Bull Series – The ROI Paradox: Translating AI’s Returns in Finance

Last week in New York, I found myself moving between two very different worlds.

Earlier in the week, I was at an AI × Fintech conference, surrounded by product leads, engineers, and builders debating how to push boundaries and where the next wave of AI innovation will come from. Later that week, I was at a finance symposium & gala dinner, in a room full of investment bankers, PE professionals, LPs, and venture capitalists discussing leadership, mentorship, and the future of the industry.

At the AI conference, I found myself explaining what private credit actually is, how capital gets raised and deployed, and where the finance industry’s pain points lie – all based on my years on Wall Street. At the finance symposium, I was explaining the capabilities and limits of AI, drawing from my work through The Bull and The Bot: what today’s models can actually do, how workflows could evolve, and where “human in the loop” is still needed.

So in one room, I was translating finance to the AI crowd. In the other, I was translating AI to finance.

The contrast between the two was striking. The AI crowd understood the technology deeply and engaged thoughtfully with finance, but often from a data-first perspective rather than the lived workflow reality that drives decisions inside firms. The finance crowd, meanwhile, understood markets intuitively and was curious about AI, but hadn’t yet found the mindset or structure to practically apply it across workflows. Many felt that the technology wasn’t quite there yet, or that the costs simply didn’t seem to outweigh the benefits.

And that made me think. While finance firms are interested and investing in AI, the measure for success still keeps coming back to one question: are we actually seeing ROI? As one finance attendee put it, “I get that people are more efficient using AI, but where are the ROI numbers in dollar form?”

The ROI Paradox

Now, ‘what’s the dollar return on my investment?’ is the most natural question for any finance professional to ask, because it’s how we’re wired. But the more I engaged in conversations across these two conferences, the more it became clear to me that ROI in AI isn’t missing -it’s just being measured in the wrong units right now.

Because the reality is, some returns show up first as edge: in how fast you learn, how well you decide, or how effectively your teams adapt.

What that means is, not every AI return can, or should, be measured in dollars.

Instead, I think about ROI in three levels:

1. Dollar value: purely financial - revenue up, cost down.

o This is what leaders fixate on (“show me the dollars”).

o This is lagging ROI, not leading ROI.

2. Numerical value: quantifiable but not dollarized.

o Productivity metrics, time saved, number of deals screened, user adoption, etc.

o This can be measured numerically - but doesn’t translate neatly into P&L yet.

o It’s “ROI you can count, but not cash.”

3. Intangible value: cultural, behavioral, reputational.

o Curiosity, collaboration, experimentation, power-user influence.

o These are the compounding drivers of all future ROI, but they’re invisible on balance sheets.

Early-stage ROI in AI is numerical and intangible, not financial. It’s measured in learning velocity, decision leverage, and cultural momentum – things that don’t have price tags yet but eventually shape the bottom line.

And this idea is echoed by something Lloyd Blankfein (former CEO of Goldman Sachs) said during his keynote at the AI x Fintech conference:

“In trading, if you want to win a bid in a market, the person who’s one millisecond faster – whose machines are half a block closer to the exchange – wins 100% of the time. So how could you not invest in the technology that gets you closer? The same applies to people exercising judgment over investment decisions. You want the best tools and resources to at least be able to have an arms race and try to stay ahead.”

Firms keep asking for ROI in dollar form, but what Lloyd was describing is the ROI of edge. It’s not about whether AI has “paid off” yet – it’s about whether you’re compounding your learning faster than the competition. The edge doesn’t have to be big, it just has to exist. And over time, those milliseconds of advantage – in knowledge, workflow, or insight – become the gap between staying relevant and falling behind. Edge matters, no matter how minute it seems at the present.

And that’s the paradox of AI ROI in this moment: firms want to see the dollars now, when the real returns show up in edges that only compound and become obvious later.

Invisible Alpha

Lets take the idea of ‘Decision Leverage’ as example.

Every established firm already has decades of proprietary data – investment memos, analyses, deal notes – all of which are a latent form of alpha that no human team can fully digest.

AI becomes the tool that translates that buried knowledge into leverage and active intelligence. That’s a massive form of ROI that doesn’t appear on P&L. Rather, it appears in sharper decisions. By surfacing patterns humans couldn’t possibly see, AI fills in blind spots we didn’t know existed. The edge AI offers isn’t in replacing judgment but is in amplifying it.

