11 Questions to Ask Before Engaging an Healthcare AI Vendor

Long before AI implementation begins, assumptions are made about workflows, integrations, user behavior, timelines and risk ownership – assumptions that quietly shape the outcome of the project. By the time they are tested, they are often contractually and politically difficult to unwind.

Most AI initiatives in healthcare don’t fail because the technology underperforms. They fail because the organization and the vendor never develop a shared understanding of what “success” actually means in practice.

Buyers speak in outcomes, vendors speak in capabilities, IT teams hear integrations, while clinicians experience workflow. Each group believes it is aligned, yet they are often responding to entirely different interpretations of the same words.

This is not a governance problem, and it’s not a readiness problem. Those are internal disciplines that determine whether an organization should pursue AI and how decisions are managed. This is a buyer–vendor communication problem, and it emerges at the moment of evaluation.

Organizations that succeed with AI treat vendor engagement as a discipline, not a sales exercise. They use early conversations to surface hidden complexity, clarify ownership and test whether promises will survive in facility-specific environments.

The questions that follow are designed to do exactly that: reduce ambiguity before momentum makes it expensive, and ensure alignment before execution begins.

1. Are We Aligned on the Problem AI Is Solving?

Before diving into features, integrations or timelines, clarify intent:

• What specific workflows or pain points are we expecting AI to improve?
• Which users will interact with the AI system daily?
• What outcomes would define success six months after go-live?

If success cannot be defined operationally, it cannot be implemented or measured.

2. Who Are the Stakeholders Required for Success?

AI implementations are rarely owned by one team alone:

• Who represents clinical leadership and end-user needs?
  
• Who owns IT systems, interfaces and security reviews?
• Who has authority to make tradeoff decisions when clinical, technical and operational priorities conflict?

Clear ownership prevents downstream delays and stalled decisions.

3. How Will This AI Fit Into Our Existing Workflow?

Vendors design for workflows, but every organization’s reality is different:

• Where exactly does this tool appear in the user’s daily workflow?
• Does it replace steps or introduce new ones?
• How are alert fatigue, false positives or rework handled?

Even highly accurate AI fails if it slows clinicians down or forces work outside their normal systems.

4. What Work Happens Outside the AI System?

AI often shifts work rather than eliminating it:

• What steps remain manual after deployment?
• What exceptions or reviews occur outside the AI platform?
• Who owns those workflows?

Invisible work is one of the most common sources of post-go-live dissatisfaction.

5. What Systems Will This AI Need to Integrate With?

Integration assumptions are a major source of delay:

• Which core systems (e.g., RIS, PACS, worklists, EMRs) must integrate with AI?
• What data flows are required in and out of the system?
• Are these integrations standard, custom or net-new?

Understanding scope early enables realistic timelines and avoids surprises.

6. What Technical and Infrastructure Requirements Should We Plan For?

AI often uncovers hidden infrastructure dependencies:

• What infrastructure is required (cloud, on-prem, GPUs, storage)?
• Who is responsible for hosting, monitoring and uptime?
• How are updates, patches and performance changes handled?

Clarity here prevents last-minute infrastructure escalations or security delays.

7. Do External Partners Affect the Deployment?

Some of the biggest risks sit outside your direct control:

• Do we rely on third-party providers that generate or consume data?
• Will external organizations need to participate in testing or validation?
• Are there dependencies that could delay integration or approval?

Knowing where coordination is required allows for smarter planning.

8. How Will Users Authenticate and Access the System?

Security and access are foundational:

• Do we support modern identity providers or centralized authentication?
• How are user roles and permissions managed today?
• Are security reviews or compliance checks required before go-live?

AI vendors should adapt to your security policies, not redefine them late in the process.

9. What Are Our Timeline Expectations and Constraints?

Timelines fail when assumptions go unspoken:

• Do we have a target start date and go-live date?
• Is there a hard deadline tied to contracts or operational changes?
• What other initiatives could compete for the same resources?

Early alignment allows vendors to be realistic and accountable.

10. What Would Prevent Us from Going Live?

These questions forces realism:

• Are there mandatory features required before adoption?
• Are there workflows that must be supported on day one?
  
• What risks could delay or derail implementation?

Surfacing blockers early reduces surprises later.

11. What Changes After Go-Live and Who Owns It?

Go-live is the beginning, not the end:

• How is performance monitored over time?
• Who owns tuning, optimization and workflow refinement?
• What happens if adoption or outcomes fall short of expectations?

Explicit ownership here is critical to sustained value.

Discovery Is a Two-Way Street

Established AI vendors don’t rush past discovery, they lean into it. The best partnerships are formed when buyers and vendors collaborate early to understand systems, workflows and constraints before commitments are made.

If you can confidently answer most of the questions above, you’re not just ready to evaluate AI, you’re ready to implement it successfully. Interested in learning more? Let’s connect.