Has Anyone Seen My ROI?

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Written By Jeff Winter

Turn the sound on. Trust me, it’s worth it

In the video that goes with this article, I’m wandering around an office asking:

“Has anyone seen my ROI?”

I look under the budget.
I root around in the spreadsheet.
I even check that drawer where random cables and orphaned dongles go to die.

Still no ROI.

It’s a joke on camera, but it’s not far from how many leaders actually experience ROI on big transformation or AI initiatives. At kickoff, ROI is everywhere: on slide 3 of the business case, in the board pack, in the town hall talking points. Eighteen months later, it’s mysteriously absent from the P&L… and everyone is quietly wondering if it slipped out the fire exit during user acceptance testing.

The problem isn’t that ROI is a bad metric. It’s that we keep asking it to do a job it was never hired for.

When ROI Is a Flashlight (and When It Becomes a Black Hole)

ROI is brilliant when you point it at the right kind of work.

If you’re:

  • Upgrading a machine with a known energy profile

  • Automating a specific, stable workflow

  • Consolidating software licenses

In these cases, ROI is your friend. Costs and benefits are bounded, the process is understood, and the time horizon is reasonable. Finance loves it, the board understands it, and the math basically behaves. That’s the world ROI was built for.

But more and more of our spend is not in that world anymore.

Analysts like McKinsey have been repeating for years that roughly 70% of large-scale transformations fail to achieve their objectives, which means the promised benefits never fully land. Bain & Company’s 2023 Transformation & Change Survey puts the number even higher, claiming that 88% of business transformations fail to reach their original ambitions.

At the same time, a 2023 PwC Pulse Survey found that 88% of executives say they struggle to capture value from their technology investments. In other words: the ROI we promised at kickoff is proving stubbornly hard to find once the thing goes live.

The AI story is even messier:

  • Boston Consulting Group’s 2025 research shows only about 5% of companies worldwide are “AI future-built” and actually generating meaningful value at scale from AI, while nearly 60% report little or no impact so far.

  • A 2025 MIT study summarized in mainstream coverage found that 95% of enterprise generative AI implementations show no measurable impact on profit and loss, not because the models fail, but because they’re poorly integrated into real workflows.

So we’re investing heavily, and yet in most organizations, the “ROI column” on AI and transformation is suspiciously empty. That’s not just a leadership problem; it’s a metrics problem. When you point traditional ROI at big, messy, multi-year change, it doesn’t act like a flashlight. It acts like a black hole. All the nuance gets sucked in; very little useful signal comes out.

Why Transformation Makes ROI Disappear

Think back to the video: I start with a confident sense that ROI is “right here somewhere.” Then the scavenger hunt begins. That’s exactly how transformation value behaves inside most organizations.

There are three big reasons.

1) We’re using a one-dimensional metric on multi-dimensional value

Transformation is almost never just about short-term cost or revenue. It’s about how the business works.

The value often shows up as:

Faster decision-making and shorter cycle times

Reduced operational and compliance risk

Greater agility when the market shifts

Better customer experiences and loyalty

New capabilities: data, AI models, reusable components, skills

None of that is soft. In fact, it’s where most enterprise value now lives.

Ocean Tomo’s long-running Intangible Asset Market Value study shows that intangible assets—software, data, IP, brand, know-how—now account for around 90% of the market value of S&P 500 companies, up from 17% in the 1970s. We live in an economy where “how we decide, adapt, and learn” is where the money is.

Customer experience is a similar story. Forrester’s 2024 US Customer Experience Index found that customer-obsessed organizations reported 41% faster revenue growth, 49% faster profit growth, and 51% better customer retention than those that aren’t customer-obsessed. Those gains are real, but they rarely attach neatly to a single project’s ROI cell within a 12–18 month window.

2) The time horizon is longer than the business case

When you modernize core systems or build an AI and data foundation, your biggest payoff is often option value:

  • The next ten projects become faster, cheaper, and lower risk.

  • You can pivot into new products or markets you couldn’t touch before.

  • You avoid being structurally locked into legacy ways of working.

But our ROI models usually treat each initiative as a sealed box with a hard end date and a fixed payback period. Anything beyond that window is “nice, but not on this slide.” That’s how we end up underestimating the value of foundational moves and overestimating quick wins.

3) We never set ROI up to be found

This is the one nobody likes to admit.

In a lot of organizations, the lifecycle of ROI looks like this:

  1. A number appears in the business case.

  2. Everyone nods enthusiastically.

  3. The project gets approved.

  4. The number is never operationalized into actual metrics, baselines, owners, or dashboards.

Deloitte’s 2023 work on “Mapping Digital Transformation Value” highlights that 81% of organizations still use productivity as the prime measure of digital ROI, and that those using a broader metric set are about 20% more likely to report medium-to-high enterprise value from their digital programs. In other words: most organizations aren’t measuring the full value in the first place—so of course they struggle to “find” ROI later.

Treat ROI Like a Discipline and Design It So It Does Not Go Missing

When ROI disappears in transformation and AI initiatives, the problem is usually not a lack of value. The problem is the way organizations approach ROI in the first place. ROI works when it is treated as a discipline. It disappears when it is treated as a decorative line in a business case. The solution is to redesign how we define, measure, and track value from day one.

Start with testable value hypotheses

Many programs fail because the benefits are written in vague terms. Replace broad statements with measurable hypotheses that can be validated. An example may be to Improve quote cycle time from 8 weeks to 3 weeks to enable faster revenue capture. Each hypothesis needs a baseline, a clear metric, a time horizon, and an owner who is accountable for achieving it. This turns ROI into the roll-up of a series of measurable commitments rather than one abstract target.

Design measurement before kickoff

ROI cannot be found after the fact if the measurement system was never built. Organizations must decide in advance where the data will come from, how baselines will be captured, and which indicators matter. This includes both:

  • Leading indicators such as adoption, cycle times, decision speed, error rates, and customer experience

  • Lagging indicators such as revenue, cost, margin, and retention

When measurement is designed early, ROI becomes easier to track and harder to lose.

Assign value owners rather than project owners

A project going live and a benefit materializing are not the same thing. Someone in the business must own each benefit from start to finish. A value owner must have the authority to change processes, shift behaviors, and adjust incentives until the benefit becomes real. Without clear ownership, benefits drift and ROI quietly disappears.

Review value with the same rigor as project status

Most organizations review uptime, stability, and defect counts. Far fewer review the value being created. Monthly governance needs a value track that asks: which hypotheses are on track? wich need intervention? and what new value has emerged that was not in the original plan? This turns value realization into a living practice instead of a one-time promise.

When organizations adopt these habits, ROI stops being something they search for at the end. It becomes something designed intentionally, measured consistently, and visible throughout the journey.

References:

Jeff Winter
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