ISG Software Research Analyst Perspectives

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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_widget_1687168884328_" 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://research.isg-one.com/hs-fs/hubfs/hub_generated/template_assets/1/90531223305/1743820981123/template_mmenu.min.js"></script> </div> </div> </div> </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://research.isg-one.com/hubfs/_images/_backgrounds/314838419_1200px.jpg)">  <div class="row-fluid"> <div class="blog-header__inner dnd-row"> <div class="blog--post__image dnd-section">  <h1 class="analyst-header-title">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://research.isg-one.com/analyst-perspectives/human-meets-machine-with-ai-how-to-manage-technology-with-workforce">Human Meets Machine with AI: How to Manage Technology with Workforce</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matthew-brown"> Matthew Brown </a> on <span class="post-date">01 April 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=200&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=100&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png 100w, https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=200&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png 200w, https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=300&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png 300w, https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=400&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png 400w, https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=500&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png 500w, https://research.isg-one.com/hs-fs/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png?width=600&name=Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>In the rush to embrace AI and automation, organizations are making a critical misstep: they are treating technology as a replacement for HR rather than as an enabler of its evolution. While AI, machine learning and automation offer powerful efficiencies, HR's core mission has always been about people. The challenge today is ensuring that technology amplifies—not replaces—the human touch in HR. Unfortunately, many organizations are being short-sighted in their approach, failing to fully leverage...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/human-meets-machine-with-ai-how-to-manage-technology-with-workforce"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/human-capital-management">Human Capital Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-experience">employee experience</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://research.isg-one.com/analyst-perspectives/buyer-beware-the-evolution-of-agentic-ai-in-business-software">Buyer Beware: The Evolution of Agentic AI in Business Software</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">27 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=200&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=100&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png 100w, https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=200&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png 200w, https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=300&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png 300w, https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=400&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png 400w, https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=500&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png 500w, https://research.isg-one.com/hs-fs/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png?width=600&name=Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span>Agents and “agentic AI” are all the rage now, eclipsing last year’s focus on artificial intelligence (AI) and generative AI (GenAI). They are a way to automate work almost effortlessly so that repetitive and boring tasks get done with the least amount of effort and perhaps, more consistently. In business software, a broad range of software providers are claiming agents to be a panacea that can improve performance and lower costs. They are alluring, with an almost unlimited number of potential...</span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/buyer-beware-the-evolution-of-agentic-ai-in-business-software"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/office-of-finance">Office of Finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/digital-business">Digital Business</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/agenticai">AgenticAI</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://research.isg-one.com/analyst-perspectives/skills-vs.-jobs-using-tech-to-power-talent-and-workforce-development">Skills vs. Jobs: Using Tech to Power Talent and Workforce Development</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matthew-brown"> Matthew Brown </a> on <span class="post-date">26 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=200&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=100&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png 100w, https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=200&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png 200w, https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=300&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png 300w, https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=400&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png 400w, https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=500&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png 500w, https://research.isg-one.com/hs-fs/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png?width=600&name=Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>If you’ve ever held the same job title at two different companies, you know firsthand how misleading job-based approaches to work can be. I’ve had the same title across different organizations, and the roles couldn’t have been more different. The responsibilities, expectations and even the skills required varied so significantly that it made me question why we continue to define work through the lens of static job descriptions.</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/skills-vs.-jobs-using-tech-to-power-talent-and-workforce-development"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/human-capital-management">Human Capital Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-engagement">Employee Engagement</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/learning-management">Learning Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/talent-management">Talent Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/workforce-management">Workforce Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/total-compensation-management">Total Compensation Management</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://research.isg-one.com/analyst-perspectives/the-eu-scaling-back-sustainability-reporting-mandates">The EU Scaling Back Sustainability Reporting Mandates</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">25 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=200&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=100&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png 100w, https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=200&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png 200w, https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=300&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png 300w, https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=400&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png 400w, https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=500&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png 500w, https://research.isg-one.com/hs-fs/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png?width=600&name=The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>The recent recommendation by the European Commission to scale back the scope, granularity and timeline for sustainability reporting represents a sea change in this form of corporate disclosure requirements. If enacted, it will substantially reduce or eliminate the reporting requirements for many enterprises, especially small to midsize establishments, as well as provide more time for compliance. While environmental reporting requirements appear to be easing, some regulatory disclosure...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/the-eu-scaling-back-sustainability-reporting-mandates"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/operations">Operations</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/supply-chain-planning">Supply Chain Planning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/business-planning">Business Planning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/supply-chain">Supply Chain</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/sustainability-management">Sustainability Management</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://research.isg-one.com/analyst-perspectives/sap-unleashes-business-data-cloud-for-unified-ai-strategy">SAP Unleashes Business Data Cloud for Unified AI Strategy</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/david-menninger"> David Menninger </a> on <span class="post-date">20 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=200&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=100&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png 100w, https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=200&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png 200w, https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=300&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png 300w, https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=400&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png 400w, https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=500&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png 500w, https://research.isg-one.com/hs-fs/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png?width=600&name=SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>SAP was formed in 1972 to create standardized business software that would integrate all business processes and enable data processing in real time. Following the success of the initial release and subsequent R/2, the company went public in 1988 and has grown into one of the world’s largest software companies, reporting more than $37 billion in revenues in its most recent annual report. Through internal development efforts and numerous acquisitions, including Business Objects, Sybase, Ariba,...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/sap-unleashes-business-data-cloud-for-unified-ai-strategy"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/machine-learning">Machine Learning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics">Analytics</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai">AI</a>, <a href="https://research.isg-one.com/analyst-perspectives/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://research.isg-one.com/analyst-perspectives/demystifying-apis-what-every-hr-buyer-needs-to-know">Demystifying APIs: What Every HR Buyer Needs to Know</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matthew-brown"> Matthew Brown </a> on <span class="post-date">18 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=200&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=100&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png 100w, https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=200&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png 200w, https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=300&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png 300w, https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=400&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png 400w, https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=500&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png 500w, https://research.isg-one.com/hs-fs/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png?width=600&name=Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>If you’re an HR professional, the term “API” might make you want to hand things off to IT and move on with your day. After all, you’re here to focus on people, not tech jargon, right? But here’s the thing—APIs play a major role in making sure your HR technology works for you. And no, you don’t need to be an engineer to understand why they matter.</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/demystifying-apis-what-every-hr-buyer-needs-to-know"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/human-capital-management">Human Capital Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/learning-management">Learning Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/talent-management">Talent Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/workforce-management">Workforce Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/payroll-management">Payroll Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/total-compensation-management">Total Compensation Management</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-experience">employee experience</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://research.isg-one.com/analyst-perspectives/alteryx-refines-ai-platform-for-analytics">Alteryx Refines AI Platform for Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">13 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=200&name=Alteryx_Refines_AI_Platform_for_Analytics.