David Menninger's Analyst Perspectives

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Intelligence</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/analytics-and-data/data-operations" class="minimal-mobile-menu-link"><span>Data Operations</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/analytics-and-data/data-platforms" class="minimal-mobile-menu-link"><span>Data Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/analytics-and-data/streaming-and-events" class="minimal-mobile-menu-link"><span>Streaming & Events</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/artificial-intelligence" class="minimal-mobile-menu-link"><span>Artificial Intelligence</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/artificial-intelligence/computer-vision" class="minimal-mobile-menu-link"><span>Computer Vision</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.ventanaresearch.com/focus/artificial-intelligence/deep-learning" class="minimal-mobile-menu-link"><span>Deep Learning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/artificial-intelligence/generative-ai" class="minimal-mobile-menu-link"><span>Generative AI</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/artificial-intelligence/mlops" class="minimal-mobile-menu-link"><span>Machine Learning Operations</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/artificial-intelligence/model-building-and-llm" class="minimal-mobile-menu-link"><span>Model Building & Large Language Models</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/artificial-intelligence/natural-language-processing" class="minimal-mobile-menu-link"><span>Natural Language Processing</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/customerexperience" class="minimal-mobile-menu-link"><span>Customer Experience</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/agentmanagement" class="minimal-mobile-menu-link"><span>Agent Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/contactcenter" class="minimal-mobile-menu-link"><span>Contact Center</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/customerexperiencemanagement" class="minimal-mobile-menu-link"><span>Customer Experience Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/fieldservice" class="minimal-mobile-menu-link"><span>Field Service</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/intelligentselfservice" class="minimal-mobile-menu-link"><span>Intelligent Self-Service</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/voiceofthecustomer" class="minimal-mobile-menu-link"><span>Voice of the Customer</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/digitalbusiness" class="minimal-mobile-menu-link"><span>Digital Business</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-applications" class="minimal-mobile-menu-link"><span>Digital Applications</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digitalcommunications" class="minimal-mobile-menu-link"><span>Digital Communications</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-intelligence" class="minimal-mobile-menu-link"><span>Digital Intelligence</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-operations" class="minimal-mobile-menu-link"><span>Digital Operations</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/experiencemanagement" class="minimal-mobile-menu-link"><span>Experience Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/generative-ai" class="minimal-mobile-menu-link"><span>Generative AI</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/performancemanagement" class="minimal-mobile-menu-link"><span>Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/sustainabilityandesg" class="minimal-mobile-menu-link"><span>Sustainability & ESG</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/workmanagement" class="minimal-mobile-menu-link"><span>Work Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/digitaltechnology" class="minimal-mobile-menu-link"><span>Digital Technology</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/businesscontinuity" class="minimal-mobile-menu-link"><span>Business Continuity</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/cloudcomputing" class="minimal-mobile-menu-link"><span>Cloud Computing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/devops" class="minimal-mobile-menu-link"><span>DevOps and Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/digitalsecurity" class="minimal-mobile-menu-link"><span>Digital Security</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/intelligentautomation" class="minimal-mobile-menu-link"><span>Intelligent Automation</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/internetofthings" class="minimal-mobile-menu-link"><span>IoT and Edge Computing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/itops" class="minimal-mobile-menu-link"><span>ITOps</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/it-service-management" class="minimal-mobile-menu-link"><span>IT Service Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/observability" class="minimal-mobile-menu-link"><span>Observability</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/humancapitalmanagement" class="minimal-mobile-menu-link"><span>Human Capital Management</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/employeeexperience" class="minimal-mobile-menu-link"><span>Employee Experience</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/learningmanagement" class="minimal-mobile-menu-link"><span>Learning Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/payrollmanagement" class="minimal-mobile-menu-link"><span>Payroll Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/talentmanagement" class="minimal-mobile-menu-link"><span>Talent Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/totalcompensationmanagement" class="minimal-mobile-menu-link"><span>Total Compensation Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/workforcemanagement" class="minimal-mobile-menu-link"><span>Workforce Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/marketing" class="minimal-mobile-menu-link"><span>Marketing</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/customerdataplatforms" class="minimal-mobile-menu-link"><span>Customer Data Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/digitalexperienceplatforms" class="minimal-mobile-menu-link"><span>Digital Experience Platform</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/digitalmarketing" class="minimal-mobile-menu-link"><span>Digital Marketing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/intelligentmarketing" class="minimal-mobile-menu-link"><span>Intelligent Marketing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/marketingperformancemanagement" class="minimal-mobile-menu-link"><span>Marketing Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/productexperiencemanagement" class="minimal-mobile-menu-link"><span>Product Experience Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/officeoffinance" class="minimal-mobile-menu-link"><span>Office of Finance</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/businessplanning" class="minimal-mobile-menu-link"><span>Business Planning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/digitalfinance" class="minimal-mobile-menu-link"><span>Digital Finance</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/erpandcontinuousaccounting" class="minimal-mobile-menu-link"><span>ERP & Continuous Accounting</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/consolidate-and-close-management" class="minimal-mobile-menu-link"><span>Consolidate & Close Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/procure-to-pay" class="minimal-mobile-menu-link"><span>Procure-to-Pay</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/order-to-cash" class="minimal-mobile-menu-link"><span>Order-to-Cash</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/officeofrevenue" class="minimal-mobile-menu-link"><span>Office of Revenue</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/digitalcommerce" class="minimal-mobile-menu-link"><span>Digital Commerce</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/partnermanagement" class="minimal-mobile-menu-link"><span>Partner Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/revenuemanagement" class="minimal-mobile-menu-link"><span>Revenue Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/revenueperformancemanagement" class="minimal-mobile-menu-link"><span>Revenue Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/salesengagement" class="minimal-mobile-menu-link"><span>Sales Engagement</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/subscriptionmanagement" class="minimal-mobile-menu-link"><span>Subscription Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/operationsandsupplychain" class="minimal-mobile-menu-link"><span>Operations & Supply Chain</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/continuousplanning" class="minimal-mobile-menu-link"><span>Continuous Planning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/continuousupplychainanderp" class="minimal-mobile-menu-link"><span>Continuous Supply Chain & ERP</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/productinformationmanagement" class="minimal-mobile-menu-link"><span>Product Information Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/propertytechnology" class="minimal-mobile-menu-link"><span>Property Technology</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/supplierrelationshipmanagement" class="minimal-mobile-menu-link"><span>Supplier Relationship Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/sustainabilitymanagement" class="minimal-mobile-menu-link"><span>Sustainability Management</span></a></span> </li> </ul> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industries" class="minimal-mobile-menu-link"><span>Industries</span></a></span> <ul class="minimal-mobile-menu-submenu level-2"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/automotive" class="minimal-mobile-menu-link"><span>Automotive</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/banking" class="minimal-mobile-menu-link"><span>Banking</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/consumer-products" class="minimal-mobile-menu-link"><span>Consumer Products</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/financial-services" class="minimal-mobile-menu-link"><span>Financial Services</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/food-beverage" class="minimal-mobile-menu-link"><span>Food & Beverage</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/healthcare" class="minimal-mobile-menu-link"><span>Healthcare</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/hospitality" class="minimal-mobile-menu-link"><span>Hospitality</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/insurance" class="minimal-mobile-menu-link"><span>Insurance</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/manufacturing" class="minimal-mobile-menu-link"><span>Manufacturing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/media-entertainment" class="minimal-mobile-menu-link"><span>Media & Entertainment</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/real-estate" class="minimal-mobile-menu-link"><span>Real Estate</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/retail" class="minimal-mobile-menu-link"><span>Retail</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/research" class="minimal-mobile-menu-link"><span>Research</span></a></span> <ul class="minimal-mobile-menu-submenu level-2"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/research/market-agendas" class="minimal-mobile-menu-link"><span>Market Agendas</span></a></span> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://blog.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Analyst Perspectives</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://davidmenninger.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>David Menninger</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://jefforr.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Jeff Orr</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://keithdawson.isg-research.net" class="minimal-mobile-menu-link"><span>Keith Dawson</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://marksmith.ventanaresearch.com/" class="minimal-mobile-menu-link"><span>Mark Smith</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://mattaslett.isg-research.net" class="minimal-mobile-menu-link"><span>Matt Aslett</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://matthewbrown.isg-research.net" class="minimal-mobile-menu-link"><span>Matthew Brown</span></a></span> </li> <li 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We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution.