How to get Your AI work more effectively 🤖🦾🦿

Love this — turning AI into your actual teammate (not just a fancy tool) is one of the highest-leverage moves you can make in sales. Below is a practical, step-by-step playbook you can run with today plus copy-and-paste prompts, KPIs, quick wins, and risk controls.

1) Start with outcomes and KPIs (don’t start with tech)

Decide the 2–3 concrete outcomes you want AI to move in the next 90 days (examples: reduce lead response time to <1 hour, improve MQL→SQL conversion by 15%, or save 4 hours/week per rep). Clear goals keep pilots focused and measurable. AI in sales is proven to boost rep productivity and enablement when tied to clear outcomes.

Suggested KPIs

• Lead response time (hrs)

• MQL → SQL conversion (%)

• Close rate on AI-assisted deals (%)

• Time saved per rep (hrs/week)

• Forecast accuracy (%)

2) Audit your data & stack (10–14 days)

AI needs good glue: clean CRM data, call transcripts, product usage, marketing touchpoints.

Checklist:

• Identify CRM(s) and data owners (Salesforce, HubSpot, etc.).

• Confirm call recording/transcript availability (Gong/Chorus/Zoom).

• Map where leads enter (forms, outbound sequences, inbound chat).

• Note GDPR/CCPA or customer privacy rules for your region.

Why this matters: most useful AI features (lead scoring, next-best action, automated notes) depend on integrated, reliable data.

3) Pick 3 high-impact use cases to pilot (2–6 week pilots)

Start small, show value, scale.

Top proven use cases

1. Lead scoring + routing — AI ranks leads by intent & fit so SDRs call best leads first.

2. Conversation intelligence & coaching — real-time cues and post-call summaries for reps.

3. Automated outreach personalization — AI drafts personalized emails/LinkedIn messages at scale.

4. Follow-up assistant — auto-generate next steps, tasks, and follow-up emails after calls.

5. Forecasting & deal risk signals — AI surfaces deals that need attention to avoid slippage.

Pick 1–2 seller-facing (coaching/outreach) + 1 ops-facing (scoring/forecasting) for your first pilots.

4) Integration & workflow design (do this

before

buying)

Design how AI outputs will appear in reps’ flows (notifications, CRM tasks, Slack pings, in-call prompts). Poor UX kills adoption.

Example flow: Lead → AI scores → SDR queue in CRM → AI suggests 3 personalized talking points → rep gets one-click email template → AI logs outcome in CRM. Use native integrations where possible (Salesforce/HubSpot + AI vendor connectors).

5) Pilot checklist (2–6 weeks)

• Define success metrics & baseline.

• Run pilot with a small cohort (3–6 reps).

• Collect qualitative feedback daily (friction points).

• Iterate rules/prompts weekly; retrain models if possible