In this episode, Etienne Nichols sits down with Michelle Wu, Founder and CEO of Nyquist AI and one of the top 100 women in AI, to discuss the transformative state of artificial intelligence within the MedTech regulatory and quality space. Reflecting on her recent personal experience as a surgical patient, Michelle shares a unique perspective on the critical importance of the devices and quality systems that keep the industry running.
The conversation dives deep into the "Great Rewiring" of the medical device industry. Michelle outlines how we have moved past the initial phase of AI skepticism and "AI fatigue" into a period of hyper-acceleration. With the introduction of the FDA’s ELSA and the implementation of the EU AI Act, the industry has reached a point where AI is no longer a side project but a fundamental requirement for operational longevity.
Finally, the episode provides a roadmap for both organizations and individual contributors. Michelle introduces her "Holy Trinity" framework for AI implementation—Data, Workflow, and Agents—and explains why the next two years will be defined by the "Invisible Colleague" or AI copilot. For junior professionals, the message is clear: knowledge is now a commodity, and the real value lies in the ability to ask high-quality, strategic questions.
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Key timestamps
- 00:00 – Introduction and Michelle Wu’s background in MedTech and AI.
- 03:45 – A founder’s perspective: Michelle’s personal experience in the OR seeing her clients' devices.
- 08:12 – The 2025 Inflection Point: FDA ELSA, EU AI Act, and global AI expectations.
- 11:50 – From billable hours to value-based output: How AI is disrupting the consulting business model.
- 15:35 – Micro-timestamp: 2026 Predictions. The shift toward universal AI Copilots and Agents for every MedTech role.
- 18:22 – The Holy Trinity of AI: Breaking down Data Layers, Workflow Automation, and AI Agents.
- 22:10 – Case Study: How a top-tier MedTech company automated 17,000 quality and regulatory tasks.
- 27:45 – The 56.8% Salary Premium: Why AI literacy is the most important skill for young RAQA professionals.
- 31:15 – Shifting from memorization to "Clarity of Mind" and high-quality inquiry.
Top takeaways from this episode
- AI Literacy is a Financial Multiplier: LinkedIn data shows that non-engineering knowledge workers with AI literacy can command a salary premium of up to 56.8%.
- The 80/20 Rule of Automation: Approximately 80% of current RAQA tasks are tedious, manual, or administrative. Successful teams are using AI to automate that 80%, allowing humans to focus on the 20% that is strategic and high-value.
- The Three-Layer AI Strategy: To effectively implement AI, companies should look at the Data Layer (intelligence), the Workflow Layer (automation of specific tasks), and the Agent Layer (autonomous "employees").
- Value-Based Billing: As AI reduces the time required for regulatory submissions and gap analyses, the industry is moving away from the "billable hour" toward pricing based on the value and quality of the output.
References:
- Nyquist AI: Michelle Wu’s platform specializing in global regulatory intelligence and AI-driven workflow automation for MedTech.
- FDA ELSA: The FDA’s internal AI tool that marked a significant shift in the agency's embrace of the technology.
- IMDRF GMLP: Good Machine Learning Practice guidelines for AI-enabled medical devices.
- EU AI Act: The first comprehensive legal framework for AI, affecting MedTech compliance timelines.
- Etienne Nichols: Connect with Etienne on LinkedIn.
MedTech 101: AI Agents vs. AI Chatbots
While many people are familiar with AI Chatbots (like the early versions of ChatGPT), the industry is moving toward AI Agents.
Think of a Chatbot like a very smart encyclopedia. You ask it a question about a 510(k) submission, and it gives you information.
An AI Agent, however, is like an AI Intern. It doesn't just give you information; it performs a multi-step task. For example, you can tell an Agent to "find the three most relevant predicates for this new device, summarize their 510(k) summaries, and draft a gap analysis table." The Agent "thinks" through the steps and delivers a finished product, not just a response.