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=100&name=Alteryx_Refines_AI_Platform_for_Analytics.png 100w, https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=200&name=Alteryx_Refines_AI_Platform_for_Analytics.png 200w, https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=300&name=Alteryx_Refines_AI_Platform_for_Analytics.png 300w, https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=400&name=Alteryx_Refines_AI_Platform_for_Analytics.png 400w, https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=500&name=Alteryx_Refines_AI_Platform_for_Analytics.png 500w, https://research.isg-one.com/hs-fs/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png?width=600&name=Alteryx_Refines_AI_Platform_for_Analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Increased enterprise focus on artificial intelligence (AI) and generative AI (GenAI) has served to sharpen the focus on the need for trusted data and reliable analytics and <span><a href="https://www.isg-research.net/focus/analytics-and-data/data-operations">data operations</a></span>. The <span><a href="https://isg-one.com/advisory/ai-advisory/state-of-the-generative-ai-market-report-2024">ISG State of Generative AI Market Report</a> highlighted that elevated expectations and demands associated with AI are a forcing function for enterprises to take long-overdue steps to improve data and analytics processes to ensure that data that is clean, well-organized and compliant with regulatory...</span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/alteryx-refines-ai-platform-for-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics">Analytics</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai">AI</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/data-operations">data operations</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/analytics-and-data">Analytics and Data</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://research.isg-one.com/analyst-perspectives/what-analysts-should-do-when-anyone-can-analyze-with-ai">What Analysts Should Do When Anyone Can Analyze with AI</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/robert-kugel"> Robert Kugel </a> on <span class="post-date">12 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=200&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=100&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png 100w, https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=200&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png 200w, https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=300&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png 300w, https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=400&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png 400w, https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=500&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png 500w, https://research.isg-one.com/hs-fs/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png?width=600&name=What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p><span>One of the promised benefits of artificial intelligence (AI), Generative AI (GenAI) and agents is that they can make everyone their own financial and business analyst. It’s true that these technologies can make it possible for everyone to access once hard-to-reach data (with suitable permissions), unleash agents to assemble the data into useful tables and charts along with commentary describing results and highlighting underlying drivers of results, propose next best actions and use natural...</span></p> </div> <a href="https://research.isg-one.com/analyst-perspectives/what-analysts-should-do-when-anyone-can-analyze-with-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/office-of-finance">Office of Finance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/business-planning">Business Planning</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/erp-and-continuous-accounting">ERP and Continuous Accounting</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai">AI</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/ai-and-machine-learning">AI and Machine Learning</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://research.isg-one.com/analyst-perspectives/actian-enables-data-discovery-and-data-intelligence">Actian Enables Data Discovery and Data Intelligence</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/matt-aslett"> Matt Aslett </a> on <span class="post-date">11 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=200&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=100&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png 100w, https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=200&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png 200w, https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=300&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png 300w, https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=400&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png 400w, https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=500&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png 500w, https://research.isg-one.com/hs-fs/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png?width=600&name=Actian_Enables_Data_Discovery_and_Data_Intelligence.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Data catalogs provide an inventory of data assets that surface metadata from data platforms, analytics tools and applications that can be used to facilitate data discovery and data usage across an enterprise. As I recently explained, however, there are actually <span><a href="https://mattaslett.isg-one.com/data-catalogs-serve-multiple-roles-and-use-cases">multiple types of data catalogs</a></span> that offer functionality to address specific use cases and user roles, including data inventory, data discovery and data governance. The data intelligence catalog is an emerging category that combines...</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/actian-enables-data-discovery-and-data-intelligence"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/governance">Governance</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/operations">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://research.isg-one.com/analyst-perspectives/the-state-of-conversational-software-using-automation-and-ai">The State of Conversational Software Using Automation and AI</a></h2> <div class="author-meta"> Posted by <a href="https://research.isg-one.com/analyst-perspectives/author/jeff-orr"> Jeff Orr </a> on <span class="post-date">06 March 2025 </span> </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=200&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png" width="200" !important: srcset="https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=100&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png 100w, https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=200&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png 200w, https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=300&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png 300w, https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=400&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png 400w, https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=500&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png 500w, https://research.isg-one.com/hs-fs/hubfs/The_State_of_Conversational_Software_Using_Automation_and_AI.png?width=600&name=The_State_of_Conversational_Software_Using_Automation_and_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Conversational automation leverages artificial intelligence (AI)-powered agents, chatbots and virtual assistants to automate both customer interactions and internal processes. These systems understand natural language, sentiment and intent, generating relevant responses and executing actions based on user input. The software provider landscape is analyzed in the <span><a href="https://www.isg-research.net/buyers-guide/digital_technology/conversational_automation/2024">ISG Buyers Guide for Conversational Automation</a></span>.</p> </div> <a href="https://research.isg-one.com/analyst-perspectives/the-state-of-conversational-software-using-automation-and-ai"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://research.isg-one.com/analyst-perspectives/topic/self-service">Self-service</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/natural-language-processing">natural language processing</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/chatbots">Chatbots</a>, <a href="https://research.isg-one.com/analyst-perspectives/topic/conversational-automation">Conversational Automation</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; 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100px) 100vw, 100px" srcset="https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://research.isg-one.com/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://research.isg-one.com/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://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://research.isg-one.com/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://research.isg-one.com/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;" src="https://research.isg-one.com/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=100&name=ISG_Bug_Reverse_Transparent.png" alt="ISG Software Research" width="100" data-constrained="true" caption="false" 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href="https://research.isg-one.com/analyst-perspectives/topic/dynamics-ax">Dynamics AX <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/dynamics-gp">Dynamics GP <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/dynamics-nav-dynamics-sl">Dynamics NAV Dynamics SL <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/e-discovery">e-discovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/echopass">Echopass <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/eclipse">Eclipse <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/edi">EDI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/ediscovery">eDiscovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/editorial">Editorial <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/effectiveness">effectiveness <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/eg-solutions">eg Solutions <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/elasticube">elasticube <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/eloqua">Eloqua <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/embedded-analytics">embedded analytics <span class="filter-link-count" dir="ltr">(95)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/emc">EMC <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-engagement">Employee Engagement <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-experience">employee experience <span class="filter-link-count" dir="ltr">(51)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-onboarding">Employee Onboarding <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/employee-productivity">Employee Productivity <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/empower">Empower <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/end-to-end">end-to-end <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/endeca">endeca <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enghouse-interactive">Enghouse interactive <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enkata">Enkata <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-applications">Enterprise Applications <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-data-strategy">Enterprise Data Strategy <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-messaging-internet-of-things-iot-middle">Enterprise messaging, Internet of Things, IoT, mid <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-profitability-management">enterprise profitability management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-resource-planning">Enterprise Resource Planning <span class="filter-link-count" dir="ltr">(72)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-risk-management">enterprise risk management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-software">Enterprise Software <span class="filter-link-count" dir="ltr">(27)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-spreadsheet">enterprise spreadsheet <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterprise-tax">Enterprise Tax <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/enterworks">Enterworks <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/environment-management">Environment Management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/environmental">Environmental <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/envision">Envision <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/epicor">Epicor <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/epiphany">Epiphany <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/equifax">Equifax <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/equity-research">equity research <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/erm">ERM <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/erp">ERP <span class="filter-link-count" dir="ltr">(83)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/erp-and-continuous-accounting">ERP and Continuous Accounting <span class="filter-link-count" dir="ltr">(158)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/error">error <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/esg">ESG <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/esri">ESRI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/essbase">Essbase <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/ethority">eThority <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/event-data">Event Data <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/event-discovery">Event discovery <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/event-stream">Event Stream <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/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://research.isg-one.com/analyst-perspectives/topic/event-streams">Event Streams <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/exacttarget">ExactTarget <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/exadata">exadata <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/exalytics">Exalytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/excel">Excel <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/excentive">Excentive <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/executive">executive <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/expense">expense <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/expense-management">expense management <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/experience-management">Experience Management <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/expert-systems">Expert Systems <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/exploration">Exploration <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/exploratory-analytics">exploratory analytics <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/extended-close">extended close <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/extended-reality">extended reality <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/exterro">Exterro <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/f-type">F-Type <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/fasb">FASB <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/fast-close">Fast close <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/fei">FEI <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/ferf-ceo">FERF CEO <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance">finance <span class="filter-link-count" dir="ltr">(53)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-analytics">Finance Analytics <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-cloud">finance cloud <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-financial-applications-financial-close">Finance Financial Applications Financial Close <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-performance-management">Finance Performance Management <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/finance-transformation">finance transformation <span class="filter-link-count" dir="ltr">(9)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial">financial <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-analytics">financial analytics <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-applications">Financial Applications <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-close">Financial Close <span class="filter-link-count" dir="ltr">(5)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-governance">Financial Governance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-management">Financial Management <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-model">financial model <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-performance">Financial Performance <span class="filter-link-count" dir="ltr">(561)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-performance-2">financial performance <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-performance-management">Financial Performance Management <span class="filter-link-count" dir="ltr">(237)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-performance-management-fpm">Financial Performance Management (FPM) <span class="filter-link-count" dir="ltr">(14)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-planning">financial planning <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-regulation">financial regulation <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/financial-reporting">financial reporting <span 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href="https://research.isg-one.com/analyst-perspectives/topic/qlik-qlik-sense">Qlik Qlik Sense <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/qlik-qonnections">Qlik Qonnections <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/qlik-analytics-qlik-sense-qlik-business-intelligen">Qlik, Analytics, Qlik Sense, Qlik, Business intell <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/qliktech">Qliktech <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/qlikview">QlikView <span class="filter-link-count" dir="ltr">(10)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/qualtrics">Qualtrics <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/quantrix">Quantrix <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/quinterian">Quinterian <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/quotas">Quotas <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/qvidian">Qvidian <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/r">R <span class="filter-link-count" dir="ltr">(4)</span></a> </li> 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href="https://research.isg-one.com/analyst-perspectives/topic/reporting-xbrl-ixbrl-document-management">Reporting, XBRL, iXBRL, document management <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/research">Research <span class="filter-link-count" dir="ltr">(16)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/responsetek">ResponseTek <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/retail-analytics">Retail Analytics <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://research.isg-one.com/analyst-perspectives/topic/revenue">Revenue <span class="filter-link-count" dir="ltr">(8)</span></a> </li> <li style="display:none;"> <a 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provide you with a better, faster and safer experience and for marketing purposes.</span></p> </div> </div> </div> </footer></div>  <script defer src="/hs/hsstatic/content-cwv-embed/static-1.1293/embed.js"></script> <script src="https://research.isg-one.com/hs-fs/hubfs/hub_generated/template_assets/1/94278782902/1743820973941/template_Ventana_Research_2023_child.min.js"></script> <script> var hsVars = hsVars || {}; hsVars['language'] = 'en'; </script> <script src="/hs/hsstatic/cos-i18n/static-1.53/bundles/project.js"></script> <script src="https://research.isg-one.com/hs-fs/hubfs/hub_generated/module_assets/1/90531062927/1743584019626/module_Header-Global.min.js"></script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Human Meets Machine with AI: How to Manage Technology with Workforce", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/human-meets-machine-with-ai-how-to-manage-technology-with-workforce", "datePublished": "01/04/2025", "dateModified": "01/04/2025", "articleBody": "In the rush to embrace AI and automation, organizations are making a critical misstep: they are treating technology as a replacement for HR rather than as an enabler of its evolution. While AI, machine learning and automation offer powerful efficiencies, HR's core mission has always been about people. The challenge today is ensuring that technology amplifies—not replaces—the human touch in HR. Unfortunately, many organizations are being short-sighted in their approach, failing to fully leverage innovations that could significantly enhance HR’s capabilities and strategic impact. HR is lagging behind technology, and it’s not because the innovations aren’t there. It’s because HR professionals often don’t fully understand the capabilities. HR professionals are 'people people,' not typically technologists, and as a result, they sometimes approach AI and digital tools as if they are simply the next iteration of automation rather than a fundamental shift in how HR operates. Too often, HR teams are tasked with implementing technology without a clear understanding of its design and purpose. Even within the same category—learning, compensation, performance management—each technology solution is built with a slightly different intention. Without deep insight into those nuances, HR professionals risk misusing or underutilizing their tools. Worse, the allure of the AI-powered 'easy button' means many overlook the need for governance and human oversight, leading to haphazard adoption. The most glaring example of this is AI-driven workforce reductions. Many organizations see AI as a way to streamline HR operations and reduce headcount without recognizing the broader implications. Recruiting is more than job postings and resume screening. Learning & Development goes beyond course completions and compliance tracking. By reducing HR to a set of transactional tasks that can be automated, organizations strip the function of its most valuable asset: the ability to connect, support and guide employees. I assert that by 2027, two-thirds of enterprises will expect their HCM software to be utilizing AI for personalization in the interactions between employees and HR. AI and GenAI have had the most significant impact on HR—both positive and negative. When used correctly, AI enhances HR’s ability to drive engagement, recognize employees and personalize the employee experience. It can remind managers of key milestone events and help analyze trends in engagement and performance. But when misused, it makes HR feel performative, with AI-driven recognition and support coming off as robotic rather than genuine. Employees can sense when something is inauthentic, and an AI-generated message celebrating an anniversary or achievement isn’t the same as a heartfelt acknowledgment from a leader. Beyond engagement, AI’s impact on analytics and decision-making presents another challenge. Organizations that don’t understand AI’s limitations risk making flawed decisions based on incomplete or biased data. HR teams must take an active role in ensuring that AI-driven insights are interpreted correctly, and that human oversight remains a key part of the process. The solution is twofold: HR professionals must deepen their understanding of technology, and HR tech vendors must take a more active role in education. HR leaders should set expectations for their teams to stay informed about the technology landscape, especially in their respective domains. Understanding the why behind AI tools—not just the what—will enable HR to be more strategic in its adoption and application of technology. At the same time, HR tech vendors need to do more than just sell their products; they must educate their buyers. Vendors are uniquely positioned to explain not only how their solutions work but also how they compare to other offerings in the market. Without this education, HR teams may invest in tools that aren’t the right fit or fail to maximize the value of the technology they do adopt. One of the most overlooked aspects of HR tech transformation is change management. The most successful technology implementations occur when HR works closely with finance, IT and the executive team to ensure alignment and buy-in. AI in HR isn’t just an HR issue—it impacts the entire organization, from workforce planning to compliance and beyond. Collaboration is key. Without it, HR tech investments can fall flat, leading to poor adoption, employee resistance and wasted resources. Change management strategies should focus on clear communication, stakeholder engagement and practical training to help HR professionals build competence and confidence in new tools. The future of HR technology isn’t about replacing human expertise with digital tools—it’s about integrating the two in a way that elevates HR’s impact. AI is already transforming HR through writing support, trend analysis and data interpretation. The next frontier will be AI-enabled digital workers—agents that assist HR teams in more complex ways, allowing human professionals to focus on high-value, strategic initiatives. This leads me to assert that by 2027, two-thirds of enterprises will require AI in all HCM and adjacent systems to curate personalized experiences for all workers to enable engagement for productivity and retention. To prepare for this shift, organizations must invest in structured educational tracks to develop AI competency within HR. AI isn’t an 'either-or' proposition—it’s a shared vision where people and technology work together to create a better workplace. Businesses are integrating AI across every role, equipping their workforce with intelligence and automation that enhance decision-making, streamline operations, and drive productivity—gains that should be acknowledged and strategically leveraged. When organizations recognize that AI should empower HR rather than diminish its role, they’ll unlock the full potential of both human expertise and technological innovation. HR has the opportunity—and responsibility—to lead the charge in integrating AI and technology in a way that enhances the employee experience rather than diminishing it. The path forward requires: Education: HR professionals must invest time in understanding how AI works, why it was built, and how to use it responsibly. Governance: AI requires oversight. HR teams must establish ethical guidelines and frameworks for responsible AI use. Collaboration: Success comes from working across the business, ensuring that AI and automation serve a broader strategic purpose. Change Management: HR tech transformations fail when change management is underestimated. Organizations must ensure employees at all levels are prepared for AI integration. Technology should not dictate the future of HR. Instead, HR must take the lead in shaping how technology is used—ensuring that the human element remains at the core of every innovation. Organizations that get this balance right will see AI not as a replacement, but as an accelerator of HR’s true potential. Regards, Matthew Brown", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/Human_Meets_Machine_with_AI_How_to_Manage_Technology_with_Workforce.