AI moved from the lab to the boardroom. In 2025, speed and safety both matter. If you go too slow, you lose the market. If you go too fast without controls, you create risk and rework.

This playbook gives a simple, repeatable way to choose the right bets, launch them fast, and prove ROI. It’s written for executives who want results this quarter and a strong foundation for the next three years.

I, Gustavo Gabriel Dolfino, have seen the same pattern across industries: the winners turn AI into daily habits tied to revenue, cost, risk, and customer love. The rest get stuck in slide decks.

The 3 Outcomes That Matter

1. Grow Revenue
   • Smarter cross-sell, faster quoting, better personalization.
   • Sales and marketing teams close more deals with the same headcount.
2. Cut Cost to Serve
   • Automate parts of support, finance close, scheduling, and forecasting.
   • Reduce rework and waste with predictive insights.
3. Reduce Risk
   • Catch fraud, compliance breaches, and quality issues early.
   • Keep a human in the loop where it counts.

Tie every AI idea to one (or more) of these outcomes. If it doesn’t connect, it’s not a priority.

Dolfino’s 3×3 Playbook

Three horizons × three levers to stay balanced:

Horizons

• H1 (0–90 days): low-risk, data-light quick wins.
• H2 (3–12 months): integrated workflows that touch data and process.
• H3 (12–24 months): platform plays and new business models.

Levers

• Data: quality, access, lineage, and consent.
• Products: AI features embedded in real work.
• People: training, incentives, and change management.

Keep at least one bet in each horizon, and move them forward in parallel.

The Data First Aid Kit (Build This Before Models)

You do not need a data lake to start. You do need the basics:

• Inventory: list top 10 data sources used by sales, ops, finance, and support.
• Access: secure, least-privilege access for the AI team.
• Quality checks: spot-check freshness, completeness, and duplicates weekly.
• Lineage notes: where did each key field come from? Who owns it?
• Consent & rights: can this data be used for training or inference? Document it.
• Redaction rules: define what must be masked (PII, secrets) before any prompt or pipeline.

This kit turns messy data into “safe enough to get value” data.

Pick the Right Projects with a Scorecard

Score each idea 1–5 on the factors below, then add them up:

Factor	1 (Low)	3 (Medium)	5 (High)
Impact (revenue/cost/risk)	Nice-to-have	Helps a team	Moves a KPI
Feasibility (90 days)	Hard	Medium	Straightforward
Data Readiness	Missing	Partial	Ready
Risk Level	High	Medium	Low
Executive Support	None	Some	Strong

Pick the top 3 ideas with the highest total that also hit different outcomes (revenue, cost, risk). This avoids tunnel vision.

The Minimum Viable Governance (MVG)

Governance isn’t red tape. It’s how you move fast with confidence. Start small:

• Use policy: who can use which tools, for what, and with what data.
• Human-in-the-loop points: define where a person must approve.
• Model cards: short docs with source, version, limits, and known risks.
• Prompt and output logging: capture inputs/outputs for audits.
• Safety tests: run weekly tests for bias, hallucinations, and data leaks.
• Rollback plan: be ready to disable a feature in minutes, not days.

MVG is enough to ship. You can mature it as you scale.

Operating Model: Treat AI Like a Product

The structure that works:

• AI Product Owner (business leader): owns the KPI, not the tech.
• Tech Lead / Architect: chooses stack, ensures security and scale.
• Data Lead: manages pipelines, quality checks, and access.
• Applied AI Engineer(s): prompts, fine-tuning, and integration.
• Risk & Compliance Partner: embedded from day one.
• Change Lead: training, comms, and adoption metrics.

Stand-ups are short. Demos happen biweekly. Every sprint ends with a visible upgrade.

Build, Buy, or Partner (A Simple Rule)

• Buy when the process is standard (support deflection, meeting notes, document search) and vendors have strong controls.
• Build when your data or workflow is unique and creates a moat (pricing models, proprietary scoring, internal knowledge).
• Partner when speed matters, but you need heavy integration or custom guardrails.

Negotiate proof, not slides: sample outputs on your data, time-to-value, and exit options.

The Pilot-to-Production Checklist

1. Baseline the KPI (e.g., average handle time, win rate, forecast accuracy).
2. Define guardrails (blocked terms, PII masking, escalation steps).
3. Ship to a small group (10–50 users).
4. Measure adoption (daily/weekly active users, tasks completed).
5. Compare before/after (KPI lift and error rate).
6. Fix, then scale (train-the-trainer, update SOPs, expand access).
7. Automate monitoring (drift, quality, cost per task).

No pilot lasts longer than 8 weeks. If the KPI moves, graduate it. If not, stop it.

