ISG Software Research Analyst Perspectives | Model Building and Large Language Models

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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. <a href="https://www.ventanaresearch.com/services/vendors/analyst_relations" rel="noopener">Analyst Relations</a> <a href="https://www.ventanaresearch.com/services/vendors/demand_generation" rel="noopener">Demand Generation</a> <a href="https://www.ventanaresearch.com/services/vendors/product-marketing" rel="noopener">Product Marketing</a> <a href="https://www.ventanaresearch.com/vendor-coverage" target="_self" role="menuitem">Market Coverage</a> <a href="https://www.ventanaresearch.com/briefing" target="_self" role="menuitem">Request a Briefing</a> </div> <ul></ul> <div class="bottom_rich_text"> </div> </div> </div> </div> </div> </div> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/90531223305/1725882478811/Ventana_Research_2023/js/mmenu.min.js"></script> </div> </div> </div> </div> </div> </div> </div> </div> </div> </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://blog.isg-research.net/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://blog.isg-research.net/snowflake-offers-a-platform-for-ai-as-well-as-data">Snowflake Offers a Platform for AI as well as Data</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/matt-aslett"> Matt Aslett </a> on 19 September 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=200&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=100&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=200&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=300&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=400&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=500&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png?width=600&name=Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> As I explained in our recent <a href="https://www.ventanaresearch.com/buyers_guide/analytics_and_data/data_platforms">Buyers Guide for Data Platforms</a>, the popularization of generative artificial intelligence (GenAI) has had a significant impact on the requirements for data platforms in the last 18 months. While there is an ongoing need for data platforms to support data warehousing workloads involving analytic reports and dashboards, there is increasing demand for analytic data platform providers to add dedicated functionality for data engineering, including the development,... </div> <a href="https://blog.isg-research.net/snowflake-offers-a-platform-for-ai-as-well-as-data"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/analytics">Analytics</a>, <a href="https://blog.isg-research.net/topic/natural-language-processing">natural language processing</a>, <a href="https://blog.isg-research.net/topic/data-platforms">data platforms</a>, <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="">AI and Machine Learning</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://blog.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/competitive-innovation-is-improving-the-economics-of-genai">Competitive Innovation is Improving the Economics of GenAI</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/robert-kugel"> Robert Kugel </a> on 09 August 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=200&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=100&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=200&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=300&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=400&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=500&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png?width=600&name=Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> At this stage of the development of Generative AI, there’s much we can see clearly (at least we think we can), but there’s even more likely to surprise us. As that great 20th-century philosopher and hall-of-fame catcher, Yogi Berra, famously said, “It’s tough to make predictions, especially about the future.” The breadth and speed of innovation today in all aspects of GenAI rivals that of the internet in the 1990s, and for the same reason. </div> <a href="https://blog.isg-research.net/competitive-innovation-is-improving-the-economics-of-genai"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/office-of-finance">Office of Finance</a>, <a href="https://blog.isg-research.net/topic/erp-and-continuous-accounting">ERP and Continuous Accounting</a>, <a href="https://blog.isg-research.net/topic/digital-finance">digital finance</a>, <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://blog.isg-research.net/topic/procure-to-pay">Procure-to-Pay</a>, <a href="https://blog.isg-research.net/topic/order-to-cash">Order-to-Cash</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://blog.isg-research.net/who-poisoned-the-ai-watering-hole-adversarial-threats-in-ai">Who Poisoned the AI Watering Hole: Adversarial Threats in AI</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/jeff-orr"> Jeff Orr </a> on 09 July 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=200&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=100&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=200&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=300&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=400&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=500&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png?width=600&name=Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> Embracing artificial intelligence technologies opens doors for innovation and efficiency. Alongside these opportunities, however, come risks. Threat actors are keenly aware of the potential impact of AI systems and are actively exploring ways to manipulate them. In this Analyst Perspective, I explore the world of adversarial machine-learning threats and provide practical guidance for securing AI systems. </div> <a href="https://blog.isg-research.net/who-poisoned-the-ai-watering-hole-adversarial-threats-in-ai"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology</a>, <a href="https://blog.isg-research.net/topic/digital-security">Digital Security</a>, <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="">AI and Machine Learning</a>, <a href="https://blog.isg-research.net/topic/devops-and-platforms">DevOps and Platforms</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://blog.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a>, <a href="https://blog.isg-research.net/topic/nist">NIST</a>, <a href="https://blog.isg-research.net/topic/model-training">Model Training</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://blog.isg-research.net/salesforce-wants-everyone-to-be-an-einstein-with-ai">Salesforce Wants Everyone to be an Einstein with AI</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on 11 April 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=200&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=100&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=200&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=300&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=400&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=500&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png?width=600&name=Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> I recently attended the Salesforce Trailblazer DX event to learn more about Salesforce’s artificial intelligence products and strategy. Fueled by generative AI, awareness and investment in AI seems to be exploding. ISG research shows that enterprises plan to nearly triple the portion of budgets allocated to AI over the next two years. This doesn’t come as a big surprise when you look at the outcomes enterprises are achieving: Of those that have invested in AI, more than 8 in 10 (84%) have had... </div> <a href="https://blog.isg-research.net/salesforce-wants-everyone-to-be-an-einstein-with-ai"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/ai">AI</a>, <a href="https://blog.isg-research.net/topic/natural-language-processing">natural language processing</a>, <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://blog.isg-research.net/topic/computer-vision">Computer Vision</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://blog.isg-research.net/topic/deep-learning">Deep Learning</a>, <a href="https://blog.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/refocus-on-ai-skill-building-for-the-greatest-operational-impact">Refocus on AI, Skill-Building for the Greatest Operational Impact</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on 21 March 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=200&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=100&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 100w, https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=200&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 200w, https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=300&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 300w, https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=400&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 400w, https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=500&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 500w, https://blog.isg-research.net/hs-fs/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png?width=600&name=Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> In the technology industry, 2023 will be remembered as the year of <a href="https://davidmenninger.ventanaresearch.com/generative-ai-ushers-in-a-new-age-of-content-and-model-creation">generative artificial intelligence</a>. Yes, the world was made aware of GenAI when ChatGPT was publicly launched in November of 2022, but few knew the impact it would have at that point in time. Since then, GenAI has taken the world by storm, with vendors applying the technology to make it easier to ask questions about data, write code (including SQL), prepare data for analyses, document data pipelines and use software products... </div> <a href="https://blog.isg-research.net/refocus-on-ai-skill-building-for-the-greatest-operational-impact"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/artificial-intelligence">Artificial intelligence</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="">AI and Machine Learning</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/as-interest-in-ai-scales-so-does-domino-data-lab">As Interest in AI Scales, So Does Domino Data Lab</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on 13 March 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=200&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=100&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 100w, https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=200&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 200w, https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=300&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 300w, https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=400&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 400w, https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=500&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 500w, https://blog.isg-research.net/hs-fs/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png?width=600&name=As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> Interest in <a href="https://www.ventanaresearch.com/artificial-intelligence">artificial intelligence</a> (AI) is exploding driven in large part by the <a href="https://davidmenninger.ventanaresearch.com/large-language-models-take-ai-world-by-storm">widespread interest in generative AI</a>. ISG’s AI Buyer Behavior Survey reported that more than 6 in 10 participants have at least one AI application in production. However, despite