... Generative AI (GenAI) has been surrounded by many exaggerated claims, yet software engineering is one area where tangible benefits are being realized. To truly capitalize on these AI tools it is crucial to apply them correctly and in suitable scenarios. Many organizations face challenges in leveraging ...
Analyst(s): Matt Brasier
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... Explaining AI/ML models to nontechnical stakeholders is challenging and affects their trust and adoption in production. Data scientists must adopt three practices to enhance model explanations, gain trust, and improve the responsible and ethical use of AI/ML. Overview Analysis Recommendations Conclusion ...
... The expanding landscape of LLMs offers new solutions with continuous capability enhancements and increasing sophistication. However, with more options comes more confusion. This research presents a framework to help data scientists define requirements and match use cases with the most suitable LLMs. ...
... The analytics mesh paradigm is an organizational model addressing the challenges of scaling and governing distributed analytics operations. Analytics specialists should use this model to achieve consistent, compliant, and scalable analytics, maximizing ROI and supporting data-driven decision making. ...
... Organizations are under increased pressure as demand for insights expands to include AI. Analytics specialists should use this research to establish a framework that ensures metric quality and strategic alignment while unlocking the power of AI-augmented analytics, enabling confident data-driven decisions ...
... Emerging developments in GenAI can impact solutions your organization uses that are based on large language models. Data scientists responsible for these solutions should read this research to understand the impact of these developments and the importance to their organizations. Overview Analysis Recommendations ...
... GenAI inputs and outputs can be negatively impacted by different factors. To manage these factors, guardrails must be deployed to ensure that inputs and outputs are validated. This research discusses how data scientists should think holistically about GenAI guardrails and their implementation. Overvi ...
... Failure to evaluate the outputs of GenAI and agentic AI solutions leads to deployment challenges and limits the business impact of the solutions. Data scientists and AI architects must evaluate these outputs using the framework provided in this research to ensure the success of their solutions. Overview ...
... architecture design patterns •How to build up your GenAI strategy, including providing training and establishing safeguards •Steps to establish success metrics for GenAI utilization and drive continuous innovation Return to this web page to watch the webinar. Contact us at gartnerwebinars@gartner.com with questions ...