Operations Utopia: Striving for Practical Excellence in Life Sciences Operations

Jake Doran is SVP, Client Success & Transformation at MResult, where he helps healthcare and life sciences organizations turn data, AI, and technology ambition into operating impact. He has more than 25 years in life sciences technology. Most recently he was Vice President, Digital and Head of IT at Lykos Therapeutics (formerly MAPS PBC), leading technology as the company moved from clinical research startup toward commercial readiness. Before that he was Head of Global R&D IT at Bausch Health, and Chief Technology Officer for the Regulatory Affairs business at Genpact, where he launched one of the industry's first AI-powered intelligent automation platforms for Regulatory Affairs.

Jake founded LiST Innovations, a consultancy focused on regulatory data standards, governance, and RIM transformation planning, and still takes select advisory work through it, including a fractional CIO role at an emerging biotech. Earlier he spent eight years at Janssen (Johnson & Johnson), where he led a regulatory platform implementation that consolidated 21 legacy systems, and seven years at Schering-Plough managing Regulatory CMC Publishing & Technology. He has served on the Board of Directors of the IRISS Forum and holds a bachelor's degree in Biology from Fairleigh Dickinson University.

Small companies, big advantage? Jake argues that early- and late-stage clinical companies can build an AI-forward organization in a way legacy enterprises can't. Legacy companies all have systems now, but they lack process discipline, standards adoption, and enforcement of people actually putting data into those systems. In his framing, AI doesn't fix garbage in, garbage out. It just lets you see the mess faster.

Why start with the chaos? Matt calls change management the bigger issue. He floats a reference-modeled starting database, even a templated spreadsheet, where maybe 60% of data points are identical across every pharma company. His question for Jake: when you walk into a blank-slate startup with a small budget and one product, is information management even a priority?

Pick your mess, and when to pay for it. Jake's answer is "yes and no." Manufacturing has to be buttoned up early, whether in-house or through a CDMO, because batch records and product release are coming. The shared drives, Gmail accounts, Box, and Dropbox sprawl from a company's early days? Some of that just gets pushed aside. Why does the industry let startups suffer first? His simple answer is budget and resources. At some point, maybe late Phase 2, a company becomes an acquisition target or a likely approval and has to clean house. Until then, why buy an eTMF when you can build a folder structure that mirrors the TMF Reference Model yourself?

The shed with no floor. Matt's metaphor: nobody outside a regulated industry would throw up a plywood shed, forget the floor, then bolt on a kitchen and electrical. Jake's addition: you don't get to go back and add the floor. You buy the next parcel of land and build it again. He calls data remediation probably the most expensive activity the industry goes through.

AI-assisted cleanup, with a human in the loop. Jake walks through migrating a home-built TMF into a real system. Train an LLM on the reference model, run your extract through it, have someone compare the output against your master data model to catch hallucination, then document and validate the whole process before loading it into a validated system. That's far easier now than it used to be.

Standards take forever. Jake works closely with the IRISS Forum and talks standards constantly. He recalls a roughly 10-year standards initiative presented at the DIA RSIDM Forum in February, and can't see why it takes a decade to define standards for things the industry has done for 100 years. Matt argues the industry keeps letting perfect be the enemy of good. Ship a good-enough standard, update it annually, stop handing out five-year adoption windows. Jake names the root cause as bureaucracy and fiefdoms. Functions do things their own way, throw it over the fence, and let regulatory piece it together. The real "secret sauce," in his view, is CROs, sponsors, academia, and health authorities working in the same environment.

The patient angle. Matt recalls a DIA panel 15 to 18 years ago declaring patient records would never be shared. Now we hand our data to banking apps, social media, and AI tools. Jake notes that de-identification isn't that hard, asks why large practices don't run closed-loop record sharing, and admits he uploads his ring, scale, and physical results to his AI companion anyway.

Clever isn't enough. Jake is bullish on AI. He cites a recent article on clinical control towers that pulled data from disparate systems into one view, and argues dashboards give point-in-time visibility, not trends, analytics, or checks against protocol expectations. He describes an AI conference in New York where people claiming healthcare expertise couldn't explain what a protocol is. AI still has to be defensible, traceable, and backed by an audit trail. And just as the industry moved from data centers to cloud faster than anyone expected, he thinks the jump to predictive, connected analytics is coming sooner rather than later.

"AI just accelerates how much faster you can see the mess, right? But it... doesn't fix the mess." — Jake Doran

"They'll deploy Copilot to 75,000 people, which may help you draft an email and schedule a meeting and whatnot." — Jake Doran

"Why start with the chaos? I guess is my point." — Matt Neal

"I can build a folder structure anywhere that replicates a TMF reference model. So why would I go buy?" — Jake Doran

"You make bad decisions initially; they just get compounded over time." — Jake Doran

"I don't understand why it takes 10 years to define standards for things that we've been doing for 100 years." — Jake Doran

"I can remember vividly conversations that I had in previous career where we couldn't come to agreement internally on what we call the product." — Jake Doran

"Everybody do what you need to do, throw it over the fence, and let regulatory piece it all together." — Jake Doran

"We know as technologists that the limitation is not technology." — Matt Neal

"It can't be just clever, right?... It still has to be defensible. It still has to be traceable." — Jake Doran

Quotes are verbatim from the transcript; ellipses mark removed filler or repeated words.

Heads of regulatory operations and RIM, especially anyone building the function at an early-stage or single-asset biotech. Clinical operations and TMF leads weighing build vs. buy. IT and digital leaders in life sciences deciding where AI actually moves the needle. Standards people at IRISS, DIA, and CDISC who are tired of decade-long timelines. And founders and executives who will eventually pay for the cleanup they skip today.

Guest

• Jake Doran on LinkedIn

Organizations & Standards

• IRISS Forum — non-profit for implementation of regulatory information and submission standards
• CDISC TMF Reference Model — industry taxonomy for Trial Master File content, now under CDISC

Events

• DIA Regulatory Submissions, Information, and Document Management (RSIDM) Forum 2026 — February 2–4, 2026, North Bethesda, MD

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

John Jones is the founder and creator of the Nidura platform. He is Chief Executive Officer of Entitech Solutions and SVP, Head of Life Sciences Consulting at Tandym Group. Earlier roles include Practice Lead for IT Strategy & Implementation at Quintiles, Vice President of Application & Integration Solutions at HighPoint Solutions, Solution Partner for Life Sciences at Business Edge Solutions, and IT Manager at Aventis Pharmaceuticals. He started out as a mechanical engineer at Ford Motor Company and holds a BS in Mechanical Engineering and an MBA from Rutgers. John spent a good five years working on the DIA RIM Reference Model alongside Bala Balasubramanian, Joel Finkle, and others. Find him on LinkedIn.