the ease with which individuals can use AI as a result of <a href="https://davidmenninger.ventanaresearch.com/generative-ai-makes-natural-language-analytics-a-reality">natural language processing</a>, creating and managing AI models is still a challenge. First, there is a <a href="https://davidmenninger.ventanaresearch.com/recognize-and-plan-for-the-ai-and-machine-learning-skills-gap">shortage of skills</a>. Second, the process itself involves many parts, each of... </div> <a href="https://blog.isg-research.net/as-interest-in-ai-scales-so-does-domino-data-lab"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/data-science">Data Science</a>, <a href="https://blog.isg-research.net/topic/ai">AI</a>, <a href="https://blog.isg-research.net/topic/analytics-data">Analytics & Data</a>, <a href="">AI and Machine Learning</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://blog.isg-research.net/topic/machine-learning-operations">Machine Learning Operations</a>, <a href="https://blog.isg-research.net/topic/mlops">MLOps</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://blog.isg-research.net/2024-market-agenda-for-artificial-intelligence-improving-productivity-and-experiences">2024 Market Agenda for Artificial Intelligence: Improving Productivity and Experiences</a></h2> <div class="author-meta"> Posted by <a href="https://blog.isg-research.net/author/david-menninger"> David Menninger </a> on 02 February 2024 </div> <hr>  </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=200&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png" width="200" !important: srcset="https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=100&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png 100w, https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=200&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png 200w, https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=300&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png 300w, https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=400&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png 400w, https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=500&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png 500w, https://blog.isg-research.net/hs-fs/hubfs/2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png?width=600&name=2024_Market_Agenda_for_Artificial_Intelligence_Improving_Productivity_and_Experiences.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> Ventana Research recently announced its 2024 Market Agenda for <a href="https://www.ventanaresearch.com/artificial-intelligence">Artificial Intelligence</a>, continuing the guidance we have offered for two decades to help enterprises derive optimal value from technology and improve business outcomes. </div> <a href="https://blog.isg-research.net/2024-market-agenda-for-artificial-intelligence-improving-productivity-and-experiences"> Read More </a> Topics: <a href="https://blog.isg-research.net/topic/artificial-intelligence">Artificial intelligence</a>, <a href="https://blog.isg-research.net/topic/natural-language-processing">natural language processing</a>, <a href="https://blog.isg-research.net/topic/generative-ai">Generative AI</a>, <a href="https://blog.isg-research.net/topic/computer-vision">Computer Vision</a>, <a href="https://blog.isg-research.net/topic/model-building-and-large-language-models">Model Building and Large Language Models</a>, <a href="https://blog.isg-research.net/topic/deep-learning">Deep Learning</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); 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If you have any issue with an Analyst Perspective, please email them to <a href="mailto:ChiefResearchOfficer@ventanaresearch.com" style="color: #284a7b;">ChiefResearchOfficer@ventanaresearch.com</a> </li> </ul> <a class="btn-secondary" href="https://www.ventanaresearch.com/analyst-perspective-policy">View Policy</a>  <div id="hs_cos_wrapper_module_168621267066296" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> </style> <h3 style="color: #255a89;">Our Analysts</h3>  <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://davidmenninger.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png" alt="David Menninger" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=300&name=Menninger.David%20Pic.Circle.png 300w"> <h5 class="analyst_name">David Menninger</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://jefforr.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png" alt="Jeff Orr" width="100" data-constrained="true" caption="false" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=300&name=Orr.Jeff.Circle.png 300w" sizes="(max-width: 100px) 100vw, 100px"> <h5 class="analyst_name">Jeff Orr</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://keithdawson.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png" alt="Keith Dawson" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=300&name=Dawson.Keith%20Pic.Circle.png 300w"> <h5 class="analyst_name">Keith Dawson</h5> </a> </div> <div class="span6" style="text-align:center;"> <a href="https://marksmith.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png" alt="Mark Smith" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=300&name=Smith.Mark%20Pic.Circle.png 300w"> <h5 class="analyst_name">Mark Smith</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://mattaslett.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png" alt="Matt Aslett" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=300&name=Aslett.Matt%20Pic.Circle.png 300w"> <h5 class="analyst_name">Matt Aslett</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://matthewbrown.isg-research.net/"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png" alt="Matthew Brown" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=300&name=profile_vr_matthew_brown_circle_900x900.png 300w"> <h5 class="analyst_name">Matthew Brown</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png" alt="Robert Kugel" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=300&name=Kugel.Robert%20Pic.Circle.png 300w"> <h5 class="analyst_name">Robert Kugel</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://stephenhurrell.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png" alt="Stephen Hurrell" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://blog.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=300&name=Hurrell.Stephen%20Pic.Circle.png 300w"> <h5 class="analyst_name">Stephen Hurrell</h5> </a> </div> </div>   <div class="row-fluid">  <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://blog.isg-research.net/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://blog.isg-research.net/archive/2010/06">June 2010 (1)</a> </li> </ul> <a class="filter-expand-link" href="#">see all</a> </div> </div> <div class="block"> <h3>Posts by Topic</h3> <div class="widget-module"> <ul> <li> <a href="https://blog.isg-research.net/topic/10q">10Q (1)</a> </li> <li> <a href="https://blog.isg-research.net/topic/vendor-references">[Vendor references] (2)</a> </li> <li> <a href="https://blog.isg-research.net/topic/absolute-data">Absolute Data (1)</a> </li> <li> <a href="https://blog.isg-research.net/topic/accessibility">Accessibility (1)</a> </li> <li> <a href="https://blog.isg-research.net/topic/accountants">accountants (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounting">Accounting (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/accounting-close">accounting close (2)</a> </li> <li style="display:none;"> <a 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<a href="https://blog.isg-research.net/topic/corvisa-cloud">Corvisa Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cost-management">Cost Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/costing">costing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cpaas">CPaaS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/cpq">CPQ (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/credit">credit (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crisis">crisis (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crm">CRM (267)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/crm-customer-service">CRM customer service (1)</a> </li> <li style="display:none;"> <a 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href="https://blog.isg-research.net/topic/digital-marketing">Digital Marketing (28)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-operations">Digital Operations (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-publishing">Digital Publishing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-security">Digital Security (23)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-technology">Digital Technology (292)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/digital-transformation">Digital transformation (20)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/direct-data-mapping">Direct Data Mapping (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/disaster-recovery">Disaster Recovery (1)</a> </li> <li 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(5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dovetail">Dovetail (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dreamforce">Dreamforce (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/driver-analysis">driver analysis (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/driver-based">driver-based (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/driver-based-planning">driver-based planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics">Dynamics (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics-ax">Dynamics AX (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics-gp">Dynamics GP (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/dynamics-nav-dynamics-sl">Dynamics NAV Dynamics SL (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/e-discovery">e-discovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/echopass">Echopass (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eclipse">Eclipse (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/edi">EDI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ediscovery">eDiscovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/editorial">Editorial (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/effectiveness">effectiveness (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eg-solutions">eg Solutions (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/elasticube">elasticube (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/eloqua">Eloqua (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/embedded-analytics">embedded analytics (95)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/emc">EMC (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-engagement">Employee Engagement (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-experience">employee experience (47)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-onboarding">Employee Onboarding (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/employee-productivity">Employee Productivity (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/empower">Empower (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/end-to-end">end-to-end (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/endeca">endeca (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enghouse-interactive">Enghouse interactive (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enkata">Enkata (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-applications">Enterprise Applications (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-data-strategy">Enterprise Data Strategy (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-messaging-internet-of-things-iot-middle">Enterprise messaging, Internet of Things, IoT, mid (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-profitability-management">enterprise profitability management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-resource-planning">Enterprise Resource Planning (72)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-risk-management">enterprise risk management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-software">Enterprise Software (27)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-spreadsheet">enterprise spreadsheet (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterprise-tax">Enterprise Tax (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/enterworks">Enterworks (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/environment-management">Environment Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/environmental">Environmental (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/envision">Envision (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/epicor">Epicor (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/epiphany">Epiphany (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/equifax">Equifax (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/equity-research">equity research (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erm">ERM (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erp">ERP (83)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/erp-and-continuous-accounting">ERP and Continuous Accounting (151)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/error">error (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/esg">ESG (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/esri">ESRI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/essbase">Essbase (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ethority">eThority (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-data">Event Data (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-discovery">Event discovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-stream">Event Stream (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-stream-processing">Event Stream Processing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/event-streams">Event Streams (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exacttarget">ExactTarget (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exadata">exadata (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exalytics">Exalytics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/excel">Excel (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/excentive">Excentive (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/executive">executive (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expense">expense (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expense-management">expense management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/experience-management">Experience Management (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/expert-systems">Expert Systems (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exploration">Exploration (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exploratory-analytics">exploratory analytics (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/explorium">Explorium (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/extended-close">extended close (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/extended-reality">extended reality (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/external-data-platform">External Data Platform (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/exterro">Exterro (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/f-type">F-Type (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fasb">FASB (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fast-close">Fast close (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/feedback">Feedback (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fei">FEI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ferf-ceo">FERF CEO (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/field-service">Field Service (29)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance">finance (53)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-analytics">Finance Analytics (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-cloud">finance cloud (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-financial-applications-financial-close">Finance Financial Applications Financial Close (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-performance-management">Finance Performance Management (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/finance-transformation">finance transformation (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial">financial (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-analytics">financial analytics 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href="https://blog.isg-research.net/topic/financial-performance-management">Financial Performance Management (237)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-performance-management-fpm">Financial Performance Management (FPM) (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-planning">financial planning (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-regulation">financial regulation (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-reporting">financial reporting (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-risk-management">financial risk management (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-services">Financial Services (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-software">financial software (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-standards">financial standards (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-statement">financial statement (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financial-systems">financial systems (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financialforce">FinancialForce (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financialforce-com">FinancialForce.com (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/financials">financials (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/five9">Five9 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/flow">flow (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fms">FMS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/for-example-genesys">for example Genesys (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/forecast">Forecast (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/forecasting">forecasting (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fpa">FP&A (22)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fpm">FPM (83)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fraud">Fraud (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fuel50">Fuel50 (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/fusion-applications">Fusion 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compliance, risk management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/green">Green (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/greenplum">Greenplum (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/gui">GUI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hadoop">Hadoop (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hana">HANA (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/harvard-business-review">Harvard Business Review (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hawq">HAWQ (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm">HCM (57)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-analytics">HCM Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-systems">HCM Systems (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hcm-hr-hrms-workforce-management-learning-manageme">HCM, HR, HRMS, Workforce Management, Learning Mana (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdfs">HDFS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdinsights">HDinsights (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/hdp">HDP (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/headcount-planning">headcount planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/healthcare-compliance">Healthcare Compliance (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/healthcare-reform">Healthcare Reform (1)</a> </li> <li 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<li style="display:none;"> <a href="https://blog.isg-research.net/topic/process-mining">Process Mining (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/procure-to-pay">Procure-to-Pay (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/procurement">procurement (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/product-experience-management">product experience management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/product-information-management">Product Information Management (90)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/professional-services">Professional Services (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/professional-services-automation">Professional Services Automation (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profit-velocity">Profit Velocity (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profitability">Profitability (14)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/profitability-management">profitability management (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-management">Project Management (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-planning">Project Planning (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/project-portfolio-management">Project Portfolio Management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projecthelp">ProjectHelp (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projectops">ProjectOps (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/projector">Projector (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/promptbook">Promptbook (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/property-technology">Property Technology (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pros">PROS (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pros-pricing">PROS Pricing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/psa">PSA (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/publishing">Publishing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/purchasing">purchasing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/purchasing-sourcing-payments">Purchasing/Sourcing/Payments (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pv-accelerator">PV Accelerator (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/pxm">PXM (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qad">QAD (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik">Qlik (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-qlik-sense">Qlik Qlik Sense (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-qonnections">Qlik Qonnections (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlik-analytics-qlik-sense-qlik-business-intelligen">Qlik, Analytics, Qlik Sense, Qlik, Business intell (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qliktech">Qliktech (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qlikview">QlikView (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qualtrics">Qualtrics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quantrix">Quantrix (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quinterian">Quinterian (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/quotas">Quotas (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/qvidian">Qvidian (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/r">R (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/ransomware">Ransomware (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rapide">Rapide (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rapidminer">Rapidminer (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rdbms">RDBMS (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/real-estate">real estate (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/real-time">Real-time (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reconciliation">Reconciliation (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recovery">Recovery (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recruiting">Recruiting (41)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/recurring-revenue">Recurring Revenue (19)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/report">report (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reporting">Reporting (78)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/reporting-xbrl-ixbrl-document-management">Reporting, XBRL, iXBRL, document management (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/research">Research (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/resilience">Resilience (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/responsetek">ResponseTek (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/retail-analytics">Retail Analytics (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue">Revenue (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-and-lease-accounting">revenue and lease accounting (15)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-management">Revenue Management (33)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-performance">Revenue Performance (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-performance-management">Revenue Performance Management (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-recognition">revenue recognition (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revenue-lease-and-tax-accounting">Revenue, Lease and Tax Accounting (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revolution">Revolution (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/revolution-analtyics">Revolution Analtyics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rightnow">RightNow (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rim">RIM (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk">Risk (37)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk-compliance-grc">Risk & Compliance (GRC) (17)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/risk-analytics">Risk Analytics (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/riversand">Riversand (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/roambi">Roambi (7)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-automation">robotic automation (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-finance">robotic finance (38)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/robotic-process-automation">Robotic Process Automation (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/roi">ROI (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rpa">RPA (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/rypple">Rypple (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sop">S&OP (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/saas">SaaS (27)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sab-miller">SAB Miller (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/saba">Saba (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sage">Sage (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sage-software">Sage Software (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales">Sales (169)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-and-operations-planning">Sales and Operations Planning (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-cloud">Sales Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-coaching">Sales Coaching (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-commission">Sales Commission (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-compensation">Sales Compensation (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-effectiveness">Sales Effectiveness (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-enablement">sales enablement (16)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-enablement-and-execution">Sales Enablement and Execution (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-engagement">Sales Engagement (25)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-force-automation">Sales Force Automation (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-forecasting">Sales Forecasting (11)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-operations">Sales Operations (12)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance">Sales Performance (446)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance-management">Sales Performance Management (77)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-performance-management-spm">Sales Performance Management (SPM) (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-planning">Sales Planning (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-planning-and-analytics">Sales Planning and Analytics (18)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-tax">Sales Tax (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-marketing-sales-performance-management-marke">Sales, Marketing, Sales Performance Management, Ma (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sales-sfa-spm-sales-performance-management-sales-c">Sales, SFA, SPM, Sales Performance Management, Sal (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/salescloud">SalesCloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/salesforce-com">Salesforce.com (58)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap">SAP (76)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-epm">SAP EPM (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-hana">SAP HANA (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-insider">SAP insider (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-lumira">SAP Lumira (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-netweaver">SAP NetWeaver (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-predictive-analytics">SAP predictive analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sap-teched">SAP TechEd (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sapphire">SAPPHIRE (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sarbanes-oxley">Sarbanes Oxley (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sas">SAS (26)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sas-institute">SAS institute (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sasb">SASB (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sase">SASE (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/savi">Savi (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scm">SCM (6)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scm-demand-planning">SCM Demand Planning (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scout-rfp">Scout RFP (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/scribe-software">Scribe Software (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/search">Search (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sec">SEC (19)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sec-software">SEC Software (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/secfinops">SecFinOps (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/secops">SecOps (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/security">Security (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/security-posture">Security Posture (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sedar">SEDAR (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/self-service">Self-service (41)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/semantic-model">semantic model (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/service-supply-chain">Service & Supply Chain (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/service-cloud">Service Cloud (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/servigistics">Servigistics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sfa">SFA (24)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sfdc">SFDC (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sharepoint">SharePoint (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/shoretel">ShoreTel (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/siemens-enterprise-communications">Siemens Enterprise Communications (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/signal-demand">Signal Demand (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/silverpop">SilverPop (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/simba">Simba (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-financial-close">Smart Financial Close (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-manufacturing">Smart Manufacturing (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-phones">Smart Phones (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smart-watches">Smart Watches (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smarter-analytics">Smarter Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smarter-workforce">Smarter Workforce (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/smb">SMB (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/snaplogic">SnapLogic (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/snowfly">Snowfly (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/soa">SOA (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/soc">SOC (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social">Social (13)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-business">Social Business (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-collaboration">Social Collaboration (17)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-crm">Social CRM (128)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-dynamx">Social Dynamx (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media">Social Media (365)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media-analytics">Social Media Analytics (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-media-intelligence">Social Media Intelligence (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/social-recognition">Social Recognition (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/socialtext">SocialText (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sococare">SoCoCare (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/softscape">Softscape (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/software">Software (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/software-as-a-service">Software as a Service (5)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sourcing">sourcing (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sovereign-cloud">Sovereign Cloud (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sox">SOX (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spago-solutions">Spago Solutions (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spatial-data-quality">Spatial Data Quality (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/speech-analytics">Speech Analytics (177)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/splunk">Splunk (8)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spm">SPM (2)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spotfire">Spotfire (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spreadsheets">Spreadsheets (10)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/spss">SPSS (9)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sql-h">SQL-H (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/sqlstream">SQLstream (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/star-analytics">Star Analytics (1)</a> </li> <li style="display:none;"> <a 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<a href="https://blog.isg-research.net/topic/visi">Visi (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vistaar-technologies">Vistaar Technologies (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/visual-insight">Visual Insight (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/visualization">Visualization (17)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vitria">Vitria (4)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vivisimo">Vivisimo (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vmware">VMWare (1)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vocalcom">Vocalcom (3)</a> </li> <li style="display:none;"> <a href="https://blog.isg-research.net/topic/vocalcom-and-zeacom">Vocalcom and Zeacom (1)</a> </li> <li style="display:none;"> <a 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href="https://www.youtube.com/c/Ventanaresearch"></a></li> <li><a class="no-decoration" target="_blank" href="https://www.instagram.com/ventana_research/"></a></li> </ul> </div> </div> </div> </div> </div> </div> <a href="javascript:" id="return-to-top"> </a> </footer> </div>  <script defer src="/hs/hsstatic/content-cwv-embed/static-1.1293/embed.js"></script> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/94278782902/1725882474552/Ventana_Research_2023/Ventana_Research_2023_child.min.js"></script> <script> var hsVars = hsVars || {}; hsVars['language'] = 'en-us'; </script> <script src="/hs/hsstatic/cos-i18n/static-1.53/bundles/project.js"></script> <script src="https://blog.isg-research.net/hs-fs/hub/2035844/hub_generated/module_assets/90531062927/1702386872146/module_90531062927_Header-Global.min.js"></script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Snowflake Offers a Platform for AI as well as Data", "mainEntityOfPage": "https://blog.isg-research.net/snowflake-offers-a-platform-for-ai-as-well-as-data", "datePublished": "19/09/2024", "dateModified": "19/09/2024", "articleBody": "As I explained in our recent Buyers Guide for Data Platforms, the popularization of generative artificial intelligence (GenAI) has had a significant impact on the requirements for data platforms in the last 18 months. While there is an ongoing need for data platforms to support data warehousing workloads involving analytic reports and dashboards, there is increasing demand for analytic data platform providers to add dedicated functionality for data engineering, including the development, training and tuning of machine learning (ML) and GenAI models. Snowflake is a prime example. The company remains best known as a cloud data platform provider for data warehousing workloads but, like many other data platform providers, has improved its support for AI workloads in the last year. In particular, the company made several announcements at its Snowflake Summit 2024 customer event in June that highlighted considerable advancements to its capabilities in relation to AI and GenAI. Snowflake was founded in 2012 to build a business around its cloud-based data warehouse with built-in data-sharing capabilities. Snowflake has expanded its reach over the years to address data engineering and data science, and long ago moved beyond being seen as just a cloud data warehouse. The company was rated Exemplary in our recent Buyers Guide for Analytic Data Platforms and a Provider of Assurance in our Buyers Guide for AI Platforms. Snowflake is not alone in adding support for AI workloads to its data platform. I assert that through 2026, analytic data platform software providers will increase their native support for AI and ML algorithms, functions and tools to enable in-database data science. The extent to which the company is positioning itself for AI and GenAI workloads was a focus at Snowflake Summit this year, with Snowflake describing its platform as an AI Data Cloud. The company did more than just add the letters A and I to its former Data Cloud branding, however, as it also made numerous announcements that justified its increased emphasis on AI. These included improvements to its Snowflake Cortex managed service for developing applications based on large language models (LLMs), as well as its Snowflake ML offering for training and operationalizing ML models for predictive analytics. Snowflake also enhanced its capabilities for data management and data governance, which will improve support for both analytics and AI workloads. Snowflake has been hugely successful in helping to drive adoption of cloud-based analytic databases, establishing itself as one of the major cloud data platforms for SQL-based data warehousing. The launch of the Snowpark developer environment in 2020 was significant in widening the target workloads by enabling data engineers, data scientists and developers to execute custom Python, Java and Scala code against data in Snowflake. This was followed in 2023 by the launch of Snowpark Container Services, which enables customers to run their own choice of third-party software, including programming languages, data science libraries and GenAI models, on the platform. This avoids the need for users to move data out of Snowflake for processing and potentially reduces complexity and infrastructure resource requirements. I noted in the wake of the Snowflake Summit customer event last year that the company’s plans for native support for GenAI were comparatively nascent relative to some rival providers that had been quicker to add support for GenAI models and development. Snowflake’s GenAI strategy took a leap forward in late 2023 with the launch of the Cortex AI development platform, which provides users with access to LLMs, AI models and vector search functionality. That was followed in April by the delivery of Snowflake’s own Arctic family of LLMs. At Snowflake Summit 2024, the company announced a number of enhancements to Cortex, including the preview of Snowflake Cortex Analyst, which provides an interface for natural language querying of structured data by business users, and Snowflake Cortex Search, which provides developers with a combination of keyword and vector search to support enterprise search and retrieval augmented generation workloads. Natural language search and query are amongst the most popular early use cases for GenAI, with 99% of participants in the ISG Market Lens AI Study having seen positive outcomes from natural language search and 97% having seen positive outcomes from the interpretation of data. Snowflake also added Snowflake Cortex Guard to automatically identify inappropriate language in the output of LLMs. In addition to GenAI enhancements, Snowflake also expanded the MLOps capabilities of its Snowflake ML offering, including the general availability of Snowflake Model Registry and the preview of Snowflake Feature Store and ML Lineage. Snowflake also announced several data management and governance enhancements that will support both analytic and AI workloads, including the general availability of support for Apache Iceberg Tables and the launch of the Polaris Catalog open-source project built on the Apache Iceberg REST protocol. Polaris is designed to provide a vendor-neutral technical catalog for Iceberg tables but complements the data governance and discovery capabilities of Snowflake Horizon, which was also recently enhanced with the preview availability of Internal Marketplace, which enables the creation, sharing and consumption of data products. Many of the new capabilities announced at Snowflake Summit 2024 remain in private or public preview. As such, they were not included for assessment in our recent Buyers Guide reports. They nevertheless represent a significant enhancement of the company’s native support for AI and GenAI that will improve our evaluation of Snowflake in future reports. As a result, I recommend that enterprises include Snowflake in their evaluations for AI platforms, as well as analytic data platforms. Regards, Matt Aslett", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Snowflake_Offers_a_Platform_for_AI_as_well_as_Data.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Matt Aslett", "url": "https://blog.isg-research.net/author/matt-aslett", "sameAs": "https://www.linkedin.com/in/matt-aslett/" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Competitive Innovation is Improving the Economics of GenAI", "mainEntityOfPage": "https://blog.isg-research.net/competitive-innovation-is-improving-the-economics-of-genai", "datePublished": "09/08/2024", "dateModified": "09/08/2024", "articleBody": "At this stage of the development of Generative AI, there’s much we can see clearly (at least we think we can), but there’s even more likely to surprise us. As that great 20th-century philosopher and hall-of-fame catcher, Yogi Berra, famously said, “It’s tough to make predictions, especially about the future.” The breadth and speed of innovation today in all aspects of GenAI rivals that of the internet in the 1990s, and for the same reason. Most of the necessary foundational elements were already in place when ChatGPT raised public awareness of artificial intelligence almost overnight. This event galvanized a broad, competitive effort to exploit the potential of the technology to its fullest. ISG Research asserts that, by 2027, almost all providers of software designed for finance organizations will incorporate some AI capabilities to reduce workloads and improve performance. Because so much is happening out of sight of one individual’s area of immediate expertise, and because so many improvements in the technology are individually small but in aggregate constitute important advances, there is a constant stream of unexpected reverberations from events that change the outlook for the practical uses of GenAI in business. As has been the case in every major technology shift, economics is the invisible hand controlling how GenAI is applied. The cost of GenAI-driven capabilities will determine when, where and to what extent enterprises will use it, either through proprietary initiatives or in GenAI-enabled applications. Recent announcements suggest that a declining cost curve might be steeper in the near term than expected by the skeptical. This includes open-source models' ability to compete closely or on par with proprietary models, improved techniques for using synthetic data in training models and model distillation. All of these may make a broader set of business computing use cases cost-effective sooner. First, some background on the cost of AI and its impact on demand and adoption. A year ago, I raised the question of how the cost of generative AI would affect demand, supply and where those costs would be borne across the full value chain. At that point, few were raising the question because we’ve grown accustomed to these sorts of costs being more than made up for by gains in productivity or other sources of value or socialized, like the cost of email. Over the past year, because of the eye-watering sums spent on the hardware needed to evolve and support GenAI, questions have been raised as to whether all its potential applications can be realized over the next three to five years because many may fail a cost/benefit test. ISG Research recently quantified attitudes around spend in our Market Lens AI Study, which asked participants how much more they would be willing to pay for AI capabilities. The research provides insight into how enterprises currently expect to gain value from AI and, therefore, the propensity to pay for AI capability in applications. The research finds the greatest inclination to