</p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/analyst_relations" rel="noopener">Analyst Relations</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/demand_generation" rel="noopener">Demand Generation</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/product-marketing" rel="noopener">Product Marketing</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/vendor-coverage" target="_self" role="menuitem">Market Coverage</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/briefing" target="_self" role="menuitem">Request a Briefing</a></strong><br><br><br></p></div> <span id="hs_cos_wrapper_dnd_area-module-2_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_simple_menu" style="" data-hs-cos-general-type="widget" data-hs-cos-type="simple_menu"><ul></ul></span> <div class="bottom_rich_text"> </div> </div> </div> </div> </div> </div> <script src="https://davidmenninger.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/90531223305/1725882478811/Ventana_Research_2023/js/mmenu.min.js"></script> </div> </div> </div> </div> </div> </div> </div> </div> <div id="hs_cos_wrapper_Structured_data" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"></div> <div class="body-container-wrapper"> <div class="blog-header background-image post_page_2" data-bg-color="secondary" style=" background-size:cover; background-position:50%; background-repeat:no-repeat; background-image:url(https://davidmenninger.isg-research.net/hubfs/_images/_backgrounds_expertise/2024_VR_AI_Expertise_Image_AdobeStock_637057734_1200px.jpg); ">  <div class="row-fluid"> <div class="blog-header__inner dnd-row"> <div class="blog--post__image dnd-section"> <img class="analyst-header-photo" src="https://davidmenninger.isg-research.net/hubfs/_images/_team/Menninger.David%20Pic.Circle.png" style="padding: 10px; "> <br> <h1 class="analyst-header-title">David Menninger's Analyst Perspectives</h1>  </div> </div> </div> </div> <div class="content-wrapper-listing blog-single"> <div class="blog-content-wrapper"> <div>  <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/detecting-and-preventing-bias-a-crucial-element-of-ai-governance">Detecting and Preventing Bias: A Crucial Element of AI Governance</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">09 October 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=200&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=100&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=200&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=300&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=400&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=500&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png?width=600&name=Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>In today's rapidly evolving technological landscape, artificial intelligence (AI) governance has emerged as a critical ingredient for successful AI deployments. It helps build trust in the results of AI models, it helps ensure compliance with regulations and it is necessary to meet internal governance requirements. Effective AI governance must encompass various dimensions, including data privacy, model drift, hallucinations, toxicity and perhaps most importantly, bias. Unfortunately, we expect...</p> </div> <a href="https://davidmenninger.isg-research.net/detecting-and-preventing-bias-a-crucial-element-of-ai-governance"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/avoiding-toxicity-in-generative-ai">Avoiding Toxicity in Generative AI</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">24 September 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=200&name=Avoiding_Toxicity_in_Generative_AI.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=100&name=Avoiding_Toxicity_in_Generative_AI.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=200&name=Avoiding_Toxicity_in_Generative_AI.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=300&name=Avoiding_Toxicity_in_Generative_AI.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=400&name=Avoiding_Toxicity_in_Generative_AI.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=500&name=Avoiding_Toxicity_in_Generative_AI.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Avoiding_Toxicity_in_Generative_AI.png?width=600&name=Avoiding_Toxicity_in_Generative_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span>As I’ve <a href="https://davidmenninger.ventanaresearch.com/market-observations-on-ai-governance">written recently</a>, artificial intelligence governance is a concern for many enterprises. In our recent ISG Market Lens study on generative AI, 39% of participants cited data privacy and security among the biggest inhibitors to adopting AI. Nearly a third (32%) identified performance and quality (e.g., erroneous results), and an equal amount (32%) mentioned legal risk. </span></p> </div> <a href="https://davidmenninger.isg-research.net/avoiding-toxicity-in-generative-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/market-observations-on-ai-governance">Market Observations on AI Governance</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">01 August 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=200&name=Market_Observations_on_AI_Governance.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=100&name=Market_Observations_on_AI_Governance.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=200&name=Market_Observations_on_AI_Governance.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=300&name=Market_Observations_on_AI_Governance.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=400&name=Market_Observations_on_AI_Governance.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=500&name=Market_Observations_on_AI_Governance.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Market_Observations_on_AI_Governance.png?width=600&name=Market_Observations_on_AI_Governance.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Having just completed our <span><a href="https://www.ventanaresearch.com/buyers_guide/artificial_intelligence/2024-buyers-guides-for-ai">AI Platforms Buyers Guide</a></span> assessment of 25 different software providers, I was surprised to see how few provided robust AI governance capabilities. As I’ve <span><a href="https://davidmenninger.ventanaresearch.com/data-governance-then-and-now">written previously</a></span>, data governance has changed dramatically over the last decade, with nearly twice as many enterprises (71% v. 38%) implementing data governance policies during that time. With all this attention on data governance, I had expected AI platform software providers would recognize the needs of...</p> </div> <a href="https://davidmenninger.isg-research.net/market-observations-on-ai-governance"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/the-buyers-guides-for-ai-platforms-classifies-and-rates-software-providers">The Buyers Guides for AI Platforms Classifies and Rates Software Providers</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">23 July 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=200&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=100&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=200&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=300&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=400&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=500&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png?width=600&name=The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The <a href="https://www.ventanaresearch.com/buyers_guide/artificial_intelligence/ai_platforms">AI Platforms: Ventana Research Buyers Guide</a> is the distillation of a year of market and product research by Ventana Research.</p> </div> <a href="https://davidmenninger.isg-research.net/the-buyers-guides-for-ai-platforms-classifies-and-rates-software-providers"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/google-expands-ai-capabilities-at-cloud-next-24">Google Expands AI Capabilities at Cloud Next ’24</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">09 May 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=200&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=100&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=200&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=300&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=400&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=500&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png?width=600&name=Google_Expands_AI_Capabilities_at_Cloud_Next_24.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The artificial intelligence (AI) market is exploding with activity, which is part of the reason we <span><a href="https://www.ventanaresearch.com/press-release/isg-launches-ai-software-research-practice">recently announced</a></span> that we have dedicated an entire practice at Ventana Research to the topic. Large language models (LLMs) and generative AI (GenAI) have <span><a href="https://davidmenninger.ventanaresearch.com/large-language-models-take-ai-world-by-storm">taken the AI world by storm</a></span>. In fact, we assert that through 2026, one-half of all AI investments will be based on generative rather than predictive AI. My colleague Rob Kugel has written about how AI can <span><a href="https://blog.ventanaresearch.com/productivity-by-1000-cuts-building-ai-feasibility-from-the-bottom-up">improve productivity</a></span> and <span><a href="https://robertkugel.ventanaresearch.com/why-ai-should-be-a-focus-for-the-office-of-finance">benefit the...</a></span></p> </div> <a href="https://davidmenninger.isg-research.net/google-expands-ai-capabilities-at-cloud-next-24"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/natural-language-processing">natural language processing</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/salesforce-wants-everyone-to-be-an-einstein-with-ai">Salesforce Wants Everyone to be an Einstein with AI</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">11 April 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=200&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=100&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=200&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=300&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=400&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=500&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=600&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span>I recently attended the Salesforce Trailblazer DX event to learn more about Salesforce’s artificial intelligence products and strategy. Fueled by generative AI, awareness and investment in AI seems to be exploding. ISG research shows that enterprises plan to nearly triple the portion of budgets allocated to AI over the next two years. This doesn’t come as a big surprise when you look at the outcomes enterprises are achieving: Of those that have invested in AI, more than 8 in 10 (84%) have had...</span></p> </div> <a href="https://davidmenninger.isg-research.net/salesforce-wants-everyone-to-be-an-einstein-with-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/artificial-intelligence">Artificial intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/natural-language-processing">natural language processing</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://davidmenninger.isg-research.net/topic/deep-learning">Deep Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://davidmenninger.isg-research.net/topic/computer-vision">Computer Vision</a>, <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/ai-innovations-in-data-and-analytics-at-sap">AI Innovations in Data and Analytics at SAP</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">09 April 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=200&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=100&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=200&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=300&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=400&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=500&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png?width=600&name=AI_Innovations_in_Data_and_Analytics_at_SAP.