What If the Problem Isn't the System, but the Process?

We recently sat down with a pharmaceutical company that wanted us to help redesign part of its Market Access process. The initial brief was relatively straightforward: they wanted a better way to track, change and manage their Market Access assumptions.

So naturally, we started by looking at what they already had. And what we found was interesting.

Their existing solution was actually pretty good.

It captured what the Market Access team needed. It had been built around their requirements and, within that part of the business, it did its job.

Then we started looking at the wider commercial ecosystem. Forecasting had a good system. Sales had good reporting. The CRM held valuable customer information. Commercial teams had dashboards. Other functions had their own models and processes. Individually, there wasn't necessarily anything fundamentally wrong with any of them.

The problem was that none of them were really speaking to each other.

Maybe we're asking the wrong question

That got us thinking. When we approach digital transformation, we usually start by asking: "how can we improve this task?" How can we build a better Market Access tool? How can we automate this forecast? How can we improve this dashboard? How can we make this particular process faster?

But with the technology now available to us, we think there's a more important question we should be asking:

Should this commercial process work this way at all?

Because improving each individual component only gets us so far. We now have the technology to start connecting them.

Think about a Market Access assumption

Imagine something changes within Market Access. Perhaps access is restricted in a particular market. That shouldn't simply result in someone updating a Market Access model, because the change doesn't stop there:

• It could alter the addressable patient population.
• Which could affect the commercial forecast.
• Which could change the opportunity attached to particular accounts.
• Which could influence customer prioritisation.
• Which could ultimately change where commercial resources are deployed.

These aren't five separate events. They're consequences of the same event.

Yet we've historically built systems that require people to connect those consequences manually. Someone updates one model. Someone tells another team. Another assumption gets changed. A dashboard gets refreshed. A forecast gets updated. Eventually, the information works its way through the organisation.

In many cases, people have effectively become the integration layer between our systems.

We can now design this differently

This is where we think developments in data architecture, automation and AI become genuinely exciting. Not because they allow us to rebuild the same processes slightly faster, but because they allow us to rethink the processes themselves.

Imagine a connected commercial ecosystem where a change in a Market Access assumption is understood across the wider organisation. The forecast knows something has changed. The commercial model understands which markets or accounts are affected. Relevant customer information is considered. The impact is surfaced through reporting. And eventually, intelligent agents could help explain the wider commercial implications of that change.

Suddenly, we're not improving five separate systems. We're designing one connected commercial ecosystem.

Build on what already works

Importantly, we don't think this means ripping everything out and starting again. Quite the opposite.

Pharmaceutical companies have spent years building valuable systems, datasets, models and processes, and there is enormous knowledge embedded within them. The opportunity is to build on those foundations: connect the data, standardise the relationships, automate the movement of information between processes, and then introduce intelligent layers capable of understanding what those relationships mean.

None of it works without the data foundation underneath — a point we keep coming back to. But get that right, and the possibilities become much more interesting.

From optimisation to redesign

We think this represents an important shift in how we should approach digital transformation in pharma. The first generation was largely about digitising existing processes. Then we started automating them. Then we built better analytics around them. The next stage may be about redesigning them entirely.

Rather than asking "how can technology make this process better?", perhaps we should increasingly ask:

"If we had today's technology when this process was originally designed, would we have designed it this way at all?"

In the example that prompted this thought, the answer was probably no. The individual systems weren't the problem. The problem was that we had designed each one to understand its own part of the business. The opportunity now is to build an ecosystem capable of understanding how those parts affect each other.

And we think that's where the next generation of commercial pharma technology gets really interesting.