The 90-Day AI Playbook: From Chaos to ROI

After 10+ years helping enterprises with AI, I’ve seen the same pattern: lots of AI excitement, scattered initiatives, unclear ROI.

Here’s the playbook I use to fix that in 90 days.

Phase 1: Requirements Engineering (Weeks 1-2)

Focus on KPIs that matter:

• Cost reduction
• Throughput improvements
• Inventory optimization

Align stakeholders on vision: where are we now, where do we want to be in one year, five years?

Real example: One client came with vague AI ideas. We narrowed focus to a single high-value prediction problem. Result: a clear line to seven-figure savings.

Phase 2: Fast, Functional Prototype (Weeks 3-6)

Leverage existing data and infrastructure. Get a baseline model running within four weeks.

The fail-fast methodology: Build quickly, get user feedback, iterate.

Case study: A client believed they needed custom NLP. Prototyping revealed the real bottleneck was data structure. We pivoted overnight and saved months of development.

Phase 3: Scaling for Stability (Weeks 7-12)

Now we solidify:

• Production architecture
• Data pipelines
• Compliance (GDPR)
• Monitoring

Execute phased rollout with continuous value delivery. Brief leadership syncs (15 minutes) focusing on results, risks, and next steps.

The Expected Outcome

An operational MVP generating 10-30% measurable improvement on target KPIs within 90 days.

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