--- title: "How CIOs Decide Where to Bet on AI" url: "https://atlan.com/context-and-chaos/issue/how-cios-decide-where-to-bet-on-ai/" description: "BNY's CIO and UKG's Chief Transformation Officer on the AI calls leaders can't hand off, from who owns what the data means to which bets get the money." keywords: "Enterprise AI, AI Leadership, Context Engineering, AI Readiness" --- A Context & Chaos guest take by Cindy Hoots, former Chief Digital Officer and CIO of AZ, who sits on the boards of Zoom and McCormick & Company. Published October 30, 2026. At Context Conference she moderated a panel with Leigh-Ann Russell, CIO and Global Head of Engineering at BNY, and Prakash Kota, Chief Transformation Officer at UKG. Her argument: the AI decisions that matter most can't be handed to a vendor or a platform team. Leaders have to decide who owns what the data means, who is accountable when the AI gets it wrong, how close to the tools they need to be, and which bets get the budget. ## The opening story - In her first week at her last company, the chief medical officer talked about APIs and platforms. To her, an API was an active pharmaceutical ingredient and a platform was a therapeutic platform for drug delivery. An AI agent reading company documents faces the same choice of meaning all the time. - Prukalpa Sankar's keynote that opened Context Conference: you and your biggest competitor can rent the same model at the same price. "So rent the intelligence, but own the context." - Diagram: "Same word, two meanings." ## Who should own what your data means? - Leigh-Ann Russell: data used to be a back-office job; with AI the business has to own it as much as technology does. "If we in technology own your data, we don't really know what that data is used for. So we are not going to get it right, but we're also not going to be able to commercialize that data." Context is "the translation between the tech and business people." - Prakash Kota: supporting an enterprise means switching context all day. AI needs the processes and policies that sit on top of the data, so it gives a real answer and not a hallucination. - Cindy: when each team builds its own context, a dozen AI solutions give different answers, and individual problems never scale. Ownership of context has to be a leadership call. ## Who is accountable when the AI gets it wrong? - Prakash: decades of outdated documentation become the context. "That's where a lot of hallucinations happen." AI can repeat an outdated policy word for word and still give the wrong answer. "If you give the wrong context, you're making wrong decisions and you find out later." - Cindy: missing information is the other problem. Her list of more than 20 requirements for her house still missed the factors that decided it. - Leigh-Ann has talked to a digital twin of herself every day for about 15 months. After she shared a poem about a stale dog biscuit, the twin offered condolences that Missy had died; Missy was lying at her feet. She wants a human "over the loop," and "we as humans still have to be accountable for our own outcomes." Her word for this year is coherence: "If you have chaos, AI is just going to amplify that chaos." - Diagram: "A twin that knows everything can still get it wrong." ## How are leaders staying hands-on through the shift to AI? - Leigh-Ann: "I demand of myself that I am a builder." Her push inside BNY is "idea to production in an hour." On her commute, by voice, she built a business plan, market assessment and website in 35 minutes, entering only her credit card by hand. - Prakash uses the time AI frees up to test what his teams launch like a user. "I almost treat myself as a product manager." - Cindy: technology leaders should be business leaders first; now business leaders have to know enough technology to judge it. If builders are everywhere, IT's job is the platforms and tools that let people build securely and repeatably. ## How do you place big bets when technology keeps changing? - Prakash: things change "every 48 hours," so he sorts decisions into reversible and irreversible and wants a portable stack. A vendor being cooler than what you have is not a reason to bring it in. - Leigh-Ann's word for 2027 is interlock, with four checks: intent, integrity, infrastructure and impact ("Did it show up in the wallet? Did it make my customer happy? Did it make the company safer?"). - Diagram: "Leigh-Ann Russell's four checks before a big AI bet." - Cindy: a few big bets scaled everywhere beat a thousand pilots. Foundations come first. "Everyone wants to talk about the new car they want to buy, and nobody talks about the highway they need to drive it on." For AI, the highway is the data, the context and systems that can talk to each other. ## What to invest in over the next two quarters - Prakash: a project UKG calls Rewired, rebuilding business processes for top-line impact, run on Leigh-Ann's four I's. - Leigh-Ann: the context layer of BNY's AI platform Eliza, which grew from an app-layer harness into one coherent stack of data, context and secure-by-design governance. - Diagram: "BNY's Eliza, from app layer to coherent stack." - Cindy: the foundation, making sure the business owns and maintains the context behind its three or four biggest bets. - Her questions for a leadership team before its next AI investment: Who in the business owns what this data means, and who updates it when it changes? Who's accountable when an agent acts on the wrong answer? Has anyone in this room built something with AI themselves? Can we undo it if it doesn't work, and what is it meant to change? ## Watch the panel [Why invest in context?](https://atlan.com/context-conference/why-invest-in-context/) with Cindy Hoots, Leigh-Ann Russell and Prakash Kota at Context Conference.