From News Desk
Gartner IT Symposium/Xpo 2025, Day 2 was held at Cochin in Kerala.
Top Reasons Why Generative AI Projects Fail and How to Fix it – Presented by Arun Chandrasekaran, Distinguished VP Analyst, Gartner,
AI leaders are under constant pressure to demonstrate value from their early GenAI pilots and increasingly scrutinised on these AI investments to receive ongoing funding. In this session, Arun Chandrasekaran, Distinguished VP Analyst at Gartner, discussed the common causes of failures, as well as best practices to mitigate those failures and scale AI across the enterprise.
Key Takeaways
- “The biggest risk with GenAI today is not taking any risk or waiting for the dust to settle down.”
- Reason No. 1: GenAI Alone Isn’t the Right Technique: Consider a composite approach to AI — where a combination of techniques may yield better value than one.
- Reason No. 2: Tech Obsolescence: Avoid technology obsolescence by using the right AI techniques, building a composable platform architecture and emphasizing AI-ready data.
- Reason No. 3: Responsible AI is an Afterthought: Put responsible AI at the center of all GenAI efforts to ensure robust guardrails. While it is important to identify where AI can be applied, it is equally important to recognize where AI should not be used.
- Reason No. 4: Inadequate Investment in Data and AI Literacy: Invest in data and AI literacy skills with personalized training programs that focus on the “doing” as much as the “knowing.”
The State of AI Regulation: How to Build a Unified Strategy – Presented by Anushree Verma, Sr Director Analyst, Gartner
AI regulations are emerging at the same rapid pace as new AI products. As a result, CIOs must navigate a complex landscape of regulations, working to meet compliance requirements without slowing down their AI strategies. In this session, Anushree Verma, Sr Director Analyst at Gartner, shared a global perspective on AI regulation, outlined what to expect in the next 12 months and offered guidance on preparing for an increasingly fragmented regulatory environment.
Key Takeaways
- “Most AI laws share a common structure, though each includes unique elements. Typically, these regulations define what AI is, establish one or more risk tiers and outline an associated set of obligations.”
- “When deploying higher-risk AI systems, enterprises must implement a risk management program to oversee, track and report on these systems in use and mitigate associated risks.”
- “Monitor the quality of the data exposed to high-risk AI systems, whether in the training or decision-making processes, and establish a human oversight process to monitor these systems.”
- “Establish AI training and literacy programs so users of high-risk AI systems understand risks and can report issues, should these systems display behavior outside of acceptable parameters.”
How to Address Risks in Your AI and GenAI Contracts – Presented by DD Mishra, VP Analyst, Gartner
AI and generative AI (GenAI) bring new contract risks involving customer data, responsible AI and agentic AI. These risks are made more complex by changing pricing models, which can make costs unpredictable. In this session, DD Mishra, VP Analyst at Gartner, outlined how CIOs can handle the unique challenges of AI and GenAI contracts compared to traditional software and SaaS agreements.
Key Takeaways
- “Make sure vendors are clear about how they use and access data, and how they handle responsible AI – this helps manage contract risks.”
- “Watch for market changes. Adoption enthusiasm combined with unique cost drivers make navigating commercials precarious for the foreseeable future.”
- “Buyers want predictable and transparent pricing. Vendors don’t typically provide that, so it is vital for buyers to investigate the models and negotiate.”
- “Play an active role in shaping future standards by pushing vendors to be transparent, predictable and focused on reducing risks.”
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Global Perspectives... 