Flow 7 works through your sales process one stage at a time. It finds where you're losing deals, works out what your strongest people do differently at that stage, and turns it into a standard your managers can actually hold the team to — so you convert more of the pipeline you already have, and grow revenue without growing the team.
The point isn't to make everyone try harder everywhere. It's to find the one or two places where the process is actually costing you, and fix those. Figures illustrative — yours would come from your own data, where it's good enough to rely on.
Sales optimisation for the AI age. That's the short version. Before the argument — the facts, so you can decide in thirty seconds whether to keep reading.
The software mostly does what it said it would. The question nobody can answer is whether any of it changed how selling actually happens.
Saved per seller, per week, by AI tools. The capacity gain is real and it is already happening.
Gartner, 2026 · 210 sales leadersOf sales organisations report low reinvestment of that reclaimed time into high-value sales activity.
Gartner, 2026 · same surveyOf enterprise GenAI pilots achieved revenue acceleration. The other 95% showed no measurable impact on profit and loss.
MIT NANDA, 2025 · 300 deploymentsAI is giving sales teams time back. Better capture, summarisation and automation mean sellers can spend less time on admin and more time selling. But saving time doesn't automatically improve sales performance. The missing piece is knowing what good execution actually looks like at each stage of your sales process — and making sure that extra capacity is spent on the things that genuinely improve outcomes.
MIT looked at 300 enterprise deployments and found the same thing. One detail is worth sitting with: most AI budget goes into sales and marketing, and that's where the measured return was lowest. Their explanation wasn't the technology — it was that organisations couldn't fit it into how they already worked.
Stated narrowly, because that's what the research supports. Gartner found 4.8 hours saved per seller per week and 72% of organisations reporting low reinvestment; that does not establish that AI fails to generate revenue, and we don't claim it does. MIT defined success as measurable P&L impact within six months, which excludes efficiency and pipeline effects, and the study has been publicly criticised on that basis — we'd concede the point if challenged. The direction of travel is the argument, not the decimal place.
Nobody is the best at everything. So the thing worth studying is the stage, not the salesperson.
The usual approach is to take whoever's billing most and hold them up as the model. The trouble is you get their weak stages along with their strong ones — which is why "go and shadow Dave for a week" so often doesn't work. New hires end up copying a personality instead of a method.
So we look at each stage on its own terms. What does winning look like here? Who's actually good at it? What do they do that others don't? We're looking for the small details that genuinely work — then turning those into something your whole sales team can repeat.
What you end up with is built from the best of what your own team already does, stage by stage — rather than from an assumption that one person has it all figured out.
Get 5% better at four consecutive stages and you finish roughly 22% ahead. That's multiplication rather than a forecast, and you can check it yourself in ten seconds. It's also why a handful of small, evidenced fixes tends to beat one large promise.
None of this is a new idea — good sales leaders have always known their best people do things differently. The problem was cost. Listening to enough calls to spot the pattern took weeks, so most teams did it once, from a small sample, and never went back. The playbook quietly went out of date. AI makes it cheap enough to look at every call instead of a handful, and cheap enough to look again next year.
Not an exhaustive list, but if you recognise your business here the conversation is usually a short one.
The founder sold everything up to this point, mostly on instinct and relationships. None of that is written down, and it doesn't transfer cleanly to the first three hires. Worth getting right before you scale it, because whatever you set up now is what the next twenty people will learn.
You've taken the investment and the numbers that came with it. There isn't time to work the process out by trial and error across four quarters, and the board will be asking about conversion long before that.
Going from eight sellers to twenty-five multiplies whatever inconsistency you already have. Onboarding by shadowing works at eight and stops working somewhere around fifteen, usually without anyone noticing until the ramp times slip.
Two sales teams, two processes, two different definitions of a qualified opportunity — and a combined forecast that nobody quite believes. Deciding whose process wins is a political question until somebody makes it an evidence question.
Your methodology works at home, so the assumption is that it travels. It usually does, apart from in one or two specific places — UK buying groups, procurement behaviour and how much directness a first meeting will take are the common ones. Better to find out which than to rebuild the whole thing.
If your sales process is genuinely well documented, consistently followed and already measured stage by stage, you've done this work. Tell us that on the call and we'll say so rather than sell you something.
Your CRM and whatever conversation tooling sits alongside it already do most of the capturing, summarising and automating. We're not here to replace any of it.