Money Talk: Show Real ROI

Keep the math simple and visible:

• Value Realized / Month
  • g., 8 support agents × 20% time saved × \$1,000 per agent = \$1,600
  • g., +3% sales win rate × average deal size × number of deals
  • g., -15% forecast error → lower safety stock → inventory savings
• Total Monthly Cost
  • Licenses + usage + engineering time + change management.

ROI = (Value – Cost) / Cost Aim for < 3–6 months payback on quick wins and < 12 months on big bets.

Publish a one-page ROI report every month. Wins get resources. Misses get fixed or cut.

Your 90-Day Plan (Week by Week)

Weeks 1–2: Align and Prepare

• Pick top 3 use cases with the scorecard.
• Set target KPIs and baselines.
• Stand up the Data First Aid Kit.
• Approve MVG and human-in-the-loop steps.

Weeks 3–6: Build and Pilot

• Build thin slices: one workflow per use case.
• Connect data safely. Log prompts and outputs.
• Train a small group. Capture feedback daily.
• Fix blockers fast: access, UX, speed, accuracy.

Weeks 7–9: Measure and Decide

• Compare KPIs against baseline.
• If green, plan scale (SOPs, training, automation).
• If yellow, fix and extend 2 weeks.
• If red, stop and replace with the next idea.

Weeks 10–12: Scale and Systemize

• Roll out to the next team or region.
• Add monitoring dashboards.
• Update job aids and policy.
• Publish the first monthly ROI report to the exec team.

Repeat the cycle.

The 2025 Tech Stack, Made Simple

• Foundation Models: general-purpose LLM + one fallback.
• Retrieval Layer: approved knowledge base with redaction.
• Workflow Layer: orchestration to call tools and systems.
• Data Pipelines: scheduled syncs, quality checks, lineage.
• Safety & Audit: prompt logs, model cards, access control.
• Observability: usage, cost, latency, drift, and KPI impact.

Pick boring, stable tools where you can. Save innovation for the business logic.

Change Management That Actually Works

People don’t adopt features; they adopt better days at work. Make that real:

• Train on tasks, not theory. “Here’s how to handle refunds with AI.”
• Celebrate early wins. Share before/after stories every Friday.
• Create champions. One per team to answer questions live.
• Update incentives. Reward usage tied to quality, not volume.
• Listen weekly. A two-question survey: “What helped? What hurt?”

Adoption is a metric, not a wish. Track it.

Risk, Security, and the Board

Give the board a short, steady view:

• Use cases and KPIs: what is live and what value it drives.
• Data controls: what data is in, what is out, and why.
• Model risks: known limits, tests run, and incidents.
• Costs and contracts: spend to date, unit economics, and vendor health.
• Roadmap: the next 90 days.

This turns fear into oversight and oversight into speed.

Common Pitfalls (And How to Avoid Them)

• Starting with a model, not a metric. Always tie to a KPI first.
• Over-building data platforms. Ship with the First Aid Kit, then grow.
• Endless pilots. Cap at 8 weeks and make a call.
• Shadow AI. Publish policy, approved tools, and a simple request path.
• Ignoring the humans. Train, reward, and support adoption.

Sample Executive Dashboard (One Pager)

• Top KPI: Support Average Handle Time ↓ 18% (Target 15%)
• Adoption: 63% weekly active users across support tier 1
• Quality: Human overrides at 7% (Target <10%)
• Risk: No PII incidents; weekly bias tests passed
• Spend: \$12.4k this month; cost per resolved ticket \$0.38
• Next Moves: Expand to tier 2; start sales proposal assistant pilot

If a dashboard can’t fit on one page, it’s too complex.

FAQ for Leaders

Q: Do we need a Chief AI Officer? A: You need clear ownership. Title matters less than having one accountable leader who partners tightly with security, data, and the business.

Q: How do we avoid hallucinations? A: Use retrieval from trusted sources, constrain prompts, keep a human in the loop for high-risk steps, and log outputs for review.

Q: What about jobs? A: AI shifts work. Plan for role redesign and upskilling. Focus on removing busywork and raising the ceiling of what people can do.

A Short Playbook You Can Print

1. Pick 3 use cases tied to revenue, cost, and risk.
2. Stand up the Data First Aid Kit.
3. Ship a thin slice in two weeks.
4. Measure the KPI every week.
5. Scale what works; kill what doesn’t.
6. Publish a one-page ROI report monthly.
7. Keep governance light but real.
8. Train people on tasks they do today.

Final Word from Gustavo Gabriel Dolfino

AI is not magic. It is a new kind of leverage. The companies that win in 2025 will be the ones that turn AI into daily habits that move the P\&L and protect the brand.

Start small, move fast, measure truthfully, and build trust with your teams and your customers. That’s the playbook. Now run it.