Selecting an AI voice agent in healthcare is harder than most vendor demonstrations suggest.
During AI vendor evaluations, health systems and pharmacy buyers ask the questions that are usually about price, timeline, and integration. However, the questions that should get asked during evaluation often don’t, and those are the ones that predict whether the deployment will actually work.
The AI vendor sales process is built to sell the product’s ideal future state through the demo, the roadmap, and the case study slide, without taking the buyer’s current infrastructure and constraints into consideration. By asking the right questions and shifting the conversation, health system and pharmacy buyers can determine whether the AI solution will actually eliminate bottlenecks and avoid burdening staff.
Here are 4 questions health system and pharmacy buyers should ask AI vendors:
“Does your solution recommend, or does it act?”
Some AI voice agents in healthcare surface a suggestion and hand it back to an overburdened staff member. While the suggestion is a step forward, it’s not as helpful as a system that pulls data, fills out the form, submits it, and then flags for human review only when something falls outside the parameters.
But what validates the output before it reaches a clinician? Acting systems need a quality control layer between the model’s output and a clinical decision. Output validation confirms every response actually tracks with the input data, and hallucination and reference checks verify that cited information wasn’t invented or unsupported. This validation supports confidence scoring so the system knows when to escalate to a clinician, and when to surface critical alerts for results requiring urgent action. Any system without this pipeline isn’t agentic, it’s just faster at being wrong. A vendor’s polished demo won’t show you whether the QC layer exists, and a vendor who can’t describe it probably doesn’t have it.
A March 2026 survey found that only 8% of AI healthcare pilots have reached production scale, which is the lowest of any industry. But 80% of healthcare executives believe agentic AI will deliver real value within a few years. The gap is between AI agents in healthcare that recommend and systems that act, so it’s worth asking whether you’re being sold a dashboard or a system that actually does the job.
“What happens to the workflow when your team walks away?”
High-touch clinical success teams to ensure high adoption rates. Once the launch is complete, the vendor’s team exits and the operational burden shifts internally and health system and pharmacy staff are left to handle friction, system issues, and ongoing training without vendor support. Healthcare IT has a long history of license-and-leave, and the track record shows it.
According to industry estimates, the failure rate for healthcare IT implementations is between 70 and 80%. The vendor’s solution gets installed, integration gets configured, and months later, when something goes wrong like payer rule change or an EHR update that breaks the AI, then it’s the buyer’s problem.
Buyers should ask the vendor to walk through what ongoing accountability looks like in practice: who’s responsible for monitoring after go-live, how often the system is retuned, and what’s included versus billed separately.
“Can I see this running in a health system or pharmacy like mine and not a demo environment?”
Pilots and sandbox demos are good for testing a concept, but they won’t prove it survives a buyer’s EHR, payer mix, or staffing model. The International Data Corporation, in partnership with Lenovo, found that for every 33 AI proof of concepts a company produced, only four made it to production. Most demos never get deployed at full scale, and a polished demo won’t tell a buyer if the system will hold up in their environment. Before signing anything, buyers should ask for a live reference at a comparable organization, find out what their go-live actually looked like, and request to talk to the operations team running it day to day.
“When AI voice agents get it wrong, who’s accountable and what’s the recovery process?”
In healthcare, credible AI systems keep a human in the loop for a percentage of cases because accuracy is higher. One review found that human-in-the-loop AI workflows achieved 99.5% accuracy, compared with 96% for human-only and 92% for AI-only workflows. That kind of accuracy depends on oversight being designed into the workflow, not bolted on after implementation. Yet, only 42% of medical group leaders report having a formal AI governance policy. Some states, like California and New York, are now writing human-oversight requirements directly into law.
Buyers should ask their vendors to clarify what gets flagged, who reviews flagged cases, and what happens when the system still gets something wrong. If the vendor can’t give a clear answer, that’s the answer.
These four questions aren’t about the technology; they’re about whether the vendor has done this before, whether the vendor will still be accountable after the contract is signed, and whether staff using Ai voice agents every day were a part of the conversation. That’s harder for a vendor to fake than a polished demo.