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Cover image for Your Transformation Isn't Failing. Your Evidence Is.
Debashish Ghosal
Debashish Ghosal

Posted on AI-assisted

Your Transformation Isn't Failing. Your Evidence Is.

#ai #leadership #cto #employeeexperience

There's one question that quietly ends more transformation programs than bad architecture ever will.

"What changed for the business?"

It usually arrives in budget season.
The team has been working hard.
The slides show tickets closed, services migrated, pipelines onboarded.

And the room goes quiet, because activity is not evidence.

If you can't explain the value in budget season, the organization will eventually treat the work as optional, no matter how hard the team worked.

If you're a director, or about to become one, this is for you.
At that level the job quietly changes.
You're no longer judged only on delivering the work, but on making the case for it.

The Market Data Is Blunt

Gartner's 2026 CIO and Technology Executive Survey found that only 48% of digital initiatives meet or exceed their business outcome targets.
The same research shows 57% of CIOs under pressure to improve productivity and 52% under pressure to reduce costs.
Yet only 33% consistently pursue financial outcomes from their technology initiatives, even though those who do are 25% more likely to excel.

AI has made the gap more visible, not less.

PwC's 29th Global CEO Survey (4,454 CEOs, January 2026) found that 56% of CEOs have seen no significant financial benefit from AI to date.
Only 12% report both lower costs and higher revenue.

BCG's Where's the Value in AI? study of 1,000 executives found that 74% of companies had yet to show tangible value from AI.
The reason matters more than the headline.
Roughly 70% of the challenges were people and process, 20% technology and data, and only 10% the algorithms.

The bottleneck is rarely the technology.
It's whether anyone can show, in terms other leaders trust, that the change mattered.

Value Is Lost Before the Work Starts

In McKinsey's research on organizational transformations, respondents said nearly a quarter of a transformation's value loss happens during target-setting.
That's before anything ships.
Another 55% is lost during and after implementation.

Many programs don't lose credibility in the budget review.
They lose it months earlier, when work starts without a baseline.
By the time someone asks what changed, the only answer available is memory, and memory doesn't survive a finance review.

Tip: Before any transformation work begins, write down today's number for the thing you claim you'll improve. A rough baseline beats a perfect recollection every time.

A note on a statistic you've probably seen: "70% of transformations fail."
McKinsey's own write-up attributes that figure to John Kotter's estimates rather than a single clean measurement.
It's worth retiring.
If you're asking finance to trust your numbers, hold your own sources to the same standard.

ROI Is a Test of Executive Legibility

ROI isn't mainly a finance exercise.
It's a test of whether your work makes sense to people outside your team.

Transformation creates value through reliability, speed, lower cost-to-serve, and lower risk.
None of it becomes visible on its own.
When a team reports activity, everyone else has to infer the business meaning.
In budget season, nobody infers generously.

Strong ROI communication isn't about protecting funding. It's about proving the transformation is real enough to steer.

A Practical Structure That Holds Up

Programs that keep their funding tend to share a few unglamorous habits.

Use Five Value Categories, Not Fifty Metrics

A compact value model is easier to defend than a crowded dashboard.
One workable set:

  1. Time-to-market — how long an idea takes to reach a customer.
  2. Reliability — incidents, recovery time, customer-facing disruption.
  3. Cost-to-serve — what it costs to run a service per unit of usage.
  4. Risk reduction — exposure removed, defined concretely.
  5. Adoption — whether people actually use what was built.

Every metric should roll up into one category.
If it doesn't, it probably belongs off the executive view.

Cost-to-serve is becoming non-negotiable.
Flexera's 2026 State of the Cloud report found that use of unit economics rose from 40% to 49% of organizations.
Over the same period, estimated wasted cloud spend rose to 29%, the first increase in five years, which Flexera links to growing AI workloads.
Finance is learning to ask "cost per what?"
Have the answer ready.

Capture Baselines Before the Work

This is the cheapest high-value habit available.

A baseline changes the shape of the funding conversation.
Without one, the pitch is "trust us, it's better."
With one, it becomes "here's where we started, here's where we are, here's what we still don't know."

That shifts the question from "should we keep funding this?" to "what should we do next?"

Give Every Metric a Named Owner

A metric without an owner is decoration.

At director level, that owner is usually one of your managers, not you.
When one person is accountable for each number, the data tends to get cleaner, the story sharper, and stale metrics get noticed faster.

Pair Every Number With a Sentence

Numbers rarely explain system change on their own.

A short narrative next to each metric closes the gap:
What changed.
Why it matters.
What's still uncertain.

