Will AI write our sales emails?

It can draft, and a human should send. Fully automated outbound at volume damages your domain reputation and your brand, and recipients can tell. Drafting from real deal context so a rep edits rather than composes is the version that works.

Our CRM data is a mess. Fix that first?

Yes, and it is usually the highest-return item. Deduplication, normalisation and enrichment on write, plus a backfill pass. Automation built on unreliable records produces confident nonsense, and forecasting on it is worse than not forecasting.

Can it do lead scoring?

Yes, and worth being clear about how. Rules and firmographic fit get you most of the value with an explanation a sales leader can argue with. An opaque model that says "0.73" and cannot say why does not get trusted or used.

What about our proposal process?

Drafts assembled from the deal record, the relevant case studies and your standard scope language — then edited and priced by a person. Pricing and commitments stay human, always.

By use case

Sales & Revenue Operations

Clean pipeline data and follow-ups that actually happen

Scope this for us Model the savings

Usually owned by

Sales
Revenue Operations
Marketing
Customer Success

< 5 min
Inbound lead acknowledged and routed: 8 hrs/week
Admin time recovered per rep: 90 days
Renewal risk visibility, from reactive

The change

What actually differs afterwards

Not a maturity model. The concrete difference in how the work happens.

Today

CRM data entered inconsistently, so forecasts are guesses
Inbound leads sit for a day before anyone looks
Renewals discovered in the month they expire
Proposals rebuilt from scratch each time
Reps spend a third of the week on admin

Afterwards

Records enriched and normalised automatically on creation
Inbound leads scored, routed and acknowledged in minutes
Renewal and churn risk surfaced 90 days ahead
Proposals drafted from the deal record, then edited by a human
Reps get time back for conversations

The data problem underneath

Every revenue operations project turns out to be a data project. Forecasts are wrong because stages mean different things to different reps. Territories overlap because account records are duplicated. Renewal dates are unreliable because nobody updates the field.

Automating on top of that produces faster wrong answers. So the first workstream is almost always hygiene: deduplicate, normalise, enrich on write, and enforce the definitions.

What we automate, in order

Enrichment and normalisation on creation. Company size, sector, technology signals and geography attached automatically, in a consistent format, at the point the record is created — not in a monthly clean-up.

Inbound triage. Score against fit criteria, route to the right owner, and acknowledge within minutes. Response time is the single strongest predictor of inbound conversion and the easiest thing to fix.

Renewal and risk surfacing. Usage signals, support history and engagement combined into a 90-day forward view. Renewals should never be a surprise.

Proposal drafting. Assembled from the deal record and your standard language, with the relevant case studies pulled in. A rep edits and prices it.

Explainable beats accurate

For lead scoring specifically, a transparent rules-plus-fit model that a sales leader can inspect and argue with gets adopted. A slightly more accurate opaque model gets ignored within a quarter. Adoption is the constraint, not accuracy, and we optimise for the one that actually changes behaviour.

FAQ

Questions we get asked

Let's find out what is actually automatable

Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.

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