Dairy 2026: Moving AI from pilot projects to real productivity
An Aforza point of view on McKinsey’s eighth annual dairy executive survey, titled The dairy industry’s 2026 playbook: Protect margins, pursue growth
McKinsey’s eighth annual dairy executive survey, published this month, paints a clear picture of an industry under pressure. Margins are flat or shrinking for nearly 70% of US dairy companies and 57% of European ones. Cost management tops the strategic agenda in both regions. And yet, even against that backdrop, dairy leaders are betting on two growth engines: protein-led innovation and AI.
The AI story is the one that deserves a closer look. Four in five US dairy companies are now using AI in some capacity, but 71% describe their work as pilot projects, and only 24% are using AI operationally. The three barriers they cite, security concerns, limited expertise, and uncertain ROI, are not problems with AI itself. They are problems with the kind of AI most CPG companies have been experimenting with: general-purpose, horizontal tools that start with the technology and ask the customer to find the use case.
2026 is the year that has to change. The dairy and wider CPG companies that pull away from the pack will be the ones that deploy vertical AI agents built for specific commercial processes, running inside the platforms their CIOs already trust, tied to outcomes their CFOs can measure. Deliberate, yes. But not slow.
The industry has stopped debating AI. It’s now debating how
McKinsey’s 2026 dairy playbook, published this month with the IDFA and EDA, lands on a dairy industry that looks very different from the one surveyed three years ago. Margins are flat or shrinking for nearly 70% of US dairy companies and 57% of European ones. Cost management sits at the top of the strategic agenda on both sides of the Atlantic, ahead of volume growth, talent, and sustainability. And the ESG conversation has quietly hardened into something more pragmatic: emissions reduction, energy efficiency, logistics optimisation. Measurable things.
Reference: McKinsey & Company – A majority of dairy companies reported flat or declining margins in 2025.
In that environment, what’s most striking in the McKinsey data isn’t what dairy leaders are worried about. It’s what they’re quietly getting on with. Four in five US dairy executives are now using AI or advanced analytics in some capacity. Protein is the clearest growth thesis the industry has had in years. And 77% of US dairy processors plan to increase manufacturing innovation investment over the next three to five years, even with margins under pressure.
This is an industry under cost pressure that has still decided AI and product innovation are where the next wave of competitive advantage comes from. The question is no longer whether. It’s how fast, how deep, and on what.
The adoption gap nobody’s talking about
Look more closely at the AI data and a structural problem emerges. 71% of US dairy companies describe their AI work as “pilot projects.” Only 24% say they are using AI operationally. And when asked why, the top three barriers are security concerns (20%), limited expertise (17%), and uncertain ROI (17%).
Reference: McKinsey & Company – Most US dairy companies are exploring AI, with security concerns posing
the greatest barrier to adoption.
Read those three barriers together and they describe a single, recognisable problem. Horizontal AI tools, the general-purpose copilots and analytics platforms that most CPG companies have been experimenting with, struggle on all three dimensions at once. They require in-house AI expertise to configure. They handle sensitive commercial data in ways that worry CIOs. And they produce output that’s impressive in a demo but hard to tie to a specific commercial outcome. That’s why so much of the work stays in pilot. The gap between “mind-blowing” and “ready to let loose on our plants,” as one McKinsey interviewee put it, is not a technology gap. It’s an accountability gap.
One of the McKinsey quotes that cuts through the noise comes from a North American dairy executive: “Here’s the problem, here’s the end state. Now, how do I leverage the technology to get there?” That is exactly the right question, and it’s the question horizontal AI tools are structurally bad at answering. They start with the technology and ask the customer to find the use case. Commercial leaders don’t have time for that. They have a P&L to defend.
The Vertical AI reframe
The more useful way to think about AI in consumer goods is to flip the starting point. Instead of asking “what can this model do?”, start with the commercial process you need to improve: a field rep’s store visit, a trade promotion sign-off, a distributor order, a deduction dispute. Then ask what an AI agent would need to know, see, and do to make that process faster, cheaper, or more accurate.
When AI is built that way, with the commercial workflow as the starting point and the CPG domain baked into the agent itself, the three barriers McKinsey identifies start to dissolve. Security concerns recede because the agent operates inside the customer’s existing trusted platform, with the same permissions model, data residency, and audit trail already approved by IT. The expertise gap shrinks because the agent is pre-configured for CPG use cases; commercial teams consume it, they don’t build it. And ROI becomes provable because every agent action is tied to a measurable commercial outcome: visits completed, promotions approved, deductions resolved, orders captured.
This is the shift from AI as a feature to AI as an operating model. It’s also where the next tranche of productivity in dairy and the wider CPG industry will come from.
What this looks like in practice
At Aforza, we built Ava, our vertical AI agent, specifically for this reason. Ava lives inside the commercial processes that CPG companies run every day: retail execution, trade promotion management, distributor management, deductions, field sales coaching. She operates natively inside Salesforce, where the customer, account, and commercial data already sits, which removes the integration tax and most of the security friction that blocks horizontal AI.
The results are already visible in customers that have put Ava in front of their commercial teams. AG Barr, the UK drinks group behind Irn-Bru and Rubicon, cut the time spent in each store visit from around 20 minutes to roughly 7, reallocating the time to genuine coaching and execution quality. That’s not an AI pilot. That’s an AI agent changing a core commercial process. It’s the kind of outcome that makes the ROI conversation short.
The pattern generalises. Whether the application is image recognition for shelf audits, an AI-drafted trade promotion, a deduction automatically matched to its root cause, or a sales rep briefed by Ava before every visit, the common thread is the same: the agent owns a specific commercial job, the outcome is measurable, and the customer doesn’t need an internal AI team to operate it.
This is where Aforza has done something no other CPG platform has. The Ava Library is the industry’s first packaged library of agentic AI use cases purpose-built for consumer goods. Not a toolkit. Not a set of APIs waiting for a systems integrator to assemble. A living library of pre-built agents that already know how to review a store visit for perfect store compliance, validate a retailer deduction against the originating trade promotion, surface the next best action for a key account, draft a promotional plan from post-event ROI, reconcile distributor sell-out data, and dozens of other jobs that sit at the heart of the CPG commercial day.
🔗 You can download a copy of the Ava Library here
This matters because it directly addresses the three barriers McKinsey identifies as blocking AI adoption in dairy. Security concerns recede because every agent in the Library runs inside the customer’s existing Salesforce platform, on infrastructure their CIO has already approved. The expertise gap closes because the agents are pre-built for CPG use cases; commercial teams consume them, they don’t build them. And the ROI question answers itself when every agent is tied to a specific commercial job with a measurable outcome attached.
The Ava Library is what turns McKinsey’s call for “deliberate investments in AI with clear use cases and economic accountability” from a board slide into a set of working agents that start delivering productivity inside the first quarter. 2026 is the year dairy leaders stop running pilots and start running agents.
The 2026 question for dairy leaders
McKinsey’s closing call to action for dairy leaders is to “make deliberate investments in AI and advanced analytics, with clear use cases and economic accountability.” The word doing the work in that sentence is deliberate. Too often, in CPG as much as in dairy, deliberate becomes an excuse for slow. Another year of pilots. Another round of internal debate about which horizontal platform to license.
The dairy companies that will pull away from the pack in 2026 won’t be the ones running the most pilots. They’ll be the ones who have made the harder decision: to deploy AI where it directly owns a commercial outcome, inside the processes that move their P&L, on infrastructure their CIO already trusts.
Deliberate, yes. But not slow.
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