Clinical catfishing is what happens when a vendor’s proposal does not match their operational reality. The proposal promises a team, a timeline, and a methodology. Execution reveals different staff, different timelines, and different capability. This is not always malicious. It is often a systemic problem: the team that writes the proposal is not the team that executes the work. The incentive structure rewards winning bids, not honest capability assessment. A vendor can promise excellence in the proposal because the proposal team has no accountability for execution. The execution team operates under different constraints. Kieran Engels has observed this pattern across CROs, sites, and specialized vendors. A proposal names a medical director with 15 years of experience. Execution reveals that the medical director will be reviewing work but the day-to-day team is junior and less experienced. This is not deception. This is misalignment between proposal and execution incentives.

KEY TAKEAWAYS

The Challenge

The problem with clinical vendor selection is misaligned incentives. The person writing the proposal is incentivized to win the bid. The person executing the work is incentivized to deliver profitably. These are not the same person. They often do not even know each other.

A CRO receives an RFP. The business development team responds. The proposal promises a monitoring plan with monthly on-site visits, a dedicated program manager, and a quality review cadence of twice weekly. The sponsor reads this and feels confident about execution oversight.

Then execution begins. The program manager assigned is junior and divided across three programs. Monthly on-site visits become quarterly because travel budgets are tight. Quality reviews are weekly, not twice weekly. The vendor is not breaking their contract. They are operating within its letter but not its spirit. The difference between what was proposed and what is executing is real.

This is not the vendor lying. This is the vendor operating under different constraints than what the proposal assumed.

Let’s be clear about what is happening. The proposal team is rewarded for closing deals. The execution team is rewarded for managing costs. These incentives are misaligned. And the gap between them is where execution problems emerge.

Kieran Engels has seen this across different vendor types. A CRO proposes a team with specific people. During execution, one of the named people leaves for another job. They are replaced with someone less experienced. The CRO fulfilled the contract by providing a replacement. But the execution capability decreased.

A site proposes a patient population and enrollment timeline. During execution, the patient population is harder to recruit than expected. Enrollment slows. The site did not breach the contract. They are operating in a harder environment than what the proposal assumed.

A specialized vendor proposes a novel analysis approach. During execution, the approach proves more complex than anticipated. Timelines slip. The vendor is doing the work they promised. But the delivery is delayed.

These are not vendor failures. These are proposal-execution gaps. And they are endemic to how vendor selection works.

The Infrastructure

The RFP-proposal process assumes that what is written in the proposal will be executed. But proposals are written by the people who sell. Execution is done by the people who deliver. These are different incentives operating on different timelines.

How does a sponsor address this? First, involve the execution team in the selection process. Not the business development team. The team that will actually do the work. This creates alignment. The people writing the proposal are the people who will execute it. Their incentives are the same.

Second, require specific staffing commitments. Not generic promises about a “dedicated team.” Specific names. Specific roles. Specific hours. And contractual protections if the named person is reassigned without cause.

Third, build feedback mechanisms that surface proposal-execution gaps early. A governance infrastructure with weekly feedback means that if the execution team is delivering differently than what was proposed, this becomes visible within weeks, not months.

Fourth, build governance infrastructure that clarifies expectations before execution starts. A proposal that promises a monthly on-site visit plan should result in a written monitoring plan that details which months, which sites, which activities. This forces specificity before the contract is signed.

Seuss+ works with biotech leadership navigating vendor relationships that have proposal-execution gaps. The first response is often to escalate to the vendor relationship manager. The vendor’s response is often technically accurate. But it does not close the gap between what was proposed and what is executing.

The real solution is systemic. Align the incentives of the proposal team and the execution team. Require specificity in proposals. Involve execution teams in selection. Build feedback mechanisms early.

This is the work of buying with your eyes open. It requires acknowledging that proposals are aspirations, not commitments. And commitments require specificity, accountability, and governance infrastructure to enforce.

Proposal vs. Reality

Key Industry Data

70% of clinical trials experience delays, with more than half related to site activation. (Source: Tufts CSDD)

Nearly 80% of trials fail to meet their original on time enrollment targets. (Source: Tufts CSDD)

Cancer center median trial activation time is 167 days, compared to the NCI target of 90 days. (Source: AACI/NCI)

Daily trial delays cost sponsors between $600,000 and $8 million per day in lost revenue opportunity. (Source: Tufts CSDD)

Best performing clinical facilities achieve 5% to 10% operating cost improvements through clinical standardization and productivity gains. (Source: McKinsey)

Frequently Asked Questions

About the Author

Kieran Engels is CEO and Co-Founder of Seuss+, a strategy and execution partner helping biotech sponsors optimize vendor relationships across clinical development. With more than a decade of experience in vendor governance, risk management, and clinical trial execution, Kieran works with biotech leadership teams to build the oversight systems that protect timelines, budgets, and data integrity. Learn more at seuss.plus.

The post Clinical Catfish appeared first on Kieran Engels.