spend is in sales performance management, which I interpret to mean that the participants see this area as having the highest potential to generate profit through gains in sales productivity and, therefore, increase revenue. The next five include supply chain management (to cut costs), treasury and risk management (for more accurate cash flow forecasts, to reduce risk of fraud and credit losses as well as cut the cost of regulatory compliance), IT service management (to cut costs), analytics and business intelligence (to gain productivity) as well as procurement (cut costs). In other words, enterprises are willing to pay for productivity gains that clearly generate revenue and easily achieve cost savings or reduce risk. Those offerings that promote productivity without a direct connection to increased revenue or cost savings, or where the cost savings are perceived to be limited or difficult to achieve, will not make the cut until investments become economically attractive. Recent developments suggest that the race for market share is driving innovations that can bring about a greater range of practical and affordable GenAI advances in business computing sooner. The release of Meta’s Llama 3.1 brings this open-source large language model roughly on par with proprietary models. The open-source computing model has been very successful because it’s cost-efficient and fosters rapid innovation through collaboration. Over time, the risks associated with its use (such as security and intellectual property issues) have proven manageable. A major advantage of this latest release is that it permits users to train and fine-tune models of varying size on their data without exposing it. Moreover, constantly training models on an enterprise’s real data prevents model collapse, a degenerative process caused by indiscriminately learning from data produced by other models. In essence, accurate modeling is forever in need of a reality check. The ability to economically and efficiently train and fine-tune models of various sizes for a specific use case is essential for business applications. Size matters because, for business software, some tasks in a process need a large model, but others can be more cost-effectively supported with a narrow language model or through an orchestration of narrow models. For example, a set of narrow models might be the best approach to automating the “reading” of a digital invoice attached to an email, summarizing or extracting elements of the message in the email it is attached to, and performing the transaction recording and accounting. This automation not only boosts productivity at the front end of the process but also substantially reduces data entry errors, provides important context that is otherwise lost and cuts down on the need for internal audit and other quality control efforts. Competition is having an impact on pricing. Following Llama 3.1’s release, OpenAI has fine-tuned GPT-4o Mini free. Open source also creates pressure to develop efficiencies that drive down provider costs but also structure pricing on a value chain that better reflects market demand. Rather than being a one-price bundle, services and products are disaggregated. Some basic elements become free of charge, while others are priced to reflect the value provided to the customer. This market-driven impact on pricing reduces inefficiencies and accelerates adoption and consumption. The ability to apply model distillation has, up until now, been prohibited by the terms of service of all providers. Distillation is a technique where “student” models are trained to mimic a larger and more complex “teacher” model. Student models are smaller and simpler and, therefore, less expensive to operate because they consume fewer compute resources. They make pre-training faster and less expensive than using a larger model to achieve sufficiently satisfactory results. Llama 3.1 permits distillation with limited restrictions, which will probably force others to follow suit. Despite the longer-term promise of more affordable GenAI applications, enterprises seem hesitant to make major commitments. This might be because the rapid rate of change in the technology is creating too much uncertainty and, therefore, perceived risk. Paradoxically, GenAI’s stunning early success across the board has tended to freeze the market to a noticeable degree. However, the impact of this reluctance appears mainly for internally developed projects because software providers have been moving as rapidly as possible to infuse AI features and capabilities into software. I strongly recommend that senior leadership teams have a clear understanding of the benefits of AI and GenAI, how to achieve them and what they’re worth. We are on the cusp of a generational change in the tools available to enable enterprises to do more and improve performance. Despite claims to the contrary, business challenges never change. It’s the tools available to conduct business that are always evolving, providing those clever enough to use them with a competitive advantage that challenges others. CFOs, in particular, must be prepared to incorporate AI across their department, yet most are ill-prepared to adopt new tools and their methods. Our Office of Finance Benchmark Research finds that 49% of departments are technology laggards while just 12% are innovative. This matters because the research also shows a correlation between technological competence and how well the department performs core processes. CFOs need to adopt a fast-follower approach to using AI and GenAI—adopting technology as soon as it is proven to enable their entire organization to stay ahead of the pack. Regards, Robert Kugel", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Competitive_Innovation_is_Improving_the_Economics_of_GenAI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Robert Kugel", "url": "https://blog.isg-research.net/author/robert-kugel", "sameAs": "https://www.linkedin.com/in/robertkugel" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Who Poisoned the AI Watering Hole: Adversarial Threats in AI", "mainEntityOfPage": "https://blog.isg-research.net/who-poisoned-the-ai-watering-hole-adversarial-threats-in-ai", "datePublished": "09/07/2024", "dateModified": "09/07/2024", "articleBody": "Embracing artificial intelligence technologies opens doors for innovation and efficiency. Alongside these opportunities, however, come risks. Threat actors are keenly aware of the potential impact of AI systems and are actively exploring ways to manipulate them. In this Analyst Perspective, I explore the world of adversarial machine-learning threats and provide practical guidance for securing AI systems. Generative AI applications introduce great potential to enterprises, bringing the intersection of innovation and security closer to business needs. Ventana Research asserts that by 2026, one-third of enterprises will establish governance to mitigate risks associated with GenAI software, ensuring ethical considerations, avoiding data and model bias, and safeguarding privacy and data security. GenAI systems can inadvertently generate biased or harmful content. Enterprises must ensure that ethical guidelines are in place to direct AI-generated outputs and prevent unintended consequences. GenAI models also learn from historical data, which may contain biases. Organizations need robust mechanisms to detect and rectify bias during model training and deployment. Plus, GenAI applications often process sensitive data. Implementing strong privacy controls and safeguarding data against breaches is essential. Striking a balance between innovation and digital security becomes paramount for enterprise adoption of GenAI software. Let’s explore the adversarial threats that enterprises must navigate to protect AI systems. A recent report by scientists from the National Institute of Standards and Technology and its collaborators identifies vulnerabilities in AI systems that adversaries can use to create malfunctions. The NIST report provides an overview of four major attack types that AI systems might suffer and corresponding approaches to mitigate damage. The report further classifies attacks according to multiple criteria such as the attacker's goals and objectives, capabilities and knowledge. The leading attack types include abuse, evasion, poisoning and privacy. Abuse attacks involve exploiting AI systems for unintended purposes. Using a language model to generate harmful or inappropriate content by compromising legitimate data sources is an example. Strict access controls, content filtering and regular audits can prevent abuse attacks. Evasion attacks aim to deceive AI models during inference by subtly modifying input data. For example, adversaries alter an image, such as a speed limit sign or road lane marker, to mislead an image-recognition system into misclassifying objects. Robust model architectures, input preprocessing and adversarial training can help mitigate evasion attacks. Poisoning attacks involve injecting malicious data into the training set to compromise model performance. An attacker, for example, modifies training data to bias a sentiment analysis model like a chatbot into utilizing inappropriate language. Rigorous data validation, outlier detection and monitoring during training are essential to prevent poisoning attacks. Privacy attacks exploit model outputs to infer sensitive data about individuals. A membership inference attack reveals whether a specific data point was part of the training set. By asking a chatbot legitimate questions, an attacker may use those responses to identify and exploit a weakness in the model. Differential privacy techniques, model aggregation and data anonymization enhance privacy protection. While enterprises invest significant effort in building robust AI models, attackers face lower barriers. Building attacks on AI systems requires less effort than constructing a robust AI model. Attackers can exploit vulnerabilities without the complexity of model development, training and optimization. Enterprises must recognize this asymmetry and prioritize AI security measures. A wealth of open-source tools and research papers are available for crafting attacks. Adversarial attack libraries, pre-trained models and tutorials are accessible online. Organizations must defend against these documented techniques. Threat actors can test their attacks against various models without rigorous validation. Enterprises often lack comprehensive testing for adversarial scenarios. Rigorous testing, including adversarial examples, is essential during model development. The potential impact of compromising AI systems drives attacker’s motivation. Enterprises may underestimate the attractiveness of AI assets to attackers. Organizations should assess the value of AI models from an adversary’s perspective. As organizations embrace AI, understanding the unique security challenges becomes paramount. Understanding the lower barriers faced by attackers is crucial for effective AI security. Enterprises must proactively address these challenges to safeguard AI systems and data. Essential practices to safeguard AI assets include: Leveraging existing frameworks. Extend existing governance and risk-management programs to cover AI. Rather than creating entirely new policies, integrate AI security considerations into your enterprise’s existing governance structure. This ensures consistency and alignment with overall risk-management practices. Risk assessment. Regularly assess AI-related risks. Understand the unique threats posed by AI systems, including adversarial attacks, data privacy concerns and model vulnerabilities. Update policies and risk assessments accordingly to address emerging risks. Stakeholder engagement. Involve