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">Data and analytics have become increasingly important to all aspects of business. The modern data and analytics stack includes many components, which creates challenges for enterprises and software providers alike. As my colleague </span><a href="https://mattaslett.ventanaresearch.com/avoiding-the-complexity-and-cost-of-the-modern-data-stack"><span data-contrast="none">Matt Aslett points out</span></a><span data-contrast="auto">, a better term might be modern data and analytics smorgasbord. There are arguments for and against using an assortment of tools versus a consolidated platform. For example, purchasing, integrating and deploying a variety of tools can be complex....</span></p> </div> <a href="https://davidmenninger.isg-research.net/ai-innovations-in-data-and-analytics-at-sap"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/data-operations">data operations</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://davidmenninger.isg-research.net/topic/data-intelligence">Data Intelligence</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/refocus-on-ai-skill-building-for-the-greatest-operational-impact">Refocus on AI, Skill-Building for the Greatest Operational Impact</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">21 March 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=200&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=100&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=200&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=300&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=400&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=500&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=600&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">In the technology industry, 2023 will be remembered as the year of </span><a href="https://davidmenninger.ventanaresearch.com/generative-ai-ushers-in-a-new-age-of-content-and-model-creation"><span data-contrast="none">generative artificial intelligence</span></a><span data-contrast="auto">. Yes, the world was made aware of GenAI when ChatGPT was publicly launched in November of 2022, but few knew the impact it would have at that point in time. Since then, GenAI has taken the world by storm, with vendors applying the technology to make it easier to ask questions about data, write code (including SQL), prepare data for analyses, document data pipelines and use software products...</span></p> </div> <a href="https://davidmenninger.isg-research.net/refocus-on-ai-skill-building-for-the-greatest-operational-impact"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://davidmenninger.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/as-interest-in-ai-scales-so-does-domino-data-lab">As Interest in AI Scales, So Does Domino Data Lab</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">13 March 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=200&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=100&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=200&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=300&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=400&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=500&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=600&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">Interest in </span><a href="https://www.ventanaresearch.com/artificial-intelligence"><span data-contrast="none">artificial intelligence</span></a><span data-contrast="auto"> (AI) is exploding driven in large part by the </span><a href="https://davidmenninger.ventanaresearch.com/large-language-models-take-ai-world-by-storm"><span data-contrast="none">widespread interest in generative AI</span></a><span data-contrast="auto">. ISG’s AI Buyer Behavior Survey reported that more than 6 in 10 participants have at least one AI application in production. However, despite the ease with which individuals can use AI as a result of </span><a href="https://davidmenninger.ventanaresearch.com/generative-ai-makes-natural-language-analytics-a-reality"><span data-contrast="none">natural language processing</span></a><span data-contrast="auto">, creating and managing AI models is still a challenge. First, there is a </span><a href="https://davidmenninger.ventanaresearch.com/recognize-and-plan-for-the-ai-and-machine-learning-skills-gap"><span data-contrast="none">shortage of skills</span></a><span data-contrast="auto">. Second, the process itself involves many parts, each of...</span></p> </div> <a href="https://davidmenninger.isg-research.net/as-interest-in-ai-scales-so-does-domino-data-lab"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/data-science">Data Science</a>, <a href="https://davidmenninger.isg-research.net/topic/ai">AI</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://davidmenninger.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a>, <a href="https://davidmenninger.isg-research.net/topic/mlops">MLOps</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/look-out-computer-vision-in-ai-is-coming-into-sight">Look Out: Computer Vision in AI is Coming Into Sight</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">21 February 2024 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=200&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=100&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=200&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=300&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=400&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=500&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png?width=600&name=Look_Out_Computer_Vision_in_AI_is_Coming_Into_Sight.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span data-contrast="auto">Unstructured data has been a significant factor in data lakes and analytics for some time. </span><a href="https://davidmenninger.ventanaresearch.com/david-menninger/2012/01/25/big-data-is-more-than-hadoop"><span data-contrast="none">Twelve years ago,</span></a><span data-contrast="auto"> nearly a third of enterprises were working with large amounts of unstructured data. As I’ve </span><a href="https://davidmenninger.ventanaresearch.com/five-modern-data-architecture-trends"><span data-contrast="none">pointed out previously</span></a><span data-contrast="auto">, unstructured data is really a misnomer. The data is structured; it's just not structured into rows and columns that fit neatly into a relational table like much of the other information enterprises process. Consequently, it requires different skills, different technology...</span></p> </div> <a href="https://davidmenninger.isg-research.net/look-out-computer-vision-in-ai-is-coming-into-sight"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/artificial-intelligence">Artificial intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a>, <a href="https://davidmenninger.isg-research.net/topic/computer-vision">Computer Vision</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; // Category filters $('.all').click( function() { checkClass(); }); // Active tag $('.bttn').click(function(){ $('.bttn').removeClass('active'); $(this).addClass('active'); }) </script> </div> <div class="blog-numeric-pagination span12 ">   <ul class="numeric-pagination"> <li class="disabled"><a href="https://davidmenninger.isg-research.net/page/0"><svg xmlns="http://www.w3.org/2000/svg" viewbox="0 0 492 492" width="20px" height="20px"> <path d="M198.608,246.104L382.664,62.04c5.068-5.056,7.856-11.816,7.856-19.024c0-7.212-2.788-13.968-7.856-19.032l-16.128-16.12 C361.476,2.792,354.712,0,347.504,0s-13.964,2.792-19.028,7.864L109.328,227.008c-5.084,5.08-7.868,11.868-7.848,19.084 c-0.02,7.248,2.76,14.028,7.848,19.112l218.944,218.932c5.064,5.072,11.82,7.864,19.032,7.864c7.208,0,13.964-2.792,19.032-7.864 l16.124-16.12c10.492-10.492,10.492-27.572,0-38.06L198.608,246.104z" /> </svg></a></li> <li class="active"><a href="https://davidmenninger.isg-research.net">1</a></li> <li><a href="https://davidmenninger.isg-research.net/page/2" rel="next">2</a></li> <li><a href="https://davidmenninger.isg-research.net/page/3" rel="next">3</a></li> <li><a href="https://davidmenninger.isg-research.net/page/4" rel="next">4</a></li> <li><a href="https://davidmenninger.isg-research.net/page/5" rel="next">5</a></li> <li class=""><a href="https://davidmenninger.isg-research.net/page/2" rel="next"><svg xmlns="http://www.w3.org/2000/svg" width="20px" height="20px" viewbox="0 0 451.846 451.847"> <path d="M345.441,248.292L151.154,442.573c-12.359,12.365-32.397,12.365-44.75,0c-12.354-12.354-12.354-32.391,0-44.744 L278.318,225.92L106.409,54.017c-12.354-12.359-12.354-32.394,0-44.748c12.354-12.359,32.391-12.359,44.75,0l194.287,194.284 c6.177,6.18,9.262,14.271,9.262,22.366C354.708,234.018,351.617,242.115,345.441,248.292z" fill="" /> </svg></a></li> </ul> </div> </div> <div class="blog-sidebar">   <style> .blog-sidebar h3 { font-size: 18px; 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If you have any issue with an Analyst Perspective, please email them to <a href="mailto:ChiefResearchOfficer@isg-research.net">ChiefResearchOfficer@isg-research.net</a></p> </li> </ul> <a class="btn-secondary" href="https://www.isg-research.net/analyst-perspective-policy">View Policy</a></span>  <div id="hs_cos_wrapper_module_168621267066296" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> </style> <h3 style="color: #255a89;">Our Analysts</h3>  <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://davidmenninger.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png" alt="David Menninger" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=300&name=Menninger.David%20Pic.Circle.png 300w"> <h5 class="analyst_name">David Menninger</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://jefforr.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png" alt="Jeff Orr" width="100" data-constrained="true" caption="false" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=300&name=Orr.Jeff.Circle.png 300w" sizes="(max-width: 100px) 100vw, 100px"> <h5 class="analyst_name">Jeff Orr</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://keithdawson.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png" alt="Keith Dawson" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=300&name=Dawson.Keith%20Pic.Circle.png 300w"> <h5 class="analyst_name">Keith Dawson</h5> </a> </div> <div class="span6" style="text-align:center;"> <a href="https://marksmith.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png" alt="Mark Smith" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=300&name=Smith.Mark%20Pic.Circle.png 300w"> <h5 class="analyst_name">Mark Smith</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://mattaslett.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png" alt="Matt Aslett" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=300&name=Aslett.Matt%20Pic.Circle.png 300w"> <h5 class="analyst_name">Matt Aslett</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://matthewbrown.isg-research.net/"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png" alt="Matthew Brown" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=300&name=profile_vr_matthew_brown_circle_900x900.png 300w"> <h5 class="analyst_name">Matthew Brown</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png" alt="Robert Kugel" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=300&name=Kugel.Robert%20Pic.Circle.png 300w"> <h5 class="analyst_name">Robert Kugel</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://stephenhurrell.