Memorable quotes from this episode
"Knowledge is a commodity now. Previously, regulatory consultants or professionals stood out by their knowledge. Now, with AI leveling the field, the capability lies in those who can ask high-quality questions." - Michelle Wu, Nyquist AI
Feedback Call-to-Action
We want to hear from you! How is your team currently leveraging AI in your regulatory or quality workflows? Are you feeling the "AI fatigue," or are you seeing the "salary premium" Michelle mentioned?
Send your thoughts, guest suggestions, or specific questions to podcast@greenlight.guru. Etienne personally reads and responds to listener feedback, and we would love to feature your insights in a future episode.
Sponsors
This episode is brought to you by Greenlight Guru. As Michelle and Etienne discussed, the future of MedTech is driven by data and efficiency. Whether you are navigating the complexities of the EU AI Act or scaling your R&D, Greenlight Guru’s QMS (Quality Management System) and EDC (Electronic Data Capture) solutions provide the structured data foundation necessary to leverage AI effectively. By integrating quality and clinical data, Greenlight Guru helps you move from "reactive" compliance to "proactive" innovation.
Transcript
Etienne Nichols: Hey everyone. Welcome back to the Global Medical Device Podcast. My name is Etienne Nichols. I'll be the host for today's episode. Today I'd like to talk a little bit about AI and its use in the MedTech and regulatory space, particularly for QARA and how it is kind of shaping the future of MedTech.
And with me today to talk about this is Michelle Wu. Michelle is the founder and CEO of Nyquist AI. She was named a top 100 women in AI in 2025.
Congratulations. It's awesome.
Also been featured by Forbes, spoken at life science AI conferences, contributed chapter to MIT's press, AI and healthcare. And previously she led global strategy at Novartis and worked in BCG's life sciences practices. She has an MBA from Stanford and a BA from Peking University.
And I first, I guess was exposed to Michelle at the RAPS conference where she is constantly.
She's a presence and a force who's there and helping the industry move forward. And she's recently had some, I guess, personal experiences with the medical device century, which I don't want to necessarily share that overshare anything, but I just love what you're doing, and I think you're making the industry a better place.
I'm really excited to have you with us today.
Michelle Wu: Yeah. Thank you. Thank you, Etienne. It was such an honor. Pleasure to join you. I'm just so grateful. I'm a very open person. Like, since you already prompted me, I just came out of my very first surgery in my lifetime, which was supposed to go for just one hour.
It went over four hours.
And, and, and I just feel very grateful. And before the nurse and doctor like knocked me out, I look around in the or, like I was like, oh, this is Philips monitors, Maximum patient monitors. That's, that's like all of my clients and Baxter Solutions. Like, I feel I'm liking a very good hands.
So, I'm really, really grateful for, for the journey I chose.
Etienne Nichols: Yeah, I think that speaks a lot to what you do and how you care about the quality of your work. When you look around and see all your clients and you feel better instead of some people saying, oh no, you know, I know the.
I know the skeletons in those closets. No, I, you, you, you are reassured by that, and I think that speaks volumes for what you're doing.
Michelle Wu: Yeah, yeah, awesome. I just feel very grateful. Like people in our industry, they really take pride and they, they really care about their due. That's what they do. So that's really sad. You know, medical device, biopharma, life science in general, quite apart from other industries, people who come to this industry have innate nature. They just want to help, and they just want to make the world a better place.
Etienne Nichols: Yeah, yeah, yeah.
So, I love that. And that is the mission here at Greenlight grew as well, to make, to improve the quality of life.
And that's patients, that's the people who work with the medical device companies, the regulators, everybody. Our goal really is to just improve the quality of life through, through the things that we do in MedTech. And I know that's a similar goal to many of the listeners and to you yourself as well.
So, if we jump into the topic here, if we, you know, AI is a nebulous topic. It's been something that's been.
