The real AI challenge of 2026 isn't adoption. It's proof.

In this edition of Thoughts on Tap, Eliott Friberg examines one of the defining challenges in AI strategy right now: the gap between running an AI pilot and actually scaling one, and what separates the organisations making that leap from those quietly stuck in between.

78% of enterprises have an AI pilot running. Only 14% have scaled one to organisation-wide use. We find that gap worth sitting with, because it’s where most of the organisations we speak with are quietly stuck right now.

This isn’t a story about laggards. Many of the organisations in that gap made deliberate, careful decisions. They ran sensible pilots, measured results, and built internal buy-in for the technology. What they haven’t done is make the harder decisions that scaling actually requires, and that’s a very different problem to the one they’ve been solving.

The question has changed

In 2025, most leadership teams were asking whether and how to adopt AI. That debate is largely settled. 92% of companies plan to increase their AI investment this year. The technology is accessible, the business case for exploration is established, and few senior leaders are still asking whether it matters.

The question in 2026 is harder: is what we’ve invested in actually producing anything at scale? Not in a contained pilot with a dedicated team and a sympathetic sponsor, but across real workflows, real teams, and real business outputs. That shift from adoption to proof is where competitive pressure is now building, and it’s where the gap between organisations creating durable advantage and those running increasingly expensive experiments is beginning to open up.

Why scaling fails

95% of generative AI (GenAI) pilots fail to produce measurable financial impact. Not because the models are inadequate, but because of poor workflow integration and misaligned incentives. Most scaling failures aren’t technology failures. They’re design and governance failures, and they tend to cluster around three recurring problems.

The first is that workflows were never redesigned. Pilots are, by nature, contained. They solve a specific problem in a specific context, usually built on top of existing processes rather than replacing them. Scaling requires something more demanding: rethinking how work actually happens, who does what, when, and with what inputs. Organisations that scale successfully are 55% more likely to redesign workflows around AI rather than layer it on top of what already exists. Most don’t, and they pay for it.

The second problem is data. 64% of organisations cite data quality as their top scaling challenge, and 77% rate their own data as average or worse. AI running on poor-quality, siloed data doesn’t accelerate good decisions. It accelerates bad ones, faster and at greater scale. Most organisations understand this in theory and still underestimate how much structured data preparation scaling actually requires. The work is unglamorous, it doesn’t show up in a pilot, and it’s genuinely hard to fund when the board is asking for speed.

The third is ownership. Pilots almost always have a sponsor: a senior advocate who cares about the outcome and has enough authority to remove obstacles. Scaling rarely has an equivalent. When a pilot moves toward production, it crosses team boundaries, budget lines, and reporting structures. Without defined accountability for that journey, nobody owns the decision to commit, nobody owns the hard calls about process change, and the initiative gradually loses momentum without ever formally failing. This is a governance problem, not a technology problem. Naming it correctly matters, because governance can be fixed.

What successful scalers do differently

The organisations we see moving from pilot to scale aren’t trying to scale everything at once. They choose use cases that are both high-impact and high-visibility, the kind where results are observable, attributable, and worth talking about internally. Early wins build the organisational credibility that makes the harder changes possible.

They also treat experimentation and execution as genuinely distinct disciplines, and they build separate structures for each. One track is rapid, low-friction, and tolerant of failure, designed to generate learning. The other is disciplined, governed, and built for consistency and measurement. The mindset that runs a good pilot is actively unhelpful when you’re embedding AI into a production workflow that hundreds of people depend on, and the organisations that scale well understand this distinction clearly. They don’t try to resolve the tension. They manage it.

Crucially, these two tracks aren’t temporary and they’re not in competition. You need both, permanently. The experimentation track keeps generating new candidates for scaling; the execution track builds the muscle to scale them reliably. Most organisations default to one or the other. The ones building genuine advantage run both.

Execution is the differentiator

Access to AI is no longer what separates organisations. Everyone of any scale has access to the same models, the same platforms, and broadly the same information about what’s possible. The differentiator in 2026 is execution: the ability to move AI out of the pilot environment and into the workflows, decisions, and outputs that actually drive business performance.

That shift from isolated pilots to measurable impact across marketing and communications is what Comprend is built to help organisations make. If your pilots are done and the real work is beginning, we’d like to talk about what that looks like in practice.

Have a topic you’d like us to cover in a future issue? We’re always up for a chat.