For example, lets say a team uses AI and can now review 10 deals instead of 5, and five years later they outperform because one of those extra 5 turned into a winning investment. That’s not visible in quarterly ROI reviews because how do you quantify a better decision you never would’ve made without AI? You can’t measure the value of a deal you never reviewed, because you wouldn’t have even known it existed. And to that end, you can’t P&L the AI-enabled insight that prevented a bad deal or surfaced a good one. So the real value of AI here is counterfactual: its invisible in the short term, but defining over time.

Thus “Decision Leverage” is intangible in quality but numerically observable in behavior. You may see it in the data trail of better decisions being made faster (e.g. more opportunities screened, higher-quality ICs, fewer reversals, faster approval cycles, etc.). But you can’t yet price it in dollar terms because the causal chain to P&L is too long or complex.

ROI in AI isn’t just about cost reduction or revenue gains – it’s about the numerical and intangible values that include things like decision leverage. Are teams accessing more of the proprietary data they own to enhance their insights? Are they making decisions faster, with greater confidence? Are they compounding insight faster than competitors? Those are the real return metrics, but they just don’t fit neatly into a spreadsheet. So while leaders may be measuring for visible gains, the real ROI right now sits in invisible advantage.

Translating Leadership for the AI Era

But to see that invisible advantage, leadership itself has to evolve.

At the finance symposium, Kewsong Lee (Former CEO of The Carlyle Group) shared a growth framework that stuck with me: Think Big. Move Quick. Perform Better. Sitting in the audience, I couldn’t help but think about how directly it applies to the kind of leadership needed to guide organizations through AI adoption today.

Thinking big means expanding perspective: lifting your focus from the immediate efficiencies of AI to the broader transformation it enables. It’s the shift from asking “Where can we cut cost?” to “How do we make better decisions?” or “How do we create new kinds of value?” For AI, thinking big means seeing the entire system – data, workflows, people – as part of a continuous learning loop. If you keep measuring AI through the same dollar-cost ROI lens, and you’ll keep drawing the same conclusion: that the returns aren’t good enough. When in reality, it may be the lens that’s too narrow.

Moving quick is about rhythm and readiness. True leadership isn’t just defined by the big, high-stakes decisions, but also by the ability to make smaller ones frequently and appropriately. And that’s exactly what successful AI adoption looks like. You don’t wait for perfect certainty; you test, learn, and adjust in motion. Organizational adoption doesn’t happen through strategy decks, it happens through iteration.

And the real signal that this is happening? Power users.

The focus right now shouldn’t be on measuring AI returns in dollar form. Instead, it should be on tracking how many power users you’re creating inside a firm. Power users are the ones compounding learning inside your organization. They experiment, share prompts, and make adoption contagious. They’re the internal translators turning curiosity into capability. The intangible value they create – a forward-looking, curious, agile, creative culture – is the early ROI every firm should be optimizing for. That’s the real multiplier effect.

Performing better is where conviction and resilience come in. There’s no perfect playbook for integrating AI into established systems, and perhaps there shouldn’t be. The leaders who tend to stand out are the ones who form clear opinions about where AI truly adds value, and who have the conviction to test those opinions in the open.

It’s less about reckless risk-taking and more about ownership of perspective. Because in a landscape moving this fast, describing the trend isn’t enough; what separates strong leadership is the willingness to interpret it — and to stand by that interpretation, test it, and learn from it.

Resilience plays a part, too. Not in the motivational-poster sense, but in the operational one. The cadence of experimentation will always include failures, and performing better often comes down to recovering faster, learning from each iteration, and refining conviction through data and experience.

Conclusion

The question isn’t whether AI can prove its ROI. It’s whether we can learn to translate it.

The firms that will win aren’t the ones waiting for perfect dollar metrics – they’re the ones investing through the translation phase, building edge in how they learn, decide, and lead.

The signals are already there: rising numbers of power users, faster learning loops, and enhancements in decision leverage, learning velocity, and cultural momentum. These are the numerical and intangible metrics that define early-stage ROI. They may not fit neatly into a spreadsheet, but they shape the ones that will.

Think big. Move quick. Perform better. Because when curiosity compounds, so does adoption. And when adoption compounds, ROI follows.