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AI Planning Guide

Questions to Ask Before Adopting AI

A practical pre-adoption checklist for business owners evaluating AI tools — covering data safety, governance, fit, and realistic expectations.

6 min read · Last reviewed July 2026

Framing note: AI tools genuinely help many businesses with real tasks. These questions are not meant to discourage adoption — they're meant to ensure you adopt thoughtfully, with governance in place, rather than reacting to a security or privacy incident after the fact.

Data and privacy questions

What data will employees enter into this AI tool?

AI tools produce better output when given more context. That means employees often share client information, financial data, or internal business details to get useful results. Before deployment, map what kinds of information will realistically flow into the tool.

Where does the data go when we submit it?

Read the provider's terms of service and privacy policy — specifically the sections on data retention, training use, and data sharing. Data-use practices vary by provider, product, account type, and user settings. Some consumer services may use content to improve models, while OpenAI states that its business data is not used for training by default unless the customer opts in. This is a provider-specific example, not a promise about every AI service. Verify the exact service tier and current provider terms rather than relying on the brand name alone.

Does the tool require a Business Associate Agreement for HIPAA-regulated businesses?

If a covered entity or business associate uses an AI provider to create, receive, maintain, or transmit PHI on its behalf, the provider may be a HIPAA business associate and an appropriate BAA or other permitted arrangement may be required. Confirm the provider's HIPAA eligibility, supported service tier, data flows, and contractual terms before submitting PHI.

Do our client contracts or NDAs restrict what we can share with AI tools?

Many professional service agreements and NDAs contain provisions restricting disclosure to third parties. Review your standard agreements before exposing client information to external AI services.

Governance and control questions

Who in the business will decide which AI tools are approved?

Without a designated approval process, employees will adopt AI tools individually — exposing business data to tools that haven't been evaluated. Assign an accountable approver and involve security, privacy, legal, or domain specialists when the data and use case require it.

Will there be rules about what information can and cannot be entered?

An AI acceptable use policy doesn't need to be a complex document. A one-page policy covering: approved tools, prohibited inputs (client PII, regulated data, credentials), and review requirements for AI-generated work is a reasonable starting point.

How will AI-generated output be reviewed before use?

AI tools produce confident-sounding output that can be factually wrong. For external-facing content, financial calculations, legal documents, or technical recommendations, a human review step is not optional.

What happens when an employee leaves — do their AI accounts follow them?

If employees are using AI tools on personal accounts, access doesn't end when employment does. Business AI tool accounts should be provisioned and deprovisioned through IT as part of the standard onboarding/offboarding process.

Fit and expectation questions

What specific problem are we trying to solve?

"We want to use AI" is not a use case. The businesses that get the most value from AI adopt it for a specific, defined task first — then expand. Start with one use case you can measure.

Who will actually use this, and are they prepared to?

AI tool adoption without training produces inconsistent results and frustration. Budget time for teaching employees how to write effective prompts, when to trust outputs, and what to check.

What does success look like in 90 days?

Define a measurable outcome before you start: time saved per week, volume of a specific task, reduction in turnaround time. Without a baseline, it's impossible to evaluate whether the tool is working.

The short version

Whether you choose private processing, approved enterprise cloud, or a hybrid deployment, be able to answer these four questions: What data will employees share with it? Where does that data go? Who reviews AI output before it's used externally? What do we do when something goes wrong?

Use those answers to select a focused pilot, assign an owner, and measure whether AI improves the workflow.

Primary sources and scope

These sources support the specific regulatory, provider, or recovery distinctions discussed here, not a complete compliance assessment or a guarantee of results. Confirm applicability, current terms, and your actual configuration before acting.

Related Tools and Resources

ToolAI Readiness AssessmentGuidePublic AI vs. Private AIResourceAI Prompt LibraryServiceManaged Private AI

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