01 AI Automation use cases

Ten ways B2B
teams actually use
AI automation.

Not keynote demos. Not speculative futures. These are the ten AI automation patterns we've shipped, watched stick, and measured outcomes against for real B2B teams. Each card shows what it replaces, a typical gain, and a "Do it for us" button — because if any of these look like you, we'd rather build it than explain it.

02 The playbook

Ten AI automation patterns.
One delivery system.

Every use case below ships with evals, observability, and handover documentation your team can own. No black boxes. No vendor lock-in.

01
Sales

Sales Research Agent

Pre-briefs every rep before every meeting — pulling the prospect's 10-K, latest product releases, adjacent tech stack, and recent exec activity into a one-page brief in under 60 seconds. Your AEs walk in informed, not improvising.

Replaces
30–90 min of manual SDR/AE research per call
Typical gain
-85% rep prep time · +3× demo quality
Do it for us
02
RevOps

Lead Enrichment & ICP Scoring

Every inbound lead — form fills, event lists, cold imports — gets enriched with firmographic data, tech stack signals, and intent, then scored against your ICP and routed to the right rep inside 90 seconds.

Replaces
8–20 SDR hours/week of manual enrichment
Typical gain
+62% lead-to-meeting · -40% CPL
Do it for us
03
Outbound

AI Outreach Copilot

Personalized first-touch emails grounded in the prospect's public signals — recent product launches, hiring activity, funding news — not "Hi {firstname}". Editor-in-the-loop by default; full autonomy once your rules are stable.

Replaces
Generic sequences and junior SDR manual drafting
Typical gain
+200% reply rate · 3.9× meetings booked
Do it for us
04
Support

Tier-1 Support Autoresolve

AI answers the repetitive 40% of your tickets autonomously, and hands off the rest to humans with full context and a drafted response. No policy hallucinations — the agent only answers from your actual help center.

Replaces
Tier-1 queue volume · after-hours backlog
Typical gain
41% auto-resolve · -52% first-response time
Do it for us
05
Internal

Internal Knowledge Agent

A Slack- or Teams-native assistant trained on your Notion, Confluence, internal wikis, and past Slack history. Answers the twenty questions new hires ask every ninety days — so your senior team stops repeating themselves.

Replaces
Repeat Q&A eating senior engineer time
Typical gain
~70% auto-answer · faster new-hire ramp
Do it for us
06
Meetings

Meeting Intelligence & Follow-up

Every sales or success call is recorded, transcribed, summarized, and turned into action items, CRM notes, and a drafted follow-up sequence — before the rep is back at their desk.

Replaces
Manual CRM hygiene, forgotten follow-ups
Typical gain
+5 rep hours/week · +28% follow-up rate
Do it for us
07
Content

Brand-Trained Content Operations

An AI system trained on your brand voice, tone, and approved messaging — producing ad variants, social cutdowns, email subject lines, and landing-page copy at scale, with editor review on every output.

Replaces
Creative-team bottleneck and template fatigue
Typical gain
4–8× asset throughput · brand compliance preserved
Do it for us
08
Analytics

Predictive Lead & Deal Scoring

Custom ML models trained on your actual win/loss data — not a vendor's generic template. Accounts get a fit score and an intent score, and reps see the reason, not just the number.

Replaces
Rule-based scoring and gut-feel prioritization
Typical gain
4–5× enterprise MQL quality · -38% sales cycle
Do it for us
09
Success

Customer Success Copilot

Monitors product usage signals, support tickets, and renewal timing to flag at-risk accounts early — and surfaces the specific expansion plays your CSMs should run next quarter on each healthy account.

Replaces
Reactive renewal discovery and manual health scoring
Typical gain
-24% churn · +18% NRR · earlier save-plays
Do it for us
10
Document AI

Contract & RFP Extraction

Parses incoming contracts, RFPs, and procurement documents to extract structured data — SLAs, pricing clauses, compliance requirements — and routes them into your CRM, CLM, or deal desk for review.

Replaces
Legal + AE hours reading every RFP by hand
Typical gain
-70% RFP prep time · >95% extraction accuracy
Do it for us
03 How we ship

Production in 4–6 weeks.
Compounding for years.

Phase 01 Week 01

Map

We shadow the team currently doing the work. Identify the 80% path we can automate — and the 20% edge cases that must stay human.

Phase 02 Weeks 02–03

Spec

Prompt architecture, data contracts, eval set, integration surface. A one-page runbook before any production code is written.

Phase 03 Weeks 04–05

Ship

Agent goes live behind a feature flag on real traffic. Daily red-team review. Your team approves outputs before anything goes customer-facing.

Phase 04 Week 06+

Operate

Full handover with evals, dashboards, and runbooks. We stay on for tuning when models change or data drifts.

Ready to ship

Something here
look like you?

Pick the one use case eating your team's time and we'll send back a one-page scope — what we'd build first, what it would cost, and what payback looks like.

Do it for us