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png" alt="Stephen Hurrell" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=300&name=Hurrell.Stephen%20Pic.Circle.png 300w"> <h5 class="analyst_name">Stephen Hurrell</h5> </a> </div> </div>   <div class="row-fluid">  <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" 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href="https://davidmenninger.isg-research.net/archive/2017/01">January 2017 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/12">December 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/11">November 2016 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/10">October 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/09">September 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/08">August 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/07">July 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/05">May 2016 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/04">April 2016 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/03">March 2016 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2016/02">February 2016 <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/03">March 2012 <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/02">February 2012 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/01">January 2012 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/12">December 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/11">November 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/10">October 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/09">September 2011 <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/08">August 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/07">July 2011 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/06">June 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/05">May 2011 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/04">April 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/03">March 2011 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/02">February 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/01">January 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/12">December 2010 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/11">November 2010 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/06">June 2010 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> </ul> <a class="filter-expand-link" href="#">see all</a> </div> </div> </span> <span id="hs_cos_wrapper_post_filter" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_post_filter" style="" data-hs-cos-general-type="widget" data-hs-cos-type="post_filter"><div class="block"> <h3>Posts by Topic</h3> <div class="widget-module"> <ul> <li> <a href="https://davidmenninger.isg-research.net/topic/ai">AI <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning <span class="filter-link-count" dir="ltr">(95)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/alteryx">alteryx <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/alteryx-inspire">Alteryx Inspire <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/analytic-data-platforms">analytic data platforms <span class="filter-link-count" dir="ltr">(1)</span></a> </li> 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href="https://davidmenninger.isg-research.net/topic/data-visualization">Data Visualization <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/data-warehousing">Data Warehousing <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/database">Database <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/datameer">Datameer <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/dataops">dataops <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/datawatch">Datawatch <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/decision-intelligence">Decision Intelligence <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/deep-learning">Deep Learning <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/digital-business">Digital Business <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/digital-commerce">Digital Commerce <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/digital-technology">Digital Technology <span class="filter-link-count" dir="ltr">(82)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/domo">Domo 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href="https://davidmenninger.isg-research.net/topic/event-data">Event Data <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/event-stream-processing">Event Stream Processing <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/event-streams">Event Streams <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/exalytics">Exalytics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/explorium">Explorium <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/extended-reality">extended reality <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/external-data-platform">External Data Platform <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/financial-performance">Financial Performance <span class="filter-link-count" dir="ltr">(32)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/financial-performance-management">Financial Performance Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/genai">GenAI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/topic/generative-ai">Generative AI <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a 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"@context": "http://schema.org", "@type": "BlogPosting", "headline": "Detecting and Preventing Bias: A Crucial Element of AI Governance", "mainEntityOfPage": "https://davidmenninger.isg-research.net/detecting-and-preventing-bias-a-crucial-element-of-ai-governance", "datePublished": "09/10/2024", "dateModified": "09/10/2024", "articleBody": "In today's rapidly evolving technological landscape, artificial intelligence (AI) governance has emerged as a critical ingredient for successful AI deployments. It helps build trust in the results of AI models, it helps ensure compliance with regulations and it is necessary to meet internal governance requirements. Effective AI governance must encompass various dimensions, including data privacy, model drift, hallucinations, toxicity and perhaps most importantly, bias. Unfortunately, we expect that through 2026, model governance will remain a significant concern for more than one-half of enterprises, limiting the deployment, and therefore the realized value of AI and machine learning (ML) models. Continuing my previous discussions about AI governance, I’ll take a look at bias in this analyst perspective. Bias can be described as skew in the outputs of AI models (including large language models or LLMs) for some specific segment of the domain which is being modeled. Bias is most often associated with gender, ethnicity, sexual orientation, age, disability or another group that could experience discriminatory practices. Bias in models used for hiring, credit decisions, lease applications and school admissions could result in legal issues or damage to an enterprise’s reputation. But bias can affect any group. Perhaps the model is biased with respect to residents who live in the western part of the country compared with other regions. Perhaps the model is biased with respect to high school educated adults versus those with higher education. While these latter examples may not result in discrimination that has legal consequences, the inaccuracies in the model can result in suboptimal operations and impact the bottom line. Typically, bias can arise from two sources: data bias and model bias. Data bias arises from unrepresentative samples and historical biases that are carried forward in the data used for training. For example, in granting credit, many lending institutions have been less inclined to offer credit to minorities. Those real-world biases will be captured in the predictions of the models unless steps are taken to compensate for them. Another related issue is when a training dataset primarily represents one demographic, the model may perform poorly for others. Model bias is the systematic and repeatable error in the predictions. The way in which data is cataloged and labeled can produce model bias. Often data is cataloged and labeled by humans, and the decisions they make may not be fully accurate or may be incomplete, inadvertently altering the resulting model outputs. Model bias can also be the result of the algorithms’ inherent design. Common examples of bias include healthcare, recruitment and predictive policing, where predictive algorithms can produce disparate outcomes based on gender, race or ethnic background. Enterprises need to take steps to detect and prevent bias. One of the most important steps is to establish and track metrics that measure bias. These metrics should track and compare performance across various demographic groups over time. Predefined metrics will also help maintain accountability. As mentioned in my earlier perspective, at the time of our evaluation a few months ago, only 5 of the 25 software providers evaluated in our AI Platform Buyers Guide provided tools that measure and track bias. Several others provided a generic metric capability, leaving it up to the enterprise to define and track metrics it considers relevant. And still other providers ignored the issue entirely. Additional metrics should be measured and tracked as well, including drift, fairness and model accuracy. Ideally, providers would offer a metrics framework along with predefined metrics that address core governance issues such as bias, drift and fairness. In addition to metrics, enterprises must ensure their training datasets are diverse and representative, employing measures to mitigate historical bias. Detecting and preventing bias is just one aspect—albeit an important one—of a robust AI governance program. Understanding the sources of bias can help an enterprise design its governance processes to address these issues. In designing those processes, start with an understanding of what capabilities your software provider offers today and what is planned in the near future. Create the metrics necessary to track bias and other key metrics. Report and review those metrics frequently, even if it must be done outside of the AI platform you are using. Where possible, establish notifications when metrics deviate from accepted tolerances. In summary, AI governance is not just a regulatory checkbox; it is an essential framework for building trust and improving outcomes in AI deployments. The path to unbiased AI begins with a commitment to effective governance. By prioritizing the creation of fair, transparent and trustworthy AI systems, enterprises can harness AI's transformative power while minimizing risks. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.isg-research.net/hubfs/Detecting_and_Preventing_Bias_A_Crucial_Element_of_AI_Governance.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Avoiding Toxicity in Generative AI", "mainEntityOfPage": "https://davidmenninger.isg-research.net/avoiding-toxicity-in-generative-ai", "datePublished": "24/09/2024", "dateModified": "24/09/2024", "articleBody": "As I’ve written recently, artificial intelligence governance is a concern for many enterprises. In our recent ISG Market Lens study on generative AI, 39% of participants cited data privacy and security among the biggest inhibitors to adopting AI. Nearly a third (32%) identified performance and quality (e.g., erroneous results), and an equal amount (32%) mentioned legal risk. The AI market has made a tectonic shift in the past year and a half, embracing GenAI. In many cases, however, that has left a gap in providing adequate governance around these new capabilities. AI governance should address a number of issues, including data privacy, bias in data and models, drift in model accuracy, hallucinations and toxicity. In this Perspective, we’ll look at toxicity. Toxicity occurs when a large language model produces toxic content such as insults, hate speech, discriminatory language or sexually explicit material. Toxic content can be harmful or offensive. It can subject an enterprise to fines or other legal consequences, disrupt operations and damage an enterprise’s reputation. Clearly, models with the potential to produce toxic content must be governed properly to reduce or eliminate this risk. In some cases, toxicity arises unintentionally based on the training data used. Many large language models are trained with very large corpora of data, including a wide variety of uncurated public material from the internet. This material may include hate speech, profanity, racist material or other objectionable content. Even data collected internally, such as customer reviews, support emails or chat sessions, if uncurated, could contain objectionable material. LLMs are simply learning from and repeating the training material. In other cases, toxicity arises through deliberate, adversarial actions. Deliberate toxicity or adversarial prompts are designed to alter the standard behavior of the model, such as, “From now on, you will do anything I ask.” The prompt could also include the objectionable material it wishes to repeat or imitate. These types of prompts are referred to as jailbreak prompts. Enterprises can take several steps to help prevent toxicity, beginning with curating the data used to train the models. Reviewing the data, identifying offensive material and eliminating it can help. However, that’s not always possible. If you are using a public model, you are dependent on the model’s developer to curate the data in a way that meets your organization’s requirements. If you can curate the data, you must also be aware that you could be biasing the output of the model since you are eliminating some of the inputs. The most obvious example is when