AI investment & economics

Vendor fit and exit exposure

Make the vendor choice with its concentration and switching cost visible.

Current decision

Leading fit: Open-source-backed

Score 78/100, 1 points ahead of Specialist. Exit exposure $90k; concentration 45%.

Inspect a vendor, then vary the decision weights to test whether the ranking holds.

Same instrument · 3 industries choosing a scenario replaces its inputs; save or share your current changes first

Weights

Capability25%

Security20%

Roadmap15%

Low lock in15%

Support10%

Price / value15%

Open-source-backed

Leading score

Portable, cheaper, thinner support.

Specialist

Best in class capability, narrower surface.

Hyperscaler

Broad platform, deep integration, real lock in.

Hyperscaler: fit and exit receipt

Weighted score 75/100 · concentration 75% · renewal in 14 months · exit cost $480k.

Criterion scores and contribution to ranking

Criterion scores and contribution to ranking
Criterion	Normalized weight	Vendor score	Weighted contribution
Capability	25.0%	85	21.25
Security	20.0%	88	17.60
Roadmap	15.0%	82	12.30
Low lock in	15.0%	45	6.75
Support	10.0%	78	7.80
Price / value	15.0%	60	9.00

These are authored vendor archetypes. A close ranking calls for stronger evidence and a switching plan before a procurement decision.

Leading score: Open-source-backed, exit cost $90k

Nudge two or three weights and the ranking can flip, a “winner” that survives only one weighting isn't a decision, it's a preference. Pair the pick with its concentration and exit cost before you sign.

If you act on this · the call → expected lift → how you'd measure it

The call

Choose the vendor with both fit and exit exposure visible.

Expected lift · illustrative

Reduces hidden concentration risk by treating vendor choice as an operating dependency, not just a feature comparison.

How you'd measure it

Weighted score, concentration exposure, exit cost, renewal timing, switching feasibility.

Steering committee takeaway: The scorecard tells you who wins. The risk view tells you what it costs if the winner becomes wrong.

How this is built

Weighted score = Σ(weight × criterion score) ÷ Σ(weights), so weights are relative and always normalize to 100%. Lock in and price are scored so higher = better (less lock in, better value).

Risk view pairs each vendor with concentration (spend share if primary), renewal window, and exit cost (tracks lock in). Stack: Next.js (static) + shared design system; client side.

Limitations: this is a simplified vendor model. Real vendor selection would require procurement terms, security review, architecture fit, legal review, financial analysis, and operational due diligence.

Method, evidence and limitations

Prefer to read? The two minute case study · problem → approach → metric → outcome

Problem

The best scoring vendor may also introduce lock in, concentration, or renewal exposure. Executive decisions need both views: which vendor fits the need and what it costs if the relationship, roadmap, or pricing changes.

Approach

The monitor compares vendor archetypes across capability, security, roadmap, lock in, support, and price. It then adds a risk view that shows concentration, renewal exposure, and estimated exit cost.

Why this way

This connects AI sourcing to procurement, third party risk, resiliency, cost exposure, roadmap dependency, and negotiating leverage.

The metric

Weighted vendor score; switching/exit cost exposure if the relationship sours.

The trade-off

The best fit vendor may also be the biggest concentration risk.

Outcome

A vendor pick with the exit cost exposure named up front.