Why Faster AI Submissions Mean Slower Approvals: IQVIA on Regulatory Velocity

Sam: It sounds like a complete paradox, but Pharma and MedTech companies are using AI to generate their regulatory submissions faster than ever, and it is actually leading to more denials and slower approvals.

Maya: That is the core finding we are unpacking today from a 2026 white paper published by IQVIA, titled "Speed Can Mislead: Why Velocity Matters Most in Global Regulatory Affairs"

Sam: Before we dive into why moving faster is suddenly a bad thing, a quick reminder that our voices are AI-generated, and this is a summary of a public document.

Maya: The premise of this paper is fascinating because it challenges the primary reason most companies are buying AI tools in the first place. The expectation for AI in regulatory data has been very straightforward: faster preparation, faster responses, and faster approvals.

Sam: Right, the logic seems bulletproof. You plug your data into an AI tool, it writes the massive submission document in a fraction of the time, you send it to the regulator, and you get your product to market sooner. But IQVIA is saying that is not what is happening in practice.

Maya: Exactly. In practice, speed alone does not deliver better outcomes. The paper notes that submissions are increasing across global markets, but a growing number of those submissions are getting delayed or rejected. Many organizations are moving faster, but they are not moving in the right direction.

Sam: What do you mean by not moving in the right direction? If the AI finishes the paperwork, isn't the submission done?

Maya: That is where the illusion of speed comes in. The paper explains that even when these AI-generated submissions are technically complete, they are full of problems. When reviewers actually look at them, they find inconsistent data, unclear lineage, and huge gaps in the clinical or regulatory context.

Sam: So the AI is just filling in all the blanks to get the document off its desk.

Maya: Precisely. The report states clearly that when AI is optimized for throughput instead of contextual understanding, organizations move faster toward denials, rework, and regulatory risk rather than patient access.

Sam: It is essentially automating the process of making a bad first impression with a health authority. But why are companies pushing so hard for speed in the first place? Why the massive rush?

Maya: It comes down to a massive increase in pressure on regulatory teams. The white paper highlights that product lifecycles continue to shrink. You have less time to capitalize on a product.

Sam: Okay, shorter lifecycles mean tighter deadlines.

Maya: Yes, and at the same time, the nature of regulatory data itself is evolving. The paper lists several innovations that are adding totally new layers of complexity. It mentions software as a medical device, real-world evidence, combination products, and AI-enabled solutions.

Sam: That makes total sense. Figuring out the regulations for a piece of diagnostic software or a combination device is going to be infinitely more complex than a traditional standard submission.

Maya: Exactly. And while the complexity is skyrocketing, many organizations are dealing with limited resources and a shortage of experienced regulatory professionals to handle that workload.

Sam: So they turn to automation and AI to bridge the gap. They need to move faster to keep up with the complexity and the lack of staff.

Maya: Right. On the surface, that should help. But IQVIA notes that the results have been mixed. Submissions are being completed faster, but the approvals are not following. In many cases, approvals are actually slowing down.

Sam: Because the submissions are sloppy. But let's dig into why the AI is making them sloppy. The paper explains that speed-first AI tends to focus on completeness, not correctness.

Maya: That is the crux of the problem. Many of these AI systems are very good at checking whether required sections are present. What they struggle with is determining whether the content actually makes sense.

Sam: It is like checking to see if a book has all its chapters, but not reading it to see if the plot actually holds together.

Maya: That is a perfect analogy. The AI does not ask if the document is consistent or accurate, or if it reflects what regulators expect to see. Because it misses those questions, the submission can look complete on the surface while still containing contradictions or gaps that are very easy for regulators to spot.

Sam: What kind of contradictions are we talking about here?

Maya: The paper gives a great example. It points to inconsistencies between clinical evidence and labeling claims. A speed-first AI might pull clinical data for one section and generate labeling claims for another section, and not realize they contradict each other.

Sam: And the automated checks just wave it through because both sections have text in them.

Maya: Exactly. But when a human reviewer at a regulatory agency looks at it, that contradiction quickly surfaces during the review.

Sam: Wait, the document also mentions that regulators have their own tools now, right?

Maya: Yes, this is a crucial detail. The paper explicitly states that regulators now have access to their own AI tools for consistency checks.

Sam: So it is literally AI versus AI. The companies are using AI to pump out submissions as fast as possible, and the regulators are using AI to scan those submissions for inconsistencies.

Maya: And the regulators' AI will find those gaps instantly. The paper notes that because regulators are using more advanced tools, inconsistencies and issues are being identified earlier and more systematically.

Sam: If I am a health-system leader or someone at a Pharma company, that should terrify me. If you just rush a disjointed document out the door, the regulator's tools are going to light it up like a Christmas tree.

Maya: And the result is brutal for the company. The paper explains that problems that could have been addressed upfront instead appear all at once. This pulls the teams back into massive clarification cycles, additional questions, rework, and delays.

Sam: The exact opposite of what the AI was supposed to achieve.