The model never changes. The plumbing always does. (1:49) John has built data platforms on SQL Server and Oracle, then Redshift and Azure, now Databricks and Snowflake. His observation: the technology keeps changing, but the data and the way the business transacts it do not. The time goes into mapping today's source system into the model, and governance, data quality, and compliance get dropped when that mapping gets hard. Nidura is his "easy button on the plumbing": standard data models at the core, a data mesh framework, and business-owned data products.

The RIM Reference Model as the foundation (5:56) Following Bala's visit in OU-007, John describes the working group spending an entire night on a single field because the field needed it. That work gave Nidura a model to make physical. He uses CDISC's USDM the same way for the clinical model. He notes the RIM Reference Model documentation is now publicly available on the DIA website.

Why canonical models get a bad name (8:06) John thinks people see the value. The problem is that the word "model" makes it sound like an IT-led initiative, the work gets stuck in technical analysis paralysis, and ambitious projects fizzle. His view is that it only works with business people driving.

AI stumbles where people stumble (11:56) Matt argues there's a false perception that AI will do everything without the plumbing. John agrees: poorly defined, poorly annotated data confuses AI the same way it confuses a person. Companies pull off a few use cases and end up with tooling overfit to one situation, something he saw in the vendor booths at DIA Global this year. He favors a context layer available to every prompt over prompt engineering for one question at a time.

Getting the human out of the loop (23:06) John compares it to trusting a new employee. Accuracy goes up when AI makes lots of little decisions on small, precise, well-annotated pieces of information. He suspects ever-larger context windows make people lazy, and points to structured content such as ePI-formatted labels and structured authoring as the path.

Side quest: tokens, gyms, and pickleball (26:01) Matt compares token pricing to old cell phone calling areas and pitches a flat gym-membership model. John thinks tokens are here for a while, that compute gains are flattening, and that token efficiency is the next wave. With AI built into everything, he expects pickleball, not the gym. Matt notes that PDF versus text changes the token count, and that the "new" advice to build folder hierarchies with MD files is the folder structure we already walked away from.

A second value prop, and the same old hurdles (38:10) Standard models used to be a reporting and analytics pitch. Now AI needs the same data, which opens up delegating work: generating documents from data, or checking documents against data so XEVMPD submissions match SmPCs. The hurdles haven't moved, though. People working on the data still need to understand it, and John says newer platforms let you house more data and get to it faster without solving that.

18 months versus a button press (44:55) John's estimate for building a regulatory data hub from nothing: a couple of months on a physical RIM Reference Model, six to eight months of ETL from Veeva, a couple more of testing, then six months of edge cases. Nidura pre-built that, and he says it happens in the first hour of operation.

What users get (46:36) Three things. Ask the data questions through an AI console, with your own model or one provided, and get answers in RIM Reference Model terms. Build reports and dashboards from data products the business controls, including who gets access, how (JDBC/ODBC, API, MCP, download), and row-level security. Feed other systems for integration. Consumers can raise issues back to the data owner, and owners can publish the quality criteria a data product is meeting.

"They're the first things to go out the window when the plumbing becomes difficult." (John, 1:49, on governance, quality, and compliance)

"We would spend an entire night talking about a field, right? Because it needed to be talked about." (John, 5:56)

"The model is just a way that the business transacts data... It's not a technical thing." (John, 8:06)

"It's really a peanut butter and jelly type situation." (John, 13:55, on the RIM Reference Model and language models)

"AI doesn't complain, but it complains in the form of wrong answers." (John, 17:16)

"The plumbing has to be there first to guide AI." (Matt, 9:35)

"I think it's going to create more of a pickleball style model than a gym membership." (John, 34:53)

"We're not in the age yet where the report is dead or the dashboard is dead." (John, 47:10)

"That's honestly what I've been building for the last 30 years. And I'm like, wow, I'm building the same thing over and over again." (John, 52:01)

Regulatory operations and RIM leaders who are tired of funding the same data hub twice. Regulatory data folks working with the RIM Reference Model. IT and data teams building regulatory data products on Databricks or Snowflake. Anyone being asked to "just put AI on it" before the data is defined.

Guest and platform: John Jones on LinkedIn. 

DIA RIM Reference Model: V2.0 User Guide. DIA Global Forum overview. RSIDM 2025 session on V2.0, where John presented the deep dive.

Standards mentioned: CDISC USDM. EMA electronic product information (ePI). EMA XEVMPD.

Technology mentioned: Veeva RIM. Databricks. Snowflake. Copilot.

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

Megha Sinha is the founder and CEO of Kolter AI, an autonomous regulatory planning platform for life sciences, and managing partner and CEO of Kamet Consulting Group, which she has run since 2023. She has spent 17 years inside major life sciences lifecycle-change programs — M&A integrations, divestitures, tech transfers, post-merger rebrandings, and CMC changes across global portfolios — across 125+ cross-functional engagements. Eight of those years were at PwC, where she built and led the firm's Life Sciences Regulatory Consulting practice (Health Industries Strategy and Transformation, Global Regulatory Consulting Solutions Leader), growing it from a single capability into a cross-platform business and supporting engagements including a $63B pharmaceutical acquisition, a $25B pharma integration, and a $2.7B divestiture. At Kamet, she has led regulatory strategy for a $14B global healthcare spin-off — rebranding across 100+ countries, 45,000 SKUs, and 10,000+ label redlines — and a $14.6B consumer health carve-out. That spin-off-scale problem, building a plan by hand across tens of thousands of SKUs, became the seed for Kolter AI. Earlier in her career she held regulatory affairs and quality roles at Stryker and MD Resource Corp. She holds an MS in Biomedical Engineering/Regulatory Affairs from the University of Southern California and a BE in Chemical Engineering from R.V. College of Engineering, Bangalore, and is RAC certified.

From billing hours to giving it away. Megha built the first version of what became Kolter as an internal automation engine for her own consulting practice — the kind of tool that could have easily billed a client for a week of work but instead took a matter of hours. Rather than protect that as a competitive edge, she chose to build a product around it, calling it "cannibalizing my own consulting business."

Why the intelligence layer was the hardest part to build. The early architecture put PDFs behind a retrieval-augmented generation (RAG) layer, but Megha found that LLMs don't read documents consistently or deterministically. The team moved to a structured database schema and roughly 38 small, purpose-built computation engines instead — keeping the deterministic math and rules out of the model's hands entirely so the same input reliably produces the same output.

