How should a practice evaluate an AI growth-system provider?
A due-diligence checklist for evaluating AI website, assistant, automation, and CRM providers by mechanism, evidence, controls, ownership, and fit.
Who this is for: Practice owners and operators comparing agencies, software platforms, AI assistant vendors, CRM implementers, or integrated providers.
The 12-question due-diligence checklist
Use the same questions with every provider so charisma does not replace evidence.
- What exact buyer journey will you improve first, and why?
- Which capabilities are live, configurable, custom, planned, or unsupported?
- Can you demonstrate the full path with a realistic prospect inquiry?
- Where is each contact, conversation, consent state, and next action stored?
- What does AI handle, and what always goes to a person?
- What happens when an integration, model, webhook, or notification fails?
- Who monitors the system after launch?
- How are prompts, knowledge, workflows, and permissions changed and rolled back?
- What data is collected, retained, shared, and deleted?
- Which claims are supported by verified client results, a working demonstration, or neither?
- What will the client own if the relationship ends?
- How are lead response, bookings, search visibility, and operational outcomes measured without guarantees?
Make every capability prove its status
A provider should not present a mockup, diagram, or roadmap item as a live capability. A simple status model—Native, Configurable, Custom integration, Planned, Unsupported—forces the sales conversation to match operational reality.
Ask for dependencies and human-approval boundaries. “Can do” is incomplete if the workflow depends on another subscription, custom engineering, manual approval, or sensitive data the system should not collect.
Separate proof that it works from proof of business outcomes
A working demonstration can prove that a call is answered, context is captured, a CRM record is created, and a person receives an escalation. It does not by itself prove more revenue or a specific conversion lift.
The FTC warns businesses against overstating what AI can do. Ask what each proof point actually represents: a verified client result, controlled test, working demonstration, implementation example, or future measurement plan.
Find out who owns failures after launch
Most failures are not visible in a polished sales demo. The serious questions are who notices a silent failure, how quickly the issue reaches a human, whether data can be recovered, and whether the client receives a clear incident record.
PracticeGrowth.Tech provides managed implementation with explicit boundaries and evidence before promises. You can test Reception AI live; for every additional capability, we define the dependencies, configuration, monitoring, and human handoffs before activation.
Sources and further reading
These references ground the governance, security, and regulatory safeguards in this guide. The practical recommendations are PracticeGrowth.Tech’s own analysis.
- FTC Announces Crackdown on Deceptive AI Claims and SchemesFederal Trade Commission
Guidance for evaluating support behind AI capability and performance claims.
- AI Risk Management FrameworkNational Institute of Standards and Technology
Framework for governance, measurement, and management of AI risk.
- Cybersecurity Framework 2.0National Institute of Standards and Technology
Reference for governance and operational cybersecurity outcomes.
Turn the framework into a practice-specific plan.
Bring the website, workflow, or follow-up constraint that matters most. We will map the most useful first system around your practice instead of forcing a one-size-fits-all stack.
This resource is general information, not legal, tax, investment, security, or compliance advice. Requirements depend on the firm, jurisdiction, data, communication, and use case. Results vary by practice, market, scope, and starting point.