The STRIVE Framework for AI Tool Evaluation
Most AI tool decisions get made on the demo — the feature list looks impressive and the checkbook opens. STRIVE is a way to slow that down. It’s a six-dimension checklist that forces the question the demo skips: does this tool actually fit the strategy, the stack, and the budget you already have? Run a candidate through all six before committing.
S — Strategic fit
Start with whether the tool serves the strategy or just adds capability for its own sake. Does it solve a real problem or offer a marginal gain? Does it create an advantage a competitor can’t trivially copy? The trap to avoid is reshaping your workflow around a tool instead of buying a tool that fits your workflow.
T — Technical efficacy
Judge whether the technology is sound. How accurate and reliable are its outputs on your kind of data? What does it need to perform — how much data, and of what quality? Is the underlying model current, or are you buying something already behind the state of the art?
R — ROI and scalability
Weigh the full cost against the return. Total cost of ownership is subscription plus implementation plus training, not just the sticker price. The return is concrete: time saved, conversion lift, or lower cost per acquisition. And it has to hold up as you grow — a tool that buckles at higher data volume is a short-term purchase.
I — Integration and usability
Check that it fits the stack you run. Does it offer real APIs and native connectors to your CRM and analytics? Is the interface something the team will actually adopt, or will it sit unused after onboarding? Can data move in and out cleanly, or does it trap your data inside the vendor’s walls?
V — Vendor viability
Assess the partner, not just the product. Is the vendor reputable and financially stable enough to still be here next year? Is there a credible roadmap and a steady release cadence? Are the documentation and support good enough that you won’t be stranded when something breaks?
E — Ethics and compliance
Confirm it’s safe and defensible. Where and how is data stored, and does that meet GDPR, CCPA, and emerging AI rules? What does the vendor do about algorithmic bias? And can its decisions — ad targeting, moderation calls — be explained when someone asks? This dimension is expanded in AI ethics and governance in social media.
Using STRIVE
The point isn’t a numeric score; it’s a structured conversation that surfaces the weak spot before you buy. A tool can be technically excellent and still fail on integration or vendor stability — and STRIVE makes that trade-off visible while it’s still cheap to walk away. Applying it well is one of the core competencies covered in AI leadership and skills.