The uncertainty line feels risky.
It's often the part that earns the most trust, because it signals the rest isn't spin.

Report it as a monthly trend, not a dramatic quarterly snapshot.
And skip composite "transformation scores": if nobody outside the team can explain the formula, nobody outside the team will believe it.

Watch for the Comfortable Vanity Metric

The most dangerous metrics are the ones that always go up.

A classic example is counting things onboarded to a platform.
The number climbs every month.
Executives don't care, and they're right not to.

Onboarded is not adopted.
A service can be "on" a platform and still route around every capability it offers.
Unused transformation isn't transformation.

Goodhart's law applies here: when a measure becomes a target, it stops being a good measure.
Swapping a count for a usage-based signal often makes the chart look worse and the conversation better.

Price the Whole Lifecycle, Not Just the Build

Most business cases are written around build cost.
Finance and your CTO care about total cost of ownership (TCO): what it costs to build, run, support, secure, migrate, and eventually retire.

AI has widened that gap.
Gartner found that more than 90% of CIOs say managing cost limits their ability to get value from AI.
It estimates that CIOs who don't understand how GenAI costs scale could make a 500%–1,000% error in their cost calculations.
Gartner also predicts AI inference costs per agentic workflow will rise more than fivefold through 2028, even as model prices fall, because agents consume far more tokens than simple chatbots.
Its advice: build proofs of concept that test how costs scale, not just how the technology works.

The status quo has a TCO too, and it's often the strongest part of the case.
In a McKinsey survey of 50 CIOs, respondents said 10–20% of the technology budget meant for new products is diverted to resolving tech debt.
They estimated tech debt at 20–40% of the value of their entire technology estate.
That's the cost of standing still, and it rarely appears on the slide.

A defensible TCO view usually covers:

  • Build and migration cost, including the parallel-run period.
  • Run cost per unit of usage, projected as adoption grows.
  • Support, on-call, security, and compliance effort.
  • The cost of the old system, until it's actually switched off.

Tip: Count decommissioning savings only once the old system is off. A planned retirement isn't a saving yet.

Honest Tradeoffs Build More Trust Than Optimism

Transformation often costs more before it costs less.
Running old and new systems in parallel is expensive.
Some benefits lag by several quarters.

Smoothing that over rarely works.
Finance teams review spending for a living and are good at spotting a polished story.

Plain framing works better:
"Cost rose this quarter because we're running both systems in parallel. Here's when that ends. Here's what we'll stop if it doesn't."

That framing builds credibility for the next request, and turns finance from an auditor into a partner who helps shape the case.

Think One Level Up: What Your CTO Gets Asked

Directors who keep their programs funded tend to answer the question their CTO will face next, not just the one in front of them.

At the top, the question isn't "did this program work?"
It's "is this the best use of the money compared to everything else?"

That changes what a strong value case includes:

  • Comparable categories. If every team reports against the same five categories, your CTO can compare programs side by side. If each team invents its own metrics, nobody can.
  • Multi-year TCO for both options. Compare the cost of the change with the cost of standing still, over the same horizon.
  • Explicit kill criteria. BCG found AI leaders pursue about half as many opportunities as other companies, and expect more than twice the ROI. Saying what you'd stop makes what you keep more credible.
  • Evidence early enough to change a decision. Gartner found only 18% of CIOs embrace dynamic, off-cycle reprioritization, yet those who do are 24% more likely to be top performers. Bring data while it can still redirect money, not only defend it.
  • A board-ready sentence. Your CTO will compress your program into one line for the CEO or board. Write that line yourself, so the nuance survives.

The Limits of All This

Risk reduction is still the hardest category.
You can define exposure concretely, such as how many critical services lack a tested recovery path.
Converting that into dollars finance fully trusts is still more judgment than math.

Attribution is messy.
When several teams and a market shift all touch the same outcome, claiming full credit is overreach.
A credible contribution beats an inflated total.

Some value is slow.
Platform work often pays off quarters later, in decisions other teams make faster.
Be skeptical of any framework claiming to capture that cleanly.

The goal isn't false precision. It's making value legible enough that the organization chooses to keep investing.

The Takeaway

The work and the translation are two different jobs.
At director level, the second one is yours.

Transformation doesn't become real when the work is hard.
It becomes real when the value is visible enough that the organization chooses to keep funding it.

So, a question for anyone who has sat through a budget review recently:

What's the one transformation metric executives in your org actually believe, and which vanity metric did you have to let go of?

Share yours in the comments. The answers will likely be more useful than this article.

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