Why a Structured Scorecard Beats a Gut-Feel Bake-Off

Every pharma organization we speak with runs some version of the same AI vendor selection process. A business sponsor identifies a use case. Two or three vendors get invited to demo. Someone from IT joins the second demo. Quality and information security are looped in when the paperwork lands on someone’s desk. The scoring is a mix of demo pizzazz, existing relationships, and whichever champion had the most political capital that quarter.

That process worked when the tools in question were traditional enterprise software. It does not work for AI, and the reasons are worth naming plainly. AI systems introduce risks that traditional software validation frameworks were not built to catch: models can degrade silently between formal change events, training data can carry provenance defects invisible to the buyer, and the boundary between vendor-controlled logic and customer-controlled configuration becomes blurry the moment you plug in a foundation model.2

A structured scorecard forces three things that a demo-driven process never does. First, it makes vendors answer the same questions in the same words, so comparison becomes possible. Second, it surfaces the questions that quality, security, legal, and integration teams would eventually ask anyway, but earlier in the cycle when the cost of walking away is still low. Third, it produces an artifact your organization can defend in front of a regulator or an internal audit committee. When an FDA investigator asks how you selected the AI tool that generated a document in your submission, “we ran a demo and liked them” is not an answer. A completed scorecard with vendor responses and internal scoring is.

~5% of life sciences firms have realized gen AI as a competitive differentiator with consistent financial value, per McKinsey
77% of companies now factor country of origin into AI vendor selection, according to Deloitte’s State of AI
76% of pharma organizations report being hindered by siloed and outdated data infrastructures when scaling AI, per Deloitte Insights

The scorecard model also creates a shared vocabulary across functions. Business sponsors speak in outcomes, quality speaks in validation deliverables, security speaks in control frameworks, and procurement speaks in commercial terms. Without a shared instrument, each function evaluates the vendor against its own priorities, and the sponsor’s timeline pressure usually wins. A single scorecard used by all four functions from the first meeting forward keeps everyone honest and compresses the total decision cycle rather than extending it.

The Eleven Categories That Actually Matter

Before diving into the 30 questions, it helps to understand why we grouped them into eleven categories rather than a flat checklist. Each category maps to a specific failure mode that has cost pharma organizations real money in the last five years. The categories cascade: earlier ones ask whether the vendor can be trusted with regulated work at all; later ones ask whether the commercial relationship will survive the inevitable ups and downs of a multi-year engagement.

CATEGORY 1

Model Provenance

Who built the model, what data trained it, how the vendor documents training data lineage, and what rights they can prove to that data. The single largest source of downstream IP and bias risk.

CATEGORY 2

Validation Posture

Whether the vendor’s SDLC and change control produce the artifacts your CSV or CSA framework needs, and whether they treat model updates as regulated events or invisible improvements.

CATEGORY 3

GxP Readiness

Concrete demonstration of 21 CFR Part 11 controls, EU Annex 11 alignment, audit trail depth, and whether the vendor understands their role in a regulated computerized system.

CATEGORY 4

Security & IP Protection

Data segregation, encryption, whether your prompts and outputs train the vendor’s model, indemnification against IP infringement claims from model outputs, and breach notification terms.

CATEGORY 5

Integration Approach

How the tool actually connects to Veeva, Benchling, SAP, your document management system, and your identity provider. Whether integrations are supported or DIY.

CATEGORY 6

Pricing Model Transparency

Consumption vs. seat, price escalators, overage terms, whether foundation model pass-through costs are visible or hidden inside a wrapper markup.

CATEGORY 7

Roadmap Alignment

Whether the vendor’s product direction serves your industry or a broader horizontal market, and how much influence you get over their backlog as a strategic customer.

CATEGORY 8

References from Regulated Industries

Not just any customer references, but named references from pharma, biotech, medical device, or comparable regulated environments where the vendor has survived at least one audit.

CATEGORY 9

Team Stability

Tenure of senior engineering and product leadership, financial runway, revenue diversification, and whether the vendor is one funding round away from a strategic pivot.

CATEGORY 10

Sub-Processor Stack

The full list of downstream providers the vendor depends on to deliver the service: foundation model providers, cloud infrastructure, vector databases, observability tools. Each is a risk you inherit.

CATEGORY 11

Exit Clauses

Data return format, migration support timeline, source content preservation, and whether you can leave the relationship without paying a ransom for your own historical inputs and outputs.

