Most "AI agent" content stops at the demo. A chatbot answers a question, everyone claps, nothing ships. The gap between a flashy prototype and a system that moves revenue is engineering discipline: clear inputs, tool access, guardrails, and a place to write the output.
Here are 12 use cases we've actually deployed for B2B clients, grouped by function. Each one has a measurable outcome, not a vibe.
Sales: agents that shorten the cycle
1. Inbound lead triage and routing
An agent reads incoming form submissions, enriches them (company size, tech stack, funding), scores intent, and routes to the right rep with context. No lead sits in a queue overnight.
2. Follow-up sequencing
The biggest revenue leak in B2B is dropped follow-up. An agent watches CRM activity, detects stalled deals, and drafts contextual follow-ups the rep can approve.
def draft_followup(deal):
days_stale = (now() - deal.last_touch).days
if days_stale < 3:
return None # too soon, don't nag
context = crm.get_thread(deal.id)
prompt = f"""Write a 3-sentence follow-up.
Deal stage: {deal.stage}
Last message: {context.last_message}
Objection raised: {deal.objections or 'none'}
Tone: helpful, not pushy."""
draft = llm.generate(prompt)
return queue_for_approval(rep=deal.owner, text=draft)
3. Meeting prep briefs
Before every call, the agent compiles a one-pager: company news, past interactions, open support tickets, and likely objections. Reps walk in informed instead of winging it.
4. Proposal and quote drafting
Pull line items from the CRM, apply pricing rules, generate a first-draft proposal in your template. Turns a two-hour task into a five-minute review.
Marketing: agents that compound
5. Content repurposing pipelines
One webinar becomes a blog post, five LinkedIn posts, an email, and a set of clips. The agent handles the mechanical transformation; a human edits the top 20%.
6. SEO brief generation
Give it a target keyword and it returns a structured brief: intent, competitor gaps, headings, and internal link suggestions. Writers start from a plan, not a blank page.
7. Lead nurture personalization
Instead of one generic drip, the agent adjusts messaging based on the segment, the pages a prospect viewed, and where they stalled.
Operations: agents that remove drag
8. Ticket classification and first-response
Support tickets get tagged, prioritized, and routed automatically. Common issues get an instant, accurate first response drawn from your docs.
9. Data entry and CRM hygiene
Agents reconcile duplicate records, fill missing fields from enrichment sources, and flag stale deals. Clean data is the difference between forecasts you trust and ones you guess at.
10. Invoice and contract processing
Extract structured data from PDFs, validate against POs, and push to your accounting system with exceptions flagged for a human.
def process_invoice(pdf):
fields = extract_structured(pdf, schema={
"vendor": str, "total": float,
"po_number": str, "due_date": "date"
})
po = erp.lookup(fields["po_number"])
if not po:
return flag("Missing PO", fields)
if abs(po.amount - fields["total"]) > 0.01:
return flag("Amount mismatch", {"po": po.amount, "invoice": fields["total"]})
return erp.post_payable(fields) # clean path, no human needed
11. Internal knowledge assistant
An agent wired to your docs, wikis, and Slack history answers "how do we handle X" so employees stop pinging each other. Onboarding time drops noticeably.
12. Reporting and anomaly alerts
Instead of someone building the same dashboard summary every Monday, the agent writes it, and flags when a metric moves outside its normal range.
What separates the winners from the demos
Every one of these works because of three things, not the model choice.
A narrow scope. "Handle all customer questions" fails. "Classify and route support tickets, draft first responses for the top 10 issue types" ships. Constrain the job.
Real tool access. An agent that can only talk is a chatbot. An agent that can read your CRM, call your enrichment API, and write back is a coworker. The value is in the actions.
A human checkpoint where stakes are high. Approval queues for anything customer-facing or financial. Full automation only where errors are cheap and reversible.
const agent = createAgent({
scope: "lead-triage",
tools: [crm.read, enrich.company, crm.write, slack.notify],
autonomy: {
routeLeads: "auto", // low risk, reversible
sendEmail: "require_approval" // customer-facing
},
fallback: (err) => slack.notify("#ops", `Agent stuck: ${err}`)
});
Where to start
Don't try to build all 12. Pick the one with the clearest, most painful bottleneck and the cleanest data. For most B2B teams that's sales follow-up or ticket triage - high volume, repetitive, and directly tied to revenue or retention.
Ship it narrow, measure it, then expand. The companies getting ROI from AI agents aren't the ones with the fanciest architecture. They're the ones who picked a real problem and wired the agent to actually do something about it.
Originally published at getmichaelai.com
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