Read the business model behind the AI pitch.
Build a Reverse Business Model Canvas for any AI vendor, expose the missing evidence, and take the next safe governance step. Made for the people who sign, vote, and answer for the outcome.
Eight blocks answered. The amber one is your next evidence request.
Vendor-neutral. No technical background required. Do not enter PHI.
Build a Reverse Canvas prompt
One field. The complete prompt is generated here in your browser. Nothing you type is stored or sent anywhere.
Your Reverse Canvas prompt
Paste the prompt into the AI tool your organization has approved. The output is a research draft for human review, not a decision.
Three blocks carry most of the signal.
You do not need the whole canvas to start asking better questions. These three tell you who the vendor really works for.
Revenue Streams
Per seat, per transaction, share of savings, or paid in your data? Pricing is the vendor's incentive structure.
Key Resources
Who owns the model after it learns from your pharmacy? Is it proprietary, or a wrapper on someone else's model?
Customer Segments
Are you the core customer, the beta site, or the training set? Is pharmacy their business, or a side bet?
The block you cannot fill is your next question.
Nine blocks, read from the buyer's side.
The Business Model Canvas is how vendors plan their business. Read in reverse, it is how buyers find the questions a pitch is built to avoid. Select a block to see its buyer question, the evidence to request, and a common red flag.
Key Partnerships
Whose model and cloud sit underneath the product?
- Buyer question
- Which parts of this product do you build, and which parts do you rent from other companies?
- Evidence request
- A plain diagram of the technology stack: the model provider, the cloud host, and any data processors, with the contracts that stand behind each one.
- Common red flag
- The pitch cannot name the model provider, or the answer changes between meetings.
Key Activities
Do they build and validate, or just sell?
- Buyer question
- What does your team actually do every week: research, validation, integration, or reselling?
- Evidence request
- Validation studies, release notes, and the review process for changes that touch medication use.
- Common red flag
- All the engineering language is about demos and onboarding, and none of it is about testing.
Key Resources
Is your data their key asset? Who owns the tuned model?
- Buyer question
- Who owns the model after it learns from our pharmacy, and what reuse rights do you keep?
- Evidence request
- The data-rights clauses in writing: training use, de-identified reuse, model ownership, and what is returned or destroyed at exit.
- Common red flag
- De-identified reuse rights buried in the master services agreement.
Value Proposition
Stated in operational units, or hype?
- Buyer question
- What outcome do you commit to, measured in hours, error rates, or dollars we can audit?
- Evidence request
- A worked example from a comparable hospital pharmacy with the baseline, the measurement window, and who verified the result.
- Common red flag
- Benefits quoted only as percentages, with no baseline and no reference site available even under agreement.
Customer Relationships
Who answers at 2 a.m.?
- Buyer question
- When the tool gives a wrong answer during a night shift, who do we call and what happens next?
- Evidence request
- The support model, escalation path, response-time commitments, and a real incident report the vendor has handled.
- Common red flag
- Support is a ticket queue, and the error process has never been exercised.
Channels
Direct team, GPO, or EHR marketplace?
- Buyer question
- How do you sell and deliver: your own team, a group purchasing organization, or an EHR marketplace listing?
- Evidence request
- The contracting path, and who holds responsibility for implementation, training, and updates in each channel.
- Common red flag
- Nobody can say who owns the relationship after the contract is signed.
Customer Segments
Are you the customer, or the training data?
- Buyer question
- Are hospital pharmacies your core market, one vertical among many, or the data source for a different business?
- Evidence request
- Customer counts by segment, the product roadmap, and where pharmacy sits in it.
- Common red flag
- Pharmacy is one of twelve verticals on the website.
Cost Structure
Are they viable? Burn rate, loss per query, runway.
- Buyer question
- What does it cost you to serve us, and how long can you operate at that cost?
- Evidence request
- Funding and runway signals, pricing that covers the cost of inference, and continuity commitments if the company is acquired or fails.
- Common red flag
- Each query costs the vendor more than it charges, and growth is the only plan.
Revenue Streams
How exactly do you get charged? Do incentives align with outcomes?
- Buyer question
- How exactly do we get charged, and do your incentives align with our outcomes?
- Evidence request
- The complete pricing model in writing: seats, transactions, savings share, data rights, and every fee that appears after year one.
- Common red flag
- A free pilot that is paid for in data rights.
Six tools you can put to work this week.
Each tool opens here and prints cleanly. No email address, no account, no gate.
