When the Sponsor Changes, Does the Transformation Change Too?

By Rishi Dixit, Head of Strategic Solutions, Innovation & Partnerships, Atlas

How life sciences organizations can sustain transformation through leadership turnover, restructuring, and shifting priorities

I've seen transformation programs deal with budget cuts, missed milestones, technology failures, and shifting business priorities. But there's another disruption that doesn't always appear in the RAID log. And that is leadership change (a daily dose of transformation).

A new executive sponsor arrives, reviews the roadmap, listens to the team, and asks a perfectly reasonable question: "Why did we not do it another way?"

And suddenly, a program that has been moving forward for eighteen months starts looking backward.

All of this can quickly throw a transformation off the curve, the PM digs through old steering committee decks. A BA searches for the original business case. The PMO reaches out to a colleague who moved to another department eight months ago. Subject matter experts are invited to meetings to reconstruct a discussion everyone thought was settled.

Nobody is necessarily doing anything wrong. The new sponsor has every right to challenge decisions. In fact, I would be concerned if they didn't.

This is what bothers me. Why does a change in leadership so often requires an organization to reconstruct it’s thinking? We remember what we decided but we forget why? And what alternatives were rejected and why?

Most organizations have plenty of documentation, RAID logs, financial trackers, steerco presentations, the roadmap etc. These may show what was decided, including the milestone, owner, dependency, funding, and target date, but what it often does not show is the journey behind the decision. It rarely captures what alternatives were considered, why some were rejected, what assumptions were made, or what quality, compliance, operational, financial, or adoption risks influenced the final choice.

And that missing context matters, because without it, a leadership transition can quickly become a transformation pain.

Think about a large transformation that has been running for two or three years. A new sponsor comes in and asks a completely reasonable question: "Why are we taking three years to deploy something we could roll out globally?"

The team knows the answer exists somewhere, but maybe it is buried in an old steering committee deck, discussed in a workshop fourteen months ago, embedded in an email thread, or stored in the memory of a PM who has since moved into another role.

Instead of answering the question with confidence, the team begins reconstructing the decision. Perhaps some regions weren't ready. Validation resources were already stretched. Data remediation was taking longer than expected. Training thousands of employees simultaneously would have created unacceptable operational risk. Those considerations shaped the decision. But if nobody saved that reasoning for a later access, the organization may have to repeat the same analysis to arrive at the same conclusion.

For pharmaceutical, biotechnology, medical device, and CRO organizations, this is more than a PMO inconvenience. These decisions are often connected to validation, quality, regulatory requirements, process design, training, systems, operating models, and adoption. Reopening one major decision can create ripple effects across an entire transformation.

The roadmap tells you where you are going. It may not always tell you why.

Most transformation teams are very good at maintaining delivery artifacts. We have roadmaps, RAID logs, program plans, financial trackers, steering committee decks, milestone reports, and dashboards. These are important because they tell us whether the transformation is moving, where risks are emerging, and what requires leadership attention.

What they do not always tell us is why the transformation is moving in that particular direction.

Take a global quality transformation as an example. The organization may have decided on a phased deployment rather than one global go-live, and the roadmap may clearly show Wave 1, Wave 2, and Wave 3.

What it may not show is that the team also evaluated a single global deployment and decided against it because of local readiness, data remediation, validation capacity, training absorption, regional requirements, or simply because the regions were already managing several competing priorities and events that would have made a global deployment an unrealistic resource and change burden.

Fast-forward a year. A new sponsor looks at the roadmap and asks, “Why are we moving so slowly? Why didn't we plan deploy globally?”

And that is a completely legitimate question. However, an important distinction is whether the organization is challenging an old decision because circumstances have changed or reopening it because nobody can explain why it was made.

If the assumptions have changed, revisit the decision. If the regulatory environment has changed, revisit it. If the operating model, funding, technology, or business priorities have materially changed, revisit it. But if nothing has changed except the person asking the question, the organization should not have to restart the entire analysis.