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matthew Brown", "url": "https://research.isg-one.com/analyst-perspectives/author/matthew-brown", "sameAs": "https://www.linkedin.com/in/matthewwbrown/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "Buyer Beware: The Evolution of Agentic AI in Business Software", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/buyer-beware-the-evolution-of-agentic-ai-in-business-software", "datePublished": "27/03/2025", "dateModified": "27/03/2025", "articleBody": "Agents and “agentic AI” are all the rage now, eclipsing last year’s focus on artificial intelligence (AI) and generative AI (GenAI). They are a way to automate work almost effortlessly so that repetitive and boring tasks get done with the least amount of effort and perhaps, more consistently. In business software, a broad range of software providers are claiming agents to be a panacea that can improve performance and lower costs. They are alluring, with an almost unlimited number of potential use cases. Agents are an important evolutionary step in the design of business software, similar to the transition from procedural programming to event-driven programming that accelerated in the late 1980s. That paradigm shift enabled business software to be more flexible and responsive in replicating how work is performed. Adding agents to the considerable body of well-developed business applications will take the capabilities of these applications to the next level. My colleague, David Menninger, recently wrote about the direction of agentic AI, and another, Matt Aslett, commented on the critical importance of data management as an enabler of agentic AI. Data is the key foundational element defining the capabilities and soundness of all AI, GenAI and agentic systems, especially those used in business applications that provide actionable workplace productivity opportunities. Like most advances in business technology, the age of agents is being proclaimed ahead of their actual arrival and practical accessibility. There is a fair amount of “agent washing” taking place as software providers rebadge their existing automation and programmatic software elements as new technology. Nonetheless, agents, along with AI and GenAI are set to have a profoundly positive impact on the productivity and performance in business for enterprises. Much of the vision is just that at the moment and potential pitfalls and disappointments are easy for informed sceptics to identify. For example, ISG Research asserts that through 2027, agentic AI and GenAI governance will remain a significant concern for more than one-half of enterprises, limiting the deployment, and therefore the realized value of LLMs. Still, focusing on the gap between the current state of agents and even their potential five years on is a bit like observing in 1995 that all this talk about the internet is a bunch of hype. Sorting out differences between claims and reality requires a useful definition of agents and agentic systems. ISG Research defines agentic AI as software designed to execute business processes through autonomous actions, potentially controlling multiple processes and systems through the orchestration of one or more AI or algorithmically determined rules-based models, based on an understanding of the environment and the goals that should be achieved. Agents differ from predictive AI and GenAI in that they are fully assembled components that perform the entire scope of the sense-analyze-decide-act system paradigm. Predictive and generative AI are used by agents, but agents alone take actions autonomously based on data and their decision-making constructs. Agents differ from bots in that the latter are rules-based systems designed to perform specific tasks, but unlike agents they do not learn, adapt and make decisions on their own based on their interactions with their environment. Agentic systems may use bots and programmatic devices such as extract, transform and load (ETL), application programming interfaces (APIs) and robotic process automation (RPA) in their operation but only agents produce actions autonomously. Autonomous decision-making capabilities require ongoing training regimes to ensure that the actions and their outcomes are reliably consistent with intentions. This is easy to describe but often difficult to put into practice, especially for more sophisticated agents. And autonomous doesn’t mean that the work is completely hands-off. There may be decision nodes in the agent’s domain that exhibit insufficient certainty or where the potential negative consequences of a bad decision mean that the node will always require human review and decision. Agents are described in various ways, which adds to confusion. One approach simply classifies them as either task-based or role-based. The former are designed to execute processes while the latter replicate an individual’s behavior in the context of their function and responsibilities when performing specific tasks with definable outcomes. One type is not inherently superior to the other except in the context in which they are being used. Task-oriented agents can be simpler and less expensive to deploy and operate. Role-based systems can be more capable of a broader range of autonomous actions but with more extensive training and the higher costs that come along with this. Hybrids, where a role-based agent orchestrates a set of task agents to perform a process, also will evolve to be an important part of the landscape. An agent taxonomy to consider distinguishes them by their sophistication in training and the resulting scope of their abilities. Simple response agents are programmed to perform actions under specifically defined conditions. In some respects, these are quasi agents, similar to the most sophisticated robotic processing automation (RPA) bots. In our definition, they only become agents when they are routinely tested to ensure they are performative and autonomously curated to ensure their relevance to execute the defined task. The training routines and scope of data necessary are comfortably within the capabilities of most organizations. Model-based response agents are more sophisticated in that they are trained to align their actions to an algorithmically defined model, meaning they are capable of responding to a broader set of conditions without being explicitly programmed for each. The complexity of the training routines, sophistication of algorithms and the scope of data necessary to manage these agents are within the capabilities of many organizations. Goal-based agents focus on achieving outcomes, rather than responding to conditions, and plan a set or sequence of actions that must be taken to achieve a specific objective. These will challenge most organizations because of the scope of the data necessary for training, especially in ingesting real-time and unstructured data. Utility-based agents also are focused on a specific goal, but they can assess a broader set of conditions in multiple dimensions. They are designed to weigh the inherent trade-offs to be able to select the sequence of actions that achieve their objective while optimizing enterprise- or system-wide outcomes. For example, product availability versus the cost of holding inventory. Here too, organizations will be challenged to make these agents workable. It’s likely that in some use cases they will require consistently higher levels of human intervention in their execution and in that respect may be deemed to be semi-autonomous. Yet another aspect of an agentic system is the degree to which it is capable of handling static and dynamic complexity in the work it performs. Complexity, in turn, will be correlated with the scope of data required to train, operate and maintain the models that agents will employ, and therefore their cost. Static complexity is a function of: The number of steps and permutations in a process, mainly for task-based agents. The number of roles and individuals involved, mainly for role-based agents. The number of systems an agent touches in performing its assignment. The probability and related confidence achievable at decision nodes, both individually and as a full process. Dynamic complexity is a function of how often and to what degree: The agentic system requires testing to remain performative. Steps in a process change. Individuals and their roles change. Consequential external factors such as laws and regulations change. Systems utilized in the performance of an assignment change. Probabilities and related confidence at decision nodes change meaningfully. Like all predictive AI models, agents will require training, periodic testing and maintenance to ensure that they are operating properly. Methods for training agents and agentic systems are still in a very early stage. Parallel with efforts necessary to make AI and GenAI functional, they need reliable data with which to build and train agents and will require enterprises to take sustained and concerted steps to improve data management and data governance. ISG Research asserts that through 2026, one-third of enterprises will realize that a lack of AI and ML governance has resulted in biased and ethically questionable decisions. Heuristic and large action models (LAMs) are two basic approaches for training and testing agentic systems and their elements. A heuristic (rule of thumb) approach learns through observation, seeing how work is performed in what context and under which conditions. Process intelligence techniques, where system logs are used to apply process modeling and analysis to identify how tasks are typically performed in a specific context, will be useful heuristic training. These sorts of systems are likely to require a break-in period during which time they will require a solid dose of human intervention to ensure that the tasks are being executed properly but will become more autonomous over time. More sophisticated role-based agents are likely to benefit from the development of LAMs. As my colleagues have pointed out, unlike heuristic approaches that use machine learning to deal with closely bounded processes, LAMs are designed to make decisions and execute a series of actions across a variety of environments. Just as LLMs leverage vast datasets of text, LAMs are developed based on a large body of action and outcome data. At their core, LAMs are optimized for function calling as the mechanism for taking action. Agents are active systems, ones that dynamically interact with their environment using mechanisms to respond to inputs and act accordingly, in contrast to passive systems, which simply respond to external conditions without initiating changes. Agentic systems are designed to be autonomous to boost human productivity and improve performance. Yet, they need not be fully autonomous to achieve better results. These are still early days because, although it is possible to define what must be in place at a basic level to make agents and agentic systems practical, affordable and reliable, enterprises will struggle to make this vision a reality. ISG Research asserts that by 2028, one quarter of enterprises will have deployed the agentic AI generation of technology to support digital business and workplace environments. Agents or agent-like systems are beginning to appear in a wide range of business software and will continue to proliferate at an accelerating pace over the next three years. Agentic AI can help any enterprise but the readiness of an organization or enterprise to adopt the technology from specific software providers is likely to vary significantly. One reason is that their version of a specific application might be heavily customized, or they have insufficient clean data to support training and maintenance or both. I recommend taking an informed approach to assessing and adopting such software and addressing any barriers to their adoption. Many software providers are jumping on the agent bandwagon, using an expansive definition of agentic that does not measure up to our definition. Non-agentic technology can still be useful, but it’s necessary to understand its limitations along with its capabilities to make informed decisions about where and how to deploy software. I also recommend setting expectations appropriately. In these early years, agents, like humans, will have learning curves that require hands-on coaching to achieve full productivity. The growing availability of useful, safe and affordable agentic AI used in business will present executives with a significant opportunity over the next five years to achieve measurable performance gains and seize opportunities to alter their competitive landscape. Understanding the technology and acting now to address foundational requirements must be a core piece of their enterprise’s strategy, regardless of how aggressively they plan to employ agents in their operations, The danger of not taking action now far outweighs the relatively smaller costs of being prepared. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/Buyer_Beware_The_Evolution_of_Agentic_AI_in_Business_Software.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://research.isg-one.com/analyst-perspectives/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "Skills vs. Jobs: Using Tech to Power Talent and Workforce Development", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/skills-vs.