Modular by design. An early beta produced an entire cross-functional plan — regulatory, labeling, supply chain — in one shot. Client pushback ("I don't want to tell supply chain what to do") led Megha to split Kolter into four or five separate agents, so each function can pull only the modules it wants.

Trust in increments. Megha's current framework: administrative decisions get automated outright, genuinely ambiguous regulatory judgment gets escalated to a human, and once a human has approved the same kind of mitigation enough times, the system starts presenting it as a precedent to accept rather than a question to answer from scratch. She frames this as a recursive self-improvement loop, with human-in-the-loop as today's checkpoint rather than a permanent one.

Strategic advantage versus tribal knowledge. If most of the industry ends up running the same planning engine, what's left to compete on? Megha's answer: the mechanics of building a plan quickly stop being a differentiator once they're commoditized. What stays proprietary is company-specific tribal knowledge and negotiated precedent, which customers can feed back into the platform as custom computation logic without a code change.

The cross-functional bottleneck hiding behind regulatory timelines. Megha points to the EMA's pilot on parallel post-approval-change submissions across reference and rest-of-world markets as an example of a shift the industry isn't operationally ready for: if health authorities start approving submissions in parallel, the burden shifts overnight from regulatory drafting to manufacturing and supply-chain readiness — and most organizations have no current line of sight into those downstream implications.

What's next. Megha describes early interest in extending Kolter into clinical — protocol amendments, IRB changes — and eventually letting agentic systems handle regulatory data entry directly, freeing operations teams for higher-value, patient-facing work.

Megha Sinha, on why she thinks regulatory belongs at the strategy table: "I truly believe that the regulatory folks in our industry are the true strategists... we take a drug and asset and build strategy, right? So we are true problem solvers."

Megha Sinha, on the design choice to keep LLMs out of the decision-making: "LLMs are pretty bad in math... I don't want them to make the decision, right? Because I wanted when I give the same input, I get the same output."

Megha Sinha, on choosing to build a product instead of protecting a consulting revenue stream: "I'm cannibalizing my own consulting business in some respect, but the expertise that I've developed over the years that will stay with me... I want to democratize it."

Megha Sinha, on where she draws the line for AI agents today: "If you are not sure, don't make the decision, right?"

Megha Sinha, on the industry's biggest objection to a shared planning engine: "Why would people want to give me their tribal knowledge? You know, that's their secret sauce, right?"

Matt, on why he named the podcast Operations Utopia: "If the patient is the point, and speed is the point, we should not have any excuse. We are obligated to not waste time if we don't have to."

Regulatory operations leaders, RIM and submission-planning teams, life sciences data and technology leaders evaluating AI for compliance-critical workflows, and founders building vertical AI tools for regulated industries.

Guest & Company

• Megha Sinha — LinkedIn
• Kamet Consulting Group — LinkedIn
• Life Science Success Podcast — Transforming Pharma Regulatory Operations with Megha Sinha

Related

• European Medicines Agency — Using EMA Assessments for Reliance in Post-Approval Changes: Insights from PAC Reliance Pilots

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

Leslie LaCosta is a fractional operations leader who embeds directly with client teams to fix broken processes and remove friction in customer experience, rather than handing over a deck and walking away. She spent 31 years in retail, working her way from a store-level associate to district manager to nearly 13 years at Bed Bath & Beyond corporate — where she organically built and ran the company's Project Management Office across roughly 1,500 stores — before spending over six years at Party City in store operations and customer experience. She holds a patent for point-of-sale coupon-recovery technology. She now runs her own consulting practice and posts short-form operations content that's built a following for calling out what most operators only say quietly in meetings.

Connect with Leslie on LinkedIn.

Note: Leslie mentioned her TikTok content directly in the episode, but we don't have a verified handle to link here — if you're listening, Leslie, send it our way and we'll get it added.

From the sales floor to the boardroom. Leslie describes going from part-time retail work at 15 to store manager at 18 to nearly three decades of climbing every level of retail operations without ever leaving the industry — and why that ground-level experience never left her, even years into corporate strategy roles.

Building the PMO — and seeing the cracks. When Bed Bath & Beyond hit a ceiling on store growth and started acquiring brands (Buy Buy Baby, Christmas Tree Shops, Harmon), leadership needed someone with enough internal credibility to run cross-functional initiatives and report straight to the C-suite. Leslie got the job organically — and the seat gave her an unfiltered view into leadership's thinking that eventually told her the company was headed for trouble.

Why stacking a transformation on someone's day job doesn't work. Leslie's core operating belief: if you're calling something a transformation, you need to bring in dedicated (often temporary) resources for it — not ask an SVP to run their business unit, lead the initiative, and project manage the cross-functional team, all as one job.

The fractional model, on purpose. Leslie built her post-corporate career around embedding temporarily, moving fast because she doesn't own the politics or the day-to-day, and leaving once the fix sticks — a deliberate contrast to being pulled into the org chart permanently.

Point of sale, coupons, and designing from a distance. Leslie tells the story of a Bed Bath & Beyond coupon bug that was refunding customers more than they'd paid — and why sitting between IT, finance, and the register (rather than just taking a requirement and building it) was the only way to catch it before it hit stores.

Standing behind the register. Every six to eight weeks, Leslie would pull the most disconnected corporate stakeholders out of their meetings and put them behind an actual register for an hour, working real customers. She'd found someone in charge of a process who'd never once seen it run. Her fix wasn't a training deck — it was making them stand there.

The light bulb, not the lecture. Leslie doesn't tell frontline staff what to do — she asks questions until they arrive at it themselves. She walks through how she gets buy-in on something as small as scanning a coupon by tracing the chain all the way to the employee's own bonus, because "everybody wants to know what's in it for me."

AI as a tool for separating the science from the art. Leslie frames operations as having both a black-and-white, executable "science" side and a harder-to-replicate "art" side — reading people, seeing the pattern, knowing what to say to whom. Her take: AI's value is taking the science off her plate so the art has more room to work, not replacing the art itself.

"We're too busy to get good." Both Matt and Leslie return to this as the excuse that quietly runs most operations teams — the two hours you don't have time to invest now costing five hours a week forever.