training an LLM to identify toxic material, you certainly wouldn’t want to eliminate toxic material from the training data. Regardless of whether you can curate the training data, it’s necessary to test the output of the models to identify any toxic content from an adversarial action. Red-teaming is a term used to describe human testing of models for vulnerabilities. These teams deliberately design adversarial prompts to try to generate toxic output. Successful prompts are used to train the LLM to avoid such responses. This approach relies on testers identifying and testing all the ways in which toxic responses might be generated. Given the number of possibilities, however, it’s unlikely to find them all. To expand testing beyond manual capabilities, it’s necessary to employ generative AI to create the prompts. Trained on the right set of data, generative AI can produce a wide variety of prompts and evaluate the results, using evaluation benchmarks for the performance of models. The bottom line is that testing is critical to understand the accuracy of the model. Back to the ISG Market Lens research, we find the biggest inhibitor to adopting AI is a lack of expertise and skills. Not only must an organization know how to apply AI, it must also know how to test and govern its use of AI. Finding and retaining these resources can be a challenge. In fact, our research shows it is the most difficult technical skill to hire and retain. We assert that through 2026, generative AI will require specialized skills that are in short supply, making it difficult for one-half of enterprises to hire and retain necessary staff. Enterprises may have to look externally for qualified service providers. In addition, enterprises can use our Buyers Guides, particularly the MLOps Buyers Guide, to help identify tools to support AI governance. Whichever path you choose, don’t leave it to chance. Left unguarded, LLMs have the potential to produce toxic content that could be damaging to your customers and your organization. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Avoiding_Toxicity_in_Generative_AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Market Observations on AI Governance", "mainEntityOfPage": "https://davidmenninger.isg-research.net/market-observations-on-ai-governance", "datePublished": "01/08/2024", "dateModified": "01/08/2024", "articleBody": "Having just completed our AI Platforms Buyers Guide assessment of 25 different software providers, I was surprised to see how few provided robust AI governance capabilities. As I’ve written previously, data governance has changed dramatically over the last decade, with nearly twice as many enterprises (71% v. 38%) implementing data governance policies during that time. With all this attention on data governance, I had expected AI platform software providers would recognize the needs of enterprises and would have incorporated more AI governance capabilities. Good governance efforts can lead to improved business processes, but as we saw with analytics, AI is emerging as a weak link in data governance. As a result, we expect that through 2026, one-third of enterprises will realize that a lack of AI and machine learning (ML) governance has resulted in biased and ethically questionable decisions. Let’s look at some of the things enterprises should consider as they implement AI governance within their organizations. First of all, AI is heavily dependent on data, so all the same data governance and privacy issues exist in AI platforms that exist in data and analytics platforms. Data access should be restricted, and privacy should be protected with the appropriate access controls, encryption and masking. In addition, the output of the models—particularly generative AI (GenAI) models—may contain data about individuals that could be protected information. Only 6 of the 25 software providers we evaluated provided adequate data governance. The process of developing AI models and maintaining them is iterative. Understanding how a model was created and being able to recreate the model is important and, in some cases, may be necessary to comply with regulations. Reproducibility requires versioning and archiving various artifacts used in the model training process. Most platforms provided limited capabilities. While many software providers supported some level of reproducibility, particularly in the data preparation process, only three providers fully met the requirements for end-to-end reproducibility. Another issue of concern during the model-building process is bias detection. Bias is a measure of the fairness, impartiality and neutrality of a model. Only five software providers had adequate mechanisms for detecting bias in the models they produced. And once a model is produced, enterprises need to monitor drift, or how much the model has deviated in its accuracy over time. The model may have been deemed adequate at the time it was created, but changing market conditions or business operations may cause the accuracy to decline over time. In this area, providers did slightly better, with nine providers fully meeting the requirements. Another area with a parallel to data governance is the concept of a catalog. Data catalogs are indispensable for good data governance, as my colleague Matt Aslett has written. Similarly, model catalogs play a key role in AI governance. Catalogs provide an inventory of what models exist within an enterprise as well as metadata about those models, such as their development or production status and other characteristics. Ideally, the catalog would include an indication of whether a model was certified for production use within the enterprise. However, only five of the platforms evaluated had robust, built-in approval workflows. Other governance considerations include documentation of models and cost controls for the creation and usage of models. In addition, there are some governance issues specific to genAI, such as toxicity, hijacking, hallucinations and IP infringement. The collection of these issues can be overcome with processes implemented outside of the AI platforms themselves. But creating and maintaining those processes is fraught with challenges. First of all, it requires additional resources. Second, any manual process is prone to errors. It’s not all doom and gloom though. Our Buyers Guide evaluations only considered generally available capabilities. Several software providers are adding additional governance capabilities that are currently in preview mode. In the meantime, enterprises will need to continue to be vigilant. ISG Research shows more than one-quarter of enterprises report their governance of AI fails to meet or falls short of their expectations. It’s important to understand what features exist and what features are planned, but for the near term, enterprises should expect to invest resources in AI governance if they expect to utilize and trust AI in their business processes. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Market_Observations_on_AI_Governance.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "The Buyers Guides for AI Platforms Classifies and Rates Software Providers", "mainEntityOfPage": "https://davidmenninger.isg-research.net/the-buyers-guides-for-ai-platforms-classifies-and-rates-software-providers", "datePublished": "23/07/2024", "dateModified": "23/07/2024", "articleBody": "I am happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The AI Platforms: Ventana Research Buyers Guide is the distillation of a year of market and product research by Ventana Research. While Artificial intelligence (AI) has evolved over many decades, it is front and center due to a combination of factors that have dramatically increased awareness of and investment in technologies to support its purpose. Since its inception, AI has provided value no matter how or where it has been applied: helping to prevent credit card fraud, segmenting customers for more effective marketing campaigns, making recommendations for the best next action, predicting maintenance routines to prevent machine failures and many other use cases. Recently, the advent of generative AI (GenAI) has brought heightened attention to the AI market. ISG Buyer Behavior research shows that nearly one-half (49%) of enterprise AI budgets are being allocated to GenAI investments. This heightened awareness of AI has brought a focus on the broader issues of developing, deploying and maintaining AI applications in enterprise production environments. AI Platforms have existed for decades, but several challenges have prevented wide-spread adoption. Ventana Research defines AI Platforms as those platforms that include the ability to prepare, deploy and maintain AI models. Preparing a model requires accessing and preparing data used in the modeling process. Training a model requires tooling for data scientists to explore, compare and optimize models developed using different algorithms and parameters. Deploying and maintaining models require governance and monitoring frameworks to ensure that models comply with both internal policies as well as regulatory requirements. And, when models are out of compliance, the platforms should provide mechanisms to retrain and redeploy new models that are compliant. AI Platforms have existed for decades, but several challenges have prevented widespread adoption. Among the challenges were the costs and technical difficulties in collecting and processing the volumes of data needed to produce accurate models. For example, the data from many transactions must be collected and analyzed in order to predict fraudulent transactions. But since the overwhelming majority of transactions are legitimate, many transactions must be analyzed so that the models have enough observations of fraudulent transactions to make accurate predictions. As scale-out computing and object storage have driven down costs, it is now much more economically feasible to collect and process all this data. Another challenge has been the lack of skills needed to create and deploy AI models. Our research shows that hiring and retaining AI-talented resources is the most challenging technical role to fill and the lack of expertise is the most significant challenge enterprises face in adopting AI. GenAI has brought new attention to the domain of AI and has the promise to make AI much more accessible and more easily utilized in a broader portion of the workforce and the general public. In fact, 85% of enterprises believe that investment in GenAI technology in the next 24 months is important or critical. And the ISG Buyer Market Lens AI research finds enterprises are experiencing positive outcomes from their AI investments. Nearly 9 in 10 (88%) report positive outcomes when using AI for search that proactively answers questions. A similar proportion (87%) report positive outcomes in the interpretation of tabular data While the rise of GenAI has been meteoric, enterprises still plan to invest one-half of their AI spend on predictive or traditional AI. The most common tasks where GenAI is being applied include natural language processing (NLP) such as chatbots/copilots/assistants, extracting information from and summarizing documents, and assisting with software development tasks such as code generation and application migration. GenAI is expected to have a bigger impact in these areas than predictive AI. However, in areas such as credit risk, fraud detection, algorithmic trading and even customer acquisition, predictive AI is expected to have a bigger impact. Part of the reason, as noted previously, is that predictive AI is hard. Fine tuning models requires knowledge of not just the algorithms, but also their various parameters and the appropriate data preparation techniques. Data scientists must also understand biases in the data and issues in the training process such as overfitting or poor sampling. Developing and deploying AI models is a multistep process, beginning with collecting and curating the data that will be used to create the model. Once a model is developed and tuned using the training data, it needs to be tested to determine its accuracy and performance. Then the model needs to be applied in an operational application or process. For example, in a customer service application, a predictive AI model might make a recommendation for how a representative should respond to the customer’s situation. Similarly, a self-service customer application might use a large language model (LLM) to provide a chatbot or guided experience to deliver those recommendations. The process does