Why Reimagine

ROI in year one was on the table, and they said “No”!

Not because the technology couldn’t deliver. But because nobody wanted to enforce consistency across an ecosystem of external partners. So Instead of fixing the upstream problem, months were spent trying to code around a problem that didn’t need to exist.

This was 2017.

The pattern hasn’t changed, it’s just got more expensive.

When automation meets reality

I was working for a global consultancy on one of its major public-sector accounts. Our task was to automate a core process, the kind of initiative that looks simple on paper, but rarely is.

Technically, we could have automated 60-70% of it and delivered returns inside twelve months. But the problem wasn’t technical. It was upstream dependencies spread across an ecosystem of external partners who each interpreted the same “standard” process differently.

The central organisation could see the inefficiency but wouldn’t enforce alignment. So the project spent months and significant budget trying to automate around inconsistency that could have been eliminated with a handful of difficult conversations.

This is what Gartner now documents as the “10 ancillary, hidden costs” problem: organisations budget for the technology, but not for the ecosystem coordination it exposes.

The “$1.9 million AI deployment“ quickly becomes something far larger and more complex because automation doesn’t hide disorder, it reveals it.

What the travel industry taught me

I saw the same pattern in 2014, leading a redesign for an online travel provider.

This wasn’t even AI. It was a pricing engine rebuilt from first principles so they could onboard new hotel chains in days instead of months, and handle special rates and non-standard pricing as it arrived.

They had competitive rates from hotels, including special offers and non-standard pricing models that could’ve won them business. But their old system couldn’t handle the variety, so they just threw it away. All of it.

10% revenue loss. Every year. Not because competitors had better deals, but because they couldn’t surface what they already had fast enough.

We built the new engine in a month. It sat unused for two years.

The technology worked. Leadership wanted it. But on the ground, people had built careers around the old platform. It defined their expertise, their status, their value to the organisation.

The resistance wasn’t about feasibility, it was about fear. Fear of redundancy. Fear of losing what had once made them important.

This maps directly to what Gartner found:

74% of CFOs report productivity gains from AI, but only 5% have managed to cut costs and just 6% saw any revenue uplift.

The technology delivers capability, but translating that into business value requires confronting how work, power, and incentives are structured, and most organisations stop short.

The pattern

This keeps repeating.

Organisations say they want transformation, then stop short of confronting what it actually requires. AI is just the latest chapter. When systems, incentives, and power structures are built around the past, new technology doesn’t fix the problem, it exposes it in sharper detail.

That’s when leaders face the real test: redesign how value flows, or just automate faster and hope for the best.

What it actually takes

The problem isn’t technical. It never was.

It starts with making the cost of inaction impossible to ignore, not through another deck, but by showing executives exactly what staying still is worth. Then you pull the conversation out of IT entirely, because technology teams were never going to fix ecosystem dynamics or boardroom misalignment.

This requires senior leaders willing to sit across the table from each other first, before they ever talk to partners. If the exec team can’t align on what problem they’re actually solving, external coordination is a fantasy.

Once internal incentives line up, then you can find the levers that move partners. Not mandate. Not compliance theatre. The actual incentives that make alignment worth their while.

And you have to help people see that their value isn’t locked into the old system, it’s in being the ones with enough context to architect what comes next.

Reimagining isn’t about fixing tech or what’s just inside your walls. It’s about redesigning how value flows through the ecosystem and how people create value within it. That means someone has to own the conversations, internal and external, that technology can’t solve.

Reimagining: The discipline

That’s what “reimagining” really means. It’s not a slogan. It’s a discipline.

[It’s] the willingness to ask which barriers are real and which are just comfortable habits dressed as constraints. Then it’s about designing the right sequence, connecting the pieces that matter, step by step, so small shifts compound into momentum.

When you take this approach, you win either way. Even if you’re skeptical about AI, you end up with cleaner operations and teams that spend time creating value instead of managing workarounds. AI just becomes the accelerant on top.

But if you’re all-in on AI, this gives you the foundation where intelligence can actually compound. Because data, process, and people already move in the same direction.

Reimagining isn’t about a single breakthrough. It’s the compounding effect of design, discipline, and momentum working together over time.

The question isn’t whether your organisation can change.

It’s whether you’re willing to see clearly what you’ve stopped questioning, and start there.

If you’re unclear on the cost of inaction, reply to this email and let’s talk