AI-generated RFPs and proposals are becoming common. They sound polished. They are structurally complete. And they are often fundamentally misleading. When a vendor uses AI to respond to an RFP, the sponsor receives answers that are technically correct but operationally empty. The polish masks whether the vendor actually understands the program. The truth is, AI is a tool for efficiency. But vendor selection requires human judgment, specificity, and the ability to detect when a vendor is telling you what you want to hear rather than what is true. Kieran Engels has observed proposals generated by AI that hit every requirement on the RFP yet offered no specific insight into how the vendor would actually execute the work. The proposals looked complete. They were complete. They just were not true. This is not a criticism of AI. It is a recognition that tools are inputs, not authority. AI can make a proposal polished. Only people can make a proposal honest.

KEY TAKEAWAYS

The Challenge

AI-generated proposals are becoming standard practice in vendor selection. A vendor receives an RFP. They feed it into an AI system. The system generates a response that addresses every requirement, uses professional language, and is internally consistent. The sponsor reads it and sees a vendor that understands their program.

But here is what is actually happening. The AI system is reading the RFP and generating text that mirrors the structure of the RFP back to you. It is not generating insight. It is generating reflection.

Let’s be clear about what this means operationally. An RFP asks: How will you manage monitoring in a decentralized trial? The AI-generated response says: We will implement a comprehensive monitoring strategy that leverages remote tools to ensure data quality while minimizing site burden. This is technically correct. It is also operationally empty. It does not say what tools, how often, what happens when something goes wrong, or what this will cost.

A human-generated response from someone who has actually managed monitoring in decentralized trials would say something different. It would say: We manage decentralized monitoring through three touchpoints. First, real-time EDC review with alerts for out-of-range values, flagged within 24 hours of entry. Second, weekly video monitoring calls with sites, focused on high-risk data points. Third, monthly on-site visits to a subset of sites, rotating based on enrollment and data quality trends. This approach costs more than central monitoring but catches problems early.

The difference is specificity. Specificity requires experience. Specificity requires judgment. Specificity is what you need to evaluate whether the vendor can actually execute.

AI-generated proposals optimize for surface-level correctness. They address every requirement. They use language that mirrors the sponsor’s values. They show up on time. But they hide the operational reality beneath a layer of polish.

Kieran Engels has seen this pattern repeatedly. A vendor submits a proposal that sounds excellent. It covers all the bases. The sponsor moves forward. Execution begins. The vendor’s actual approach is different from what the proposal suggested. The monitoring plan is less frequent. The team is different. The escalation path is not what was promised. The vendor is not lying. They are implementing a different approach than what they proposed. But by the time this becomes clear, the vendor has been selected and implementation has started.

This is a selection problem, not a vendor problem. The selection process failed to distinguish between AI-generated polish and operational truth.

The Infrastructure

How does a sponsor address this? Several approaches work. First, ask for specificity. When a vendor proposes a monitoring strategy, ask them to name the monitoring coordinator, describe their experience, and outline the monitoring visit calendar by program month. Specificity is hard to fake. AI-generated proposals dodge specificity.

Second, ask about constraints. Every operational approach has constraints. A monitoring strategy that promises weekly visits has cost implications. It requires staffing. It requires logistical coordination. A vendor that does not acknowledge constraints is hiding from operational reality.

Third, involve the team that will do the work in the selection process. The person who will actually manage monitoring should meet the vendor’s proposed monitoring team. Not the vendor’s business development person. The actual monitoring team. This is uncomfortable for vendors. It is also revealing. This is where operational truth becomes visible.

Fourth, require references that check operational capability, not just vendor viability. Ask references: Did the vendor deliver what they proposed? Were there surprises? What would you do differently? References often reveal gaps between proposal and reality.

Tools are inputs, not authority. AI is a tool. It can make a proposal efficient. It can make a proposal polished. But only human judgment can make a proposal honest.

Seuss+ works with biotech leadership teams navigating vendor selection in an AI-abundant environment. The risk is not that AI will generate false statements. The risk is that AI will generate technically correct but operationally empty statements that obscure whether the vendor actually knows how to execute.

The solution is not to ban AI. The solution is to change the selection process. Make proposals more specific. Require constraint acknowledgment. Involve execution teams. Check references on operational capability.

This is the work of buying with your eyes open. It requires more work than reading polished proposals. But it prevents the expensive surprise of discovering after selection that the vendor’s proposal did not match their operational reality.

