Columbia-based advisor and author of The Practical AI Playbook outlines a business-first vendor evaluation approach that prioritizes problem fit, data readiness, ownership, and measurable outcomes over demo theater
COLUMBIA — As mid-market and SMB organizations face a crowded market of AI products, technology consultant and AI strategist Tzvi Boxer is releasing a practical framework for evaluating vendors before budgets are approved — designed to protect teams from unused licenses, unclear ownership, and pilots that never define success.
Boxer, who works remotely with Optimal Targeting and advises organizations on systems modernization, workflow automation, and practical AI implementation, says the most costly vendor mistakes he sees are not technical. They are sequential: companies invite demos before they can state the problem, assess data readiness, or name who will own the system after onboarding ends.
“Vendor evaluation should feel more like due diligence and less like a feature bake-off,” said Tzvi Boxer. “Before you compare models or pricing tiers, you should be able to explain the operational problem, the quality of your inputs, who owns the tool internally, and how you will measure success in 30 to 60 days. If those answers are fuzzy, you are not ready to evaluate vendors — you are ready to clarify your own work.”
Boxer’s vendor evaluation framework emphasizes five business-first checks:
- Problem fit — Can the workflow be stated in one sentence that frontline teams recognize?
- Data readiness — Are inputs usable, and can wrong outputs be explained?
- Ownership — Is there a named operator with calendar time, not only a budget sponsor?
- Measurement — Are baseline metrics, success targets, and kill criteria written before the pilot?
- Risk and oversight — Do privacy, security, transparency, and human review match the use case? The framework is intended for operators, finance leaders, and executives who want AI where it earns its keep — and simpler automation or process fixes where it does not. Boxer notes that many organizations reach for AI platforms when rules-based automation, cleaner definitions, or better use of existing tools would deliver more value with less complexity.
With more than 20 years of experience and work across 100+ client engagements, Boxer focuses on technology that evolves with the business rather than one-size-fits-all recommendations driven by trend cycles. He is the author of The Practical AI Playbook: How Smart Businesses Use AI Without the Hype.
“The goal is not to pick the flashiest vendor,” Boxer added. “The goal is to pick the smallest intervention that improves a measurable workflow — and to walk away when the business is not ready. Judgment is part of evaluation.”
Additional resources on practical AI strategy, systems optimization, and business-first technology decisions are available at https://www.tzviboxer.com/.
About Tzvi Boxer / Optimal Targeting
Tzvi Boxer is a technology consultant and AI strategist based in Columbia. He helps organizations modernize systems, streamline operations, and decide where AI and automation actually add value. Remotely, he collaborates with Optimal Targeting on practical AI strategy and high-authority content work grounded in clear expertise and operational reality. He is the author of The Practical AI Playbook. Learn more at https://www.tzviboxer.com/.
Media contact
Tzvi Boxer
Optimal Targeting (remote)
Email: tzviboxer1@gmail.com
Web: https://www.tzviboxer.com/
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