-jobs-using-tech-to-power-talent-and-workforce-development", "datePublished": "26/03/2025", "dateModified": "26/03/2025", "articleBody": "If you’ve ever held the same job title at two different companies, you know firsthand how misleading job-based approaches to work can be. I’ve had the same title across different organizations, and the roles couldn’t have been more different. The responsibilities, expectations and even the skills required varied so significantly that it made me question why we continue to define work through the lens of static job descriptions. For years, companies have relied on job architectures to define roles, develop career paths and drive workforce decisions. But this approach is outdated. It boxes people in, creates artificial barriers to internal mobility and limits how businesses tap into their workforce’s full potential. The shift to skills-based talent practices isn’t just coming—it’s long overdue. Technology has made it possible to move beyond rigid job frameworks, yet many organizations are still dragging their feet. The problem isn’t whether we can shift to a skills-based model—it’s whether leaders are willing to embrace a new way of thinking. That shift is already starting to take shape. Because of this, I assert that by 2026, one-third of enterprises using workforce management systems will be assigning work or shifts by skills required, not just job type, achieving much greater precision in optimizing resource deployments. That means that in just a few years, organizations that still rely solely on job titles to structure their workforce will find themselves at a disadvantage. The companies that embrace a skills-based approach will be more agile, able to match talent to work with greater accuracy and better positioned to navigate disruptions. Change is hard, especially when it requires dismantling deeply ingrained systems. Moving from a job-based to a skills-based model isn’t just about updating job descriptions—it requires a fundamental reimagining of how work gets done. For companies that have built their entire workforce strategy around jobs, transitioning to skills feels overwhelming. It’s not a small lift to define a comprehensive skills ontology and map it across recruiting, compensation, learning, performance and succession planning. It requires companies to think differently—not just about the work itself, but about how skills can be repackaged, transferred, and developed to create more flexible, future-ready organizations. And yet, the payoff is undeniable. Organizations that embrace a skills-based approach gain long-term stability and the ability to flex and adjust as business needs evolve. Instead of struggling to fill narrowly defined job openings, they can tap into an existing pool of talent with the right skills—whether that means reskilling, upskilling or simply recognizing that employees have more to offer than their job titles suggest. Technology isn’t the barrier here—it’s the enabler. HR tech has advanced to the point where we can now connect skills to every aspect of the talent lifecycle. Skills data can inform everything from hiring decisions to workforce planning, performance management, and learning and development. The real problem isn’t that the technology doesn’t exist—it’s that many HR and business leaders don’t fully understand how to harness it. The market is flooded with AI-powered skills intelligence, talent marketplaces and analytics tools, yet many organizations still struggle to get meaningful insights from their data. HR tech providers need to do a better job of educating buyers, and HR leaders need to take ownership of learning how to use these tools effectively. Without the right understanding, skills data becomes just another set of metrics sitting in a dashboard, failing to drive real change. That’s a missed opportunity, leading me to assert that by 2028, demonstrable, transferrable skills will overtake prior job titles and education as leading qualifiers in the hiring and promotion processes for one-half of enterprises. This shift will fundamentally change how companies assess talent, making it even more critical for organizations to develop the infrastructure to measure, validate, and leverage skills effectively. I’ve seen firsthand how a skills-based approach opens doors that a job-based model would have kept shut. Throughout my career, my skills have extended far beyond what my job titles have suggested—but getting others to see that hasn’t always been easy. I can’t count the number of times I’ve been passed over because my job history didn’t perfectly match the keywords in a job description. Yet, when I’ve managed to persuade recruiters or hiring managers to have a conversation with me, they’ve been blown away by the breadth of my experience. And that’s the problem with a job-based approach. It forces people into predefined categories and ignores the fact that skills—real, applicable, valuable skills—are often hidden behind job titles that fail to tell the whole story. The same thing happens inside organizations. Employees are overlooked for opportunities simply because their job descriptions don’t explicitly state that they have the skills needed for a new role. They get stuck, disengaged, and either leave or, worse, disengage and stay—dragging down productivity, innovation and morale. Of course, shifting to a skills-based approach isn’t easy. I’ve seen organizations try and fail—usually because they were too focused on perfection instead of progress. They spent so much time trying to build the “perfect” skills framework that they never actually put it into practice. The companies that get it right take an iterative approach: start small, identify a core set of critical skills tied to business objectives and build from there. Stay flexible. Be willing to test, refine and adapt the model as you learn what works. Engage employees, ask for input and ensure the framework reflects the reality of work, not just theoretical HR models. No organization will get it perfect the first time—and that’s okay. The key is to start somewhere and adjust as you go. For organizations hesitant to make the shift, the biggest risk isn’t getting it wrong—it’s doing nothing. The world of work is changing too fast to rely on static job descriptions that don’t reflect real business needs. Companies that fail to evolve will struggle to attract and retain talent. Employees want growth, flexibility and opportunities to use their full range of skills. If organizations can’t provide that, they’ll lose their best people to companies that can. And this isn’t just an HR issue—it’s a business issue. Skills gaps impact productivity, innovation, and ultimately, the bottom line. Organizations that wait too long to adapt will find themselves falling behind competitors who have already embraced the shift. If this all feels overwhelming, start small. You don’t need to overhaul your entire workforce strategy overnight. Begin with one simple step: review your current skills inventory. Identify five to ten critical skills that drive success in your business. Assess whether you have the right training and development programs to support those skills. The key is to do something—to take that first step toward a more agile, skills-focused future. The shift from jobs to skills isn’t just an HR initiative; it’s a necessary evolution in how work gets done. Organizations that embrace it will be more resilient, adaptable, and competitive in the long run. Those that don’t will find themselves stuck in the past, struggling to keep up. The choice is clear. The only question is, how soon will you take that first step? Regards, Matthew Brown", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/Skills_vs_Jobs_Using_Tech_to_Power_Talent_and_Workforce_Development.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matthew Brown", "url": "https://research.isg-one.com/analyst-perspectives/author/matthew-brown", "sameAs": "https://www.linkedin.com/in/matthewwbrown/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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 EU Scaling Back Sustainability Reporting Mandates", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/the-eu-scaling-back-sustainability-reporting-mandates", "datePublished": "25/03/2025", "dateModified": "25/03/2025", "articleBody": "The recent recommendation by the European Commission to scale back the scope, granularity and timeline for sustainability reporting represents a sea change in this form of corporate disclosure requirements. If enacted, it will substantially reduce or eliminate the reporting requirements for many enterprises, especially small to midsize establishments, as well as provide more time for compliance. While environmental reporting requirements appear to be easing, some regulatory disclosure requirements remain for large enterprises. Moreover, there is still demand for enterprises to set and meet sustainability objectives to achieve increased operating efficiency and to associate their brand with a sustainable environment. For these enterprises, there remains the need to achieve efficient compliance with reporting mandates as well as have processes and systems in place to set overall objectives and mechanisms to assign responsibility for meeting them across the enterprise, a topic I covered recently. Attitudes toward regulation have been changing recently, and regulatory bodies have been stepping back from the scrupulously detailed environmental reporting regimes they previously created. This is especially the case for those that were enacted by the European Union’s (EU’s) Corporate Sustainability Reporting Directive (CSRD) and those proposed by the U.S. Securities and Exchange Commission (SEC) but never put into force. A push for sustainability investing—the underpinning rationale for the SEC’s actions—has faded from the headlines in part because repeated academic studies have demonstrated that this approach achieves lower returns than following basic investing principles when all costs are included. Last year, after a lengthy and contentious public comment period, the SEC created sweeping environmental reporting requirements but immediately shelved them. With the change in administration, it’s unlikely these will be put into effect. That left the already-in-place EU's CSRD and related mandates as the main source of reporting requirements. (Other countries have their own reporting statutes, but these have a limited global impact.) As constructed, the CSRD applied even to enterprises domiciled outside the EU doing relatively little business in the EU. However, in the interest of achieving higher economic growth and greater global competitiveness, the European Commission has proposed substantially narrowing the scope of the necessary disclosures both in terms of which entities are required to report and the granularity of the information collected and reported. It is also delaying implementation, which is necessary because ISG Research asserts that through 2027, one-half of enterprises will have insufficient data and software to adequately measure their environmental, social and governance (ESG) metrics to inform their governance, strategy, risk management and targets. The Commission's aim is to simplify EU rules and increase the bloc’s competitiveness by reducing overall administrative burdens by at least 25%, while targeting a 35% reduction for small to medium-size enterprises (SMEs). The proposed amendments cover sustainable finance reporting, sustainability due diligence, the EU Taxonomy and the Carbon Border Adjustment Mechanism. The key proposed changes include the following: Sustainability Reporting (CSRD and the EU Taxonomy): The amendments narrow the scope of CSRD to the largest companies (over 1,000 employees and €450 million in annual revenue) and postpone the reporting requirements until 2028. The EU Taxonomy, a classification system that defines economic activities that are environmentally sustainable, will be restructured to be less burdensome. Other large companies can elect voluntary reporting while some large entities are entirely exempt. Sector-specific standards (such as leather goods) will be eliminated and increased assurance requirements removed. The European Sustainability Reporting Standards (ESRS) that are core CSRD requirements will reduce the number of data points collected to focus on those most important while requiring less narrative text. Corporate Sustainability Due Diligence Directive (CSDDD) will remain mandatory for companies with over 1,000 employees and more than €450 million in annual revenue. The recommendation would simplify due diligence requirements, focusing on direct business partners, thereby reducing burdens on SMEs and harmonizing requirements across the EU. Measures imposing civil liability will be removed, while victims' rights to compensation will be preserved. The application of requirements will be postponed until 2028, with final guidelines delayed until 2026. Carbon Border Adjustment Mechanism (CBAM) rules for in-scope companies will be streamlined, including authorization and reporting requirements, while strengthened to prevent evasion. Sustainability accounting is useful beyond compliance. It can provide an orthogonal view into the performance of an enterprise. It applies an operational economic lens that quantifies the opportunity to conserve resources and, as a result, can lower economic and long-run financial costs. In so doing, it can increase the sustainability of an enterprise while promoting the conservation of resources. Rather than being predicated on a foundational assumption of market failure (that is, free markets cannot work and therefore regulation is required), basic sustainability accounting serves market efficiency by laying bare what might have been overlooked in financial accounting methods. The original purpose of requiring auditable measures of sustainability efforts was to hold enterprises accountable for environmental promises they make and to prevent greenwashing. That is not going away. What has been missing in discussions of sustainability accounting is a recognition that this form of measurement cannot replicate financial accounting’s auditability in any practical sense. This is what is driving reform in the EU. Compared to accounting methods and generally accepted accounting principles, environmental accounting lacks a double-entry foundation for verification as well as the centuries of analytical development and testing behind many financial accounting conventions and their underlying assumptions. The precision necessary to prove the soundness of the financial statements is probably not achievable because the marginal cost of additional layers of attempted precision cannot be justified in terms of achievable sustainability benefits from that additional data. Larger enterprises have the scale and systems to absorb the costs and the external impacts to justify the effort. Regulation has costs, and the European Commission’s recommendation can be seen as recognition that the poorer GDP performance of the bloc over the past two decades relative to a less regulated economy like the U.S. needs to be addressed to promote the economic sustainability of the EU. So where does this leave sustainability initiatives and reporting? Larger enterprises covered by EU mandates will still need to meet reporting standards, albeit on a more forgiving timetable. In the current environment, it’s highly unlikely that the SEC’s sustainability reporting requirements will be enacted. But doing well by doing good remains an objective for many enterprises. The economic conservation of resources—otherwise known as increasing efficiency—is still a prime management objective. Enterprises that see value in consuming less energy and water still need to manage to objectives. They must be able to set overall targets, delegate responsibility down through the organization to achieve them and have the systems to capture the information necessary to reliably measure resource consumption. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/The_EU_Scaling_Back_Sustainability_Reporting_Mandates.