"I never forgot where I came from, because every single block built on the previous one." — Leslie LaCosta

"If you're really willing to call it a transformation, you need to bring in temporary resources to support it for a distinct amount of time, and then let them leave." — Leslie LaCosta

"I'm here for a good time, not a long time." — Leslie LaCosta

"You can't pressure test anything until you're standing in front of a customer who's in a rush." — Leslie LaCosta

"By the time they walked out of the store, they would usually go like, 'Holy shit, that's hard.'" — Leslie LaCosta

"Everybody wants to know what's in it for me." — Leslie LaCosta

"Everybody perceives operations as always science, because it's mostly black and white. But there is an art to it." — Leslie LaCosta

"If AI can take some of the workload of the science off of my plate... that to me is all it does is make me better and lets me shine on what I bring to the table." — Leslie LaCosta

"Miraculously, when it comes time for me to take PTO, somehow I was able to take five business days off, and it all still ran." — Leslie LaCosta

Anyone running or absorbing the fallout of a cross-functional transformation initiative; regulatory operations and RIM leaders who manage vendor relationships and system change control; operations and CX leaders trying to close the gap between headquarters intent and frontline reality; anyone deciding whether their next hire should be permanent or fractional; and anyone using AI to offload repeatable work so they can spend more time on judgment calls.

• Leslie LaCosta — LinkedIn, TikTok
• The Metamorphosis Studios Real Experience. Real Transformation. Really Different. Business Consulting and Services

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

Francisco (Frank) Nogueira is CEO of Accumulus Technologies, where he leads the continued evolution and global adoption of the Accumulus Platform, helping transform regulatory collaboration to accelerate patient access to medicines. Prior to leading Accumulus Technologies, he served as Chief Executive Officer of the nonprofit Accumulus Synergy. He joined Accumulus in 2020 from Genentech, where he served as he served as Vice President of Regulatory Affairs at Roche. Accumulus Technologies is reimagining how life sciences organizations and national regulatory authorities work together to accelerate the drug development lifecycle, from drug discovery through global availability. Connecting industry to 75+ regulators around the globe, the Accumulus platform is a secure, purpose-built, SaaS platform that enables real-time collaboration and continuous data streaming - powering faster decisions, global alignment, and accelerated access to therapies for patients worldwide. 

The four connective tissues. Frank's frame for the last thirty years: the internet connected people, cloud connected enterprises, AI is connecting knowledge, and the next layer — the one still missing — is connected scientific evidence. That is what Accumulus is building. Frank says the community today is "data rich but information poor," and the volume of scientific evidence generated worldwide has become astonishing. Somebody has to move it, securely, in real time, without waiting three years per region.

The collision course. Large pharma is betting hard on AI, generating more data than ever, presenting it more ways than ever, with fewer people. Meanwhile some regulators are still receiving paper dossiers. Frank thinks the gap between leading and lagging regions doesn't just persist — it accelerates unless the digital infrastructure catches up. Gene and cell therapies mean smaller trials with smaller patient populations but larger information volumes per submission. His prediction for the next decade: regulator workload goes logarithmic.

Trust, reliance, and the manufacturing change problem. Frank spends real time on the reliance framework — one regulator leaning on another's review work. Manufacturing changes today can take three to five years to work through global regions, forcing companies to maintain parallel manufacturing lines or accept country-level shortages. What Accumulus is seeing on the platform: regulators watching those changes in real time, seeing the questions and answers as they happen, and concluding "they did everything we were going to do. They asked every question we were going to ask. In fact, they asked more. There's nothing for us to wait three years for."

Sovereignty is not the same as isolation. Frank is clear he isn't arguing for one mega-regulator. Each regulator maintains sovereignty and independence. But sovereignty is compatible with reliance, work-sharing, and specialization. Five countries in a region don't need five reviewers doing the same work in parallel — they can carve up the modules. Low-to-middle income regulators increasingly tell Frank they're relieved to be getting the same scientific package the FDA and EMA got, which increases trust in the system rather than eroding it.

The three camps. Frank divides industry into three groups. Camp one: the naysayers with a thousand reasons change won't work. Camp two: the curious middle, who Frank says used to be curious about whether it would work and are now curious about how to make it work in their own company. Camp three: the "let's go" people who see a thousand reasons this will be problematic and decide to figure it out anyway. Matt and Frank are both in camp three.

Technology isn't the bottleneck anymore. Frank's closing thesis, and Matt's too: this is no longer a technology problem. It's change management, vision, and incentives. The infrastructure exists. What determines the speed of adoption is how many companies and regulators move from wait-and-see to "let's go."

How Accumulus got here. The nonprofit Accumulus Synergy was formally formed on July 13, 2020 out of quarterly regulator-industry conversations that had been running since 2019. The original scope was small — a handful of people and a large cloud provider. They ran a full year-long RFI/RFP with the three main cloud providers. The winner worked with them for three months and pulled out, saying it was too complex and outside their core business. That refusal is what forced Accumulus to become a technology company itself.

Nonprofit skepticism and the spinout. The nonprofit ran into skepticism from two directions. Inside the U.S., people said big pharma funds it, so how nonprofit is it really. Outside the U.S., the concept didn't translate. Regulators told Frank they didn't want to work one way for large pharma, another for mid-size, another for small biotech. Building a growth-hungry SaaS platform inside a nonprofit shell got harder every quarter. On August 4, 2025, Accumulus Synergy spun out Accumulus Technologies as a commercial entity — turning one year old as a commercial company right around the time this was recorded.

Public benefit corporation: a side quest worth taking. Matt asks if Frank considered a PBC for the spinout. He did, but sees PBCs today as more branding than structural — many are still fully driven by revenue and shareholder value. Matt pushes back with his own PBC pharma experience: the incentive is real, but no publicly endorsed measurement standard exists for pharma-specific impact. B Corp doesn't quite fit. Matt's take: there's opportunity for existing PBCs to define and publicize the standard themselves as a differentiator.

Frank on the real barrier: "Technology is absolutely no longer a factor. It is mostly change management."

Frank on the four-layer progression: "The internet connected people... cloud connected enterprises... AI is connecting knowledge... our next step is to connect scientific evidence."

Frank on where the industry actually sits: "The community in general is data rich but information poor."

Frank on reliance in the wild: "They did everything we were going to do. They asked every question we were going to ask. In fact, they asked more. There's nothing for us to wait three years for."

Frank on the paper-dossier gap: "You have regulators who are still receiving paper dossiers, and you can see why those two are on a collision course."

Frank on the wait-and-see camp: "Change is even more uncomfortable than the parts of my job I don't like."

Frank on the closing frame: "We no longer believe that every country is an island."

