Discovery field guide / Department
How to find AI opportunities in Sales & Marketing
Where should discovery start?
Trace one prospect journey from its source through qualification, seller follow-up, and the next commercial decision. Look for repeated research, missing handoff context, and content-review rework before proposing more generated outreach. Interview sales and marketing together so a faster local task does not create a noisier downstream queue. Validate consent, facts, and attribution before claiming that an AI-assisted activity caused revenue.
The shared framework
Fourteen stages, from objective to portfolio.
Every guide uses this sequence to connect a business problem to an accountable investment. Revisit earlier stages when new evidence changes the assessment.
Stage 1: Business Objective
Name the outcome the organization is trying to move, so every later idea has something to be measured against.
Stage 2: Department
Locate the team that owns the objective and the work that feeds it.
Stage 3: Process
Describe how the work actually runs today: steps, handoffs, systems, and exceptions.
Stage 4: People
Capture who performs and depends on the work, and what they know that no document records.
Stage 5: Problem
State the friction in plain terms: delay, rework, error, cost, or risk, with evidence.
Stage 6: Opportunity
Frame a specific improvement that addresses the problem, not yet tied to any technology.
Stage 7: AI/Automation Fit
Compare AI, RPA, IDP, workflow automation, process redesign, and no technology change.
Stage 8: Value
Estimate benefit in the objective's own terms, with assumptions written down.
Stage 9: Feasibility
Check data, systems, skills, and change capacity needed to deliver.
Stage 10: Risk
Surface compliance, quality, security, and adoption risks and the controls they need.
Stage 11: Scoring
Rate each opportunity on the same dimensions so comparisons are consistent and explainable.
Stage 12: Prioritization
Sequence opportunities by score, dependencies, and capacity, with reasons people can challenge.
Stage 13: Investment
Match approved effort to the sequence; people with budget authority decide what gets funded.
Stage 14: Portfolio
Track opportunities over time as a living set: delivered, in flight, waiting, and retired.
Start with one observable journey
Pick one segment and campaign or inbound route. Compare a progressed opportunity with a stalled handoff and a disqualified lead. Use approved CRM records and published account information, and distinguish recorded activity from verified customer intent.
This guide addresses go-to-market discovery across marketing and sales. It does not treat customer-service conversations as freely reusable prospecting data or assume permission to send automated outreach.
Where to look in the work
The handoff between campaign and seller
Inspect duplicated account research, missing buying-context notes, stale fields, and conflicting qualification criteria. A lead may stall because no owner accepts it, not because it needs a better score. Follow what the seller actually uses before adding another generated summary to the CRM.
Approval loops around claims and collateral
Trace how teams verify product facts, adapt content for a segment, and get approval for regulated or contractual claims. Find repeated searching and corrections. If product information is outdated or contradictory, fix the source and its ownership rather than increasing the volume of draft content.
Who to interview and what each person knows
Seller, marketer, and revenue operations
Ask a seller to demonstrate preparation for a recent meeting, then ask marketing what context they supplied. Include revenue operations to explain field definitions, deduplication, routing, and data access. A shared vocabulary is a prerequisite for comparing the same prospect across systems.
Approvers and recipient-facing owners
Interview the product or legal claim approver and the owner of consent, deliverability, or suppression rules. Include customer success when sales promises affect delivery. Ask who will detect and correct a wrong statement after it reaches a prospect rather than stopping the review at publication.
What to ask: follow the evidence
What evidence changed the next action?
Ask: Which account fact mattered in this opportunity? Where did it come from and when was it verified? What made a lead inappropriate for follow-up? Identify whether research, missing customer context, or a commercial decision consumed the effort; they need different evaluation methods.
Where does a draft become a commitment?
Ask which product, pricing, security, or performance statements need approval. How are opt-outs and channel permissions enforced? What happens when two systems disagree about consent? Document the exact boundary between an internal suggestion and a message sent on the company's behalf.