That is what strong transformation governance should enable. It should not force new leaders to blindly accept yesterday's decisions. It should give them enough context to challenge those decisions intelligently and make better decisions today.

The PMO should preserve decision intelligence, not just program history

Traditional PMO governance is very good at telling us whether a program is on track, which milestones are at risk, where the organization stands against budget, what needs to be escalated, and what decisions the steering committee needs to make.

Those questions are essential for managing execution. But during a leadership transition, a new sponsor also needs to quickly understand:

• What did we decide?

• What alternatives did we seriously consider?

• Why did we reject them?

• What assumptions or evidence supported the decision?

• What would justify reopening it?

And no, this does not mean creating another fifty-page governance document that nobody reads. The answer is not more documentation. It is more useful documentation.

A strong decision record should be simple enough for an executive to understand quickly, yet detailed enough for the organization to reconstruct the logic behind the choice. More importantly, it should preserve not just the decision that was made, but the options that were not. Because the decisions we did not make can be just as valuable as the ones we did. Rejected alternatives often disappear into meeting notes, PowerPoint slides, Teams chats, email threads, or someone's memory, yet they tell us something extremely important: what the organization has already thought through.

If nothing material has changed, the organization can preserve momentum and keep moving. If something has changed, leadership can make a deliberate decision with the benefit of the previous analysis.

That is how governance protects momentum without limiting leadership judgment.

This is also why decision history cannot become a static archive. It should be part of active governance, reviewed at major stage gates, used when material scope changes are proposed, connected to risks and requirements, and brought directly into leadership transitions. This creates continuity without handcuffing the new leader. And more importantly, it builds trust.

People need to understand what is changing, why it is changing, what it means for them, and which decisions they can trust to remain stable. That is why organizational change management and transformation governance cannot live in separate worlds. Role-based communications, stakeholder impact assessments, readiness criteria, training, feedback loops, governance, and decision rights all need to work together.

At Atlas, this is a big part of how we think about transformation. People, process, tools, and context are connected. When governance changes, there is almost always a downstream human impact that needs to be understood and managed.

And yes, all of this becomes even more important with AI

The same governance challenge is beginning to appear with AI as organizations introduce AI-enabled workflows, intelligent agents, analytics, and automation across life sciences functions.

Imagine an AI agent taking actions inside a process, but nobody can clearly explain the approved use case, who owns it, what data it is using, where human review is required, what evidence exists, or how performance is monitored.

This is not very different from the decision problem. A decision nobody properly recorded and an AI agent nobody governed create the same fundamental gap: the organization loses context and traceability. In a regulated environment, both matter.

AI governance therefore needs the same basic discipline we expect from strong transformation governance: clear ownership, intended use, validation, data lineage, human oversight, approval points, monitoring, evidence, and lifecycle management.

Governance cannot be something we bolt on after the transformation is underway. It has to be designed into how the organization works. And we should be able to tell whether that governance is working.

Transformation will always have uncertainty, but good governance makes that uncertainty manageable.

Here's a quick test I'd suggest for your next steerco.

Pick one significant decision made twelve months ago and ask the team why the other options were rejected and what would have to change to reconsider them.

If answering requires finding three former project managers and going through fourteen PowerPoints, you may have discovered a governance problem hiding in plain sight.

And if the only person who remembers the rationale is the consultant, I suppose that's good for my business. But, is it good for yours?

About the Author

Rishi Dixit is Head of Strategic Solutions, Innovation & Partnerships at Atlas where he helps build new advisory practices, partnerships, products, and solutions that solve complex business challenges. With more than 30 years of experience across life sciences, technology, consumer goods, and global business, Rishi has led large-scale transformations across people, digital, and organizational change.

He is a portfolio, program, and product management expert, change practitioner, mentor, and AI enthusiast who believes the hardest challenges are rarely just technology problems. They are people problems shaped by technology, process, and change.