Regulatory strategy and operations leaders inside pharma, health authority technologists thinking about digital submissions and reliance pathways, RIM teams, and anyone building or buying tech that has to interoperate across sovereign regulators. Also for anyone who has looked at a three-to-five-year approval tail and quietly wondered why the industry treats it as physics rather than a decision.

Accumulus Technologies (commercial spinout)

Francisco Nogueira on LinkedIn

Accumulus Synergy spins out Accumulus Technologies (Aug 4, 2025)

Nogueira appointed CEO of Accumulus Synergy (May 13, 2021)

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

Jeff Sovis is founder and principal consultant of Mindful FDA Compliance LLC, which he started in 2015. He began his career in the lab at Genzyme Biosurgery manufacturing cell-therapy products for burn victims, then moved into life sciences quality and validation consulting — roughly 12 years of it, specializing in MasterControl, Veeva Vault, and ETQ eQMS implementations, CSV, SOP harmonization, and audit remediation.

Six or seven years ago Jeff was validation lead on a single-instance MasterControl harmonization at one of the largest companies in the world. He had visibility into both the validation work and the executive-level operations meetings — and the real issues almost never surfaced in the validation document. They showed up in operational communication, in decisions made by stakeholders operating in silos, in documents that got split or lost. That gap between the paperwork and the operation is what he calls operational drift.

For GAMP 5 Category 3 systems — where most SaaS lives — Jeff argues validation has drifted into a pass-through exercise instead of a tool for actually finding the seams in an implementation. His framing: risk correlates directly to the business process, so validation should be used to work out ways into and out of every foreseeable situation. Post-go-live matters just as much as day one because features keep landing.

Matt traces how the industry dragged on-premise validation habits into SaaS and made a mess, then dragged that mess into AI. Jeff recalls the 2017 V-model era — test scripts due before anyone had access to the system, no sandbox — as an exercise that turned validation into paperwork theater. Today's ability to spin up sandbox environments changes the ceiling if the operating model catches up.

Jeff's line: automation you can prove works correctly. AI is different — it introduces a third unknown entity making final decisions. His starting pattern for organizations is AI as an outside system, not connected to production, used for education and trust-building. The human moving data across the boundary eliminates most of the risk. Matt pushes on what happens when we let agents operate where humans did — the scale of a mistake changes fast when the click-throughs disappear.

Both agree the "validate on install and walk away" model is dead for modern systems. Matt frames it as always-on monitoring; Jeff notes he's hearing "continuous validation" more and more. This aligns with the direction of the FDA's Computer Software Assurance guidance, finalized in September 2025.

Matt goes off on the lifecycle math: a biotech product spans years — sometimes a decade — from lab to marketed product. Assuming a single human holds the information across that arc is fragile. Jeff picks up the thread with the "last mile" problem: workarounds harden into someone's job. He reframes it as misallocation rather than overallocation — that resource could be improving the system instead of patching around it.

Biotech organizations weren't built for this. Ten to fifteen years ago the enterprise ran on file cabinets. The transition to modern software requires people who understand a little of everything — quality, IT, business, GxP — rather than departments operating in silos. Jeff has seen audit findings tied to putting non-GxP experienced people into GxP roles as a fix.

Jeff's concrete offering: a two-to-three-week engagement, a roughly 20-page report focused on systems, testing, and procedures, with actionable feedback. Positioned as a low-investment way to align philosophies before something breaks in an audit.

Jeff: "It was almost never in the validation document."

Jeff: "Validation has kind of become like a checkbox exercise."

Jeff: "With AI there's a third unknown entity making final decisions."

Jeff: "I still think we're basically at square one."

Jeff: "It's not overallocation… it's misallocation."

Matt: "The longer you're not operating at an optimal level, the longer you're creating risk."

Matt: "We have to hold it to a higher standard."

Quality and validation leaders, regulatory operations professionals, life sciences IT, GxP software vendors and implementers, and anyone stewarding an eQMS through go-live and beyond. Especially useful if your organization is caught between traditional CSV instincts and the reality of SaaS plus AI.

Guest

Guidance and Standards

Concepts Referenced

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

V. "Bala" Balasubramanian, PhD, MBA, is a strategic advisor in healthcare and life sciences and the subteam lead for the DIA RIM Reference Model. He has been associated with DIA for more than 14 years and also leads the DIA AI Consortium Regulatory Frameworks and Terminology workstream.

Bala spent his earlier career inside big pharma, then led the Healthcare and Life Sciences Industry Solutions Group at Orion Innovation as Senior Vice President before moving into independent advisory work. Across that arc — sponsor, vendor, standards leader — he has been consistently focused on the same problem: shifting regulatory affairs from a document-centric practice to a data-driven one. He's a co-author on the DIA RIM white paper (V2.0) and the RIM Reference Model V2.0 conceptual data model that came out of it.

The seven pieces of information. Bala opens with the story that has stayed with him for twenty years: circa 2004–2005, working for a global sponsor that couldn't reliably answer seven basic questions about its own products across markets. Each affiliate tracked things its own way. Free-text fields everywhere. "This is humanly impossible" — his message to leadership at the time. The problem was never system availability. It was data discipline.

How the RIM Reference Model actually got built. The origin story: a DIA working group formed around 2015, but it stalled on the definitional question — what is RIM? — before it could even talk about data elements. Bala joined the effort in 2018 as a second workstream launched alongside the white paper. Version 1.0 (released 2022–23) was a single, comprehensive Excel workbook that became unusable through filter-fatigue. The team pivoted to a proper conceptual data model — an entity-relationship diagram, objects and attributes catalogued on individual tabs, definitions and controlled vocabularies aligned where possible with IDMP and FHIR. That's Version 2.0.

Not a standard — a common terminology. Bala is careful to define what the reference model is and isn't. It's not a mandated standard. It's a platform-agnostic common terminology and taxonomy that industry can pick up as a starter kit for a new RIM implementation, as a backbone for a system migration, or as a comparison layer in M&A. Two vendors have told the team their own offerings are subsets of it. Sponsors have used it to build their own regulatory product hubs.

Data citizenship — treat it like money. Bala's most repeated line in the episode: in 40 years of banking in America, he's never received a wrong bank statement. Financial institutions reconcile trades every night because the discipline is embedded. Clinical does the same because trial data has to be pristine. Regulatory has been the outlier — filing today, entering the record next week — because the field has "gotten away with" treating data as secondary. That has to end.

Why performance objectives matter. Bala's structural fix: put data quality in the performance objectives of regulatory professionals. Not aspirational, not project-scoped — actually tied to review and incentive. It's uncomfortable, but he's clear that without accountability at the individual level, discipline never sticks.

