AI Strategy & Roadmap for 2026

AI strategy is not "we should use AI." Every company has said that. What matters is: which use cases, in what order, with what ROI, and how do you avoid the traps that sink 70% of AI projects.

Here's the framework we use with clients adopting AI for the first time.

Step 1 — Use case selection

Not every use case is worth pursuing. Evaluate candidates on:

Business impact — What KPI does this move?

Data readiness — Do we have the data, and is it usable?

Technical feasibility — Can this be built in 6–12 weeks?

Risk — What happens when it fails?

Prioritize use cases with high impact, high data readiness, and medium risk. Skip low-impact or low-readiness use cases for phase 2.

Step 2 — Vendor evaluation

For each use case, decide:

Build vs buy — Custom model or vendor API?

Vendor shortlist — Which vendors meet your needs?

Evaluation criteria — Accuracy, cost, latency, compliance

POC — Run a 2-week proof of concept before committing

Step 3 — Build vs buy

Buy when a vendor API solves the problem at acceptable cost

Build when your domain is specialized, data is proprietary, or cost at scale justifies it

Most companies should buy 80% and build 20%.

Step 4 — ROI modeling

AI features have real costs:

Development cost (build)

Vendor/API cost (run)

Infrastructure cost (hosting, vector DB)

Ongoing maintenance (retraining, prompt updates)

Model ROI against a baseline. Without a baseline, ROI is a guess.

Step 5 — Phased roadmap

Don't launch 5 AI features at once. Sequence:

Phase 1 (0–3 months): One high-impact, low-risk use case

Phase 2 (3–6 months): Expand to 2–3 use cases

Phase 3 (6–12 months): Enterprise-wide AI capabilities

Common traps

Chasing flashy use cases instead of business-critical ones

Underestimating data readiness

Not modeling cost at scale

No plan for model drift

Treating AI as a one-time project