Five Categories of AI Risk
One shared language for classifying any proposed AI use. Each category comes with a control question to ask before approval.
- Clinical and patient safety: dosing, interactions, substitutions. Who verifies the output before it touches a patient?
- Data and privacy: PHI exposure, training-data leakage, access. Where does our data live, and who can see it?
- Model and accuracy: hallucination, drift, bias. How is accuracy measured, and how often is it rechecked?
- Operational and workflow: automation errors, over-reliance. What happens when staff stop checking?
- Compliance and regulatory: CMS, HIPAA, state board, CE integrity. Which rules does this use touch, and who owns them?
Vendor Due-Diligence Checklist
Three areas that decide whether a vendor can be trusted with medication-use work.
- Data governance: where PHI lives, retention terms, and a signed BAA.
- Model transparency: validation evidence, known limits, and human-in-the-loop design.
- Accountability: error ownership, audit logs, and exit terms.
FDA loosened its oversight of clinical decision support software in January 2026, which formally shifted the diligence burden to buyers. Ask vendors for the HTI-1 source attributes: 31 disclosures that certified EHR AI must publish. Silence is an answer.
Open the printable checklistOne Approval Gate
A single governance path every AI tool passes through. No shadow adoption.
- Intake: log every AI request in one place.
- Risk review: score the request against the five categories.
- Approve and scope: define allowed use and limits.
- Pilot and monitor: launch with metrics, owners, and kill criteria set in advance.
- Ongoing review: recheck on a set cadence.
Role-Based AI Literacy
Three levels of competency. No technical background required at any level.
- All staff: what AI is and is not, when to verify, and how to flag a concern.
- Pharmacy operators: tool procedures, checking outputs, and escalation paths.
- Governance leads: risk assessment, vendor review, and monitoring.
Incident-Response First Hour
What to do in the first hour after an AI tool produces a harmful or suspicious output.
- Detect: catch the error or the near-miss.
- Contain: pause the tool and limit exposure.
- Report: through one known channel.
- Investigate: find the root cause, not just the symptom.
- Remediate and learn: fix, document, and update the guardrail.
Near-misses are free lessons. Capture them like errors.
Open the printable checklistFirst 90 Days
A realistic sequence for standing up pharmacy AI governance from zero.
- Days 1 to 30: inventory the AI already in use, stand up the approval gate, and name an owner.
- Days 31 to 60: run vendor due diligence, draft the governance policy, and launch literacy basics.
- Days 61 to 90: run an incident-response drill, add monitoring, and report to P&T and leadership.
One vendor. One blank block. One email.
- Choose one current vendor.
The one already in your inbox works best.
- Run the prompt using approved materials.
Use the AI tool your organization has already approved.
- Mark the first unsupported block.
The one with no evidence behind it.
- Send one evidence request.
One specific document, not a general question.
- Name the approval owner.
Every AI decision needs a person who answers for it.
This is already happening.
Every example below is public record. No vendor is named, rated, or criticized here; the point is the pattern, not the party.
of hospitals had actually deployed AI or machine learning in pharmacy, per an ASHP national survey of 1,497 pharmacy directors in 2025. Adoption is early, and the pitches are not.
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Public record, December 2023
A popular chatbot answered only 10 of 39 drug-information questions acceptably
Study presented at the ASHP Midyear Clinical Meeting.
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Public record, September 2024
First state attorney general settlement over a hospital generative AI tool's overstated accuracy claims
Texas. The claims at issue were the vendor's own marketing.
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Public record, October 2024
A transcription AI used across roughly 40 health systems was found inventing content in medical notes
Reported by the Associated Press.
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Public record, March 2026
A major payer's care-denial algorithm was ordered into federal court discovery
The algorithm's design and validation are now litigation evidence.
Primary sources
Start with these. The full source register carries publication dates, review dates, and what each source is and is not.
- Professional resourceASHP Digital Health and Artificial Intelligence Resource Center
- Regulatory guidanceFDA Clinical Decision Support Software Guidance, January 2026
- RegulationONC HTI-1 Final Rule and Decision Support Interventions
- Accreditation guidanceJoint Commission and CHAI Responsible AI Guidance
Built from a conference session, kept for the work after it.
This resource accompanies "AI Risk Management for Pharmacy Leaders: Building Guardrails Before You Need Them", presented by Paul Swider at the Hospital Pharmacy Buyer Conference in August 2026. RealActivity is a healthcare AI company; this page is educational and deliberately does not sell anything.
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