Matt's baton metaphor — and the 10-to-12-year horizon. Matt's contribution to the discipline problem: a product lives 10 to 12 years minimum. Ownership passes person to person across that arc. Institutional memory has to survive the handoffs, and that requires each individual steward to keep the record current in real time — not just for themselves, but for whoever holds the baton next.

Configuring systems to fit the lifecycle. A recurring failure mode Bala flags: old RIM systems required 33 attributes on a single screen before you could save the record, without regard for what was actually knowable at that point in the product lifecycle. Progressive disclosure — capture what's available now, add fields as the record matures — plus cascading controlled lists, are table-stakes today. Matt notes a very recent feature enhancement in one major RIM platform that finally lets records start in a draft state even when downstream fields are required. In 2026.

The transformation-program cycle. Bala names a pattern anyone who has lived through a data-remediation program will recognize: three-year initiatives, big teams, big budgets, evangelists appointed. The program ends, reorgs hit, champions move on, resources shrink, data drifts, and the next remediation project starts. Fix: embed evangelism in the operational team — not the program team — so change management becomes implicit and continuous, not episodic.

When health authorities set the new bar. Matt's observation: inspectors have caught up. They now walk in expecting real-time answers from the RIM system and the TMF system, and they'll give an hour instead of days. That external pressure is doing what internal accountability couldn't.

The circle of affinity. Bala's frame for regulatory's place in the org: safety, PV, manufacturing, and labeling all depend on regulatory data being right. Regulatory sits at the center of that circle whether it wants to or not. Owning that role — and being visible in it — is the way ops earns its seat at the strategic table.

Prediction: data-driven submissions arrive, split focus is the risk. The rise of data-centric submissions is real. IDMP, structured labeling, structured CMC, structured clinical reports — all pushing industry toward MDM in R&D for the first time. But the volume of change is huge, resources are constrained, and outsourcing to captive centers can go transactional and lose data quality. His call: keep the discipline attached wherever the work goes.

"In the 40 years I've lived in America, I've never gotten a bank statement that's been wrong."

"As long as data is not treated equal to money, we'll have this situation in regulatory."

"It's not a systems issue. It's a discipline issue."

"For some reason, regulatory always has this afterthought mentality."

"You need to have data as one of your performance objectives — and either get incentivized or penalized based on that."

"In some ways, we've come full circle: we went document-centric, and now we're going back to data."

Regulatory operations, regulatory affairs, and data-governance leaders responsible for RIM strategy or implementation; system owners planning a RIM migration or M&A integration; RIM vendors benchmarking product coverage; and anyone still trying to make the case that regulatory data discipline is worth investing in.

Guest & Related Work

DIA RIM Reference Model — Downloads

Standards & Regulatory Frameworks Referenced

Concepts Referenced

Transcript provided by Otter.ai.

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

Paul Slater helps professionals, leaders, and organizations navigate the human side of AI transformation. He is the author of The AI-Ready Human: Your 90-Day Program to Stay Relevant as Technology Transforms Work, host of the Humanity Working podcast, and founder of Paul Slater Advisory.

Paul spent nearly two decades at Microsoft, leading global digital transformation initiatives — including defining strategy for the company's Life Sciences business — and authored more than 20 books and courses for senior technologists. He has contributed to AI think tanks at Harvard, Duke, and Arizona State University, and briefed Fortune 500 executives and national governments worldwide. He is currently an analyst-relations thought leader at Adobe, where the frame problem behind this episode — "how do I stay on top of what 70+ industry analysts are saying?" — is the one he ended up vibe coding a solution for.

The 12-hour build. Paul's opening story: needing a hybrid of a CRM, a CMS, and a news feed for his Adobe work — a "CIA-profile-every-analyst" system Salesforce doesn't cover — and building it end-to-end in Claude Code in about twelve hours. His advice: if you're not technical, get a bit technical. If you're technical, get more technical. Then build the thing that fits your work like a glove.

The barrier to coding has collapsed — knowing what to build has not. Paul's clearest reframe: the how-to-code is gone. What remains is the ability to think holistically enough to imagine the system that would solve the problems you have. If you can do that, everything else isn't that hard. This is the muscle worth building.

The standardization paradox in regulated industries. Matt's frame: life sciences needs standardized foundational systems it doesn't quite have — and yet if those foundations were solid, everyone could accelerate the individualized layer on top. So can AI let us leapfrog around the missing standardization? Sometimes yes; sometimes the messy reality is the whole reason your custom tool is valuable.

Products are an artifact of cost, not need. Paul's epiphany: the explosion of products since 1990 isn't a reflection of need — it's a reflection of how cheap it became to bring a solution to market and wrap it in sales and marketing. Every product then accumulates functionality to justify its existence, until — Matt's line — the product becomes the problem.

What actually needs to be centralized. A relatively small amount of data must live in highly regulated, locked-down systems. Everything else — the messy, unstructured, contextual layer that supports the art of the work rather than the science of it — has been artificially wedged into expensive applications where it never belonged. Paul's clinical-trial example: a huge amount of real-world Phase IV signal is already out in the world; you don't need to lock it down at the point of first inspection, only when you build a hypothesis on it.

Why software companies have to reinvent themselves. With AI writing an increasing share of software (Paul references Anthropic's public numbers on internal AI-written code), the moat under any SaaS resting purely on software gets very thin, very fast. Paul's bet: winners like Veeva don't get displaced — they reinvent themselves from "product for these roles" into a role-enablement layer that uses their unmatched understanding of how the work is done to build self-forming, naturalistic support for each user.

The Star Trek IV point. In the film, Scotty tries to talk to a 1980s Mac and Bones hands him the mouse like it's a microphone. The joke: the future is one where you talk to the computer and it responds. That future has arrived — and it means the same tool should present four different faces to four different people doing the same job, adjusting to how each of them actually thinks and works.

The ugly middle ground on pricing. We're paying legacy subscriptions plus token consumption plus occasional old licenses — the worst of every pricing model at once. Paul's read: pick a lane. His preference is everything is a token — like electricity. Leave the lights on all night, pay more. Cleaner accountability, cleaner incentives.

Local models vs. the data center. The recent Dell / Microsoft / NVIDIA announcements around local AI processing hint at a different future — where most inference runs on the user's expensive workstation and only escapes to the cloud for the heavy lift. That may be the shape of the cost curve that finally makes token economics work.

