Leads arrive, but deals do not close quickly

Compare de-identified enquiries, response times and deal outcomes. AI groups delay causes and drafts replies while a manager checks context and decides.

RA LAB practical guide · based on the library’s GRI / GTM sources.

GOAL

Find a verifiable delay between enquiry and payment and test a faster response.

VALUABLE FINAL DELIVERABLE

A response-time map, AI-assisted loss hypotheses and a one-week test plan with conversion and quality checks.

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The business problem and an AI use case

Why a fast reply is not yet a sale

A delay may occur before the first reply, during clarification, while agreeing an offer or before payment. First identify where the customer stops moving. Faster replies alone will not fix a poor offer or an unsuitable enquiry.

Separate enquiries by channel, task and communication language. Compare similar requests: seasonality, pricing or a different lead mix may explain an outcome better than AI.

What to ask AI to do

Start with de-identified delay analysis and draft replies based on an approved offer. Give AI explicit rules: use available facts only, flag unknowns and hand non-standard terms to a manager.

A person checks prices, availability, timing and promises before sending. For teams working in RU/EN in Kazakhstan and Central Asia, test each language separately against actual enquiry types.

Example artifact and stop rule

Illustration, not a client result. An enquiry record without a name or phone contains: customer task → first-response time → clarification → offer → outcome → loss reason → corrections to the AI draft.

Stop or revise the test if replies make unsupported promises, miss the request or increase complaints. Faster preparation is useful only with acceptable quality and a clear effect on the next stage.

Common questions

Must AI connect to the CRM immediately?

No. Test the first hypothesis using a small de-identified sample and manually prepared drafts. Discuss integration after reviewing usefulness and data requirements.

When should we discuss a pilot?

When one sales stage, a workflow owner and a baseline are defined. If the cause of losses is unclear, begin with a consultation to frame the problem.

AI helps identify patterns and generate hypotheses. Verify outputs against source evidence, use de-identified data and keep a person accountable for decisions.

GRI and GRI AI in plain language →

From problem to decision

1. Measure enquiry path

Choose a channel and week. Record first-response time, transitions and paid deals without sending personal data to AI.

Channel, baseline conversion, response time and data gaps.

Step deliverable: Channel, baseline conversion, response time and data gaps.

2. Find delay with AI

Give AI only de-identified enquiry features. Ask it to group delays and suggest alternative causes; verify examples manually.

One verified delay and an alternative hypothesis.

Step deliverable: One verified delay and an alternative hypothesis.

3. Test faster response

Prepare reply variants and a handoff rule for a manager. Run a small test while respecting communication consent and consultation quality.

Reply variant, owner, period, metric and quality guardrail.

Step deliverable: Reply variant, owner, period, metric and quality guardrail.

4. Compare deals and quality

Compare conversion and response time with baseline or a comparable group. Include complaints, cancellations and operating cost.

Actual result, limitations and decision to continue, revise or stop.

Step deliverable: Actual result, limitations and decision to continue, revise or stop.

How to judge whether it helped

You should leave with a document that supports a decision and a next action. Completing steps does not guarantee a commercial result. Measure the chosen change separately over an agreed period. If evidence is missing, state that and plan how to collect it.

  • A baseline, actual result and data limitations are recorded.
  • A person made the decision after reviewing quality and side effects.

How this differs from S01–S13

This is a user route for a specific problem. The GRI AI research stages S01–S13 and the 13-week programme remain in the library as separate methodological work. This route is not a validated GRI diagnostic scale.

Your route0 / 4 · 0%

Completed 0 · remaining 4

Next action: Measure enquiry pathThe percentage measures completed steps, not proven business growth.

Measure enquiry path

Choose a channel and week. Record first-response time, transitions and paid deals without sending personal data to AI.

Step deliverable: Channel, baseline conversion, response time and data gaps.

No names, contacts, client secrets or sensitive details. Saving happens only when you press the button.

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Data & privacy

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Sources and supporting materials

Author-provided materials: GTM Genius: lead-to-deal journey; GRI AI: Human × AI and outcome review.

Methodological references. Links lead to original materials; worksheet examples are not research findings.

Generative AI at Work ↗Brynjolfsson, Li & Raymond / NBER · Field evidence on productivity and heterogeneous effects across worker experience.Identifying and Scaling AI Use Cases ↗OpenAI · A field guide for finding, prioritizing and expanding AI use cases from tasks into end-to-end workflows.The Go-to-Market Framework ↗Go-to-Market Alliance · A cross-functional framework for aligning product, marketing, sales and customer success around one market motion.Revenue Architecture ↗Winning by Design · A six-model GTM operating system connecting revenue, customer lifecycle data, math, execution and growth stages.Open the full source catalog →
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