Maya: Exactly. The pattern becomes familiar: faster submissions, but slower approvals and higher risk. And the paper identifies cross-functional alignment as a major weak spot here.

Sam: Cross-functional meaning the different departments inside the company?

Maya: Yes. A regulatory submission is a massive compilation of inputs. You have clinical inputs, technical inputs, quality inputs, and regulatory inputs. The paper notes that all of these might be correct individually.

Sam: But if you just staple them together without checking if they match, it falls apart.

Maya: Right. They still fail to come together as a cohesive story. An experienced reviewer will see those gaps immediately, but most speed-driven systems will not.

Sam: Saying it needs to be a cohesive story is interesting. It implies that a regulatory submission is not just a data dump. It has to make a narrative argument.

Maya: That is a fundamental issue the white paper raises. Regulations are not just checklists. They are frameworks used to evaluate patient risk, clinical benefit, and real-world use.

Sam: And if an AI is built purely for throughput, it does not understand that framework.

Maya: Exactly. The document points out that when AI is built for throughput, it tends to treat regulations like a series of boxes to check rather than something to interpret and apply.

Sam: And I imagine this problem gets exponentially worse when you scale it up. If a massive global pharma company is doing this, they are making these mistakes everywhere.

Maya: The paper warns about exactly that. It says that at scale, the impact compounds. AI does not just accelerate productivity. It accelerates mistakes as well, including hallucinations.

Sam: Wow. Hallucinations in a global regulatory submission. That is the ultimate nightmare scenario for a compliance officer. The AI just makes up clinical data to fill a required section.

Maya: It is a massive risk. And the paper points out that when these flawed, AI-generated submissions are reused across regions, those issues multiply quickly.

Sam: Because you don't just submit a drug to one country. You submit to dozens. If the foundation document has a hallucination or a massive contradiction, you just sent it to regulators all over the world.

Maya: Exactly. So instead of gaining efficiency, organizations end up managing more scrutiny, more questions, and more rework globally.

Sam: Okay, so speed-first AI is a trap. What is IQVIA's proposed solution here? The title of the paper says velocity matters most. How are they defining velocity?

Maya: The paper makes a very clear distinction. Velocity is not just about moving quickly. It is about moving in the right direction. It is speed combined with intentional direction.

Sam: And what sets that direction?

Maya: Quick note before we carry on. This spot is open for a sponsor. If your company builds or sells AI for healthcare and wants to reach the clinicians, health-system leaders and industry teams who listen to this show, the link to our sponsorship page is in the show notes.

Sam: And now, back to the document.

Maya: Context. In a regulatory setting, that means building submissions that are grounded in clinical evidence, aligned with regulatory expectations, and clearly connected to real-world use.

Sam: So how do you get an AI to understand context? Because right now, the AI just sees words and data fields.

Maya: The white paper argues for a shift to what it calls patient-centric AI reasoning.

Sam: Patient-centric AI reasoning. That sounds like a bit of a buzzword. What does that actually mean at a practical level for the software?

Maya: At a practical level, it means looking at a submission as a whole and asking whether it tells a clear and credible story about patient outcomes. The paper says the AI needs to help answer specific questions.

Sam: Like what?

Maya: Are risks identified and addressed consistently? Does the clinical evidence support the claims being made? Do the technical, quality, and labeling elements align with each other?

Sam: So the AI needs to act as a rigorous editor, not just a fast writer. It needs to look for those cross-functional gaps we talked about earlier.

Maya: Exactly. The document notes that these are the exact kinds of questions regulators ask. So the AI needs to help answer them before the submission goes out the door.

Sam: From a clinical perspective, as a former clinician yourself, this patient-centric reasoning must resonate. Because at the end of the day, all this paperwork is just a proxy for figuring out if a device or drug will hurt someone.

Maya: It resonates deeply. The white paper states that at its core, regulatory decision-making is about one thing: patient safety. Speed only matters if it moves businesses towards that outcome.

Sam: And if the submission is a mess, either an unsafe product slips through, or a safe product gets delayed by months of rework, which hurts patients either way.

Maya: Right. Ultimately, the document says regulatory work comes down to evaluating whether patients and healthcare professionals can trust the product to be safe, effective, and used appropriately. Every global submission must address that fundamental baseline.

Sam: So if an AI cannot help answer those underlying questions, it is just generating noise. But technically speaking, how does a company actually build this kind of patient-centric reasoning into their workflow? It cannot just be a prompt you type into a chatbot.

Maya: No, it requires connecting data across sources, not treating documents as isolated inputs. The paper argues that AI has the most impact when it is built directly into the way regulatory work actually happens, rather than layered on top as a way to just move faster.

Sam: Embedding it rather than layering it. What does that look like in practice?

Maya: The paper points to embedding AI into strong Regulatory Information Management solutions, or RIM solutions. These systems do more than process documents. They work within the full regulatory context.

Sam: So the AI lives inside the database where all the clinical, technical, and quality data is already stored.