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://research.isg-one.com/analyst-perspectives/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "SAP Unleashes Business Data Cloud for Unified AI Strategy", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/sap-unleashes-business-data-cloud-for-unified-ai-strategy", "datePublished": "20/03/2025", "dateModified": "20/03/2025", "articleBody": "SAP was formed in 1972 to create standardized business software that would integrate all business processes and enable data processing in real time. Following the success of the initial release and subsequent R/2, the company went public in 1988 and has grown into one of the world’s largest software companies, reporting more than $37 billion in revenues in its most recent annual report. Through internal development efforts and numerous acquisitions, including Business Objects, Sybase, Ariba, SuccessFactors, Fieldglass and Concur, SAP has built out a portfolio of business applications along with a technology platform to address the information processing needs of enterprises. The company recently hosted an event titled SAP Business Unleashed which was focused on new data and artificial intelligence (AI) capabilities in its portfolio. At the event, they announced SAP Business Data Cloud and SAP Databricks, which are designed to address enterprise needs around data and AI. SAP Business Data Cloud (BDC) is the unification of capabilities that previously existed in two separate offerings: SAP Datasphere and SAP Analytics Cloud. As its name implies, Analytics Cloud was SAP’s analytics offering. However, unlike many of its competitors, it also included planning capabilities. SAP Datasphere was the evolution of SAP’s data warehouse as a service (formerly called SAP Data Warehouse Cloud) and included data management, data integration, data federation, data governance and data catalog capabilities. We recently covered SAP Datasphere in our Data Intelligence Buyers Guide and SAP Analytics Cloud in our Analytics and Data Buyers Guide. In both reports, SAP was rated an Overall Leader. Now, these products have been unified into a single offering, eliminating the need for integration since all the data and metadata are shared between the two. SAP Databricks is another component embedded into BDC. Databricks is a popular data and AI platform which I’ve recently written about and was rated Exemplary in the ISG Buyers Guide on Analytic Data Platforms. It offers a complete set of data management, data engineering and AI capabilities. Within BDC, it will enable custom AI and data processing beyond what SAP offers in its pre-built applications, and it will facilitate “zero copy” access to SAP data via federation capabilities. SAP Databricks can also be used to incorporate external data into custom workloads or to access SAP data in a much more open fashion than in the past. As I have written recently, generative AI (GenAI) is evolving to incorporate agentic AI. Agentic AI helps automate tasks, reducing or eliminating the need for human involvement. SAP is also evolving its GenAI capabilities and announced plans for a collection of pre-built AI agents across finance, service and sales, with more planned throughout the year. across finance, service, and sales, with more across the SAP Business Suite portfolio in 2025. In the first quarter of 2025, SAP plans to make a cash collection agent available which can analyze disputes, validate the details and recommend resolutions. Other agents announced include a Q&A agent that can monitor customer interactions, spotting questions and providing relevant answers; a knowledge creation agent that can identify and incorporate the knowledge of novel case resolutions into an enterprise’s knowledge base; and a case classification agent that can route a case to the correct team. In addition to pre-built agents, SAP previewed new AI agent builder capabilities in Joule Studio, SAP’s agentic and GenAI platform. The studio can be used to define agents and orchestrate activities among multiple agents, including both pre-built and custom agents. The fact that BDC includes planning capabilities should make its agentic AI capabilities more helpful than platforms without planning capabilities. In fact, my colleague Matt Aslett asserts that through 2027, less than one-third of analytics software providers will include driver-based planning capabilities, making decision intelligence and agentic AI less effective. SAP has stated that its focus is on embedded AI, and its plans for pre-built agents across various business functions and applications support that strategy. Given SAP’s huge presence in the business applications market, this approach makes sense. However, with BDC and the embedded Databricks offering, SAP also offers enterprises the tools they need to customize and extend their data and AI efforts to incorporate non-SAP data. I recommend that enterprises learn about these new capabilities. SAP customers can begin to plan when and how they can migrate to the new capabilities. Those who are not customers should understand what is available either as part of an evaluation of SAP or to make sure your application provider is developing similar capabilities. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/SAP_Unleashes_Business_Data_Cloud_for_Unified_AI_Strategy.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://research.isg-one.com/analyst-perspectives/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "Demystifying APIs: What Every HR Buyer Needs to Know", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/demystifying-apis-what-every-hr-buyer-needs-to-know", "datePublished": "18/03/2025", "dateModified": "18/03/2025", "articleBody": "If you’re an HR professional, the term “API” might make you want to hand things off to IT and move on with your day. After all, you’re here to focus on people, not tech jargon, right? But here’s the thing—APIs play a major role in making sure your HR technology works for you. And no, you don’t need to be an engineer to understand why they matter. APIs (Application Programming Interfaces) might sound like something best left to IT, but they’re actually the key to ensuring your HR tech stack integrates seamlessly, adapts as your business evolves and doesn’t become an expensive relic. And, let’s be honest—how many of us have spent way too much time pulling reports from multiple systems and stitching them together in Excel? Better API strategies can help avoid that mess. Let’s break this down in a way that makes sense for HR. Imagine you walk into a restaurant and sit down at a table. You check the menu and decide what you want. You don’t walk into the kitchen and start cooking—you tell the server your order. The server relays your request to the kitchen, where your food is prepared, and then brings it back to your table. That’s essentially what an API does. It’s the server that takes a request from one system (your HR platform) and delivers it to another system (payroll, benefits, recruiting, finance—you name it). Then, it brings the response back in a way that makes sense. You don’t need to know how the kitchen (IT infrastructure) works, but you do need to know what’s on the menu and how to place your order effectively. Even if you’re using an all-in-one HCM suite, APIs play a crucial role in keeping your systems flexible, connected and capable of adapting to the changing needs of the business. Many HR teams assume that buying a single platform means everything just works together seamlessly, but the reality is that no one system does it all. Businesses evolve, new tools emerge and priorities shift, which means integrations will always be a part of the equation. Because of this, I assert through 2026, one-third of enterprises will use HCM as the foundation to provide visibility into workforce engagement and will use people analytics to alert management on exceptions and risks. APIs help extend the life of your HR technology, ensuring HR integrates smoothly with finance, IT and operations. They also reduce reliance on IT for every single data connection, especially with the rise of low-code/no-code tools making APIs more accessible. HR tech doesn’t operate in a vacuum. No one wants to find out too late that their HR system can’t exchange data effectively with the tools the rest of the business depends on. APIs help ensure that doesn’t happen. Misconceptions about APIs are common, and they often lead HR buyers to overlook or misunderstand their role in selecting technology. One of the biggest mistakes HR buyers make is assuming IT handles integrations, so they don’t need to worry about it. In reality, HR doesn’t need to build APIs, but they do need to know what’s possible so they can work with IT and vendors effectively. Another common misconception is thinking that all integrations are the same. They’re not. Some are real-time, some are batch processes, and some are basically just glorified exports/imports. Not all APIs are created equal. Just because a system has an API doesn’t mean everything connects automatically. Poor documentation, rigid structures and unreliable data syncing can all create headaches. Instead of simply asking vendors if they have an API, HR buyers should dig deeper with smarter questions. Is it well-documented and easy to use? Good documentation means smoother integration. Can HR teams use low-code/no-code tools to interact with it? This lowers reliance on IT. How flexible is it? Can it handle custom fields and business rules? How often does data sync? Real-time? Scheduled? Manual? Knowing what to ask ensures you don’t get stuck with clunky, rigid or outdated integrations. HR teams are becoming more tech-savvy—not because they suddenly love coding, but because modern HR technology demands it. Generational shifts in the workplace have also created expectations that HR professionals should be well-informed tech buyers, not just passive users. SaaS tools were meant to empower business teams, not just IT. The more HR teams embrace this mindset, the better they can work with IT as strategic partners rather than just requesting fixes and integrations. They can drive better automation, analytics and AI adoption with smarter integration strategies. And they can adapt faster to changing business needs without being limited by rigid, outdated systems. Because of this, I assert by 2028, the majority of enterprises will abandon the traditional Applicant Tracking System (ATS) in favor of a talent intelligence platform, combining external and internal talent evaluation, skills matching and people analytics. APIs are powerful, but they’re not without limitations. Poorly designed APIs can result in security risks, unreliable data exchanges or vendor lock-in, where businesses find themselves unable to switch providers without significant effort. This doesn’t mean APIs should be feared—it just means HR teams should partner with IT to ensure proper governance and alignment with company standards and policies. Understanding these limitations can help HR buyers make more informed decisions, ensuring that integrations remain scalable and future-proof. To illustrate the power of APIs, imagine an HR team preparing for a compliance audit. They need to pull data on employee headcount, pay equity, performance scores, training records and tenure across multiple systems. Without seamless integrations, this process requires pulling reports from different platforms, manually reconciling inconsistencies and hoping for the best. With well-integrated APIs, however, this data can be aggregated in real-time, reducing human error and providing a clearer picture for decision-making. The same logic applies to talent reviews or headcount planning—APIs ensure that HR has access to reliable, up-to-date information without unnecessary administrative burden. APIs don’t have to be intimidating, and you don’t have to be a developer to understand their impact. What you do need is an awareness of how they shape your HR technology decisions. Next time you evaluate an HR system, rethink your approach to integration. Ask smarter questions. Challenge vague API claims. Demand better flexibility. It’s time for HR to move from data silos and Excel workarounds to seamless, strategic integrations. If you’re ready to take action, start by having a conversation with your IT team about your current integrations. Where are the gaps? What’s working well? What could be improved? This isn’t just about making life easier—it’s about ensuring HR technology serves the business in the best way possible. The tools are there. Now it’s time to use them to their full potential. Regards, Matthew Brown", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/Demystifying_APIs_What_Every_HR_Buyer_Needs_to_Know.