It starts to infiltrate nearly every conversation recently. And when I look back over the last two or two and a half, three years, when OpenAI first came out with ChatGPT, there was a lot of skepticism around that. People just kind of, that will never take away anything I'm doing as far as a regulatory perspective. And I know you've been working with Nyquist AI for quite, quite some time now.
It seems to me there's been kind of a.
We've gone maybe a full 180 in that people at one point they're like, oh, this is ridiculous, but I'll, I'll entertain the idea. But then they got some AI fatigue and then finally the FDA introduced Elsa and people started sitting up and taking notice and saying, maybe this is something I need to consider for my, my job, my career longevity, my company's longevity. When we look back at 2025, are there any moments in your mind that you feel like were true inflection points for MedTech RAQA as it relates to AI?
Any thoughts there?
Michelle Wu: Yeah, that's really a powerful question, especially for me. Have, you know, going through my very first surgery in life and it's quite, you know, it's a big deal for me. I think about the life and death, and we are at the end of this year.
It's definitely moments for reflection.
And I think like 2025 definitely made the AI and the digital world like, you know, a necessary part for the medical device industry, especially for the RA QMS.
And so, a little bit about our company. We build AI tools dedicated to regulatory and Quality medical professionals. We take, we provide data, we work provide workflow automation to help people take the tedious repetitive work away from their day to day and really focus their brain power like mission critical tasks.
So, for 2025 they are like kind of one or two moments that really jumped out to me and one is like the global AI expectation got really real with the IMDRF GMLP, you know the final version which is a shared baseline of how AI abled AI enabled devices should be built across the life cycles.
And also, the EU AI act timeline that really put the AI literacy or the AI governance into the conversation.
And also, as you mentioned the FDA embracing Elsa and more and more people start to talk about AI and share but more willing to share their use case of AI.
That was not the case back in 2020 when I first started.
When we first started people don't care whether it's AI or not, they just care that we can provide a solution. So, 2020 to 2022 it was like AI naive but friendly face.
And after the introduction of ChatGPT I feel like there is moments of scare and confusion and all of a sudden there are so many tools that overwhelmed end users, especially medical device companies.
And I remember I have a conversation with the VP of regulatory affair from the top three medical device company, and he was like oh my God, like if every time I can get one $1 from like a cold inbound of AI tool I don't need to work again.
I could just use retirement like so from 2020 like since like introduction of ChatGPT and two years after introduction of ChatGPT I feel this like AI overload process and like a phase and now we are in the phase three or phase four where people who actually adopt AI are running so far at and you will not believe like what I've seen and people like.
Even though on the surface a lot of people are still like hey wait and see or just like flip through the newsletter the headline about AI and do not even bother to give it a second thoughts.
But there are people who running very far with AI.
Etienne Nichols: Yeah, what's amazing to me is you know we.
I just recently read the book the Anxiety Generation which really talks about the convergence of social media and high-speed Internet and the iPhone coming out all at the same time around 2010 I think it is.
Michelle Wu: Yeah.
Etienne Nichols: In 2007-2012 in that era and the generation prior and the generation after there was an inflection point and a hockey stick and the change in thinking and they he called it, the great, the great resetting I think it was or the great rewiring.
And I almost wonder if there's going to be another great rewiring from prior to AI and after AI because even my own kids, I have a 7-year-old who he says, you know, let's start a T-shirt company, dad.
I'm like, so in the evening we, in three hours we had set up a drop shipping company with about 50 T-shirts, you know, with designs and everything that he had made.
Just, just sitting there with ChatGPT or Claude or Gemini and all these different things. They just immediately these kids seem to think I'm just going to go straight to the AI tool.
Let's make a movie now. Last night, you know we made quick, you know, three scene movie with I can't remember it was Google Flow or something like that. So, there's, there's these, these. I think we're going to see this AI first culture coming on at least if nothing else the kids.
But I'm, I'm curious when we look at that from a MedTech regulatory standpoint, are you seeing that now with, with regulatory and quality professionals as well?