Why this order matters: The categories run from technical to commercial, but they also run from “can we trust them at all” to “can we live with them for five years.” If a vendor fails category one or two, no amount of commercial flexibility saves the deal. If they pass those but fail category ten or eleven, you might still buy, but you buy with your eyes open and shorter contract terms.

The 30-Question Scorecard

Each question below is scored 0 to 5. A zero is a non-answer or a red flag. A five is a specific, documented, defensible answer that the vendor can back with an artifact. The full scoring rubric appears in the methodology section further down. The columns show what to look for in a strong answer, the deflection you should expect from a weak vendor, and the follow-up question you use when the initial answer is thin.

Category 1: Model Provenance (Questions 1-3)

# Question Strong Answer Looks Like Common Deflection Follow-Up
1 Which specific foundation model or models does your product depend on, and are you using them off the shelf, fine-tuned, or retrieval-augmented? Named model with version, deployment mode (API vs. self-hosted), and a written explanation of fine-tuning and RAG architecture. “We use best-of-breed models” or “our model is proprietary.” Ask for the specific model card and the URL of the provider’s documentation for that model.
2 Where did the data used to fine-tune or train your model originate, and how do you document lineage and license rights for that data? A written data provenance document identifying sources, license type, whether web-scraped or licensed, and retention of provenance records. “Our training data comes from public sources and is legally cleared.” Ask for a sample data provenance record and the SOP that governs training data intake.
3 How do you handle model drift and version control? When a model is updated, do you notify customers before the change is deployed? Documented model versioning with semantic versioning, advance notification windows, and the option to pin to a specific version for regulated workloads. “We update the model when our provider updates it.” Ask what notice you get, whether you can defer an update, and what happens to your validated configuration when the underlying model changes.

Category 2: Validation Posture (Questions 4-6)

# Question Strong Answer Looks Like Common Deflection Follow-Up
4 What SDLC do you follow, and what validation artifacts can you provide to support our CSV or CSA effort? Documented SDLC aligned with GAMP 5 Second Edition, availability of a supplier assessment package including URS, FS, DS, and test evidence. “We follow agile best practices.” Ask for the table of contents of the vendor’s most recent internal audit report or their ISO 9001 or SOC 2 attestation covering the SDLC.
5 How do you handle change control, and specifically how are model retraining events classified and communicated? Explicit change control SOP that treats model retraining as a regulated change with impact assessment, testing, and customer notification. “Our platform continuously improves.” Ask for a redacted change control record from a recent model update. If they cannot produce one, the change control is aspirational.
6 Do you support a Predetermined Change Control Plan approach for regulated features, or a model card that specifies the boundaries within which the system can change without re-validation? Written PCCP or equivalent boundary document, referenced in the vendor’s product documentation and available to customers pre-purchase. “We can work with you on that.” Ask whether any other customer has implemented a PCCP with them, and if yes, for a sanitized version.

Category 3: GxP Readiness (Questions 7-9)

# Question Strong Answer Looks Like Common Deflection Follow-Up
7 How does your product support 21 CFR Part 11 requirements around electronic records, electronic signatures, and audit trails? Feature-level mapping of Part 11 requirements to specific controls, including whether AI-generated outputs capture the model version, prompt, retrieval context, and human reviewer identity in the audit trail. “We are Part 11 compliant.” Ask for the compliance matrix and specifically what a Part 11-compliant audit trail entry looks like when an AI action generated the record.
8 Have you engaged with EMA’s reflection paper on AI in the medicinal product lifecycle, and how does your product help our organization document AI governance under Annex 22 or comparable frameworks? Named awareness of EMA guidance, product features supporting AI governance documentation, and content librarians who understand the difference between GMP and clinical use cases. “Our tool is designed for pharma.” Ask which specific EMA or FDA guidance documents the product team has read in the last twelve months.
9 Who at your company holds the accountable role for GxP compliance, and how many years have they spent in regulated life sciences? Named person, title, tenure at the vendor, and prior GxP experience at pharma, biotech, or a regulated software vendor. “Our head of quality handles that.” Ask for a LinkedIn URL or bio, and whether that person will be available for customer audits.