non-technical executives and stakeholders in security discussions. Effective AI security requires collaboration across departments. Engage business leaders, legal teams and compliance officers to ensure a holistic approach to risk management. Quality control. Rigorously validate and curate training data. Poisoning attacks often exploit vulnerabilities in training data. Implement strict data validation processes to prevent malicious injections. Regularly review and clean training datasets. Data privacy. Implement privacy-preserving techniques. Sensitive data used for training AI models must be protected. Techniques like differential privacy can help safeguard individual privacy while maintaining model performance. Consider anonymization methods as well. Data retention. Define data retention policies. Minimize the exposure of sensitive data by retaining it only as long as necessary. Regularly review and update retention policies to align with changing business needs and compliance requirements. Adversarial training. Train models with adversarial examples. Adversarial training exposes models to intentionally crafted adversarial inputs during training. This helps improve model robustness and resilience against attacks. Monitoring and alerts. Continuously monitor model behavior. Set up alerts for suspicious activities, such as unexpected prediction shifts or sudden drops in performance that may indicate tampering. Regularly review model outputs to detect signs of an adversarial attack. Regular updates. Keep models up-to-date with security patches. Just like any enterprise software, AI models may have vulnerabilities. Stay informed about security updates and apply them promptly to mitigate known risks. The convergence of AI’s transformative potential and the increasing sophistication of threat actors requires a proactive approach to safeguarding AI systems. Securing AI systems is an ongoing journey that demands collaboration and adaptability. By following these best practices, enterprises can secure the AI watering hole and contribute to a safer digital future. Regards, Jeff Orr", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Who_Poisoned_the_AI_Watering_Hole_Adversarial_Threats_in_AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "Jeff Orr", "url": "https://blog.isg-research.net/author/jeff-orr", "sameAs": "https://www.linkedin.com/in/jefforr" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Salesforce Wants Everyone to be an Einstein with AI", "mainEntityOfPage": "https://blog.isg-research.net/salesforce-wants-everyone-to-be-an-einstein-with-ai", "datePublished": "11/04/2024", "dateModified": "11/04/2024", "articleBody": "I recently attended the Salesforce Trailblazer DX event to learn more about Salesforce’s artificial intelligence products and strategy. Fueled by generative AI, awareness and investment in AI seems to be exploding. ISG research shows that enterprises plan to nearly triple the portion of budgets allocated to AI over the next two years. This doesn’t come as a big surprise when you look at the outcomes enterprises are achieving: Of those that have invested in AI, more than 8 in 10 (84%) have had positive outcomes. However, AI is not without its challenges. A lack of skills is the top issue organizations face. AI-related skills are the most difficult technical skills to hire and retain. There are also issues with trust and accuracy, among others. Salesforce is attempting to address some of these issues with its introduction of Einstein 1 Studio. The Trailblazer event is oriented more towards developers compared with the company’s flagship event, Dreamforce. And you will see that new capabilities are geared toward those building AI but with the objective of making more AI functionality available to end users of Salesforce applications. Salesforce’s Einstein represents a different, innovative approach to AI. Shortly after Einstein was introduced, we recognized it with our 2018 Digital Innovation Award. Einstein is an AI platform that is tightly coupled with the various applications Salesforce offers. The advantage of this approach is that it gets AI into the hands of line-of-business workers, where it can have the most impact. The disadvantage is that if you want to use it elsewhere, you will be disappointed. Einstein 1 Studio is a set of low-code/no-code tools that can configure and customize Einstein Copilot as well as the prompts and actions associated with it. The tools enable developers to work with both generative AI and predictive AI. Developers can choose which large language models to work with and import predictive models developed in other platforms. Prompt Builder enables the creation of reusable prompts to make it easier for end users to quickly generate responses they know are reliable. The product also includes prerelease capabilities to create prompt templates so a single prompt can be used across a variety of similar situations. Copilot Builder and Copilot Actions, both prerelease, support configuration and customization of Einstein Copilot. Developers can tie in other Salesforce tools, such as Apex, Flow and Mulesoft, to coordinate and complete tasks based on interactions with the Copilot. A number of significant enhancements to Salesforce Data Cloud are coupled with the Einstein functionality. Salesforce recognizes the need to have trusted data−often external to Salesforce−to support AI initiatives. As a result, it has invested in Data Cloud to provide an extensible customer data platform that meets those needs. As my colleague Matt Aslett explains, one of the ways to improve trust in generative AI is with vector search and retrieval-augmented generation. Data Cloud now includes a preview of vector database and vector search capabilities. It also includes a preview of support for unstructured content, an important element in generative AI implementations. Enterprises can now bring their data lake into the data cloud environment. The data does not have to be moved or copied; it can be accessed in place and treated like other Data Cloud data. Einstein 1 Studio and Data Cloud are certainly capable products that could potentially stand on their own. That’s not the way they are being delivered. These products are for use within the Salesforce ecosystem. Salesforce customers should be pleased with the new functionality, which is easily consumed from within the applications. The challenge will be for enterprises looking to combine AI efforts across the entire information estate. The new capabilities provide mechanisms to bring in external data and models, but there will still be a divide. Nonetheless, the capabilities of Einstein 1 Studio are compelling, and I encourage Salesforce customers to evaluate how to use them to enhance the value of applications with AI. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Salesforce_Wants_Everyone_to_be_an_Einstein_with_AI.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Refocus on AI, Skill-Building for the Greatest Operational Impact", "mainEntityOfPage": "https://blog.isg-research.net/refocus-on-ai-skill-building-for-the-greatest-operational-impact", "datePublished": "21/03/2024", "dateModified": "21/03/2024", "articleBody": "In the technology industry, 2023 will be remembered as the year of generative artificial intelligence. Yes, the world was made aware of GenAI when ChatGPT was publicly launched in November of 2022, but few knew the impact it would have at that point in time. Since then, GenAI has taken the world by storm, with vendors applying the technology to make it easier to ask questions about data, write code (including SQL), prepare data for analyses, document data pipelines and use software products more effectively. More than three-quarters of enterprises are using or plan to evaluate GenAI across these different types of usage. As I’ve written previously, natural language analytics may finally become a reality. Unfortunately, all the focus on GenAI may divert attention away from “traditional AI” or predictive analytics. One could say that GenAI is sucking all the air out of the room. I anticipate that in 2024, GenAI will continue to enhance many aspects of business processes, but in the coming year I think we will also see a backlash about some of the shortcomings of GenAI relative to traditional, predictive AI. Because GenAI has been so successful at many of the tasks associated with data and analytics, it is perceived as a panacea. However, I do not anticipate that GenAI will dominate the use cases typically associated with predictive AI such as predictive medicine, fraud detection, predictive maintenance, intrusion detection and other similarly complex, highly valuable use cases. ISG’s Banking study confirms this split, and ISG’s AI Buyer Behavior study further reinforces this notion, showing that enterprises expect that one-half (51%) of AI spend in 2024 will be on predictive AI, and one-half (49%) will be on GenAI. AI was a hot topic before ChatGPT. Our research showed that three-quarters of organizations planned to increase investment in AI and machine learning, and none planned to decrease investment–and that was before the GenAI phenomenon. Unfortunately, as I’ve written previously, enterprises don’t have the skills to support the application of traditional AI/ML to operations. Only one-quarter of organizations indicated having the skills needed, and only one-third (31%) cited having adequate AI/ML technology. This problem is likely to continue. We assert that by 2026, more than one-half of enterprises will realize AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage. What does this mean for enterprises? While you need to invest in GenAI and the benefits it brings to all types of business processes, you also need to continue investing in good old-fashioned traditional AI/ML and supporting data technologies. But it’s not just about technology; enterprises also need to address the shortage of skills to use traditional AI successfully. Eventually, GenAI will make it easier to develop predictive AI models, just as it makes it easier to develop code. But for the near term, the highest value AI/ML use cases will still require a human in the loop, armed with specialized skills and technology. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Refocus_on_AI_Skill-Building_for_the_Greatest_Operational_Impact.