Churn prediction. Anomaly and abuse detection. CRM lifecycle automation. Support deflection. Compliance monitoring. Content localisation.
What your platform covers depends on which one you're on, which edition, and how it's set up — and it's changing fast. Before paying anyone else to build these, it's worth asking your account manager what's already there or coming.
We don't sell these, and we'd rather say so than find out three weeks into an engagement.
What none of them give you is a definition of what good looks like in your business, drawn from your own results.
That's what Flow 7 builds — clear stage definitions, findings you can trust, specific behaviours to start and stop, all of it dropped into the tools your team already opens every morning.
If your existing platform can deliver that standard, use it. Building anything separate is optional, and only worth it when the numbers say so.
Flow 7 identifies behaviours associated with stronger and weaker outcomes at each stage, grades the evidence, and turns what survives scrutiny into observable operating standards. Each phase produces a usable output and ends at a decision gate.
What the available data can and cannot support. Sometimes the honest answer is that it can't yet — you'd get that verdict and a remediation plan rather than findings built on unreliable inputs.
The process as it is actually executed, with entry, exit and measurement criteria, and a baseline view by stage where the data supports it.
Candidate signals from calls, CRM, correspondence, wins, losses and stalls — lost and stalled opportunities can be particularly instructive.
Test alternative explanations against the obvious confounders, and grade every finding for evidence strength.
Defensible findings become observable standards, scoreable rubrics and an anti-pattern set — specific behaviours to stop, which may deliver value faster than adding advanced ones.
Pilot the standard inside the tools your team already uses — pre-call briefs, post-call review against the rubric, alerts on known failure patterns, and a manager view that replaces generic one-to-ones.
Stage metrics tracked against the pre-engagement baseline where comparison is credible, with adoption, process movement and commercial outcome reported separately. Scheduled re-extraction keeps the standard current.
It's hard to buy something you can't picture. So here's the shape of what you'd actually get — the structure is real, the numbers are made up.
Evidence. Opportunities where the economic buyer was named before call two progressed to proposal at 34% against 19% where they weren't. The difference held after segmenting for account size and lead source.
Illustrative operating change. Named economic buyer becomes a mandatory exit criterion for Stage 03 — a gate, not a target.
Evidence. 62% of Stage 05 losses had no recorded conversation between proposal issue and client response. Won opportunities included a walkthrough in 81% of cases.
Illustrative operating change. Proposals are not issued cold. The walkthrough is booked before the document is sent, not after.
Evidence. Question count roughly 30% below team mean; proportion of open questions roughly double. Sample size limits confidence — the pattern is consistent but not statistically established.
Illustrative operating change. Framework issued and coached, flagged for re-testing at the next cycle before being treated as settled.
You're not buying insight. Insight is what a consultant leaves behind on a slide. You're buying a change to how the work gets done, that someone can be held to.
It's a fair question, and for some teams the answer is that you should. Here's what an outside pair of hands actually buys you.
A seller who thinks the discovery framework is a waste of time isn't going to say so to the person who wrote it. Saying it to us costs them nothing. That's not a comment on your culture — it's true almost everywhere, and the most useful thing we hear is often the thing nobody has said out loud internally.
Everyone on your team learned the process the same way, so the assumptions baked into it are invisible from where you're sitting. And the person who designed it is rarely the right person to judge whether it works — not through any lack of capability, just because nobody can be neutral about their own thinking.
Most sales ops teams could do a version of this. They also have a quarter to close. Analysis loses to pipeline every single time, which is exactly why the playbook gets written once and never revisited. A fair amount of what you're buying is simply the fact that it happens.
"The CRO thinks we should qualify harder" is an opinion from someone with authority over your career. "Here's what happened across four hundred opportunities" is evidence. Same conclusion, very different reception — and only one of them tends to change what people actually do on Monday.
Buyers now research, compare and shortlist before you know the opportunity exists. A vendor can lose without ever appearing in the pipeline.
On the 6sense evidence, buyers purchase from a Day One shortlist vendor 95% of the time, and 94% rank that shortlist by preference before contacting anyone. Meanwhile 67% of B2B buyers now prefer a rep-free buying experience.
Conventional pipeline, conversion and win-rate analysis begins once an opportunity has been recorded — so that loss does not appear in it.