Category 4: Security & IP Protection (Questions 10-13)

# Question Strong Answer Looks Like Common Deflection Follow-Up
10 Do our prompts, uploaded documents, or model outputs get used to train any of your models or your sub-processors’ models? Can you contractually commit to a zero data retention posture? Contractual commitment to zero data retention with the foundation model provider, no use of customer data for training, and a specific clause we can point to in the DPA. “We don’t train on customer data.” Ask which specific contract clause captures the commitment, and whether their upstream model provider agreement contains the same commitment.
11 Do you indemnify us against third-party claims that your model outputs infringe intellectual property rights, and is that indemnification uncapped for willful misuse of training data? Explicit IP indemnity clause covering both the software and the model outputs, with either uncapped or generously capped liability for training data claims. “Standard mutual indemnification.” Ask for the redlined language and whether the cap is the total contract value or something smaller.
12 What is your data segregation architecture? Is customer data logically or physically separated, and can we get a copy of your SOC 2 Type II report or ISO 27001 certificate? SOC 2 Type II covering the past 12 months, ISO 27001, HITRUST, or equivalent, with a described logical or physical tenancy model. “We are SOC 2 compliant.” Ask for the actual report under NDA and check for exceptions in the auditor’s opinion.
13 What is your breach notification commitment, both in terms of timeline and detail, and does it survive termination of the contract? Written commitment to notify within a fixed window (72 hours is a common benchmark) with defined content requirements and post-termination survival for at least 12 months. “We follow all applicable notification laws.” Ask what constitutes a notifiable breach in their view and whether they will accept your definition.

Category 5: Integration Approach (Questions 14-16)

# Question Strong Answer Looks Like Common Deflection Follow-Up
14 What integrations do you support natively for Veeva Vault, Benchling, SAP, ServiceNow, or the specific systems in our stack, and how are those integrations maintained through vendor upgrades? Named integrations with version compatibility matrix, dedicated support for keeping integrations current, and a stated position on breaking changes. “We have an open API.” Ask for a customer who runs the specific integration you need and how much internal engineering effort it required.
15 How does your product integrate with our identity provider for SSO and how granular is the role and permission model? SAML and OIDC support, SCIM for user provisioning, and a role model that maps to real regulated use case separations (e.g., author, reviewer, approver). “We support SSO.” Ask whether they support Just-In-Time provisioning and how permissions map to the specific workflow states you need to enforce.
16 What data can we export in bulk, in what format, and can we do so via API on demand without vendor involvement? Documented bulk export via API, standard formats (CSV, JSON, or PDF for records requiring human readability), and self-service capability. “We can arrange an export if you need one.” Ask for the API documentation and the estimated time to complete a full export of a typical customer’s data.

Category 6: Pricing Model Transparency (Questions 17-19)

# Question Strong Answer Looks Like Common Deflection Follow-Up
17 What is your pricing model, and how do foundation model consumption costs flow through to us? Clear disclosure of whether pricing is per-seat, per-consumption, or hybrid, and whether foundation model tokens are marked up or passed through at cost. “Our pricing is competitive.” Ask for a written sample invoice for a customer of your size and how the price would move if usage doubled.
18 What is your annual price escalator, and are there overage charges we should model into a three-year total cost of ownership? Fixed annual escalator (CPI or a stated cap), disclosed overage rates, and a written commitment to notify at defined thresholds. “We handle overages case by case.” Ask for the total contract value at year one, year two, and year three assuming typical usage growth.
19 Do you offer volume commitments, and what happens if we do not consume our full committed capacity? Rollover of unused capacity within a quarter or year, credits toward next term, or a documented true-up process. “Use it or lose it.” Ask what percentage of their customers hit their commitment in year one, which reveals whether the model is set up to punish over-purchasing.

Category 7: Roadmap Alignment (Questions 20-21)

# Question Strong Answer Looks Like Common Deflection Follow-Up
20 What percentage of your revenue comes from life sciences customers, and how does that inform your product roadmap? Specific percentage disclosed under NDA, named life sciences advisory board or user group, and roadmap items driven by pharma-specific requirements. “Life sciences is a strategic focus for us.” Ask for a copy of the last roadmap review presentation shared with life sciences customers.
21 What are the three features on your roadmap for the next twelve months that are most relevant to regulated workflows? Three named features with target quarters, and how each addresses a specific regulated requirement. “We can share our roadmap under NDA in a follow-up.” Push for at least a written summary before contracting; a vendor that will not disclose roadmap directionally is a vendor that will change direction without you.

