CFO Talk: Building AI that Delivers
Below are some highlights from our recent CFO Talk: Building AI that Delivers, featuring:
• Prashanth Southekal (Managing Director, DBP Institute; Founding Partner, D2A2, Guest)
• Shikha Mohindra (Data Science Manager, Delta Dental of Michigan, Panelist)
• Sreenivas Gadhar (Global Data & Analytics, Marsh Risk, Panelist)
• James Kulesa (Head of Innovation & Design, Regeneron, Panelist)
• Steve Rosvold (Chief Learning Officer, CFO.University, Host)
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Why Are Enterprise AI Initiatives Failing?
Steve Rosvold opened with a reality check:
“Let’s be honest right from the start, most enterprise AI initiatives are failing to deliver actual financial value.”
o 95% of enterprise GenAI pilots fail to show measurable returns (MIT)
o Over 50% of GenAI projects fail or never scale (Gartner)
o Nearly two-thirds of organizations haven’t begun scaling AI (McKinsey)
Emphasis: Too many focus on technology inputs—not business outcomes, risk, or adoption.
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The Panel’s Prescriptions for Real Value
Prashanth Southekal: Start With the Business Outcome
• Most AI projects fail because teams start from technology, not the business problem
• Focus on what you want to achieve (“start with the end in mind”)
• Prioritize the intersection of high-value transactions and high-value decisions—not all data or processes matter equally
• “You just need a small percentage of data—about 15-20%—to power your real analytics and AI”
• Use your KPIs to guide this focus
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James Kulesa: Adoption Over Activity
• Measuring AI ROI is tricky—it’s not about pilot launches or API counts, but lasting, meaningful workflow change
• Ask: Are users leveraging AI in a way that drives actual results?
• Pay attention to “hidden human costs” of AI change
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Shikha Mohindra: Build Organizational Capability
• The analogy: Nobody celebrated electricity—just what it enabled (the refrigerator, the lightbulb, etc.)
• Four requirements for sustainable AI:
1. Business ownership—biz champions, not just IT
2. Data readiness—trustworthy, scalable inputs
3. Security and risk—start with this, not after the fact
4. Change management—adoption should be day-one, not mile ten
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Sreenivas Gadhar: Governance as a Steering Wheel
• Treat governance as a steering wheel, not a brake pedal
• Balance innovation and control by managing, not avoiding, risk
• Use risk-based governance. Tailor rigor to the use case (low, medium, high risk)
• Build guardrails (privacy, security, human oversight) into the process, not as hurdles
• Measure innovation and downside: track productivity, savings, AND incidents, compliance, user trust
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Debunking Data Myths (Prashanth Southekal)
• “There is no such thing as 100% data quality.” Perfect data is a myth
• Focus on “good enough” data tied to business decisions
• Sometimes you’ll need data from outside your company
• AI is probabilistic—not every decision has a deterministic answer
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BONUS: The 5x5x5 AI Compass
Prashanth Southekal shared his framework:
• 5 Capabilities: Predict, Perceive, Create, Act, Reason
• 5 Solution Types: Assistants, Chatbots, Automated Agents, and more
• 5 Enabler Areas: Data, Algorithms, Applications/Models, Output, and Ethics
• Focus less on automation, more on decision-making
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The Panelist’s ONE Final Message
• Sreenivas Gadhar: Don’t block all risk, controlled risk is the only way to innovate
• James Kulesa: Measure real adoption and human workflow change, not just activity
• Shikha Mohindra: Organizational capability turns AI possibility into value
• Prashanth Southekal: Identify root causes. Sometimes it’s not an AI problem after all (“not every nail needs an AI hammer”)
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