The Buyer-Side AI Readiness audit is a separate engagement that tests what leading AI systems currently tell prospective buyers about you, using a controlled and dated question set — then identifies what to change, and re-tests.
£3,000 – £5,000 indicative. Faster and cheaper than the Flow 7 diagnostic, and can be commissioned independently of it.
Hiring a sales lead costs a six-figure salary plus fees. Then they spend their first quarter working out what they've inherited — largely by asking the people whose work they're assessing.
It's nobody's fault. A new leader has no independent view of the process they've taken on, so they build one from conversations, and those conversations are with people who have a stake in the answer. Meanwhile the clock on their targets is already running.
Some of the biggest decisions they'll make — who to keep, what to restructure, where to invest — get made in that window, on the least reliable information they'll ever have.
Sales Leader Support hands them an independent read of the sales operation, a roadmap of what to address in what order, and a timeline built around the targets they've actually been given.
£6,000 – £9,000 indicative. Commissioned either before they start, so they land with a map, or in their first ninety days, when they know what they need to test.
Illustrative figures and arithmetic examples are labelled where they appear. Four defensible figures beat twelve of mixed provenance.
Sales performance improvement, delivered first-hand. Seven-stage decomposition inside an enterprise AI platform business serving regulated industries — run manually, before comparable tooling existed.
Throughwork · founder-ledSaved per seller per week by AI tools, with 72% of organisations reporting low reinvestment of that reclaimed time. An association, not our outcome claim.
Gartner, 2026 · 210 sales leadersOf enterprise GenAI pilots achieved revenue acceleration. Diagnosis was workflow integration, not model quality.
MIT NANDA, 2025 · 300 deployments, 52 interviews, 153 surveysWhat we won't claim. Flow 7 has one measured delivery behind it, not a portfolio. We don't quote a blended average improvement across clients, because there isn't one. Any figure specific to your business would come from your own baseline at the diagnostic — and from nowhere else.
Illustrative client findings on this page are not evidence of a Throughwork outcome. The 22% compounding example is arithmetic, not a forecast. Any client-specific performance figure comes from that client's own measured baseline.
The method analyses customer conversations and employee performance data. In regulated businesses, these controls can determine whether a project is permitted to proceed at all.
Automated extraction generates candidates, not conclusions. Any material finding requires traceable source examples and human review before it is codified.
Where automation is used, the system assesses before it acts. Write access and stage gating are granted by client policy, not by the model.
Findings are framed at process level. Individual-level output is used for coaching, not for disciplinary or capability proceedings. Agreed in writing before extraction begins.
Sellers and managers have a defined route to contest a classification and supply missing context. Disagreement is logged as evidence about the analysis, not treated as resistance.
You've probably been pitched AI a dozen times this year. Here's where ours doesn't help.
If the product isn't landing, the pricing is off, or you're chasing the wrong customers, tightening the sales process just helps you lose faster. The diagnostic is partly there to catch that — and if it does, we'll tell you and stop.
Below a certain deal volume the numbers won't support firm conclusions, and we won't pretend otherwise. It's not simply headcount — it's how many deals you close, how consistent the process is, and how good the records are. Where the maths doesn't hold, we compare cases openly instead, and price it as the lighter piece of work it is.
People who know they're being scored will play to the score. That's half the point and half the danger — a rubric rewards what you can count, and some of what makes a good seller good doesn't count easily. We treat it as a floor, never a ceiling, and we say that to the team out loud.
The full version needs lawful access to recorded calls, with whatever transparency or consent that requires sorted out first. Without recordings we work from CRM and email alone — that's genuinely weaker evidence, and we'd price and promise it that way rather than pretend otherwise.
Twenty-five years in enterprise B2B sales across regulated markets — iGaming, financial services and payments. Built £1.5m ARR from zero as a sole commercial hire. Sold an enterprise AI platform into tier-one financial services. Earlier career at KPMG, Barclays and Hargreaves Lansdown, with financial services qualifications.
Flow 7 came out of that work rather than out of a framework. The seven-stage decomposition was first delivered by hand inside an enterprise AI platform business, and produced a measured 15%+ improvement. AI didn't create the method — it made it affordable to run properly, and to run twice.
Regular chair and moderator on innovation and technology panels across the major industry conferences.
If you can't answer that with evidence, that's the conversation. Thirty minutes, no slides — and you'll come away knowing whether a diagnostic would tell you anything you don't already know.
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Last updated July 2026.