Category 8: References from Regulated Industries (Questions 22-23)

# Question Strong Answer Looks Like Common Deflection Follow-Up
22 Can you provide three named references from pharma, biotech, or medical device customers running the same use case at production scale? Three named companies with contactable references, ideally including at least one that has been through a regulatory inspection while using the product. “We can provide references under NDA at the appropriate stage.” Ask specifically for references who have hosted an FDA or EMA inspector while running the vendor’s product.
23 Has your product been used to generate content or evidence that appeared in a regulatory submission, and what governance did the customer wrap around it? Case studies (redacted or anonymized if needed) showing the product’s role in a submission workflow with the human review and control steps. “Customers use us in various ways for submissions.” Ask for one specific submission workflow described end to end.

Category 9: Team Stability (Questions 24-25)

# Question Strong Answer Looks Like Common Deflection Follow-Up
24 What is your current funding position and runway, and what percentage of ARR comes from your top three customers? Willingness to share funding stage, months of runway at current burn, and confirmation that customer concentration is below industry alarm levels. “We are well capitalized.” Ask when the last funding round closed and what the announced use of proceeds was.
25 Who are your named senior engineering, product, and quality leaders, and how long have they been in role? Bios with tenure, LinkedIn URLs, and any turnover in the last twelve months disclosed proactively. “We have a great team.” Ask about the last three departures at the VP level or above and why they left.

Category 10: Sub-Processor Stack (Questions 26-27)

# Question Strong Answer Looks Like Common Deflection Follow-Up
26 Can you provide your complete sub-processor list, including foundation model providers, cloud infrastructure, vector databases, observability, and any offshore support? Published sub-processor list on the vendor’s website with notification commitment for changes and geography of each processor. “We have a sub-processor list available on request.” Ask for the URL, the last update date, and how customers are notified of additions.
27 Do you have a business continuity plan for foundation model outages, and can you fail over between providers if your primary model provider has an incident? Documented BCP that names the primary and secondary model provider, with a target time to fail over and evidence the plan has been tested. “We have a business continuity plan.” Ask when the last failover test was conducted and what the observed impact on customer workloads was.

Category 11: Exit Clauses (Questions 28-30)

# Question Strong Answer Looks Like Common Deflection Follow-Up
28 What data return provisions are in your standard contract, in what format, and over what timeframe after termination? Minimum 90 days of transition support, bulk export in standard formats, and no additional fee for standard export. “We help customers export their data at termination.” Ask for the specific contract clause and whether a customer has ever exercised it.
29 What is your data deletion commitment after termination, and can you provide certification of deletion to satisfy our records retention policy? Documented data deletion within a defined window (30 to 90 days after transition), certification available on request, and confirmation that sub-processors follow the same protocol. “We follow our data retention policy at termination.” Ask for a sample deletion certificate and whether it covers backups and archive tapes.
30 Do you retain any customer content, embeddings, or derived model weights after termination, and if so, on what basis? No retention of customer content, embeddings, or derived weights beyond a short transition window, unless explicitly agreed for a defined purpose. “We may retain anonymized data for product improvement.” Ask what “anonymized” means in their view and whether you can opt out entirely without commercial penalty.

Common Vendor Deflections and How to Cut Through Them

Some deflections come up so often that they deserve dedicated attention. These are not signs of a bad vendor. They are signs of a vendor whose sales motion has not yet caught up to what a regulated buyer needs to hear. A good vendor, gently pushed, will produce the specifics you need. A weak vendor will keep restating the same generic answer.

Deflection 1: “We are HIPAA compliant.”

HIPAA is often not the relevant regulation for your use case. Ask instead about the specific pharma or biotech regulation that applies (21 CFR Part 11, EU Annex 11, Good Documentation Practices, GDPR for EU personal data). A vendor who leads with HIPAA has likely sold mostly to healthcare providers or payers and may not have depth in pharma-specific requirements.

Deflection 2: “We take security very seriously.”

Every vendor takes security seriously. Ask instead for the SOC 2 Type II report, the ISO 27001 certificate, and the penetration test summary from the last twelve months. If any of these three artifacts do not exist, security is aspirational.

Deflection 3: “We can provide that under NDA at the appropriate stage.”

Sometimes true, sometimes a stall. If a vendor cannot provide sample validation documents, sub-processor lists, or contract terms before you sign, you are signing blind. Reasonable stages: sub-processor list before purchase order, sample validation package before contract signature, redacted DPA text at first mutual NDA.

Deflection 4: “We built this specifically for pharma.”

