The pressure to "do something with AI" is real. The businesses getting value tend to start with a clear problem, test before they trust, keep people in the loop and measure results. This checklist pulls those habits together.
First, a reality check
- Adoption is real but uneven. The U.S. Census Bureau's Business Trends and Outlook Survey found overall business AI use hovered between 17% and 20% from December 2025 through May 2026, with larger firms the biggest users.
- Use is not the same as payoff. In McKinsey's 2026 State of AI survey, nearly nine in ten respondents said their organizations use AI regularly, but 37% attributed any EBIT impact to it. Survey responses are self-reported.
- Workflow changes matter. McKinsey reports that nearly three-quarters of its "AI high performers" redesigned workflows for AI, compared with about one-quarter of other respondents. High performers were also about twice as likely to have defined processes for measuring AI's impact.
- Training is uneven. In the U.S. Chamber of Commerce's 2026 small-business technology report, around half of small businesses said they are actively working to upskill employees: 47% provide on-the-job training, while 48% provide AI tools and hope employees learn on their own.
- Rules are a concern. The same report found six in ten small businesses worry about increased litigation and compliance costs from a patchwork of state privacy and AI laws.
A framework to lean on
The National Institute of Standards and Technology's AI Risk Management Framework organizes AI risk work into four functions: Govern, Map, Measure and Manage. NIST says its suggested actions are not a checklist. Use the framework to organize decisions and controls, not as a certification.
Opinion — Vlogic Advisors: the checklist
- Define one problem. Name the task, the team and the outcome to improve.
- Measure the starting point. Record current time, cost, errors or customer satisfaction.
- Check data and permissions. Know which information the tool can access, store or use, and confirm that use is permitted.
- Review contracts and law. Check privacy, intellectual-property, security and industry-specific obligations before launch.
- Assign an owner. Name the person responsible for performance, problems and approval decisions.
- Pilot narrowly. Test with a small group and set clear criteria for continuing, changing or stopping.
- Test realistic cases. Include common requests, unusual requests, sensitive data and attempts to misuse the system.
- Keep human review where mistakes matter. Do not let AI make legal, financial, employment, safety or customer-impacting decisions without appropriate oversight.
- Train employees. Explain approved uses, prohibited uses, review steps and how to report problems.
- Measure results. Compare pilot results with the starting point, including quality, cost and risk.
- Keep an inventory. NIST's framework calls for mechanisms to inventory AI systems. Track tools, owners, data, versions and settings.
- Plan a shutdown path. NIST also calls for ways to disengage or deactivate AI systems that show outcomes inconsistent with intended use. Know who can turn it off and what the fallback is.
Our view: start small, test carefully, keep people accountable and expand only when results justify it.
Want help planning a pilot? Talk to Vlogic Advisors about a review of your stack.
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