People
Hoyoung Pak
Chief AI Officer, Partner & Managing Director
Chicago
People
Hoyoung Pak
Partner & Managing Director - Chicago
“The companies that win with AI aren't the ones spending the most. They're the ones who know exactly which bets create enterprise value and drive them forward relentlessly.”
Alignment to your value drivers: Concentrate capital where it compounds
Knowing which levers matter starts with an honest, granular read of where your enterprise actually makes money, and where it doesn't, across products, customers, and geographies, across your entire value chain. The parts of the business already earning above your cost of capital deserve more investment. The parts that aren't need a harder conversation before they get an AI pilot.
Anchor funding to a number rather than a narrative. Deploy capital where economic profit is deepest and be able to say what the last dollar returned before you commit the next one. For more on how sustained outperformers allocate capital, see our analysis of the Performance Frontier.
Stakeholder engagement: AI is a team sport
Without the right alignment, you may find that every executive pursues their own agenda, causing the entire effort to disintegrate into ineffective fragments. Worse, you may invest a great deal of effort into an investment only to find that compliance objects or that finance had planned for that spending to go elsewhere.
Alignment is just the start. Adoption is where the real work begins. Build AI literacy with the people who make key business decisions day to day, as they are best placed to know where the business already makes money: that’s exactly where you want AI to operate.
Use case selection: Prioritize delivering value and building capability
Focus on initiatives that accelerate the value drivers you identified. These may not be the easiest or the fastest, but they are the biggest wins when you get them right. Conversely, quick wins are often very easy for competitors to mimic. Keep an eye on screening criteria, as they often quietly filter out valuable projects simply for being harder than the quick, but insignificant, wins sitting beside them.
Judge every use case on two things:
- Will this use case deliver against your most important value drivers? Quantify the costs involved and identify the metrics you’ll use to prove its value down the line. Set how much ROI you expect and name who is accountable for delivering it.
- Will it build enduring capability? The best early use cases weave new capability across the business rather than improving how one or two functions work, so each project leaves the organization more capable than before.
That second test is crucial for your first few choices. Keep initial use cases thin enough to deliver quickly, but cut across how the business generates value rather than deeply into one function. This means touching on technology, processes, people, culture, and governance to improve how your business points all of them at generating value. Going too broad risks becoming clunky and impossible to prove. However, focusing too narrowly on just one area risks making an elaborate pilot rather than a true business improvement, as it is unlikely the investment will truly run from data to business results. Watch for overlap between use cases, too, since two initiatives claiming the same savings will not both deliver it.
Set the bar and the deadline at the start, as proving something will not work is far better than carrying a large sunk cost nobody wants to admit to. There is one important exception to this approach: innovation. Entirely new ideas can rarely be judged the same way as refinements or upgrades. A thoughtful, ring-fenced exploration budget lets you fund the best ideas, even if they may need an opportunity to prove feasibility before they can be properly judged against ROI. Seed it once, and expect it to earn its way into funding the next wave through its own success, rather than treating it as a standing line item.
"The point of AI was never to do the same work for less. Pick use cases that meaningfully improve how your business operates. Efficiency plays are table stakes, growth plays are where you can get a real competitive advantage."
1 quarter to an AI strategy built on value, not potential | 80 senior leaders aligned on how to win with AI
The CEO of a category-leading retailer knew AI belonged in the company's strategy but did not have a plan he could confidently commit to. The company was not cash-rich, so every AI investment had to prove its worth both in theory before investing and in practice once launched.
Their ambition was to accelerate growth, not just savings. We started from the strategy the retailer already had and asked where AI could accelerate what it already did well.
The aim was to make the company's best people even better rather than replace them, and that started with leadership. Awareness and enthusiasm for AI varied widely, so we brought the top 80 leaders through a half-day session on the main types of AI and what each could do for retailers. That allowed every leader to discuss their priorities from the same starting point.
From there, deep dives with each function scored every opportunity on financial impact, feasibility, scalability, adoption readiness, and risk, revealing where AI could push the strategy forward fastest. The result was a tiered portfolio: off-the-shelf AI tools teams could use straight away, changes that needed leaders to bring their people along, and a small number of game-changing initiatives. We homed in on the largest opportunities with the client, creating a charter for each that included an executive accountable for delivery. Each was then presented to the executive committee for alignment and commitment.
The existing capital process was built for store investments and moved too slowly for AI, so we designed a venture-style AI fund: the company seeds it once, the executive committee acts as its investment committee, and every saving Finance verifies is added back, meaning the program grows in step with what it delivers. A central AI transformation office tracks each initiative against agreed definitions of success. This fund turned AI from a drain on capital into a program that pays for itself, with money following the strongest opportunities rather than fixed allocations to each business unit.
Within one quarter, the CEO could take the board a quantified plan rather than a promise: AI tied directly to the strategy, a prioritized roadmap that funds its own next wave, and a clear way to show what every initiative returns. Because the fund only grows when Finance verifies savings, AI became part of how the business competes rather than a one-off investment.
There are four key elements to realizing value from AI. Click to explore each in more detail:
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