Ask which regulations informed the product roadmap in the last twelve months and which pharma customers sit on the product advisory board. A vendor that claims pharma focus but cannot name a pharma-specific feature shipped in the last quarter is probably horizontal software with a pharma logo on the deck.

Deflection 5: “The model doesn’t hallucinate for our use case.”

All large language models can hallucinate. The relevant question is not whether hallucinations occur but how the product design constrains their impact. Ask about retrieval grounding, citation requirements in outputs, human review checkpoints, and how the product measures accuracy on a representative test set.

Deflection 6: “We’re working on that.”

“Working on that” is neither a yes nor a no. Ask for a target quarter, a written statement of the feature scope, and whether it will require a contract amendment or price increase. If none of the three can be provided, “working on that” means “not on the near-term roadmap.”

Scoring Methodology: Turning Answers into a Decision

A scorecard without a scoring methodology is a checklist. To be useful, each of the 30 questions gets a numerical score, categories get weightings, and thresholds get set in advance so that no one is arguing about goalposts after the demos are done.

Per-Question Scoring (0-5)

0

No answer, or evasion

The vendor did not answer, provided a non-response, or refused to engage. Any zero should trigger a follow-up before the final score is set; a persistent zero is a disqualifier for regulated use cases.

1

Generic marketing answer

The vendor referenced a general capability but could not point to a specific artifact, clause, or example. Common with “we are compliant” answers that cite no evidence.

2

Verbal specifics, no documentation

The vendor described specific processes or controls, but did not produce a document, screenshot, or contract clause. Common in early sales conversations.

3

Documented but not customer-tested

The vendor produced documentation, but you cannot confirm the process is followed in practice. Common when the vendor is new to pharma and building the SOPs as they go.

4

Documented and demonstrated

The vendor produced documentation and showed you an example of the process in action, either through a system screenshot, a redacted change control record, or a customer reference confirming the practice.

5

Documented, demonstrated, and audit-tested

The vendor produced documentation, showed you an example, and provided evidence that the process survived either an external audit (SOC 2, ISO 27001, customer regulatory inspection) or an internal audit within the last twelve months.

Category Weighting

Not all categories deserve equal weight. For a regulated pharma use case (a system generating content that enters a submission, a GxP-adjacent workflow, or anything touching pharmacovigilance), we recommend the following weights:

Category Weight Rationale
Model Provenance15%Foundational: sets the ceiling on IP, bias, and regulatory risk.
Validation Posture15%Determines whether you can validate the system without rewriting the vendor’s SDLC.
GxP Readiness15%Whether the system can survive an inspection.
Security & IP Protection12%Where the largest downside financial risks live.
Integration Approach10%Where hidden implementation cost hides.
Pricing Model Transparency8%Impacts three-year TCO and renewal leverage.
Roadmap Alignment7%Determines whether the vendor grows with your needs or away from them.
References7%Independent verification of everything else.
Team Stability4%Where multi-year contract risk lives.
Sub-Processor Stack4%Inherited third-party risk.
Exit Clauses3%Where the exit option costs get set.

For non-GxP internal use cases (a marketing chat assistant that never touches regulated data, an internal research summarization tool), you can flatten the weighting: reduce Validation Posture, GxP Readiness, and Model Provenance to about 8% each, and shift the freed points to Integration, Pricing, and Roadmap. The scorecard structure stays the same; what changes is what you optimize for.

Thresholds and Decision Rules

Suggested decision rules for a regulated use case:

Vendors scoring below 60% on the weighted total do not proceed. Vendors scoring above 80% proceed to due diligence. Any category scoring below 40% triggers a governance review even if the total is passing; a strong vendor in most areas with a critical gap in one is often more dangerous than a mediocre vendor across the board, because the gap is easier to overlook. Any single question scoring zero on Model Provenance, Validation Posture, or GxP Readiness is a hard stop until resolved.

Running the Process Without Burning Six Months

A scorecard is only useful if the process around it moves. Here is a lean cycle time we have seen work when the business need is urgent and the buyer is disciplined. The total elapsed time is roughly ten to twelve weeks from long list to signed contract, which is realistic for a mid-sized pharma buying an AI tool for a well-scoped use case.

1

Weeks 1-2: Long list and screening

Assemble a long list of six to twelve vendors. Send the 30 questions in writing as a written questionnaire with a two-week response window. Score the responses and shortlist to three or four vendors. Vendors who cannot answer at least 20 of the 30 questions in writing within two weeks are out.