Maya: Exactly. This allows the AI to check for consistency across submissions, variations, and renewals. It aligns the claims with the supporting evidence, and keeps regulatory content in sync with quality and labeling.

Sam: That makes sense. If the AI is embedded in the whole system, it can see if the labeling team changes a claim, and instantly flag that the clinical evidence does not support the new claim.

Maya: Right. When this is done well, the paper says teams start to see issues earlier. Potential objections can be identified before submission. Deviations from prior approvals become easier to spot.

Sam: So you catch the errors upstream, internally, before you ever send it to the regulator.

Maya: Exactly. Risks that could affect patients can be addressed upstream instead of showing up late in the review process. And over time, the document notes that these systems get better. They learn from regulatory outcomes and become more effective at identifying patterns of risk.

Sam: That is the holy grail for a regulatory team. Anticipating the regulator's objections before they even make them. But with all this embedded AI doing the heavy lifting, where does the human regulatory professional fit into this?

Maya: The white paper is extremely clear on this point. AI does not replace regulatory expertise. It supports it. The accountability for the accuracy and completeness of a regulatory submission still sits squarely with regulatory professionals.

Sam: So you cannot blame the AI if the regulator rejects your submission.

Maya: Never. The paper emphasizes that the humans are the ones who ensure the submission tells a clear, accurate, and defensible story, grounded in both the product and its intended use.

Sam: AI just helps them do that critical thinking faster by surfacing the inconsistencies and highlighting the risks.

Maya: Exactly. Instead of simply accelerating tasks, AI needs to support the critical thinking of global regulatory affairs professionals. When that happens, the paper argues that teams are not just moving faster, they are moving with more confidence and intentional direction.

Sam: And the ultimate business outcome here? IQVIA obviously believes this patient-centric approach is better for the bottom line.

Maya: Absolutely. Organizations that take this patient-centric approach to AI see clear, measurable improvements. Submissions are accepted more often on the first pass. Review cycles become shorter, without sacrificing quality.

Sam: Which means less rework, less remediation, and lower costs.

Maya: Right. The amount of rework and remediation drops, reducing pressure on internal teams. But just as important, the paper notes that consistency starts to build trust.

Sam: Trust with the regulators.

Maya: Yes. When submissions are clear, aligned, and supported by strong data, regulators have more confidence in what they are reviewing. That translates into smoother interactions and fewer surprises.

Sam: And over time, that means approvals become more predictable. Which is huge for a company trying to plan a product launch.

Maya: Precisely. The paper states that organizations that invest in these capabilities operate very differently. Instead of reacting to issues as they appear, they anticipate them. Instead of moving quickly and fixing things later, they move with more precision from the start.

Sam: And another big advantage of this embedded approach the paper mentions is managing global regulatory changes.

Maya: Yes. Monitoring global regulatory changes and understanding their impact is only possible when the underlying data is clean, controlled, and relevant. Without strong data models, this kind of visibility just does not scale.

Sam: Because if a regulation changes mid-flight, a connected RIM system can tell you exactly which parts of your submission need to be updated. A disconnected, speed-first AI tool wouldn't know where to look.

Maya: Exactly. With the right data foundations in place, AI-enabled RIM solutions can track evolving regulations, assess what has changed, and help teams determine what actions are needed, like adjusting an approved product or planning remediation.

Sam: Okay, before we wrap up, we should address the source of this white paper. This was published by IQVIA.

Maya: Yes, and it is important to note their specific perspective. The document concludes with an overview of SmartSolve, which is IQVIA's AI-enabled, Microsoft Azure-based platform.

Sam: So they sell a platform that does exactly what this paper is recommending.

Maya: They do. SmartSolve helps Life Sciences organizations manage regulatory compliance, centralizing quality processes and managing regulatory submissions. It connects teams, data, and workflows in a single platform.

Sam: So while they have a product in this space, their fundamental argument about the danger of using AI just to write documents faster without understanding the context is incredibly sound.

Maya: The core argument stands regardless of the vendor. The future of AI in regulatory affairs will not focus on speed alone. The most effective systems will be designed to understand regulatory expectations, prioritize patient safety, and work within structured, compliant processes.

Sam: Because at the end of the day, as the paper states plainly, faster submissions do not reduce regulatory risk. Higher quality submissions do.

Maya: That is the most memorable takeaway from the document. The goal is not simply to move submissions faster. It is to achieve approvals more consistently and with greater confidence.

Sam: To recap, MedTech and Pharma companies are learning the hard way that using AI merely for speed leads to disjointed submissions and costly rejections. To actually accelerate market access, they need to shift from raw speed to intentional velocity by embedding patient-centric reasoning into their workflows.

Maya: And remember, you can find a link to the full IQVIA white paper in our show notes. As always, we are here to analyze health-tech documents, but this is not medical advice or regulatory guidance.

Sam: Thanks for joining us on this deep dive into regulatory AI. Make sure to click that link in the show notes to read the source for yourself, and we will catch you on the next episode of Smart Summaries.