Beating Decision Fatigue in Sales with AI

Welcome to the first episode of 'This is The Play' with Steven Werley, where we tackle AI in sales. This episode lays the groundwork for overcoming decision fatigue in sales by implementing practical systems and reducing time spent on decision-making. Steven emphasizes the importance of maintaining consistency and avoiding cherry-picking easy calls. He presents a structured approach using AI to score leads, classify them into hot, warm, and cold categories, and prioritize engagements through two daily 45-minute sprints. Additionally, an 'AI Sidecar' segment highlights the use of GPT to streamline tasks, allowing sales reps to focus on high-yield activities. The episode concludes with actionable steps for integrating these tactics into your routine.

00:00 Introduction to 'This is The Play'

00:43 Understanding Decision Fatigue in Sales

03:11 The Impact of Cherry-Picking Calls

06:01 Building Effective Sales Systems

08:05 Defining and Scoring Leads

10:56 Implementing the AI Sidecar

12:56 Best Practices for Using AI in Sales

19:21 Actionable Steps and Conclusion

What you’ll learn

• How to set Hot, Warm, Cold SLAs (24 h, 72 h, 7 days) and score intent 0–5.
• How to auto-build a 9-slot list at 06:00 and clear it with two 45-minute sprints.
• The guardrails and the few metrics that prove it’s working.

The Play (Next‑3 Moves Queue)

1. Set SLAs: Hot 24 h, Warm 72 h, Cold 7 days.
2. Score intent 0–5 using last reply, last call outcome, stage, inverse deal age.
3. At 06:00, auto-build a 9-slot list (3/3/3; highest intent first).
4. During sprints, use macro-only actions mapped to bucket + stage (email, SMS, call).
5. Run two 45-minute sprints (AM and PM). Track: Touched %, Next-Action %, Replies %, Booked Calls.

AI Sidecar (copy‑ready prompt)

You are my sales ops assistant. Using the table below (fields: name, stage, last_reply, last_call_outcome, deal_age_days), do three things:

1) Score INTENT 0–5 for each opp:

   - last_reply recency (0–2), last_call_outcome (0–1), stage (0–1), inverse deal_age (0–1).

2) Assign a BUCKET with SLA:

   - Hot=24h, Warm=72h, Cold=7d. Explain your bucket briefly.

3) Output:

   a) A prioritized 9-slot list for tomorrow 06:00: 3 Hot, 3 Warm, 3 Cold (highest intent first).

   b) Three plain-text macros (Hot/Warm/Cold) I can paste as email/SMS/call notes. Keep under 80 words each, no hype.

Return a clean table (name, stage, intent_score, bucket, why_bucket, next_action_macro_type), then the 9-slot list and the three macros.

Privacy tip: strip PII; never include payment data.

Guardrails

• Do not write from scratch during sprints; personalize 1–2 lines max.
• Max two passes per day through the queue.
• Close or recycle any opp with no next action.

Action in 5

“Start (≤5 min): Paste 10 real opps into GPT with that prompt and copy the 9-slot list into your CRM view. Finish: Run two 45-minute sprints before Friday 3pm.”

Links

• 1‑page checklist
• Follow‑Up Builder ($7)
• Learn more about Closable.ai