Michelle Wu: Yeah, that's for sure. And you know during my recovery time from the surgery that like a past week I was the source of comfort, like I was just lying in bed recovering is the source of my comfort is like listen to your podcast and also together you will feel proud that like together with Elon Musk podcast and you know, the micro and Sam Altman's podcast.
So, it really gave me a lot of fruitful thoughts to think. I think 2025 is definitely the year where AI stop being just a side project and whether it's within our industry or you've seen in your kids is going to rewrite how we work, is going to rewrite the whole organization structure and is even going to change how people learn education maybe in your kids generation like college education is never going to be the same or they just don't need to go to a college anymore.
They just, just like start entrepreneur and become a 7-year-old mini-Elon Musk.
Etienne Nichols: It blows my mind. I, I can't even imagine the future. I always consider myself to have a good imagination. But in your mind having talked to all the companies that you do talk with and deal with, are there any in your mind rules that are going to change as a result of AI?
Michelle Wu: Yeah, I think the rules are especially for regulatory and quality previously or any modern organization because my old job was from BCG Strategic Consulting and I think all our philosophy, all our thinking about how a business running and how our organization function will change fundamentally.
The rule of people, headcounts or budget will not apply to AI.
AI will.
It's not like oh, previously if I run regulatory consulting firms, I would like, okay, I have 10 regulatory consultants so I can handle 10 510(k) submissions and QMS projects. No, with AI, maybe you just need one or two.
We recently just talked because it's a holiday season.
We recently just talked with one of our customers who runs a consulting firm and the CEO was thrilled because he's like, wow, this is like using Nyquist. And of course, there's all the other amazing AI tools and, and they have really achieved a lot of exceptional gains.
Their consultant is, is they, they initially have the fear that oh, AI will make people less relevant. And for consultants, for regulatory consultants they will bill less like billable hours. But on the contrary, because their work is so good and they always deliver on time or under time, under budget, the client is thrilled. So, they end up working nonstop. All of their consultant has 120% of billing rate. 100% or 20% of occupancy rate for anyone who ever run any consulting business.
That's. That's amazing.
Etienne Nichols: Yeah, yeah, yeah, that's. That is amazing. And I'm curious how.
I suppose the billing model in my mind is one thing that I'm. Is that going to change then just because instead of billing hours, you're billing for your own time and how you're running these, these tools, is your rate going to go up?
I don't know. There's just lots of different questions in my mind about that.
Michelle Wu: Yeah, I think that definitely the fundamental rule or like adopt the first principle with AI, it's a fundamental rule of manpower or time is going to be irrelevant with the power of AI because when you sleep or when you do other things, AI can still run in the background.
Especially with AI agent. A lot of the regulatory work, for example predicate search, competitive landscape, post market surveillance, analysis and reporting, all those routine works can be automated quite easily.
And so that leaves us to see you know like small but mighty team that can do a lot with limited time, limited budget and limited headcount. And also, to the billings I talked to because we sell to consultants, we also sell to the like manufacturers.
So, we got a more, you know, holistic view. We also sell to academics and law firms and so on and so forth. We have a more holistic view of what, what everyone's feedback is. And for Customers or the end user, they actually don't mind.
Consultants charge more as long as is it's justified the value. So, I would imagine the old way of manpower or user based or project based, the price will be obsolete.
People will be strived to pay for value.
Etienne Nichols: Yeah, they're being, yeah. Paying for the outputs and not just the time spent on the project. That makes yeah, total sense to me.
Michelle Wu: Yeah. I'm curious like for greenlight guru like you guys are the one of the most respected solutions for QMS.
What you have seen the rule has been changing with adoption of AI from your user base.
Etienne Nichols: It's a good question. We get a lot of questions about the use of AI in our tool now. Whereas previously if we, because we actually adopted AI many years ago, you know, I think 21, we might have started utilizing AI inside our tool and we kind of pulled back a little bit because the industry wasn't quite ready for it at that time.
