If your AI programme has produced more presentations than production deployments, you are not alone and you are not behind because of your data. You are behind because of your operating model.
According to McKinsey, the clearest signal from this year's Consumer Goods Forum Global Summit in Vienna is that the companies pulling ahead in AI are not simply launching more pilots. They are redesigning how work gets done and closing the gap between business and technology teams. That is not a technology observation. It is an organisational one.
At AI Navi, we have been saying this for a while not from a conference stage but from inside the businesses where the gap lives. Haja J Deen has spent 25+ years leading digital transformation inside CPG businesses including pladis Global ($3B+ revenue), Holland & Barrett, and Saint-Gobain. The pattern we see is consistent, and it is not a technology failure.
Why Are CPG Companies Still Stuck in Pilot Mode?
The short answer: ownership without authority.
Most mid-market CPG businesses have someone nominally responsible for AI, usually a CDO, a Head of Data, or a transformation lead but they rarely have the authority, the cross-functional access, or the mandate to redesign how commercial, supply chain, or finance work actually gets done. So they procure tools. They run pilots. They produce slide decks. And the board keeps waiting for results.
What McKinsey is describing from Vienna is the structural consequence of that gap. When the business team and the technology team are not integrated under a single point of accountability, AI investments produce activity, not outcomes. The pilot succeeds in isolation and then dies at the handover.
This is not a small business problem. We have seen it at £40M food brands and at businesses approaching £500M in revenue. The pattern is the same: a well-scoped proof of concept, a vendor who delivers on the narrow brief, and then a deployment that never happens because nobody in the business owns the change.
What Does 'Redesigning Work' Actually Mean in CPG Operations?
It means changing the process, not just adding a tool to the existing one.
Here is a concrete example. A UK food brand I worked with was losing margin on unchallenged deductions, the kind that sit in accounts receivable, get reviewed manually every quarter, and quietly compound. The fix was not complicated technically. But the real work was redesigning how the commercial team and finance team interacted around deduction data. That meant changing who owned the review, at what cadence, with what decision authority.
We recovered 60% of previously unchallenged deductions in 8 weeks. The AI component was real but the redesign of the workflow is what made it stick. Without that, it would have been another pilot that produced a dashboard nobody used.
This is what operational model change means in practice. It is not about platforms. It is about the handshakes between functions, the ownership of decisions, and the question of who is accountable when the AI flags something that needs a human response.
What Is the Gap McKinsey Is Actually Describing?
The gap between business and technology teams in CPG is not a skills gap. It is a translation gap, and it gets worse the larger the business becomes.
| What Technology Teams Typically Optimise For | What Business Teams Actually Need |
|---|---|
| Model accuracy and data quality | Commercial decisions made faster |
| Platform scalability | Problem solved in this quarter |
| Clean architecture | Workflow that works with existing systems |
| Long-term data strategy | Margin impact before the next board review |
| Proof of concept completion | Production deployment and team adoption |
This table is not theoretical. It is what we see in every AI programme review I walk into. The technology team has done good work by their own measure. The business team is still waiting for something that moves the needle. And there is nobody in the room whose job it is to close that distance.
That is the ownership gap. It is structural, not personal. And it does not get solved by hiring a CAIO and putting them on the technology side of the org chart.
Why Does the Fractional CAIO Model Solve This Specifically?
Because it puts a senior operator inside the business, not outside it.
The AI Navi model, Fractional Chief AI Integration Officer embedded inside the business is the structural answer to exactly the problem McKinsey surfaced in Vienna. A fractional CAIO who carries P&L accountability, sets the problem priority based on commercial pressure, and takes responsibility for getting working AI into production is not a consultant who delivers a recommendation. They are an operator who owns the outcome.
This matters particularly for PE-backed management teams and mid-market CPG boards who have heard McKinsey's framing before and recognise the implication immediately: the question is not whether to invest in AI, it is whether you have the right ownership structure to convert that investment into margin.
How Do You Know If Your Business Has an Ownership Gap?
Here is the checklist we use in an AI FlightCheck™ diagnostic.
Signs your AI programme has an ownership gap:
- [ ] You have completed more than two AI pilots in the last 18 months with none in production at scale
- [ ] Your technology and commercial teams describe the same AI initiative differently when asked separately
- [ ] A vendor has told you your data is not ready and six months later it still is not ready
- [ ] Your AI roadmap is owned by the IT function but the business value is expected from commercial or supply chain
- [ ] Board reviews of AI progress focus on project milestones rather than commercial outcomes
- [ ] The person accountable for AI results does not have authority to change a process without committee sign-off
- [ ] You have a data strategy but no clear owner of the first deployment
If three or more of those are true, your business has the structural problem McKinsey is describing. More tools will not fix it. More pilots will not fix it. A redesigned operating model with the right senior ownership embedded will.
What Should CPG Leaders Do Differently in 2026?
Stop scoping pilots. Start scoping problems with a named owner and a production deadline.
The Consumer Goods Forum signal from Vienna should be read as a commercial instruction, not just a strategic observation. The businesses that are redesigning work are not doing it because they are more technologically ambitious. They are doing it because they have resolved the question of who is accountable when AI touches an operational decision.
For mid-market CPG businesses between £40M and £500M, the route to that is rarely a full-time CAIO hire the role is too expensive, too slow to recruit, and too often placed in the wrong part of the structure. The fractional model works because it brings the seniority, the sector depth, and the cross-functional mandate without the 6-month runway before value appears.
Abhishek C, our AI Delivery Lead and ex-Deloitte engineer, ships 30+ AI products per year. The speed is real. But speed without the right ownership structure just means you get to the wrong outcome faster. Both halves of the model have to be in place.
If you are heading into a board conversation about AI progress in the next quarter, the McKinsey framing from Vienna gives you the right language: the question is not how many pilots you have running, but whether you have redesigned any work. If the honest answer is no, that is the conversation worth having. Let's talk.