Why Data and Digital Transformations Fail: Evidence from 74 Senior Executives

Most data, digital, and now AI initiatives do not fail because the business case was wrong. They fail because the organization was never adjusted to deliver it. I surveyed 74 senior executives to find out how often that happens, and what separates the initiatives that deliver from the ones that quietly do not.

This is the research behind my capstone project for the Advanced Management Program at Harvard Business School, formally the Leadership Impact Project. The capstone focused on driving enterprise value from data, digital transformation, and AI. Respondents were senior leaders across general management, technology, finance, and commercial functions, surveyed in 2026. Responses are reported in aggregate.

What 74 executives reported

51%

report that fewer than half of their data and digital initiatives delivered the commercial outcomes that justified them.

56%

said their organization selected the technology before the business outcome had been defined.

2.6x

higher scores on ability to demonstrate ROI among organizations that redesigned incentives before launch. Only six respondents had done so.

more likely to fully deliver when the initiative had dedicated ownership and dedicated resources rather than being added to business as usual.

Zero

of the respondents whose organizations did none of the five conditions reported an initiative that fully delivered. That group was a minority of the 74, so read it as a pattern rather than a precise rate.

Survey of 74 senior executives, 2026. Figures are self-reported and describe the respondents’ own organizations.

The last finding is the one worth sitting with. Among respondents whose organizations did none of the five things, clear ownership, a dedicated team, aligned incentives, defined measurement, and an assessed readiness, not one reported an initiative that fully delivered. Not a reduced return. None.

This is a transformation problem, not a technology problem

That reading is consistent with the wider literature. Bain & Company found that 88% of business transformations fall short of their original ambitions, with only about 12% achieving what they set out to deliver. Boston Consulting Group’s 2025 study of more than 1,250 companies across 68 countries found 60% achieving no material value from AI at all, and just 5% generating value at scale. My survey adds a practitioner-level observation to both: 56% of these organizations chose the technology before the commercial outcome had been defined, which is the point at which the rest becomes hard.

The failure is rarely dramatic. Resources get quietly withdrawn to cover business as usual. Two initiatives draw on the same finite talent pool with no stated first priority. The people who stand to lose control, budget, or status do not object in the meeting, they simply do not move. None of that shows up on a milestone dashboard until the return is already gone.

AI is repeating the pattern, faster

This survey asked about data and digital initiatives, because that is the wave most of these organizations have already been through. What is striking is how exactly the AI conversation is now reproducing it. The technology is being selected before the commercial outcome is defined. Pilots are being added to teams who already have day jobs. Nobody is being paid differently if it works. Those are the same three conditions that predicted failure in the data era, and there is no reason to expect a different result from a different acronym.

If anything AI compresses the timeline. The technology is easier to acquire than a data platform was, which removes the procurement friction that used to force at least some organizational conversation. It is now entirely possible to have AI running in a dozen places in the business without anyone having defined what commercial outcome it is meant to produce, who owns it, or how the return will be verified. Ease of adoption is not the same as readiness to benefit.

Readiness, then strategy, then deployment

Readiness means diagnosing where today’s norms, power, and incentives will resist tomorrow’s strategy, and adjusting them before capital is committed. It is not a checklist run at kickoff. Four structural conditions need to be true before launch rather than after it:

An aligned senior team. If key leaders are not genuinely on board, resolve that friction before launch. It does not dissolve on contact with a roadmap.
Defined leadership. A clearly named leader, with the emotional intelligence to map where the cultural resistance sits and the standing to influence through inclusion rather than mandate.
Explicit resources and architecture. Protected capital, protected talent, and a structure insulated from business as usual, which will otherwise reclaim both.
Sustained support. Not for a year. Short-cycle support is how initiatives die before they can deliver.

The LEASH model

Where the diagnostic finds gaps, five levers close them. The framework I apply here is the LEASH model, developed by Charles O’Reilly of Stanford and taught in the Harvard Business School change and renewal curriculum. It is not mine, and I use it because in practice it is the most complete account of what actually has to move:

Leader actions

What leadership visibly does, funds, and protects, which is read as the real priority regardless of what is announced.

Employee involvement

Bringing the people who will live with the change into the design of it, early enough that their input still changes something.

Aligned rewards

Compensation and recognition that pay out on the new outcome rather than the old one.

Stories, symbols and signals

The narrative and the small visible decisions that tell the organization which way is now up.

HR systems

Hiring, promotion, and performance management adjusted so the structure sustains the change after attention moves on.

What it looks like when it works

The two openings look different and are worth naming separately. Michael Tushman and Charles O’Reilly draw the distinction in Winning Through Innovation: a performance gap is something broken or inefficient in the business you already run, an opportunity gap is new value you could create. They argue the two need structurally separate teams, because the second will always lose to the first if they compete for the same people.

At The Venetian, the performance gap was channel mix. Too much revenue was flowing through online travel agencies at high commission cost. Shifting those bookings to direct meant the same guest at a lower acquisition cost and a higher margin, which improved EBITDA without changing the product at all.

The opportunity gap was priced differently. Combining guest willingness to pay with competitive pricing data by date and segment let the right offer reach the right guest at the right price before a competitor could make one. A single use case paid for the entire program.

Neither depended on novel technology. Both depended on defining the commercial outcome first, funding a discovery phase rather than a platform, embedding the project team inside the business rather than beside it, and having an independent finance validator confirm the result rather than the project team reporting on itself.

Governance as a sensing instrument

Most governance tracks milestones and spend. That catches problems after they have cost something. Governance that works surfaces resource withdrawal, misaligned incentives, and passive resistance early, which means it has to be looking at the organization and not only at the plan. A monthly steering review comparing capital approved against independently verified result will find drift sooner than any status report.

Governance is not a stop mechanism. It is a navigation instrument, and its real job is to protect the new behaviors long enough for them to take hold.

The practical version

Before the next initiative is funded, three questions are worth answering honestly. Can we describe exactly what work needs doing and who owns each piece? Do we have a dedicated team, or are we adding this to people who already have day jobs? Will anyone be paid differently if this succeeds?

If the answer to any of those is no, the business case is not the thing to revisit. The organization is.