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Techniques & Tactics

HBR 2025: Using Gen AI to Discover What Clients Need

Apply Harvard Business Review guidance on generative AI for sales discovery when buyers won't engage — without replacing human trust and qualification.

BadgeLead Editorial Team10 min read

Summary

Buyers resist long discovery calls, but sellers still need deep context. HBR outlines how gen AI can fill the research gap — ethically and effectively.

The Discovery Paradox

In their February 2025 Harvard Business Review article, Ian Gross and Lisa Earle McLeod describe a tightening trap: sellers must understand customers deeply to prove value, yet prospects and even existing clients resist investing time in discovery conversations. Cold outreach goes unanswered; expansion calls stall at surface level.

McKinsey data cited in the piece shows in-person appetite for new suppliers dropped from 50% to 35% over five years — sellers cannot rely on face time alone to learn what matters.

Where Gen AI Helps — and Where It Cannot

HBR positions generative AI as a research accelerant: synthesizing public filings, earnings commentary, LinkedIn activity, industry news, and internal CRM notes into hypothesis-rich briefs before live conversations. The goal is smarter questions in shorter meetings, not automated faux-empathy.

Effective use cases include account planning, stakeholder mapping hypotheses, draft implication questions tailored to a prospect's stated priorities, and competitive context summaries. Poor use cases include sending AI-drafted emails without review, fabricating buyer quotes, or skipping qualification because a model produced a convincing narrative.

  • Pre-call: build a one-page 'what might matter' brief with sources cited
  • Live call: use AI prep to ask fewer, sharper SPIN-style implication questions
  • Post-call: summarize and validate insights with the buyer, not the model
  • Never substitute AI output for MEDDPICC fields you have not verified with humans

Keeping Trust Central

HBR's broader 2024–2025 sales and AI coverage warns that digital efficiency without authenticity backfires. Pair gen AI research with active listening and buyer enablement — share concise, buyer-ready assets that help committees decide, rather than dumping generic thought leadership.

Challenger-style teaching still requires human judgment: AI can suggest reframes; only a skilled seller can read the room when a reframe threatens a stakeholder's status quo.

Team Implementation Checklist

Sales enablement should publish approved prompts, data-handling rules (PII, confidentiality), and quality rubrics. Managers inspect whether AI-augmented prep correlates with better discovery scores on call recordings — not whether reps clicked 'generate.'

Integrate with conversation intelligence so winning patterns from top reps train how junior sellers use AI, rather than everyone converging on generic outputs.

References & Further Reading

This article draws on peer-reviewed research, established frameworks, and authoritative industry sources.

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Frequently Asked Questions

Does HBR recommend replacing discovery calls with AI?
No. HBR frames gen AI as preparation and synthesis support. Live discovery remains essential for trust, politics, and qualification — AI reduces research friction beforehand.
What myths does HBR warn about?
Their companion piece on gen AI myths cautions against over-automation, assuming AI output is accurate without verification, and neglecting the human skills buyers still reward.

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