Nothing about work is fit for purpose. Paul's summary line: technology infrastructure, organizational infrastructure, information infrastructure — none of it is where the puck is. On alternate mornings that terrifies him ("we're all going to be out of a job") and thrills him ("that is a lot of work for a lot of people"). Either way, it's the biggest reset since the PC.

The AI-Ready Human and the third framing. Paul's book is designed to meet people wherever they're at — from wall push-ups to elite AI users — and centered on the evergreen part: the human. In work with creatives at Adobe he's landed on a third way to think about AI, beyond productivity-enhancer and quality-improver: AI as medium. Things that couldn't have been created any other way — the Beatles' Now and Then, the short-form work of surrealist artists — are the flipside of AI slop. Same technology, different intent.

"I built something way more useful than any commercial software at all for my job in twelve hours."

"You can literally vibe code the perfect workflow support system for whatever job you have."

"The product becomes the problem."

"You wind up being held back by the very solutions that you're paying for."

"Almost every aspect of how work is structured and how work is done is not fit for purpose."

"AI can do a poor job of mimicking things humans would do, but it might do a really good job of creating things humans cannot."

Life sciences, Reg Ops, and Quality leaders trying to see around the corner of the SaaS-and-AI shift; enterprise architects and IT strategists rethinking buy-versus-build; software vendors serving regulated industries; and anyone wondering what "AI-ready" means for their own job in the next twelve months, not the next five years.

Guest & Work

Tools & Platforms Discussed

Concepts & Cultural References

Transcript provided by Otter.ai.

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

For most of life sciences history, validation has been a snapshot — freeze the configuration, prove it behaves as designed, trust the system until you change it. Nuno Valério has spent his career inside that paradigm, and he's now one of the clearest public voices on what has to change for it to survive the AI era. As Head of Innovation, R&D Quality at Merck, Nuno is building AI governance frameworks for pharma R&D in real time — and he joins Matt Neal for a wide-ranging conversation about validating probabilistic systems, the trap of blanket "human-in-the-loop" thinking, and what genuine trust looks like when the model itself keeps changing.

AI as a liberator — when you stay the driver. Nuno's framing: AI is an enabler and a modulator, but the moment you let it produce your voice instead of you being behind the voice, it becomes hollow. The F1 analogy: what was great about watching Senna and Prost wasn't the cars, it was the art of the driver — the late brake that was almost too late, but not quite. AI is a powerful car. The driver still matters.

Set the model to challenge you. A practical antidote to the validation-loop trained into LLMs: prompt the model to push back, ask clarifying questions, and always offer a different angle. Sometimes the angle is irrelevant; sometimes it reshapes the whole question. It's how you keep the tool from collapsing into agreeable blandness.

The expertise paradox. AI is hugely powerful when you know your subject deeply — you can dig with a backhoe instead of a shovel. When you don't, it sounds great and can be completely wrong, and you won't know to push back. Matt's framing: when you really know something, you notice how not great it is on first blush.

AI as the first alien intelligence. Not alien in the extraterrestrial sense — alien in the sense of an intelligence that originated outside the patterns of natural selection that produced us. We've never met one before. The implication: we shouldn't assume it will behave like the only kind of intelligence we already know.

Trust architecture — validating the workflow, not the model. The old validation paradigm — take a snapshot of a deterministic system, freeze the configuration, trust it holds — doesn't survive probabilistic models whose outputs change with each input. Nuno's framework validates the whole ecosystem around the model: the tool, the human reviewing the output, the infrastructure, the guardrails, and the drift monitoring that flags when the model wanders. The goal isn't perfection — it's predictability you can sign under.

The human-in-the-loop trap. Putting a human everywhere isn't governance — it's burnout. Picture the reviewer at 5pm with 300 outputs to validate and a partner waiting at home. The first 257 were perfect, so he clicks through 258, 259, 260. "Human-in-the-loop" needs to mean human-on-the-loop where it matters — triggered by drift, risk thresholds, or signals the model is operating outside its trusted envelope.

Risk-based proportionality. A model that summarizes a meeting doesn't carry the same risk as one producing a safety report for a submission. The validation effort should reflect the consequence of failure. Quality has been doing risk-based work for decades — sampling, focusing where it counts, accepting you can't be everywhere. AI doesn't change that principle; it raises the stakes for applying it well.

The customization trap. Nuno's pushback on Matt's optimism about Veeva and Salesforce implementations: pharma companies routinely insist they're special, customize the standard configuration to match how they already work, and then can't absorb new features. AI capabilities increasingly only work — or only work well — on standard configurations. The cost of "specialness" is now showing up in the roadmap. And requirements gathered from people doing it the old way produce new systems that look exactly like the old systems.

Data quality, compounded over decades. Pharma's data is messy because there was never an incentive to fix it. Decades of operations stack up. Synergies across silos and regions matter only if the underlying data can be connected — which is exactly why frontier AI labs see life sciences as so much opportunity. Nuno's advice: don't try to fix 50 years; cut a reasonable line and move forward from useful data.

The GIP provocation. Matt's controversial proposal: the industry is missing a standard. GMP and GCP cover their domains. The little "x" in GxP gets stretched until everything is high-risk — and the result is fear-based bottlenecks. He proposes Good Information Practice — a discipline grounded in modern systems that trace every click, every change, every reason. If a spreadsheet column took three months to add, the real risk isn't governance; it's the columns you stopped adding. Nuno's response: he's wary of more letters, but agrees the binary GxP / non-GxP switch is broken, and proportionality has to be applied inside GxP too.

Sandboxes and pre-competitive collaboration. Nuno's call for shared, experimental spaces where industry, regulators, and vendors define what "good" looks like together — modeled on aviation safety. Pre-competitive information isn't IP. We can all get better at what everyone has to do, without giving up what makes anyone different. He sees the beginnings of that maturity in the sector, and signs from regulators that make him hopeful.

The dawn of AI maturity. Quality is a culture used to knowing what it's talking about — built from decades of guidelines, mistakes, and corpus. AI shifts every professional out of that seat. The only honest path forward, in Nuno's framing, is to think out loud, share the work, and accept that nobody has it all figured out yet.

"If you use AI just to produce your voice instead of you being behind that voice, it becomes hollow."

"What I was seeing was not the cars. What I was seeing was the art of the person at the wheel."

"AI might be the first alien intelligence — not in the sense of being from outside Earth, but in the sense of being originated outside of our patterns."

"Trust, to me, is predictability that you can sign under."

"He actually was very thorough. He checked everything. This one is certainly fine as well. Click, click, click."

"Every pharma company thinks they are very special. And then they pay the price of that specialty."