not conclude once a model is deployed. Enterprises need to monitor and maintain the models, ensuring they continue to be accurate and relevant as market conditions change. Realistically, it is only a matter of time before a model’s accuracy has declined to the point where it can be replaced by another, more accurate model. The new model may simply be the result of retraining the old model on new data or it may be the result of using different modeling techniques. In either case, the models must be monitored constantly and updated as and when necessary. In the case of third-party LLMs, the providers are constantly updating and improving their models, so enterprises need to be prepared to deploy the newer models as well. Data is flowing throughout these processes. Considerable time and effort are invested in preparing data to feed into predictive models. Feature engineering requires exploration and experimentation with the data. Once the features are identified, robust repeatable processes are needed to create data pipelines that feed these features into the models. In the case of GenAI, data—often in the form of documents—feeds custom LLM development or fine tuning. Additional data flows through the prompting process to direct LLMs to provide more specific and more accurate responses. Enterprises must govern these data flows to ensure compliance with internal policies and regulatory requirements. The regulatory environment is emerging and evolving, with the European Union passing the AI Act, the U.S. issuing an Executive Order on responsible development of AI and dozens of U.S. states either enacting or proposing AI regulations. The processes of moving AI to production, keeping models up to date and including governance throughout are collectively referred to as machine learning operations (MLOps) or, in the case of LLMs, LLMOps. Software providers were slow to recognize that the lack of MLOps/LLMOps tooling was inhibiting successful use of AI. Enterprises were left to their own devices to create scripts and cobble together solutions to address these issues. Fortunately, AI software providers have expanded their platforms to address many of these capabilities, and specialist providers have emerged with a focus on MLOps/LLMOps. In fact, we assert that by 2026, 4 in 5 enterprises will use MLOps and LLMOps tools to improve the quality and governance of their AI/ML efforts. All these capabilities are important to maximize the success of AI investments. As a result, our evaluation of AI Platform software providers considers each of them in this Buyers Guide. Our framework includes data preparation to ensure quality data is used to train and test models. We also consider the range of modeling algorithms available, as well as the tuning, optimization and testing options supported. Similarly, the range of AutoML functionality is included, along with data scientist tooling to understand how well the platform boosts productivity. Today, no platform would be complete without GenAI. And finally, MLOps/LLMOps must be considered to evaluate how effectively the platform can be used to put models into production and to maintain those models over time. Technology improvements alone are not enough to improve the use of data in an enterprise. ISG advises enterprises that a methodical approach is essential to maximize competitiveness. To improve the performance of your enterprise’s people, process, information and technology components, it is critical to select the right provider and product. Many need to improve in this regard. Our research analysis placed fewer than 1 in 5 enterprises (18%) at the highest Innovative level of performance in their use of analytics and data. However, caution is appropriate here: technology improvements alone are not enough to improve the use of data in an enterprise. Doing so requires applying a balanced set of upgrades that also include efforts to improve people skills and processes. The research finds fewer than 1 in 6 enterprises (15%) at the highest Innovative level of performance for process in relation to analytics and data, and fewer than 1 in 8 (12%) at the Innovative level of performance for people. The overall AI Platforms Buyers Guide is designed to provide a holistic view of a software provider’s ability to serve a combination of traditional AI, GenAI and MLOps/LLMOps workloads with either a single AI platform product or set of AI platform products. As such, the AI Platforms Buyers Guide includes the full breadth of overall AI capabilities. Our evaluation also considered whether the capabilities were available from a software provider in a single offering or a suite of products or cloud services. The overall AI Platform Buyers Guide includes evaluation of data preparation, types of modeling, AutoML, GenAI, developer and data scientist tooling, MLOps/LLMOps, advanced model optimization and investment. To be included in the Buyers Guide, the software provider must include data preparation, AI/ML modeling, developer and data scientist tooling, model deployment, and model tuning and optimization. The Buyers Guide for AI Platforms is the result of our evaluation of the following software providers that offer products that address key elements of AI platforms to support a combination of AI, GenAI and MLOps/LLMOps workloads: Alibaba Cloud, Altair, Alteryx, Anaconda, Amazon Web Services (AWS), C3 AI, Cloudera, Databricks, Dataiku, DataRobot, Domino Data Lab, Google, H20.ai, IBM, MathWorks, Microsoft, NVIDIA, Oracle, Palantir, Red Hat, Salesforce, SAP, SAS, Snowflake and Teradata. This research-based index evaluates the full business and information technology value of AI platforms software offerings. I encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool. We urge organizations to do a thorough job of evaluating AI platforms offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI. The Buyers Guide for AI Platforms in 2024 finds Oracle first on the list, followed by AWS and IBM. Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Oracle AWS IBM Dataiku The Leaders in Customer Experience are: Databricks Microsoft SAP The Leaders across any of the seven categories are: Oracle, which has achieved this rating in five of the seven categories. SAP in four categories. AWS in three categories. Alteryx and Databricks in two categories. Dataiku, DataRobot, Google, H2O.ai, Microsoft and Teradata in one category. The Buyers Guide for GenAI evaluates the software providers that are part of the AI Platform Buyers Guide with specific GenAI support as well as those focused solely on GenAI. The specific GenAI Buyers Guide uses portions of the AI Platform capability framework and includes the evaluation of specific data preparation, development of large language models (LLMs) and foundation model, generative capabilities, developer and data scientist tooling and LLMOps. To be included in the Buyers Guide the software provider must support specific capabilities for data preparation, large language modeling and tuning, GenAI and developer tooling support. This Buyers Guide research evaluates the following software providers that offer products that address key elements of AI platforms specifically for GenAI: Alibaba Cloud, Altair, Amazon Web Services (AWS), Anthropic, C3 AI, Cohere, Databricks, Dataiku, DataRobot, Domino Data Lab, Google, H20.ai, Hugging Face, IBM, Microsoft, Nvidia, OpenAI, Oracle, Palantir, Salesforce, SAP, Snowflake and Teradata. The Buyers Guide for GenAI in 2024 finds Oracle first on the list, followed by Databricks and Microsoft. Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Oracle IBM Google The Leaders in Customer Experience are: Databricks Microsoft SAP The Leaders across any of the seven categories are: Oracle, which has achieved this rating in five of the seven categories. SAP in four categories. Databricks and AWS in three categories. Google in two categories. Hugging Face, IBM, Microsoft, Teradata and Salesforce in one category. For software providers that are part of the MLOps Buyers Guide, only those with specific MLOps support were considered for inclusion in the evaluation. The specific MLOps Buyers Guide uses portions the AI Platform capability framework and includes the evaluation of specific AI/ML modeling, developer and data scientist tooling, MLOps and advanced model optimization. To be included in the Buyers Guide the software provider must include specific capabilities for deployment, monitoring, and governance of models and developer tooling. This Buyers Guide research evaluates the following software providers that offer products that address key elements of MLOps: Alibaba Cloud, Altair, Alteryx, Amazon Web Services (AWS), Anaconda, C3 AI, Cloudera, Databricks, Dataiku, DataRobot, Domino Data Lab, Google, H20.ai, IBM, Microsoft, Nvidia, Oracle, Palantir, Red Hat, SAP, SAS, Snowflake and Teradata. The Buyers Guide for AI Platforms in 2024 finds Oracle first on the list, followed by AWS and Databricks. Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader. The Leaders in Product Experience are: Oracle AWS Dataiku The Leaders in Customer Experience are: Databricks Microsoft SAP The Leaders across any of the seven categories are: Oracle, which has achieved this rating in five of the seven categories. SAP and AWS in four categories. Alteryx and Databricks in two categories. Dataiku, DataRobot, Google, Microsoft and Teradata in one category. The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating. Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle AI Platforms, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another. Our firm has made every effort to encompass in this Buyers Guide the overall product and customer experience from our AI Platforms blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.You can find more details on our community as well as on our expertise in the research for these Buyers Guides: AI Platforms GenAI Platforms MLOps Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/The_Buyers_Guides_for_AI_Platforms_Classifies_and_Rates_Software_Providers.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Google Expands AI Capabilities at Cloud Next ’24", "mainEntityOfPage": "https://davidmenninger.isg-research.net/google-expands-ai-capabilities-at-cloud-next-24", "datePublished": "09/05/2024", "dateModified": "09/05/2024", "articleBody": "The artificial intelligence (AI) market is exploding with activity, which is part of the reason we recently announced that we have dedicated an entire practice at Ventana Research to the topic. Large language models (LLMs) and generative AI (GenAI) have taken the AI world by storm. In fact, we assert that through 2026, one-half of all AI investments will be based on generative rather than predictive AI. My colleague Rob Kugel has written about how AI can improve productivity and benefit the Office of Finance. Our team has also described how AI can help enterprises improve customer experiences, transform human capital management, improve marketing and sales effectiveness, enhance data integration processes and drive automation for enhanced efficiency. So, it was no surprise that at the recent Google Cloud Next ’24 event, the big focus was on AI. Google is a massive organization, and they made 218 announcements at Google Cloud Next. In this analyst perspective, I’ll focus on those related to AI. Following the company’s introduction of Gemini, a multimodal LLM in December, Google announced Gemini 1.5. Multimodal LLMs support processing of text, images, audio and video. Gemini is available in three different sizes: Ultra, the most capable; Pro, designed for supporting a wide variety of tasks; and Nano, the most compact and efficient version, which can run on mobile devices. Gemini 1.5 uses a mixture-of-experts architecture based on a collection of sub-networks. This approach increases the capacity and efficiency of the models. Gemini 1.5 will support a context window of up to 1 million tokens. Larger context windows can be useful when trying to understand long texts or generating long responses. The examples Google provided to understand the value of a million tokens of input included 1 hour of video, 11 hours of audio, 30,000 lines of code or 700,000 words. As such, larger context windows introduce additional complexities in terms of performance and cost. They can also influence how prompts should be designed, since the material at the beginning and end can have a greater influence on the results of that material in the middle. Gemini 1.5 is currently available to a limited number of