Signals of opportunity, and reasons to look elsewhere
Repeated research with checkable facts
A bounded research brief with named public sources and seller review can be worth investigating. Stale enrichment, weak identity matching, and invented buying intent are warning signs. More populated fields are not useful if sellers spend longer verifying them than gathering the evidence themselves.
Consistent rework in a controlled draft
Recurring corrections to the same product description may support source-grounded drafting, or reveal that the source itself needs repair. Volume of generated content and email sends are activity measures, not evidence of better opportunities. Check acceptance and downstream usefulness before attributing value.
How to evaluate what you find
Verify facts before measuring conversion
Test source accuracy, recency, consent boundaries, suppression behavior, and unsupported claims on held-out examples. Include similar company names and contradictory records. Evaluate seller editing effort and usefulness independently of any later commercial experiment so a persuasive but inaccurate draft cannot pass.
Treat revenue attribution as a separate question
Define an observation window and a defensible comparison before assessing pipeline impact. Account for segment, campaign, and seller differences. Do not extrapolate a few successful conversations into ROI or assume every minute of preparation saved translates into additional closed business.
How to compare, prioritize, and invest
Start inside the approval boundary
Prefer internally reviewed research or draft preparation over autonomous sending or pricing commitments. Repair routing, source facts, and consent synchronization first when those block a useful trial. Name the seller or marketer who will accept the output and record why they reject it.
Fund useful capacity, not output volume
Compare candidates by less verified rework, better handoff acceptance, and usable selling time. Include editorial review, data-provider cost, and reputation exposure. Pause on consent failures or false claims, even if a local activity metric improves. Commercial leaders decide whether longer-term evidence supports expansion.
Turn observations into a comparable opportunity record
Describe the current process before naming a solution. Record the trigger, owner, input, steps, handoffs, systems, output, and exception path. Separate what you observed from what someone recalled and what you still need to verify. A vivid interview story is a useful lead, not proof that every case behaves the same way. Validate it against a sample of ordinary work and difficult exceptions, with the limits of that sample made explicit.
Measure active work separately from waiting. Note the observation period, case mix, frequency, and source of each estimate. Include the receiving team's effort, checking, correction, and escalation so a local improvement does not merely transfer work. Describe the outcome in operational terms before attaching a monetary value. Capacity released is not automatically a budget saving; explain what can actually change and which manager has authority to make that change.
Compare interventions before selecting a tool
If a rule is explicit and the input is structured, test ordinary workflow automation first. If the task repeats a stable interface action, examine RPA and an integration alternative. If the difficulty is reading documents, consider IDP with source checks. AI may help where language or variable evidence requires interpretation, but its output still needs a task-specific acceptance standard. Process redesign or no technology change may be the right answer when policy, ownership, or an unnecessary step causes the problem.
Write down why each alternative would or would not address the observed cause. Do not compare a carefully controlled AI proposal with an artificially weak version of the current process. Include data repair, access approval, integration, training, review, and upkeep in each feasible option. An unavailable prerequisite is a reason to sequence work or pause, not a detail to hide inside a feasibility score.
Agree on a decision, not just a demonstration
Before a trial, name the business owner, evaluator, permitted data, review boundary, and person who can stop it. Define acceptable outputs and failures that must trigger escalation. Use examples held out from development, including missing information and uncommon cases. Compare the complete task with the baseline, including human verification and downstream consequences. Preserve the evidence behind the result so another owner can challenge the conclusion.
Keep value, feasibility, risk, and evidence confidence visible separately. A candidate with an attractive benefit estimate is not ready if its owner cannot obtain the data or operate the controls. Record the decision to investigate, pilot, wait, reject, or retire, with a reason and a condition for revisiting it. After delivery, compare observed results with assumptions rather than leaving the original estimate in the portfolio as fact.
Use the evaluation guide for the evidence standard and the prioritization guide to compare qualified candidates. Your leaders make funding decisions; a score or a discovery tool supplies structured evidence, not investment authority.
Continue through the discovery framework
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