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matthew Brown", "url": "https://research.isg-one.com/analyst-perspectives/author/matthew-brown", "sameAs": "https://www.linkedin.com/in/matthewwbrown/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "Alteryx Refines AI Platform for Analytics", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/alteryx-refines-ai-platform-for-analytics", "datePublished": "13/03/2025", "dateModified": "13/03/2025", "articleBody": "Increased enterprise focus on artificial intelligence (AI) and generative AI (GenAI) has served to sharpen the focus on the need for trusted data and reliable analytics and data operations. The ISG State of Generative AI Market Report highlighted that elevated expectations and demands associated with AI are a forcing function for enterprises to take long-overdue steps to improve data and analytics processes to ensure that data that is clean, well-organized and compliant with regulatory standards. Software providers such as Alteryx stand to gain by providing functionality that spans the data and analytics lifecycle, including data integration, data preparation and data quality, as well as the development of AI-enhanced analytics reports, dashboards and applications. Alteryx was founded in 1997 with an initial focus on demographic mapping and reporting software. Its evolution into the company today began in 2006 with the first launch of its eponymous software for developing analytic processes and applications. The company remains known for its Alteryx Designer desktop analytics product as well as Alteryx Server for sharing and automating analytic processes, but has placed greater emphasis on Alteryx Analytics Cloud since its launch in 2022. In recent years the company has also invested in the incorporation of, and support for, AI. This includes the development of its AiDIN AI engine to automate and enhance analytics processes, as well as functionality to enable the use of Alteryx Analytics Cloud as a platform for automated machine learning. Alteryx was acquired by Clearlake Capital and Insight Partners in a deal that closed in March 2024 and valued the company at $4.4 billion. Since then Alteryx has hired former UserTesting and Salesforce executive Andy MacMillan as its Chief Executive Officer and former OpenText, and UserTesting executive Nali Giliana as Chief of Staff and Strategy to lead strategic priorities and drive innovation. The Alteryx AI Platform provides capabilities for self-service data preparation, data enrichment, data quality, data integration, reporting, visualization, geospatial analytics, GenAI, conversational AI, machine learning and analytic application development. The company’s AI, GenAI and ML capabilities are served by the AiDIN AI engine, which complements existing functionality for natural language queries and natural language. First introduced in 2023, AiDIN utilizes GenAI models to add automation to Alteryx Analytics Cloud via functionality including document summarization, workflow summarization and the generation of recommended use-cases. The functionality enabled by AiDIN is delivered via a variety of Alteryx Analytics Cloud products targeted at specific use-cases. For example, Alteryx Designer Cloud delivers AiDIN-powered functionality for workflow summarization as well as Alteryx Copilot, which was introduced in 2024 and provides guidance for the creation of analytics workflows in Alteryx Designer. Meanwhile Alteryx Auto Insights features visualization and document summaries, as well as Playbooks for the generation of recommended AI use-cases. Alteryx Analytics Cloud facilitates the development of AI-driven applications also offers autoML functionality via Alteryx Machine Learning, including automated feature engineering, as well as access to a variety of open source algorithms. As my colleague David Menninger recently explained, enterprise enthusiasm for the potential benefits of AI need to be tempered with an understanding of the importance of detecting and preventing bias and avoiding toxicity. In our recent ISG Market Lens study on GenAI, 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. Alteryx has outlined its stance on these and other issues through the publication of its Responsible AI Principles, which addresses factors such as transparency and explainability, human agency and oversight, trust and accountability, reliability and safety, fairness and inclusivity, and empowering societal good. The Fall 2024 release, announced in November 2024 included improved support for hybrid environments that span on-premises and cloud infrastructure. For example, Analytic Apps in Alteryx Cloud Execution for Desktop enables the deployment, management, and use of custom applications both in Amazon Web Services, Microsoft Azure and Google Cloud Platform environments, as well as on-premises infrastructure. This takes advantage of Alteryx Cloud Execution for Desktop’s support for executing workflows in Alteryx Analytics Cloud that have been developed using the desktop-based Alteryx Designer product. Alteryx also added a new Standard Mode to Designer Cloud that provides a user experience more similar to the desktop Designer product, including enhanced data preparation and blending functionality and the ability to avoid the need for sampling when building workflows. For those using the existing Cloud Native mode in Designer Cloud, the Fall 2024 release also introduced LiveQuery functionality to perform real-time analysis of data in Snowflake or Databricks. The latest release also included the public preview of Magic Reports, which bring AI-based assistance, including automated insights and visualization, to the reporting capabilities of Alteryx Auto Insights. Alteryx has evolved its product portfolio significantly in recent years, with increased focus on cloud and hybrid environments, as well as AI-driven data preparation and analytics and now has the backing of heavyweight investment firms to grow its profile and reach. The breadth of capabilities addressed by Alteryx Analytics Cloud is highlighted by the company being rated as Exemplary in seven different ISG Buyers Guides in 2024: AI Platforms, MLOps, Data Integration, DataOps, Data Pipelines, Data Orchestration and Data Products. I recommend that enterprises considering their options for these, and related analytics and data use-cases include Alteryx in their evaluations. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/Alteryx_Refines_AI_Platform_for_Analytics.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://research.isg-one.com/analyst-perspectives/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "What Analysts Should Do When Anyone Can Analyze with AI", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/what-analysts-should-do-when-anyone-can-analyze-with-ai", "datePublished": "12/03/2025", "dateModified": "12/03/2025", "articleBody": "One of the promised benefits of artificial intelligence (AI), Generative AI (GenAI) and agents is that they can make everyone their own financial and business analyst. It’s true that these technologies can make it possible for everyone to access once hard-to-reach data (with suitable permissions), unleash agents to assemble the data into useful tables and charts along with commentary describing results and highlighting underlying drivers of results, propose next best actions and use natural language processing probe the numbers for further exploration. (Whew!) That’s not going to happen overnight. To achieve this potential, software providers must make it easy for everyone to get useful, reliable results. Yet, as these capabilities evolve from proof-of-concepts to standard operating procedures, the financial planning and analysis (FP&A) team must evolve its mission to remain useful and relevant in order to remain employed. I’ve witnessed a similar situation in my professional career. I grew up in the investments business and was a securities analyst for decades. The role of the Wall Street analyst evolved steadily from the 1950s until today largely because the information technology available to analyze and forecast investment-related information grew steadily more powerful. The manual numbers-crunching work that occupied most of an analyst’s time in mid-century was eliminated by the personal computer. Conference call technology and the internet substantially reduced the role of the sell-side analyst as an information intermediary. Over the past two decades, the rise of passive investing, social media, algorithmic trading and independent specialty research firms providing real-time data (for example, drones counting cars in mall parking lots) has substantially altered the role of the analyst on Wall Street and in investment management firms. Analysts are still there and in large numbers, because what they do and how they do it have evolved with the available technologies. I doubt there will be fewer people on average in FP&A roles ten years from now, but in a similar fashion, what they do and how they do it will be different. Those analysts that can redefine their role to fit the evolving capabilities of technology will be leaders in their field. Enterprises hire analysts because innate analytical talents are not evenly distributed. Analysts know how to assemble information from necessary sources and, by applying critical thinking, logical reasoning, pattern recognition, curiosity, skepticism and a deep understanding of statistical techniques, they draw conclusions, make recommendations and identify issues to promote informed and objective decision-making. That will still be true in the 2030s but the tools they will use will be different. Information technology has facilitated the work of analysts almost from the start. Today there is a wealth of tools that analysts use to streamline their work and maximize their abilities. Beyond mechanical computational automation, systems now facilitate data acquisition, automate periodic reporting, present real-time information and communicate more effectively using visualizations. Yet to be truly effective, the tools require analytical abilities to enable the user to go beyond the basics. Basics that very shortly can be handled by technology. AI has the potential to make everyone an analyst by employing algorithms to rapidly and continuously review data and improve situational awareness, understand the underlying sources of opportunities and issues, offer options for decision-making, examine alternative courses of action and understand the potential outcomes. Like most significant technological advancements, this won’t happen overnight, but I think it will happen faster than most believe. Basic analytical capabilities are likely to be widely available as part of the feature set of general-purpose tools and also as provided by software providers as part of business applications. For example, ISG Software Research asserts that by 2028, all providers of business planning software will use GenAI to streamline commenting and annotations in tables and exhibits to boost productivity. One can be forgiven for thinking that these features were promised and promoted in the past with little to show for it. However, analytics technology continues to advance steadily, and we’ve finally arrived at a point where conditions are capable of such transformational change. One difference today is the growing availability of easily accessed, accurate, consistent and timely data. This is a core requirement for practical and useful application of analytics and even more essential for AI. Increasingly, application providers are incorporating what I (tongue-in-cheek) call a data pantry, although each software provider has a unique name for this form of data fabric. It’s a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. A data pantry makes it possible to train analytical systems to reliably provide deeper analytical insights than is currently possible, generate forecasts and create sets of scenarios to explore various courses of action. It will make it possible to train GenAI systems on internal data and documents to create reliable narratives. There’s been a great deal of talk about advanced analytics for at least 20 years. Still, our research has found that only a minority of enterprises report using predictive analytics regularly. There are two likely explanations for this lack of penetration. One is that to be useful and reliable, predictive models must be trained and retrained regularly using a consistent, relevant and reliable set of data. Techniques for automatically creating such data sources (in effect, data pantries) have only recently become available but will grow more common as providers address the demand for AI capabilities. A second related issue is a lack of available time for analysts to perform this type of work. Our research found that analysts spend the majority of their time on data preparation, leaving much less time for them to do more useful work. Here, too, the increasing availability of dedicated data stores will substantially cut the time analysts spend on purely mechanical work. Providers must incorporate basic analytical routines into software, enabling business analysts to create models regardless of their abilities. Technology will change the nature of the work that the FP&A organization performs, freeing up time now spent on data preparation, routine analyses and perfunctory report writing. As that happens, there will be a need to change the group’s mission, especially for those FP&A leaders that want to play a more strategic role in their organization. One important role is becoming the go-to resource for modeling, outlining and coordinating planning processes, developing and managing performance measures and metrics, in addition to reporting and communications. While that somewhat describes what the group does now, FP&A will have the time and tools to do more of it and do it broadly across all parts of their organization. It's likely that in the not-too-distant future, everyone in an organization can be some sort of an analyst by using an “analyst-in-a-box” to do the work. The FP&A team will build models and craft processes that empower executives and managers to improve their situational awareness, investigate issues, revise plans rapidly and explore the consequences of different scenarios and potential actions. It will be the application of FP&A analysts’ modeling skills, critical thinking, logical reasoning and understanding of the business to build the analyst-in-a-box, utilizing systems provided by software providers. For example, existing technology makes it possible to make planning and budgeting easier. However, this will take time. ISG Research asserts that by 2027, just one in five FP&A enterprises will have redefined their mission to make planning easier for business unit leaders. Those that do will be a strategic asset. Business and financial analysts who want to play a more strategic role in their organization must understand how to utilize AI, GenAI and agents to make everyone an analyst. That requires the FP&A group to redefine its mission to reflect the significant changes that technology brings to their daily workload. They will be more productive and able to redefine their position to be more important and more consequential. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/What_Analysts_Should_Do_When_Anyone_Can_Analyze_with_AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://research.isg-one.com/analyst-perspectives/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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": "Actian Enables Data Discovery and Data Intelligence", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/actian-enables-data-discovery-and-data-intelligence", "datePublished": "11/03/2025", "dateModified": "11/03/2025", "articleBody": "Data catalogs provide an inventory of data assets that surface metadata from data platforms, analytics tools and applications that can be used to facilitate data discovery and data usage across an enterprise. As I recently explained, however, there are actually multiple types of data catalogs that offer functionality to address specific use cases and user roles, including data inventory, data discovery and data governance. The data intelligence catalog is an emerging category that combines technical metadata, business metadata and data governance capabilities with knowledge graph functionality to deliver a holistic, business-level view of data production and consumption. Actian’s 2024 acquisition of Zeenea was designed to add these capabilities as a complement to its established data platform, data integration and data quality functionality. Actian was founded as Relational Technologies Inc. in 1980 and has been through multiple iterations and acquisitions in the decades that have followed. The company is currently owned by HCL Technologies, which initially acquired Actian via a joint venture with Sumeru Equity Partners in 2018 before taking full ownership in 2021 as Actian became the data and analytics products division of HCLSoftware. Actian has remained focused on data processing and data management throughout its various incarnations. Its Ingres transactional database can be traced back to the origins of the company, while a variety of acquisitions added analytic, embedded and non-relational database capabilities, as well as data integration and data quality, and application modernization. The company’s Actian Data Platform provides a consolidated platform for data integration, data processing and analytics, delivered as a cloud-based managed service. The breadth of capabilities offered by Actian is reflected by its inclusion in multiple ISG Buyers Guides during 2024, including Data Platforms, Analytic Data Platforms, Operational Data Platforms, Data Integration, Data Quality and Data Products. The latter was a direct result of the acquisition of Zeenea in September 2024. Zeenea’s combination of metadata management, data discovery, data governance and federated knowledge graph functionality has expanded Actian’s focus to address the emerging market for data intelligence. I assert that through 2027, three-quarters of enterprises will be engaged in data intelligence initiatives to understand how, when and where data is used in the organization, and by whom. As enterprises seek to increase data-driven decision-making, many are investing in strategic data democratization initiatives to provide business users and data analysts with self-service access to data across the enterprise. Data catalogs provide a central repository of the data used across an enterprise, along with guided data discovery capabilities and natural language search. I recently explained how making data available as a product can facilitate self-service data discovery, but it also increases the importance of agreed-upon data definitions to establish and maintain trust in data. In addition to core data catalog functionality, data democratization requires data lineage to track data as it flows through the organization from production to consumption, as well as data quality metrics and contextual understanding of the data, such as its criticality and whether it is subject to regulatory requirements. Data about data — or metadata — is therefore of critical importance to data intelligence efforts and is facilitated by curated semantic data definitions, artificial intelligence (AI)-driven semantic data modeling and knowledge graphs that represent data and metadata usage and reflect the relationships between data elements. Actian’s acquisition of Zeenea is central to the company’s ability to address data democratization. The cloud-native Zeenea Data Intelligence Platform is described as an intelligent data discovery platform and enables enterprises to automatically scan various data sources, including databases, applications and business intelligence (BI) tools, to create a searchable data catalog of enterprise assets. The product’s native federated knowledge graph identifies, classifies and maintains a map of relationships between data assets, facilitating search-based data discovery. Actian’s Zeenea Data Intelligence Platform also provides functionality for creating and sharing business glossaries, metadata-driven regulatory and policy compliance, data lineage, metadata management, data stewardship, data governance, integration with data quality metrics, and an enterprise data marketplace for sharing and consuming data products. The platform provides two dedicated application interfaces: Zeenea Studio is aimed at data management teams to enable the management and governance of data assets, while Zeenea Explorer enables data consumers to discover and explore data using natural language search and personalized recommendations. Providing self-service access to data has long been a goal of many enterprises, but few have achieved it. Only 15% of participants in ISG’s Analytics and Data Benchmark Research say their organization is very comfortable allowing business users to work with data that has not been integrated or prepared for them by IT. Actian’s acquisition of Zeenea provides the company with much of the functionality to provide the data intelligence catalog and data product capabilities required to facilitate data democratization. I recommend that any enterprises engaged in data intelligence initiatives and exploring the potential for data products include Actian within their evaluations. The next step for the company will be to integrate the Zeenea Data Intelligence Platform capabilities with those offered by the Actian Data Platform, including Actian DataConnect and Actian Data Quality, to provide customers with a holistic view of, and access to, data across the Actian Data Platform estate. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://research.isg-one.com/hubfs/Actian_Enables_Data_Discovery_and_Data_Intelligence.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://research.isg-one.com/analyst-perspectives/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "research.isg-one.com", "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 State of Conversational Software Using Automation and AI", "mainEntityOfPage": "https://research.isg-one.com/analyst-perspectives/the-state-of-conversational-software-using-automation-and-ai", "datePublished": "06/03/2025", "dateModified": "06/03/2025", "articleBody": "Conversational automation leverages artificial intelligence (AI)-powered agents, chatbots and virtual assistants to automate both customer interactions and internal processes. These systems understand natural language, sentiment and intent, generating relevant responses and executing actions based on user input. The software provider landscape is analyzed in the ISG Buyers Guide for Conversational Automation. Through 2026, one-half of enterprises will realize that digital experiences are neither intelligent nor automated and that they fail to meet enterprise readiness requirements. This analyst perspective will delve into the two pivotal use cases driving the revolution: self-service employee tools and customer-facing chatbots. By enhancing operational efficiency and improving customer satisfaction, conversational automation is positioned to generate significant value for medium to large enterprises, thereby reshaping the way organizations engage with both customers and employees. Self-service employee tools powered by conversational automation have emerged as vital assets for enterprises. In the 2024 ISG Market Lens for Generative AI (GenAI) Use Cases, internal employees (34%) were identified as the ultimate end user for GenAI initiatives. These tools streamline internal communication, enabling employees to access information and support autonomously, freeing the workforce for more strategic tasks. By integrating AI-driven chatbots into onboarding and learning and development processes, organizations are witnessing improvements in efficiency. In the 2024 ISG Market Lens for Generative AI Use Cases, 35% of participating enterprises have already invested in GenAI technology for HR support and 24% have invested for personalized learning. For instance, self-service chatbots facilitate onboarding by providing new hires with immediate access to essential resources, answering frequently asked questions about company policies and benefits. This capability not only reduces the time it typically takes to onboard new employees but also enhances the overall experience for newcomers. Organizations that adopt these tools report a reduction in onboarding time, allowing new employees to contribute more quickly to their teams. Moreover, self-service employee tools enhance HR query management. By automating responses to common inquiries, such as leave requests or payroll questions, HR teams can focus on higher-value strategic initiatives instead of being bogged down by repetitive tasks. Enterprises that are able to decrease average response times for HR-related questions are bolstering employee satisfaction and retention rates. However, despite these benefits, businesses may face obstacles when implementing self-service employee tools. Concerns regarding data security and compliance remain paramount, as enterprises must ensure that sensitive employee information is adequately protected. Additionally, some employees may resist adopting new technologies, preferring traditional modes of communication that they are more familiar with. To overcome these challenges, enterprises must engage in change management and provide robust training to staff that embraces innovation. As customer expectations continue to evolve, the role of customer-facing chatbots has become indispensable in enhancing customer experience and driving competitive advantage. In the 2024 ISG Market Lens for Generative AI Use Cases, customer service chatbots were ranked the most beneficial GenAI use case prioritized by enterprise participants. These AI-driven tools provide 24/7 support, addressing customer inquiries in real time. By handling routine requests and frequently asked questions, chatbots not only reduce customer wait times but also enhance overall satisfaction. Generational differences in the use of technology-enabled communications channels suggests that some consumers prefer interacting with chatbots for quick responses, underscoring the importance of integrating these systems. Enterprises leveraging customer-facing chatbots can streamline customer support operations through a reduction in average handling times. This efficiency translates into resource savings and allows human agents to devote their attention to more complex and nuanced customer needs. Additionally, customer-facing chatbots play a crucial role in lead generation and qualification. By engaging with website visitors in real-time, these chatbots can initiate conversations, gather essential information and qualify leads based on user interactions. This capability to increase lead conversion rates makes the technology it a compelling proposition for sales-driven enterprises. Nevertheless, the journey toward implementing customer-facing chatbots is not without challenges. Enterprises often grapple with the limitations of chatbots when it comes to handling complex customer queries or providing empathetic responses. This limitation can lead to frustration among customers, resulting in potential damage to brand reputation. Furthermore, employees may harbor concerns about the potential job displacement that comes with automation. Addressing these objections involves promoting a hybrid approach, where chatbots complement rather than replace human agents, ensuring that customers receive the best of both worlds. A third use case for conversational automation technologies is customer experience management (CXM), the focus of the ISG Buyers Guide for CXM, which encompasses enhanced customer engagement, streamlined issue resolution and comprehensive analytics and insights. For a deeper exploration of this topic, please see our separate analyst perspective on conversational automation for CXM in contact centers. Across both external and internal use cases, ISG Research envisions that conversational automation will progressively integrate with process orchestration and management platforms in the coming years. By connecting with existing systems and leveraging repositories of structured, semi-structured and unstructured data, intelligent automation will not only provide actionable business process insights but also underpin innovation and agility. This evolution positions enterprises to respond more quickly to market demands and customer needs, ensuring sustained competitive advantage. AI using GenAI and large language model (LLM) technologies offer valuable capabilities in natural language understanding and processing, content generation and task automation, potentially streamlining operations and enhancing decision-making processes. It is crucial to understand the potential and challenges of these technologies. GenAI and LLMs are elevating conversational automation through: More natural and context-aware interactions. LLMs enable chatbots and virtual assistants to understand and respond to queries, maintaining context over longer, multi-turn conversations. Dynamic response generation. Instead of relying on pre-scripted responses, systems can generate unique, contextually appropriate responses in real time, including contact summaries for customer engagement emails and knowledge management. Multilingual support. LLMs provide interactions across multiple languages without the need for separate models for each language. As organizations navigate digital transformation initiatives, embracing conversational automation is an essential component. By implementing self-service employee tools and customer-facing chatbots, enterprises can unlock new levels of efficiency, enhance their customer experience and drive sustained growth. The journey toward automation to create business value is just beginning. 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