2

Weeks 3-4: Deep dives with the shortlist

For each of the three or four shortlisted vendors, run a two-hour deep dive with product, quality, security, and integration teams present. Focus on the questions that scored below a 3 in the written round; use the follow-up columns to force specificity.

3

Weeks 5-6: Proof of value

Run a bounded proof of value with the top two vendors, using your data on your infrastructure, against a defined success metric. The POV should not be more than three weeks; if a vendor needs longer to show value, the value is not there.

4

Weeks 7-8: References and commercial diligence

Talk to at least three named references per finalist. Complete financial due diligence, insurance certificates, sub-processor validation, and legal review of the MSA and DPA.

5

Weeks 9-10: Negotiation and signature

Finalize the commercial terms, exit clauses, and validation deliverables schedule. Confirm the internal governance body has reviewed the completed scorecard. Sign.

The scorecard is not the whole process, but it makes each stage more efficient. When quality and security join the process in week 3 rather than week 8, they are reacting to a documented set of answers rather than starting from zero. When procurement negotiates in week 9, they are pushing on the specific clauses the shortlist analysis flagged. The scorecard becomes the shared artifact that keeps everyone moving in the same direction.

Red Flags That Should End a Conversation Early

Some responses should not just lower a vendor’s score. They should end the conversation. These are the patterns we have seen precede an expensive failure in the last three years of pharma AI deployments, and they are worth naming explicitly so a scoring team can walk away without agonizing.

Red Flag 1: Refusal to disclose the underlying foundation model

Any vendor that will not name the specific model behind their product is either hiding a dependency that could break their business (concentration risk on a single provider) or does not understand how their own stack works. Neither is a partner for a regulated workload. Provenance transparency starts with model transparency.3

Red Flag 2: Aggressive posture on customer data for model training

A vendor whose default contract lets them use your prompts, uploads, or outputs to improve their model is a vendor whose commercial motion is at odds with pharma’s confidentiality expectations. Zero data retention with the foundation model provider should be a default, not an upcharge.4

Red Flag 3: No named accountable person for GxP

If the answer to “who at your company owns GxP compliance” is “we have a shared responsibility model” or “our head of engineering handles that,” the vendor has not built quality into the organizational structure. A regulated buyer needs a named, tenured, contactable counterpart. Absence of one is a sign the vendor’s investment in regulated markets is superficial.5

Red Flag 4: Refusal to share sub-processor list

Sub-processor lists are standard disclosure in modern enterprise software. A vendor that will not disclose theirs is either hiding a concentration risk, using a provider they do not want you to know about, or does not maintain an accurate list. All three are third-party risk management failures you inherit.6

Red Flag 5: Data return that requires vendor cooperation to be usable

Some vendors export your data in proprietary formats that require their tools to read. Others charge substantial “transition assistance” fees. Others simply have no self-service export. All three create exit friction that erodes your leverage at every renewal. Look for standard formats, self-service capability, and a documented transition timeline in the base contract.7

Red Flag 6: No customer has ever hosted a regulator with their product in scope

You do not need every vendor to have been through an inspection, but for regulated use cases you need at least one customer who has. A vendor whose entire book of business is either pre-inspection or non-regulated is not a vendor whose Part 11 mapping has been tested. You will be the test.

None of these red flags mean a vendor is a bad company. They mean the vendor is not ready for a pharma buyer with a regulated use case. A vendor that would be a fine partner for a horizontal enterprise buyer can still fail these tests, and the fair thing is to say so early and not waste the vendor’s time or yours running a POV that will not survive due diligence.

Conclusion

The pharma AI vendor market will get more crowded before it gets clearer. Foundation model providers will continue to ship new capabilities every quarter. Wrapper products will continue to emerge, some strong and durable, others fragile. Consultancies will continue to bring point solutions to the market. The buyers who make the fewest expensive mistakes will not be the ones with the best market intelligence; they will be the ones with the most disciplined evaluation process. A 30-question scorecard, honestly answered and honestly scored, is that discipline in an artifact.

Sakara Digital works with pharma and biotech organizations who are trying to bring AI into regulated workflows without lowering the bar on quality, validation, or their commercial position at renewal. If you are running an AI vendor evaluation and want an independent perspective on how your scorecard, weightings, and shortlist decisions would hold up in front of your quality and audit committee, we are happy to have that conversation.