AI Consulting for Business Growth

With a holdout group. Concretely: before go-live we randomly split the relevant population — leads, accounts, or customers — into a treatment group that receives the AI-driven treatment and a control group that continues with your current process. Randomly, not by region or rep, because those differ in ways that quietly explain the result.

Both groups then experience the same quarter, the same market, the same campaigns and the same weather. The difference in the metric between them is the incremental lift, and it is the only number worth reporting. A model deployed to everyone at once cannot be evaluated: it will show a healthy win rate simply because it was pointed at customers who were going to convert anyway.

We agree the split size, the primary metric and the measurement window before development begins, and we hold the control group long enough for the sales cycle to complete. On a long B2B cycle that means patience — which is exactly why it has to be agreed at the start rather than negotiated once someone wants a win to announce.

The question is not how many rows you have, it is how many labelled outcomes. A propensity model learns from deals that closed and deals that did not, so what matters is the count of resolved cases with an honest label, and reasonable balance between them. A few thousand closed opportunities with clean win/loss reasons is far more valuable than millions of activity records with no outcome attached.

Three things break more projects than data volume. First, outcomes recorded inconsistently — everything marked “closed lost: no budget” because it is the fastest option in the dropdown. Second, leakage: fields that were only filled in after the deal closed, which make the model look brilliant in testing and useless in production. Third, a history so old it describes a product or market you no longer have.

We assess exactly this in the first stage and give you a straight answer on feasibility before you commit to building anything.

Both, but they are different projects. High-volume B2C gives you many outcomes quickly, so models train well, experiments reach significance in weeks, and the natural applications are next-best-offer, churn prediction and pricing.

Long B2B cycles give you fewer, slower, larger outcomes. That rules out approaches needing tens of thousands of examples, and it means measurement takes a full sales cycle rather than a month. What works instead is prioritization and timing: which accounts to work now, which opportunities are quietly stalling, which existing customers show expansion signals, and which unstructured signals — call notes, tickets, tender documents — indicate an account is in market.

The mistake in complex B2B is expecting a model to predict a single deal. It cannot, and neither can your best rep. What it can do is allocate scarce senior selling time across a portfolio better than a gut-feel ranking, which is a smaller claim and a much more reliable one.

They have to. A score that lives in a separate dashboard is a score nobody acts on, and adoption is where most growth AI quietly dies.

Technically, our approach is capability-based rather than tool-based: models read from your existing systems and write predictions back to where the commercial team already works, so a rep sees a priority and a reason on the record they were opening anyway. The integration path depends on what your platform exposes — API, data warehouse, or scheduled export — and we assess that during discovery, before committing to any particular design.

We also design for the reason, not just the number. “0.78” changes no behaviour; “usage dropped in two of three key features since the renewal date was set” does.

This deserves a blunt answer, because it is the most expensive mistake in this category.

AI amplifies a commercial motion that already works. It does not create demand where there is no product-market fit. If prospects who fully understand your offer still do not want it, a propensity model will simply rank them by how politely they decline.

It also will not fix these:

• A pricing strategy that is wrong in principle — a model can optimize within a strategy, not replace the decision about what business you want to be in.
• A funnel that leaks before the model touches it — if half of qualified leads are never contacted, better scoring changes nothing until the follow-up process works.
• A retention problem caused by the product — churn prediction tells you who is leaving and often why; if the why is a missing capability, the answer is a roadmap decision, not a retention offer.
• No usable outcome history — if nobody has recorded why deals were won or lost, there is nothing to learn from yet, though we can help you start capturing it.

We would rather lose the project than sell a model into one of these situations, because it will be judged on revenue it was never able to move.

Start where three things overlap: a lever that meaningfully moves your revenue, outcome data you already have, and a team that will actually change behaviour based on the output.

As rough guidance — if you have far more leads than selling capacity, start with prioritization. If acquisition is healthy but customers leave, start with churn. If you have a broad customer base and a narrow attach rate, start with next-best-offer. If growth is capped because every new customer needs more people to serve them, start with assistants and automation. If margins are set by habit, start with pricing.

Whichever it is, start with one. A single lever with a named owner and a measurable target beats a platform that promises to improve everything and is accountable for nothing.

A proof of concept — does your history predict this outcome at all — takes weeks, and is back-tested against periods where you already know what happened. Getting a validated model into the workflow follows the MVP stage.

After go-live, leading indicators move first: win rate on prioritized leads, save rate on flagged accounts, offer acceptance. Booked revenue follows roughly one sales cycle later. For high-volume B2C that means a quarter or two; for enterprise B2B with a nine-month cycle it means most of a year before the revenue comparison is complete, even though the leading indicators are readable much sooner.

Anyone promising measurable revenue impact in weeks on a long sales cycle is describing a demo, not a result.

Sometimes, and that is a useful validation rather than a failure. The value shows up in three places your account managers cannot reach: coverage — every account scored every week, not just the ones someone has time to think about; timing — behavioral drift is often visible months before the relationship signals it; and consistency — the same standard applied by a new hire and a fifteen-year veteran.

The honest test is the same as everywhere else on this page: hold out a control group, intervene only on the flagged accounts in the treatment group, and compare retention. If your team's intuition already captures everything the model finds, the comparison will say so and you will have learned something worth knowing about your team.

Not for a proof of concept. A PoC can run on exports — a CRM extract, a transactions table, a usage log — because at that stage the question is whether the signal exists, not whether the pipeline is production-grade.

Infrastructure becomes the constraint at the production stage, when predictions must refresh on a schedule against live data. That is a deliberate sequencing choice: proving the model is worth having costs far less than building the platform to serve it, so we prove first. Many clients discover their existing systems carry a first production version perfectly well, and only invest in consolidation once there is a working model justifying it.

You own what we build — models, code, pipelines and documentation. We do not run growth AI as a rented platform you can never leave, because a model trained on your customer behaviour is your commercial asset, not our subscription.

We also expect models to need care: markets shift, products change, and a propensity model trained on last year's motion degrades quietly rather than failing loudly. So we set up monitoring and retraining, and through the AI Academy we train your analysts to run and extend the system. Continuing support is available where it is genuinely useful — but it should be your choice, not a structural dependency.