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "As Interest in AI Scales, So Does Domino Data Lab", "mainEntityOfPage": "https://blog.isg-research.net/as-interest-in-ai-scales-so-does-domino-data-lab", "datePublished": "13/03/2024", "dateModified": "13/03/2024", "articleBody": "Interest in artificial intelligence (AI) is exploding driven in large part by the widespread interest in generative AI. ISG’s AI Buyer Behavior Survey reported that more than 6 in 10 participants have at least one AI application in production. However, despite the ease with which individuals can use AI as a result of natural language processing, creating and managing AI models is still a challenge. First, there is a shortage of skills. Second, the process itself involves many parts, each of which require discipline and governance. The process of managing all these parts is referred to as Machine Learning Operations or MLOps. Domino Data Lab was formed to provide a software platform for MLOps and has since expanded its capabilities into a broader enterprise AI platform. Founded in 2013, Domino is backed by Coatue Management, Great Hill Partners, Highland Capital, Sequoia Capital and Snowflake Ventures. The company has raised more than $200 million in funding. Domino states its objective is to help enterprises build and operate AI at scale with its Domino Enterprise AI Platform. Its customers include major companies in pharmaceuticals, financial services, insurance, manufacturing, retail and other verticals. The Domino platform was originally conceived to help create reproducibility in data science projects. Given all the moving parts, this can be a daunting task. At the heart of the platform is a container-based approach coupled with a repository of the artifacts used to create models such as data pipelines, libraries and environment variables used in the process. The containers ensure the configuration is identical to when the model was created. Reproducibility is a critical element in AI governance. Regulations may require enterprises to be able to reproduce the models, and even those enterprises not subject to regulation should have the discipline to be able to do so. We assert that through 2026, model governance will remain a significant concern for more than one-half of enterprises, limiting the deployment, and therefore the realized value, of AI and machine learning models. But Domino’s platform goes beyond reproducibility to include all aspects of model development and deployment. The application architecture is divided into four main elements: clients, a control plane, a data plane and external services. Clients include a browser-based user interface, programmatic APIs and a command-line interface. The control plane tracks and monitors data science projects, provides a model registry and has an environment image builder which creates the containers that provide reproducibility. The data plane manages Domino workspaces, jobs and applications. It also manages scaling of clusters and distributed compute clusters where needed for compute intensive workloads. External services are used for object storage, file shares, remote data sources and Git providers. Domino supports fully managed, self-managed and hybrid configurations. Domino supports a wide variety of notebooks and integrate development environments (IDEs) for model development. Models can be deployed as REST endpoints or exported to third-party products such as AWS Sagemaker and Snowflake. Developers can also publish applications that incorporate the models using any HTTP framework. The model registry can be used to register and incorporate external models. Other recent enhancements include an AI Hub for pre-built templates of common use cases, support for retrieval augmented generation (RAG) and AutoML capabilities. Domino has added GenAI-based coding assistants for jupyter and Visual Studio Code. Users can now also take advantage of cost optimization capabilities to help meet the budgets allocated to specific projects. Domino has significantly expanded its platform capabilities, providing a robust data science platform that can fit into many organizations existing tool and framework preferences. There is more work to be done. We would expect to see Domino add AI pipelines and orchestrations across multiple models. Prompt management is also an area where vendors are investing more to help with both better accuracy and responsible AI. We also expect Domino to continue to invest in its partner ecosystem with more integration with cloud-based AI services. We recommend that enterprises investing in AI consider how Domino Enterprise AI Platform could support its data science projects and machine learning operations. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/As_Interest_in_AI_Scales_So_Does_Domino_Data_Lab.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://blog.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "blog.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "2024 Market Agenda for Artificial Intelligence: Improving Productivity and Experiences", "mainEntityOfPage": "https://blog.isg-research.net/2024-market-agenda-for-artificial-intelligence-improving-productivity-and-experiences", "datePublished": "02/02/2024", "dateModified": "02/02/2024", "articleBody": "Ventana Research recently announced its 2024 Market Agenda for Artificial Intelligence, continuing the guidance we have offered for two decades to help enterprises derive optimal value from technology and improve business outcomes. The application of artificial intelligence and machine learning within enterprises is transforming analytics. Opportunities to take advantage of these technologies will increase over the coming year as software providers incorporate generative AI in products and automate more AI processes. Our upcoming research will explore the extent to which these advances reduce the skills needed to reap the full benefit of AI, but we assert that through 2026, more than one-half of enterprises will realize their AI competencies and skills are insufficient and will require new investments to avoid being at a competitive disadvantage. Only one-quarter of enterprises (23%) report they have the skills they need, while two-thirds (65%) report they need more skills. Unfortunately, enterprises are having the most difficulty finding and retaining those with AI skills. Finding and retaining those with cloud platform skills and those with data and database skills are also among the top five most challenging. AI involves the development of systems and software capable of automating tasks that have previously required human intelligence. It encompasses machine learning (ML), deep learning and GenAI to deliver capabilities including predictions, recommendations, personalization, speech and visual recognition as well as translation and summarization. To get the most out of AI systems, enterprises need to involve people in business and executive roles outside of the IT department in determining use cases and success metrics. As revealed by ISG’s recently completed AI Buyer Behavior Survey, the second most common challenge enterprises face is a lack of holistic, company-wide vision. Ventana Research offers research-based guidance on analytics and data to help enterprises enhance the value of information through the smarter use of data. Going beyond earlier methods of business intelligence, dashboards and reports is essential to ensure that everyone is able to not only access data but is also empowered to act on it to optimize the business. Our Artificial Intelligence expertise provides a holistic perspective that addresses both the use cases and requirements to successfully apply AI technologies. It includes six focus areas: Computer Vision, Deep Learning, Generative AI, Machine Learning Operations, Model Building including Large Language Models and Natural Language Processing. ISG’s AI Buyer Behavior Survey explores these topics, providing insights to guide decision-making, while we help enterprises assess, evaluate and select software providers through our Ventana Research Buyers Guides. Computer Vision Computer Vision refers to a type of AI that interprets image and video data to identify and classify objects and actions. Once interpreted, this data can provide an additional source of input to a variety of decision-making processes. Only 1 in 7 enterprises consider image or video data an important source of information for their analytics activities today, but ISG research shows that among those that have adopted AI, nearly three-quarters of enterprises (71%) report positive outcomes interpreting images. We assert that through 2026, 1 in 5 enterprises will adopt computer vision applications to monitor and improve their operations. In 2024, our Artificial Intelligence Buyers Guide research will include an assessment of a software providers’ computer vision capabilities. Deep Learning Deep Learning refers to a type of ML that utilizes neural networks with more than three layers to simulate the learning behavior of the human brain. Deep learning algorithms generally require large amounts of data and perform tasks such as image and speech recognition. Increases in processing power, including advances in GPUs, have made deep learning much more accessible. Without these advances, the cost and time required to execute deep learning algorithms were prohibitive for many use cases. As deep learning has become more affordable, in part due to cloud-based computing resources, we assert that through 2026, deep learning algorithms will continue to drive advances in natural language processing (NLP) and image recognition, enabling new use cases. In 2024, our Artificial Intelligence Buyers Guide research will include an assessment of a software providers’ deep learning capabilities. Generative AI Interest in and application of GenAI is exploding. The ISG study 2023 Future Workplace shows that 85% of enterprises believe that investment in GenAI technology in the next 24 months is important or critical. GenAI is being used successfully for NLP such as chatbots/assistants, extracting information from and summarizing documents, and assisting with software development tasks such as code generation and application migration. Nearly every software provider is incorporating GenAI into their products to make them easier to use and more efficient. Further insights into the provider landscape will be included in our 2024 Buyers Guides focused on Artificial Intelligence which contain a related Generative AI Buyers Guide. Machine Learning Operations Machine learning Operations (MLOps) focuses on the application of agile development, DevOps and lean manufacturing in support of AI. It encompasses model training, testing, deployment and orchestration of related data processing pipelines. While MLOps emerged prior to the explosion of interest in large language models (LLMs), the same techniques and concepts are being extended to encompass LLMs. I assert that by 2026, 4 in 5 enterprises will use MLOps and LLMOps to improve the quality and governance of their AI efforts. Further insights into the provider landscape will be included in our 2024 Buyers Guides focused on Artificial Intelligence which contain a related MLOps Buyers Guide. Model Building and Large Language Models The process of training, testing and tuning AI models, including LLMs, is a complicated process that requires highly skilled individuals and disciplined processes. As noted above, two-thirds of enterprises report they do not have the skills they need. Due in part to a lack of process, we assert that through 2026, model governance will remain a significant concern for more than one-half of enterprises, limiting the deployment and therefore the realized value of AI models. AI platforms have improved dramatically with capabilities such as hyperparameter optimization, but these platforms still require specialized knowledge. We expect to see the application of GenAI to further improve the ease of use. We will explore this and other elements of the model building process in our 2024 Buyers Guides focused on Artificial Intelligence. Natural Language Processing  Conversational computing is becoming an increasingly common part of user interfaces. Smart speakers and mobile assistants propelled consumer interest in natural language capabilities and now GenAI powered chatbots are creating an explosion of interest across both consumers and the workforce. Future enhancement with domain-specific and custom LLMs will make NLP more accurate and more broadly applicable over time. We also expect to see enterprises demanding more multilingual capabilities if NLP is to live up to its promise of reaching more of the workforce. We expect that by 2026, one-half of conversational analytics will incorporate multilingual capabilities to support a larger portion of enterprises’ employees. Subscribe to our Ventana Research community to stay up to date on our 2024 research efforts. Check out the Artificial Intelligence expertise and focus pages for our detailed research agenda and continuously updated 90-day calendar as well as more research facts and best practices. 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