£40M food brand · Deductions
60% of unchallenged deductions recovered in 8 weeks
A deductions triage workflow that reads each retailer deduction letter, matches it against signed promo terms and flags invalid claims.
AI FlightPath™ Sprint
AI implementation that connects to your P&L. Not another pilot.
Most mid-market companies know what AI could do. They have the budget, the mandate and the vendor shortlist. What they lack is an implementation plan that survives real data, real teams and a real board. We build that plan, and the first working product, in 10 weeks.
Not sure where you stand? Take the 3-minute AI Readiness Scorecard
- 10 weeks from kickoff to a production AI use case
- Fixed fee, below most procurement thresholds
- The same senior team from first call to handover
What is an AI implementation strategy, and why do most fail?
An AI implementation strategy connects a business outcome your board cares about to the data, technology and people changes needed to deliver it. It is not a tool selection exercise and it is not a list of pilots. It is a plan with a P&L figure at the end of it.
Most fail at one of three points, and it is rarely the technology: direction, data or adoption. Our guide to implementing AI in a mid-market business covers the process in more detail.
Why AI pilots die
The three reasons AI implementation fails in CPG and logistics
01
The strategy trap
The board asks for an AI strategy. An agency produces a long slide deck. Nobody inside can judge it, the budget was released for the deck and not for delivery, and nothing is actioned. Months later a new vendor is invited to pitch.
Root cause: no P&L-connected roadmap and no internal owner.
02
The data trap
AI needs clean, shared, labelled data. Most mid-market firms have inconsistent ERP data and three versions of the same KPI. The pilot runs on a curated subset and looks impressive, then breaks on production data.
Root cause: no data architecture before the model was commissioned.
03
The change trap
Frontline managers resist tools that feel like surveillance. Middle managers do not know how to fit AI into their day. The internal champion moves on and the tool is quietly archived.
Root cause: change management treated as an afterthought, not a deliverable.
Our delivery model
Navigate. Execute. Land. A 10-week plan from strategy to a working product.
One team across all three stages. Nothing is handed to a junior. Before the sprint starts, a one-week AI FlightCheck™ audits strategy, data and execution readiness and delivers a prioritised 90-day action plan.
Weeks 1–2
Navigate
Set the strategy, unlock the budget and align the C-suite before spending on tools.
- AI strategy tied to board P&L outcomes
- Two highest-ROI opportunities identified and scoped
- Budget-approval narrative for internal sign-off
- Vendor and platform evaluation framework
- Governance and data-readiness baseline
Weeks 3–8
Execute
Build the data foundation and ship the first working AI product, in production and not in a sandbox.
- Data audit and architecture on AWS or Azure
- One production data pipeline deployed
- First AI use case scoped, built and beta-tested
- Working V1 delivered to your team
- GDPR and data-governance controls applied
Weeks 9–10
Land
Enable the team, embed the change and measure the return.
- Change plan, team briefings and upskilling
- Board presentation with an ROI measurement framework
- Documentation and handover to your internal team
- 90-day post-launch check-in included
Results
Results from CPG and FMCG clients
£25M CPG brand · Promo decisions
£180K saved by stopping two promotions before launch
A promo pre-mortem tool that ingests last year’s data and flags likely duds in about 30 seconds.
Large CPG · Snacking category
>5% net revenue improvement in 20 weeks
A pricing and pack-size decision engine for the category lead, built despite patchy source data.
We also ship our own products, such as SalesGenius.ai and ApplyGenius.ai. See more on the CPG and FMCG page and in our case studies with demo videos.
Before you start
What needs to be in place before AI implementation can work
The riskiest moment is deploying a tool before the organisation is ready for it. You do not need a perfect data warehouse. Start from the use case and clean only what it needs.
- A named executive owner and a board-approved business outcome
- A P&L metric to measure against, such as forecast accuracy, hours eliminated or cost per delivery
- Access to the data for one priority use case, even if it is untidy
- A team willing to change one workflow
Is the AI FlightPath™ Sprint right for you?
A good fit
UK mid-market Consumer Products, FMCG and logistics companies with £25M–£250M revenue, a mandate and budget for AI, and no working AI in production yet. Often PE-backed or board-led.
Not a fit
Teams that want a strategy deck only, businesses with no data access at all, or companies that need a platform built before any use case is chosen. We will say so on the first call.
Ownership
Who should own AI implementation inside your business?
The answer depends on where you are, not on what sounds best in a board presentation.
| Option | Strength | Watch out for |
|---|---|---|
| Full-time Chief AI Officer | Long-term internal ownership | Months to recruit and a senior salary; the role is often undefined on day one |
| Large consulting firm | Brand reassurance for the board | High minimum cost; the deliverable is usually a strategy deck |
| Fractional Chief AI Officer (AI Navi) | Senior leadership, fast start, flexible commitment | Part-time by design, so you need an internal owner to work with us |
AI implementation: your questions answered
Start with a conversation, not a proposal.
Tell us the outcome your board wants. We will tell you honestly whether a 10-week sprint can deliver it.
Book a callOr take the AI Readiness Scorecard first.