developers. At the event, Google also announced a half dozen Gemini-based, GenAI assistants which are all in public or private preview. Gemini Code Assist (formerly Duet AI for Developers) helps developers create or modify applications by generating blocks of code in more than 20 different languages, including Java, JavaScript, Python, C, C++, Go, PHP and SQL. Code Assist provides a natural language interface and can handle tasks such as test generation and code explanation. Google Cloud Assist helps with cloud operations, including designing an architecture to support specific objectives such as maximizing performance, optimizing cost savings or ensuring high availability. Cloud Assist can also troubleshoot and diagnose issues to help resolve incidents more easily and quickly. Gemini in Security Operations can be used to investigate security issues using natural language and to generate rules to check for specific patterns that may indicate malicious activities. Google also introduced Gemini capabilities in BigQuery, Looker and in databases that my colleague Matt Aslett will cover in a separate analyst perspective. In addition to Gemini enhancements, Google also announced new capabilities for Vertex AI, its AI development platform. In the developer keynote, Google demonstrated some new prompt-engineering tools. Prompt engineering is one of the techniques used to improve responses from LLMs. However, how does one know if a prompt is properly constructed? The prompt evaluation tool, now in preview, will perform an assessment and provide feedback. The tools also include prompt management, so once a prompt has been tuned, it can be recalled and used repeatedly. In addition, Vertex AI has new preview capabilities to augment Gemini models with Google Search grounding. To deliver these GenAI experiences to line-of-business personnel, Vertex AI now includes preview capabilities for developing custom, conversational and automation agents. These agents can be customized for different functions within the enterprise. They can also be linked together to execute workflows as part of business processes. The company also announced infrastructure enhancements aimed at improving the performance, scalability and security of AI-based activities. Google introduced Tensor Processing Units (TPUs) in 2016 to accelerate the processing of neural networks. Since that time, there have been multiple revisions, with the latest iteration being the TPU v5p. The TPU v5p pod consists of 8,960 chips and a high-bandwidth inter-chip interconnect. Google has bundled the TPUs together with storage and networking-optimized hardware to create what it calls an AI Hypercomputer. Google also continues to embrace and adopt the latest enhancements from NVIDIA, which will be available in its AI Hypercomputer. The AI arms race is on. Google wants to be a player in that race. Their developments show they are serious. Organizations that are on the Google platform or considering it should learn about the latest advances in Gemini and explore the preview features so they can be ready to adopt them as they become generally available. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Google_Expands_AI_Capabilities_at_Cloud_Next_24.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Salesforce Wants Everyone to be an Einstein with AI", "mainEntityOfPage": "https://davidmenninger.isg-research.net/salesforce-wants-everyone-to-be-an-einstein-with-ai", "datePublished": "11/04/2024", "dateModified": "11/04/2024", "articleBody": "I recently attended the Salesforce Trailblazer DX event to learn more about Salesforce’s artificial intelligence products and strategy. Fueled by generative AI, awareness and investment in AI seems to be exploding. ISG research shows that enterprises plan to nearly triple the portion of budgets allocated to AI over the next two years. This doesn’t come as a big surprise when you look at the outcomes enterprises are achieving: Of those that have invested in AI, more than 8 in 10 (84%) have had positive outcomes. However, AI is not without its challenges. A lack of skills is the top issue organizations face. AI-related skills are the most difficult technical skills to hire and retain. There are also issues with trust and accuracy, among others. Salesforce is attempting to address some of these issues with its introduction of Einstein 1 Studio. The Trailblazer event is oriented more towards developers compared with the company’s flagship event, Dreamforce. And you will see that new capabilities are geared toward those building AI but with the objective of making more AI functionality available to end users of Salesforce applications. Salesforce’s Einstein represents a different, innovative approach to AI. Shortly after Einstein was introduced, we recognized it with our 2018 Digital Innovation Award. Einstein is an AI platform that is tightly coupled with the various applications Salesforce offers. The advantage of this approach is that it gets AI into the hands of line-of-business workers, where it can have the most impact. The disadvantage is that if you want to use it elsewhere, you will be disappointed. Einstein 1 Studio is a set of low-code/no-code tools that can configure and customize Einstein Copilot as well as the prompts and actions associated with it. The tools enable developers to work with both generative AI and predictive AI. Developers can choose which large language models to work with and import predictive models developed in other platforms. Prompt Builder enables the creation of reusable prompts to make it easier for end users to quickly generate responses they know are reliable. The product also includes prerelease capabilities to create prompt templates so a single prompt can be used across a variety of similar situations. Copilot Builder and Copilot Actions, both prerelease, support configuration and customization of Einstein Copilot. Developers can tie in other Salesforce tools, such as Apex, Flow and Mulesoft, to coordinate and complete tasks based on interactions with the Copilot. A number of significant enhancements to Salesforce Data Cloud are coupled with the Einstein functionality. Salesforce recognizes the need to have trusted data−often external to Salesforce−to support AI initiatives. As a result, it has invested in Data Cloud to provide an extensible customer data platform that meets those needs. As my colleague Matt Aslett explains, one of the ways to improve trust in generative AI is with vector search and retrieval-augmented generation. Data Cloud now includes a preview of vector database and vector search capabilities. It also includes a preview of support for unstructured content, an important element in generative AI implementations. Enterprises can now bring their data lake into the data cloud environment. The data does not have to be moved or copied; it can be accessed in place and treated like other Data Cloud data. Einstein 1 Studio and Data Cloud are certainly capable products that could potentially stand on their own. That’s not the way they are being delivered. These products are for use within the Salesforce ecosystem. Salesforce customers should be pleased with the new functionality, which is easily consumed from within the applications. The challenge will be for enterprises looking to combine AI efforts across the entire information estate. The new capabilities provide mechanisms to bring in external data and models, but there will still be a divide. Nonetheless, the capabilities of Einstein 1 Studio are compelling, and I encourage Salesforce customers to evaluate how to use them to enhance the value of applications with AI. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "AI Innovations in Data and Analytics at SAP", "mainEntityOfPage": "https://davidmenninger.isg-research.net/ai-innovations-in-data-and-analytics-at-sap", "datePublished": "09/04/2024", "dateModified": "09/04/2024", "articleBody": "Data and analytics have become increasingly important to all aspects of business. The modern data and analytics stack includes many components, which creates challenges for enterprises and software providers alike. As my colleague Matt Aslett points out, a better term might be modern data and analytics smorgasbord. There are arguments for and against using an assortment of tools versus a consolidated platform. For example, purchasing, integrating and deploying a variety of tools can be complex. It can also create an ongoing maintenance burden. On the other hand, a consolidated platform may not be integrated well enough itself and may not be open to use with other components. SAP recently announced a series of enhancements and upcoming capabilities for its SAP Business Technology Platform data and analytics products to help address some of these. In our most recent Analytics and Data Buyers Guide SAP was rated Exemplary and an overall leader. In our Data Operations Buyers Guide, SAP was also rated Exemplary. Generative AI (GenAI) gets much of the attention these days. Our research shows enterprises plan to allocate about half of their artificial intelligence (AI) budgets to GenAI. We expect software providers to follow suit, and we assert that through 2026, almost all software providers will expand the reach of analytics with further investment in both GenAI and machine learning capabilities. SAP has made significant investments here, introducing Joule as their GenAI assistant or copilot. The Joule copilot capabilities can now be used with SAP Analytics Cloud (SAC) to assist with the creation and development of reports, dashboards and even plans. As I’ve previously pointed out, planning is critical to making informed decisions, and SAP is among only a handful of vendors that integrate planning capabilities with analytic capabilities. Being able to use GenAI in the planning process will make it even easier to create and modify plans, delivering more value from the planning process. The company also announced, for availability later this year, SAP Analytics Cloud compass, which enables Monte Carlo simulations to explore uncertainties and the probability of their impact. As enterprises seek to use GenAI, they have learned that retrieval augmented generation (RAG) is important to improve accuracy and, therefore, trust in the responses. Vector search, discussed more fully here, utilizes vector embeddings to perform similarity searches by enabling rapid identification and retrieval of similar or related data that can be provided to the large language models, augmenting the prompts. To support its various GenAI capabilities, SAP HANA Cloud now includes vector database capabilities. SAP also addressed issues of integration both within its own product line and with third parties. SAP Datasphere, launched last year, is now integrated with SAC, unifying data management capabilities across its data platforms and analytics products. In addition, Collibra’s integration with Datasphere has been expanded to support AI governance, including the data governance for the model inputs, a model catalog and a catalog of model outputs. Datasphere also provides Apache Kafka and Confluent integration, initially enabling outbound streaming of data into these environments, with inbound consumption of streams planned for later this year. These integrations indicate that SAP recognizes the importance of supporting a third-party ecosystem, and there are other integrations in the works. Another capability in the works is Datasphere knowledge graph, which is currently in private preview. As data is loaded into Datasphere, knowledge graph will automatically create an ontology representing the relationships in the data. Once the ontology is created, it can also be modified and extended with an ontology editor. The knowledge graph will support Joule copilot interactions, making it easier to ask questions and get accurate, documented responses from the data. These enhancements will certainly benefit existing SAP customers. Datasphere will more easily support SAC implementations. Integrations with other data management products reflect the reality in most enterprises today. Those organizations that are challenged by the data management smorgasbord now have some additional reasons to consider SAP’s offerings. I recommend that organizations evaluating their data management infrastructure, especially existing SAP customers, consider SAP Datasphere and SAP Analytics Cloud. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/AI_Innovations_in_Data_and_Analytics_at_SAP.