"The age of AI shifts everyone — every professional — from their seat."

Guest & Company

Events & Public Work

Platforms & Tools Discussed

Concepts Referenced

Everyone relaxes when you say "human in the loop." Nobody pictures the reviewer at 5pm, 257 clean outputs deep, clicking approve on the 258th because the first 257 were fine. The loop isn't a safeguard; it's an architecture problem. Validation used to prove a system does what you specified; with probabilistic systems the spec can't save you, so the real question stops being *does it work* and becomes *under what conditions can I sign under it.* That's the shift I care about. That's what I mean by trust architecture; not a new framework I'm selling, more a way of framing what our industry already half-know.

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal

Few people have lived through more change cycles in regulatory operations than Scott Cleve. Across 20+ years at Accenture, AbbVie, Astellas, Boehringer Ingelheim, bluebird bio, and now Daiichi Sankyo, Scott has built and led global Reg Ops organizations through wave after wave of new technology — and figured out a few things about how change actually sticks.

Matt and Scott trace the arc from a 2017 Alexa pilot at Boehringer Ingelheim (RIP — voice capture for affiliate correspondence) to today's reality of AI agents working alongside humans as teammates. Along the way: the three obstacles that quietly slow every change initiative, why "training" a workforce two weeks before go-live with a PowerPoint is the corporate equivalent of asking a kid to play in the World Cup, and the backhanded compliment that defines a great Reg Ops team — you guys do your job so well, I don't even think about it.

Scott Cleve is Vice President of Regulatory Operations, Information and Compliance at Daiichi Sankyo. His 20+ year career spans consulting at Accenture and Reg Ops leadership at AbbVie, Astellas, Boehringer Ingelheim (where he led the company's global Reg Ops org from Germany), and bluebird bio — giving him a rare view across big pharma, family-owned multinationals, and cell & gene therapy.

Scott is a regular voice in the Reg Ops community — keynoting the Veeva R&D and Quality Summit, appearing on industry podcasts, and serving on conference panels (he and Matt have shared the stage at DIA RSIDM on "Regulatory 3.0 — A Data-Driven Approach"). His focus throughout: pulling organizations forward through change without breaking the people inside them.

Building a global Reg Ops org. Scott's first big leadership challenge was inheriting Boehringer Ingelheim's global Reg Ops function — a US leader living in Germany, navigating a family-owned multinational with a different time horizon and culture, and footprints across Japan, China, Europe, and the US. The playbook he refined: gather requirements, honestly assess where you're strong and where you need to grow, align investments with the company's priorities, and use outsourced centers of excellence for what fits.

The 2017 Alexa pilot. Long before Copilot and ChatGPT, Scott's team ran a pilot using Amazon Alexa to capture correspondence from global affiliates — voice in, PDF out, archived directly into document management. The catch: "Alexa has a really short attention span." It didn't roll out, but it opened doors to voice capture, voice-to-text archiving, and the broader question of how to embed new tech in real workflows.

Finding the tinkerers. Every Reg Ops team has early adopters who want to do their job easier — the people who actually read Word and Excel release notes. Scott's pattern: identify the advocate, give them a single use case on a single deliverable, demonstrate visible progress, and let the knock-on effect pull the rest of the group along.

The three obstacles to fast change. Data first — is it structured, clean, complete enough to feed into modern tools? Organizational readiness second — disrupting processes people have run for years creates real anxiety, not just resistance. Compliance and validation overhead third — the documentation and testing burden that makes iteration slow, even when the technology itself isn't the bottleneck.

The training paradox. Scott's analogy: a pro athlete gets coached and feedback from age seven onward. The corporate version is to roll out a new document management system, hand people a PowerPoint two weeks before go-live, and call it "hyper care" when it goes sideways. "We're asking people to fundamentally change the way they've worked for years with a minimal amount of support" — and Matt's revelation from a recent summit: why are you making me train people on your software?

Change as a constant. Five or ten years ago the industry talked about change fatigue. Today, between iPhone updates, Outlook updates, and the Veeva release cadence, change is just the default — and the leadership job is reframing it as continuous improvement and sharing the vision: "I've been to the beach, I know it's there. You're still on the other side of the mountain."

The Reg Ops symphony. As teams have grown, the publisher-does-everything model has bifurcated — into publishing, submission management, and an emerging data-steward track for IDMP, PQ/CMC, and future data submissions. The question Scott hasn't fully solved: how do you keep these specialists working in symphony without losing the cross-functional reach that made the old generalist Reg Ops role so valuable?

Bots as team members. RPA was the warm-up. The new mental model: think of an agent the same way you'd think of a new hire. "Bot one, you're going to do all the document formatting checks — here's how much work I expect from bot one in a typical day. Bob is overseeing bot one." Humans become managers of humans and bots, with the human-in-the-loop critical for change control, error handling, and upskilling the agents over time.

The self-driving tipping point. Matt's analogy: when self-driving is genuinely better than humans in every condition, it becomes irresponsible to drive. The same logic is coming for Reg Ops work — and the open question is the time scale. Scott's read: a lot will change in the next five years, technology is pushing the field along, and AI is going to start "eating from the bottom" of the task list.

The career arc for publishers. As automation absorbs the click-work, the people who built that expertise become the most valuable teachers in the building — data stewards, submission managers, and trainers of the next wave of both humans and bots. Retaining that knowledge in the organization is the leadership challenge of the decade.

The hidden magic of Reg Ops. When the team does its job perfectly 99.9% of the time, nobody notices — they only see the 0.1%. Submissions go out two days earlier after a Veeva upgrade, and no one outside the team knows why. Scott calls the resulting feedback "the most backhanded compliment": you guys do your job so well, I don't even think about it. The trusted-partner relationship with regulatory strategists is real and valuable — and chronically under-recognized.

"Everyone's favorite system is the one you just stopped using."

"You guys do your job so well, I don't even think about it. It's the most backhanded compliment."

Regulatory operations and regulatory affairs leaders managing change at scale; Reg Ops professionals navigating new tools, validation overhead, and shifting role definitions; R&D IT, RIM, and digital transformation leaders in life sciences; and anyone interested in what AI and automation actually look like inside a high-performing operations team.

Guest & Career

Tools & Platforms Mentioned

Industry Events & Concepts

Transcript provided by Otter.ai.

Operations Utopia - Where Regops, Innovation, Technology, and Execution Meet.

Disclaimer: This podcast reflects only the opinion of the podcaster and guests and does not reflect those of their organizations, system vendors, or service provider

Original show theme "Little Sammy" by Matt Neal