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Function pillar Practitioner-led · production AI · ships in weeks

AI lead scoring,
sales forecasting and CRM hygiene — without a 12-month RevOps program.

Mid-market VPs of Sales and RevOps leads run on rep gut-feel forecasts, lead scoring that drifts by rep, and CRM data entry that eats 25% of selling time. AI fixes all three — when the data plumbing and the eval methodology happen first. We do both, in 2–4 weeks to first production.

Book a Discovery Sprint → See how it ships ↓
1 Day 1 2 Day 2 DISCOVERY & MAPPING 3 Day 3 4 Day 4 ANALYSIS & SCORING 5 Day 5 DELIVERY 5 Days 6 Deliverables $5K Flat Fee

What ships

Three places AI moves Sales-Ops numbers.

Each one is a candidate for a Discovery sprint and a 2–4 week first-production deployment via the Fractional team.

AI lead scoring

Replace rep-gut-feel scoring with models that read deal-stage velocity, engagement signals, fit attributes and intent data. Same signal read the same way for every rep — board-defensible pipeline metrics replace anecdotes.

AI sales forecasting

Pipeline-stage probability replaces rep-overrideable single-point forecasts. Range-not-single-point outputs. Board-ready forecast variance bands. Integrates with the CRM your team already uses (HubSpot, Salesforce, Dynamics).

Call transcription → CRM auto-pop

Automated activity logging from call recordings — call summary, next-step extraction, opportunity-stage updates auto-written to the CRM. Reps stop typing notes; managers stop chasing them. Recovers the 25% of selling time spent on data entry.

How AI lead scoring actually works

Data, model, eval — in that order.

Most lead-scoring models fail not because the algorithm is wrong but because the historical-conversion data is dirty, the win-loss labels are inconsistent, and the eval methodology was never agreed before training. We do all three in the first two weeks.

What we ship

What's in a first-production AI sales-ops deployment.

Not a research paper. A scoring or forecasting workflow that runs every day and writes its outputs into the CRM your reps already use.

Win-loss labels audited and re-coded

If 40% of your closed-lost reasons are 'other', the model can't learn. We clean the labels first.

Baseline measured against current scoring

Precision at top-decile, recall at the conversion threshold, false-positive cost in rep-hours. Numbers before models.

Eval methodology signed off in writing

What constitutes a win, what's a regression, what triggers rollback. Agreed before training starts.

Rep-overrideable, audit-logged

AI proposes a score, rep can override, system records who overrode and why. Trust earned, drift detectable.

CRM-native delivery

Outputs land in HubSpot / Salesforce / Dynamics fields your team already looks at. No second tool to log into.

Production monitoring on day one

Score distribution drift, override rate, downstream win-rate by score band. Weekly digest to the RevOps lead.

Where it fits

Three buyer profiles we ship for.

If your operation looks like one of these, the Discovery sprint pays for itself in the first week of forecast-review meetings.

B2B SaaS with rep-driven sales

Mid-market AE / SDR motion, pipeline above 200 active opps, forecast variance >20% versus actuals. Lead-scoring uplift typically shows up in conversion within one quarter.

RevOps teams under board pressure

Forecast variance is now a board-meeting topic. AI doesn't fix the rep behaviour — it makes the forecast itself defensible by replacing gut-feel with model-backed probability bands.

Industries with documented rep-coverage gaps

When rep-headcount can't grow with pipeline, AI lead scoring + auto-prioritization keeps top-decile leads from falling through the cracks. Force-multiplier on the team you already have.

Industry credential

CRM customisation — sector experience.

<strong>Source Energy Partners</strong> — custom Salesforce development for acquisition-opportunity identification. Delivered by our parent group Ascendix. <strong>Work type: custom Salesforce build, not AI.</strong> We use this as <em>sector-experience evidence</em> — we have shipped CRM workflows at scale across energy, real estate, and PropTech — not as an AI case study. The first AdvantageWorks-native AI sales-ops case lands later this quarter.

Common questions

About AI in Sales Operations.

What VPs of Sales and RevOps leads ask before they pick us.

They route around it if it's a black box, if the override path is hidden, or if the score is wrong on deals they know personally. We design for trust: rep-overrideable, audit-logged, with the override reason written back into the CRM. Trust grows when the override rate falls week-over-week — and that's the metric we monitor.
AI outputs land in CRM fields your team already uses — score, recommended next action, forecast probability — via the standard API. No second platform to log into. Our parent-group practice has shipped Salesforce + Dynamics customisations for 13+ years; we extend that with AI on top.
Use-case specific. The honest answer is we measure your current forecast variance baseline in week 1 of the Discovery sprint and commit to a target only after we've seen the data. Anyone quoting a generic uplift number without your data is selling, not forecasting.
Yes. Most engagements start as an AI-assist layer on top of existing rules-based scoring — score-and-explain rather than score-and-override. As trust grows the AI score takes more weight. The rules-based layer never disappears entirely; it stays as a fallback.
Top-decile precision (of leads scored top-10%, what share converted?) and recall against the conversion threshold (of leads that converted, what share were scored above the threshold?). Plus false-positive cost in rep-hours: how many high-scored leads turned out to be unqualified, and what was the rep-time wasted on them. All measured pre-model and post-model so the uplift is defensible.

Get an AI sales-ops roadmap in one week.

$5,000 Discovery sprint — we measure your current lead-scoring and forecast baselines, identify the 5–7 highest-ROI cases across scoring, forecasting, CRM hygiene and churn prediction, and hand you a costed implementation roadmap. Service-quality commitment: if we don't identify at least 3 actionable opportunities, you don't pay.

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