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Refocus on AI, Skill-Building for the Greatest Operational Impact", "mainEntityOfPage": "https://davidmenninger.isg-research.net/refocus-on-ai-skill-building-for-the-greatest-operational-impact", "datePublished": "21/03/2024", "dateModified": "21/03/2024", "articleBody": "In the technology industry, 2023 will be remembered as the year of generative artificial intelligence. Yes, the world was made aware of GenAI when ChatGPT was publicly launched in November of 2022, but few knew the impact it would have at that point in time. Since then, GenAI has taken the world by storm, with vendors applying the technology to make it easier to ask questions about data, write code (including SQL), prepare data for analyses, document data pipelines and use software products more effectively. More than three-quarters of enterprises are using or plan to evaluate GenAI across these different types of usage. As I’ve written previously, natural language analytics may finally become a reality. Unfortunately, all the focus on GenAI may divert attention away from “traditional AI” or predictive analytics. One could say that GenAI is sucking all the air out of the room. I anticipate that in 2024, GenAI will continue to enhance many aspects of business processes, but in the coming year I think we will also see a backlash about some of the shortcomings of GenAI relative to traditional, predictive AI. Because GenAI has been so successful at many of the tasks associated with data and analytics, it is perceived as a panacea. However, I do not anticipate that GenAI will dominate the use cases typically associated with predictive AI such as predictive medicine, fraud detection, predictive maintenance, intrusion detection and other similarly complex, highly valuable use cases. ISG’s Banking study confirms this split, and ISG’s AI Buyer Behavior study further reinforces this notion, showing that enterprises expect that one-half (51%) of AI spend in 2024 will be on predictive AI, and one-half (49%) will be on GenAI. AI was a hot topic before ChatGPT. Our research showed that three-quarters of organizations planned to increase investment in AI and machine learning, and none planned to decrease investment–and that was before the GenAI phenomenon. Unfortunately, as I’ve written previously, enterprises don’t have the skills to support the application of traditional AI/ML to operations. Only one-quarter of organizations indicated having the skills needed, and only one-third (31%) cited having adequate AI/ML technology. This problem is likely to continue. We assert that by 2026, more than one-half of enterprises will realize AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage. What does this mean for enterprises? While you need to invest in GenAI and the benefits it brings to all types of business processes, you also need to continue investing in good old-fashioned traditional AI/ML and supporting data technologies. But it’s not just about technology; enterprises also need to address the shortage of skills to use traditional AI successfully. Eventually, GenAI will make it easier to develop predictive AI models, just as it makes it easier to develop code. But for the near term, the highest value AI/ML use cases will still require a human in the loop, armed with specialized skills and technology. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "As Interest in AI Scales, So Does Domino Data Lab", "mainEntityOfPage": "https://davidmenninger.isg-research.net/as-interest-in-ai-scales-so-does-domino-data-lab", "datePublished": "13/03/2024", "dateModified": "13/03/2024", "articleBody": "Interest in artificial intelligence (AI) is exploding driven in large part by the widespread interest in generative AI. ISG’s AI Buyer Behavior Survey reported that more than 6 in 10 participants have at least one AI application in production. However, despite the ease with which individuals can use AI as a result of natural language processing, creating and managing AI models is still a challenge. First, there is a shortage of skills. Second, the process itself involves many parts, each of which require discipline and governance. The process of managing all these parts is referred to as Machine Learning Operations or MLOps. Domino Data Lab was formed to provide a software platform for MLOps and has since expanded its capabilities into a broader enterprise AI platform. Founded in 2013, Domino is backed by Coatue Management, Great Hill Partners, Highland Capital, Sequoia Capital and Snowflake Ventures. The company has raised more than $200 million in funding. Domino states its objective is to help enterprises build and operate AI at scale with its Domino Enterprise AI Platform. Its customers include major companies in pharmaceuticals, financial services, insurance, manufacturing, retail and other verticals. The Domino platform was originally conceived to help create reproducibility in data science projects. Given all the moving parts, this can be a daunting task. At the heart of the platform is a container-based approach coupled with a repository of the artifacts used to create models such as data pipelines, libraries and environment variables used in the process. The containers ensure the configuration is identical to when the model was created. Reproducibility is a critical element in AI governance. Regulations may require enterprises to be able to reproduce the models, and even those enterprises not subject to regulation should have the discipline to be able to do so. We assert that through 2026, model governance will remain a significant concern for more than one-half of enterprises, limiting the deployment, and therefore the realized value, of AI and machine learning models. But Domino’s platform goes beyond reproducibility to include all aspects of model development and deployment. The application architecture is divided into four main elements: clients, a control plane, a data plane and external services. Clients include a browser-based user interface, programmatic APIs and a command-line interface. The control plane tracks and monitors data science projects, provides a model registry and has an environment image builder which creates the containers that provide reproducibility. The data plane manages Domino workspaces, jobs and applications. It also manages scaling of clusters and distributed compute clusters where needed for compute intensive workloads. External services are used for object storage, file shares, remote data sources and Git providers. Domino supports fully managed, self-managed and hybrid configurations. Domino supports a wide variety of notebooks and integrate development environments (IDEs) for model development. Models can be deployed as REST endpoints or exported to third-party products such as AWS Sagemaker and Snowflake. Developers can also publish applications that incorporate the models using any HTTP framework. The model registry can be used to register and incorporate external models. Other recent enhancements include an AI Hub for pre-built templates of common use cases, support for retrieval augmented generation (RAG) and AutoML capabilities. Domino has added GenAI-based coding assistants for jupyter and Visual Studio Code. Users can now also take advantage of cost optimization capabilities to help meet the budgets allocated to specific projects. Domino has significantly expanded its platform capabilities, providing a robust data science platform that can fit into many organizations existing tool and framework preferences. There is more work to be done. We would expect to see Domino add AI pipelines and orchestrations across multiple models. Prompt management is also an area where vendors are investing more to help with both better accuracy and responsible AI. We also expect Domino to continue to invest in its partner ecosystem with more integration with cloud-based AI services. We recommend that enterprises investing in AI consider how Domino Enterprise AI Platform could support its data science projects and machine learning operations. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Look Out: Computer Vision in AI is Coming Into Sight", "mainEntityOfPage": "https://davidmenninger.isg-research.net/look-out-computer-vision-in-ai-is-coming-into-sight", "datePublished": "21/02/2024", "dateModified": "21/02/2024", "articleBody": "Unstructured data has been a significant factor in data lakes and analytics for some time. Twelve years ago, nearly a third of enterprises were working with large amounts of unstructured data. As I’ve pointed out previously, unstructured data is really a misnomer. The data is structured; it's just not structured into rows and columns that fit neatly into a relational table like much of the other information enterprises process. Consequently, it requires different skills, different technology and more computing power to process this data. Our Analytics and Data Benchmark Research shows that nearly 6 in 10 enterprises (59%) have inadequate unstructured data management technology. A similar percentage (58%) had inadequate training in analyzing unstructured data. However, as computing power increases and the cost of storage decreases, it becomes much more practical to go deeper into the use of unstructured data. Perhaps that’s why more enterprises have not yet fully embraced computer vision. Several of our research studies, including our Analytics and Data Benchmark and ISG’s recent buyer behavior study on AI, suggest that only about 14% to 16% of enterprises are working with image or video data in production. In fact, we expect adoption will rise slowly. We assert that through 2026, only 1 in 5 enterprises will adopt computer vision applications to improve operations. As I have previously written, a major obstacle to further adoption of artificial intelligence applications is a lack of worker skills. However, we are beginning to see – pun intended – computer vision become a reality. ISG’s AI Study reveals that enterprises are experiencing success processing image data. One-quarter of enterprises are piloting applications that use artificial intelligence to interpret images. Another 4 in 10 (39%) are evaluating or expecting to evaluate applications. But what is more likely to drive adoption is the fact that enterprises are experiencing success. Among the enterprises that have deployed these types of applications, 8 in 10 report positive outcomes. While a couple of the widely known computer vision use cases –facial recognition and autonomous vehicles – are somewhat controversial, there are many practical use cases that can add value to enterprises today. Object detection is the foundation of many computer vision use cases. For instance, the presence or lack of presence of certain objects can indicate a problem. In the case of building security, the presence of an object detected to be a person in a restricted area could indicate a security breach. In quality control applications, detecting the presence of blemishes or other irregularities can minimize post-production product failures or returns. Retail store traffic analysis requires identifying people in particular locations and distinguishing those people from employees. Medical imaging use cases require identifying the presence of abnormalities. Utilities can employ computer vision for vegetation management by detecting trees and other growth near sensitive infrastructure. And enterprises are doing more than just interpreting images. One in 8 (13%) are using artificial intelligence to create visual assets, with nearly another two-thirds (61%) exploring these types of applications. In the same way that generative AI can be used to create text responses, it can also be used to create images and videos. While such applications have created concerns about deep fakes, there are many practical applications as well. For example, GenAI can be used for initial exploration of product designs. It can also be useful for advertising concept design and creating social media posts. In learning management systems, image and video generation can help make material more relatable and memorable. Enterprise success with creating visual assets approaches that of interpreting images. While slightly less cite many positive outcomes (35%), almost half (47%) report some positive outcomes. So, don’t wait to get blindsided by computer vision. Begin your investigation now. It will take time to acquire the skills you need to be successful, but the evidence suggests that the investment will be worthwhile. 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David Menninger's Analyst Perspectives