Founders face a paradox: investors expect an AI narrative, yet customers only pay for solved problems. Winning teams treat AI as a strategic capability, not a checkbox. The framework below helps you prioritize investments, allocate budget, and avoid the common traps we see in boardrooms every week.
Step 1: Clarify the Job to Be Done
Start with the customer outcome, not the model. Use the "JTBD" checklist to evaluate AI opportunities:
- What painful, frequent job are we helping customers complete?
- How do they solve it today? What workarounds exist?
- What result, if delivered instantly and accurately, would make them switch?
- Which parts of that workflow require human judgment versus automation?
Step 2: Pick an Operating Model
| Model | When to Choose It | Watch Out For |
|---|---|---|
| AI-Enhanced Product | Core product remains human-centric with AI copilot features (e.g., drafting, summarizing). | Underinvestment leads to novelty features with no retention impact. |
| AI Platform | Customers build on top of your APIs/models (e.g., vertical-specific LLM platform). | High infra burn; need a strong developer ecosystem. |
| AI Services | Professional services team delivers AI outcomes (e.g., custom copilots). | Margin compression unless you productize the most common engagements. |
Step 3: Budget for the Whole Lifecycle
Use the 40-30-20-10 rule as a planning heuristic for your first year of serious AI investment:
40% Talent
Staff engineers, ML specialists, prompt designers, AI operators.
30% Infrastructure
Model hosting, vector databases, evaluation pipelines, observability.
20% Data
Acquisition, labeling, governance, compliance, privacy tooling.
10% Experimentation
Vendor trials, hack weeks, user research to validate use cases.
Step 4: Sequence the Roadmap
- Quarter 1: Ship a proving-ground feature. Instrument everything. Capture qualitative feedback.
- Quarter 2: Industrialize successful workflows. Add evaluation pipelines, docs, and support playbooks.
- Quarter 3: Expand into adjacent jobs. Partner with marketing and sales to tell the story.
- Quarter 4: Reinvest in infrastructure. Negotiate model contracts, optimize costs, harden governance.
Pitfalls to Avoid
Feature parade
Shipping AI features that look impressive but never move retention or revenue. Tie every release to a measurable KPI.
Vendor lock-in
Relying on a single closed provider without exit options. Maintain abstraction layers and evaluation data.
Shadow AI
Departments launching AI workflows without governance. Implement a simple intake form and review council.
Neglected humans
Cutting training or support teams assuming AI will replace them. AI success requires new human skills, not fewer people.
Board & Investor Talking Points
- Our AI roadmap is anchored to customer jobs A, B, C with leading indicators X, Y, Z.
- We track model quality via evaluation metrics (accuracy, hallucination rate, human review time).
- Budget allocation follows the 40-30-20-10 model; we revisit quarterly based on impact.
- We maintain vendor optionality with API abstraction layers and internal fine-tuning experiments.
- Governance: monthly ethics review, red-teaming, and customer transparency updates.
Takeaway
AI strategy is business strategy. It is about sequencing investments, aligning teams, and proving value with real customer outcomes. Approach it with the same rigor you apply to product-market fit—hypotheses, experiments, metrics, and decisive iteration. Do that, and you will stand out from the noise.