AI-Generated vs. Human-Generated RFP Responses

Key Industry Data

The FDA conducted 6,375 domestic researcher inspections between 2007 and 2015, with 360 resulting in significant violations and 194 triggering regulatory actions. (Source: FDA)

FDA warning letters increased 59% year over year, from 190 in FY2024 to 303 in FY2025. (Source: FDA)

The most common FDA inspection violations include failing to maintain accurate case histories (10.82%), enrolling ineligible subjects (8.85%), and failing to perform required tests (8.52%). (Source: FDA warning letter analysis)

Responses to FDA Form 483 observations are due within 15 business days. (Source: FDA)

Only 5% of FY2025 warning letters concerned clinical research or IRB oversight, indicating that when violations occur in clinical settings, they are treated with heightened severity. (Source: FDA)

Frequently Asked Questions

About the Author

Kieran Engels is CEO and Co-Founder of Seuss+, a strategy and execution partner helping biotech sponsors optimize vendor relationships across clinical development. With more than a decade of experience in vendor governance, risk management, and clinical trial execution, Kieran works with biotech leadership teams to build the oversight systems that protect timelines, budgets, and data integrity. Learn more at seuss.plus.

The post When AI Writes the RFP appeared first on Kieran Engels.

KEY TAKEAWAYS

Here’s what most vendors know: Clinical sponsors are looking for vendors that feel right. Polished. Available. Responsive. So vendors optimize for that. They build great decks. They make themselves available for endless discovery calls. They promise flexibility and adaptability.

Understanding the Fundamentals

And then execution starts. The resource that was going to lead your project gets pulled onto something else. The timeline they approved starts shifting because no one was clear on what ‘done’ looked like. The governance structure you agreed to in kickoff never quite takes shape because no one defined what the sponsor’s decision rights actually are.

The problem wasn’t the vendor. The problem was that you were evaluating fit based on signals that don’t matter.

The Real Cost of Misalignment

Operational proof is different. It’s asking: How does this vendor actually work? What are their real constraints? How do they staff projects? What happens when we ask them something that contradicts something else in their proposal?

Let me be clear. The vendors that fail in clinical development aren’t failing because they lack capability. They’re failing because there’s misalignment between what the sponsor needs and what the vendor can realistically deliver. That misalignment is visible in the proposal and in how they respond to constraints. Most sponsors just don’t know how to read it.

Building Governance Infrastructure

At Seuss+, the work is helping sponsors see the governance signals in vendor proposals. A vendor that can clearly articulate their staffing model is more trustworthy than a vendor that promises unlimited flexibility. A vendor that tells you their constraints is more reliable than a vendor that says they can do anything. A vendor whose proposal is internally consistent is more aligned with your needs than a vendor whose proposal shifts depending on the conversation.

This is governance-led vendor evaluation. You’re not asking, “Do we like working with them?” You’re asking, “Is their operational model aligned with how we govern? Can we clearly define what they’re supposed to deliver? Can we hold them accountable for it?”

The Speed Advantage

The vendors that succeed in clinical development are the ones where there’s no surprises in the governance structure. You ask them about their decision cadence, and it matches what you need. You ask them how they handle scope change, and their answer aligns with your change management process. You ask them about continuity, and their staffing model supports it.

Kieran Engels has worked with dozens of sponsors who hired vendors based on best-fit feeling and then spent months in rework and negotiation. Every single time, the signs were in the proposal. The vendor was promising something that contradicted something else. Or they were being vague about staffing. Or their timeline assumed a decision-making pace that didn’t match how the sponsor actually operates.

The other vendors, the ones that deliver on time and on scope, are usually not the most impressive in the pitch room. They’re the ones who asked the most questions about your governance. They’re the ones who said no to things that didn’t fit their model. They’re the ones who were explicit about their constraints.

This is also why vendor failure isn’t really vendor failure. It’s governance failure. If you hired a vendor based on a feeling rather than operational alignment, and they fail to deliver, that’s not because they’re incapable. It’s because you didn’t diagnose what they could actually deliver before you signed.

Governance-led vendor evaluation prevents that. It forces both sponsor and vendor to get clarity on expectations before execution. It creates accountability because the expectations are explicit. And it prevents the months of misalignment and rework that come from discovering too late that the vendor’s operational model doesn’t match your governance needs.

Traditional Vendor Selection Criteria vs. Governance-Led Criteria

Key Industry Data

Daily trial delays cost sponsors between $600,000 and $8 million per day in lost revenue opportunity. (Source: Tufts CSDD)

63% of all new trial starts now come from emerging biotech companies, up from 56% in 2019. (Source: IQVIA)

The top five CRO companies hold more than 35% of total market share. (Source: IQVIA)

Between 50% and 60% of all clinical trial activities are now outsourced to CROs globally. (Source: IQVIA)

The global CRO market reached $79.5 billion in 2023 and is projected to exceed $125 billion by 2030. (Source: Grand View Research)

Frequently Asked Questions

About the Author

Kieran Engels is CEO and Co-Founder of Seuss+, a strategy and execution partner helping biotech sponsors optimize vendor relationships across clinical development. With more than a decade of experience in vendor governance, risk management, and clinical trial execution, Kieran works with biotech leadership teams to build the oversight systems that protect timelines, budgets, and data integrity. Learn more at seuss.plus.

The post Why Best-Fit Vendor Is Dead, And What to Look for Instead appeared first on Kieran Engels.