And so, we've come full circle now they're asking for it but at the same time the way in which they are asking about AI is not so much are you using AI in your tool?
Although that is a question. But it's more of what does AI accomplish for me and what are the features that, that what are the things that will actually be a better outcome as a result of the use of AI?
So, it's not just an insistent on an insistence on utilizing AI but knowing what is going to be the output or the outcome. So, there's certainly more to come as far as how we're going to be using AI.
There's a certain amount in our flagship product now, Ultralight, and I'm excited about that and how that's going to change the face of design, controls and quality management. But yeah, it's an exciting time for sure because I think to your point, companies are going to be able to work smaller, teams are going to be able to accomplish a lot more.
Michelle Wu: Yeah, yeah. Across the board, whether you are like one person startup or like the largest medical device company in the whole universe. The constant theme is like the team is pressured to use AI from the leadership team and also, they are, they are, they are incentivized or they are to do more with less.
Etienne Nichols: Yeah, yeah, yeah. So, if we look at the future, I mean looking back a little bit, I think it's fun because it's. But, but at the same time it's hard to extrapolate purely from what we've seen, especially when AI is changing the game.
So, it's a little Bit of an exponential change that we're seeing.
Michelle Wu: Yeah.
Etienne Nichols: Are you able to look into 2026 and how do you see the future of AI and capabilities?
Are there any things that you think are maybe obvious to you but wouldn't be obvious to the rest of us when it comes to regulatory and quality in AI?
Michelle Wu: Yeah. So, my biggest prediction for AI capability 2026 is that every. For every task, every role, whether you are in regulatory or quality, whether you are doing regulatory strategy, regulatory intelligence, like our space, or you're doing like a QMS or DHF, like what a green light guru is specialized. You can always find a co-pilot. And my big idea is that in what it feels common now like in 2025, like as we discussed, a lot of our customer just asked me like, hey, how should I use AI? How can AI do this?
How do you build the AI?
But in 2026 it will feel more normal. The first step, every task for the first step for every task is you just knock on the shoulder of your co-pilot. It's almost like you have your invisible colleagues like hey, I have this idea or I have this test.
How much can you auto augment this for me or automate it for me?
Etienne Nichols: Yeah, no, that. I think that's a great point. And if we were to take this one step further because this is something that's exciting to me as well because I look at, okay, I do podcasting, I also do social media, I also do event strategy.
I also do, you know, some of the market analyst approach. So, you build out an AI co-pilot for each one of those Personas. So, every time you put on your quality hat, you have your co-pilot.
Every time you put on your regulatory hat, it's not just a single co-pilot but you can get even more targeted with, with AI in those co-pilots. That's great.
Michelle Wu: Yeah, yeah, absolutely. I think we are only scratching the surface of AI tool or the whole co-pilot concept. People nowadays are mostly just using, I would say like 80% of our users from the 200 customers globally are still using co-pilot or like AI, almost like a chatbot or Q&A to hey, can you find me the latest guidance 510(k) or hey, can you help me design a gap analysis?
Do a gap analysis. But the reality is like co-pilot could also be your AI agent. Think of like a Mistral's agent that do regulatory strategy for Medtronic or like ATMs AI agents that do all the podcasting, marketing, social media strategy is almost like you have a team that is built of your AI employees that will be the I think will be the ultimate solution or ultimate dream for 2026.
When it comes to my biggest AI.
Etienne Nichols: Predictions, how do people use your tool? As far as like you talk about 80% of users using it this certain way, what would you say the top 1% who utilize it to the fullest capability, what are they accomplishing that the rest just aren't just are leaving on the table?
Michelle Wu: Wow, so different.
So, so different. Like, like I can share a story that will shock you.
So, for example 80% we have three layers of solutions.
We give it a nickname of Holy Trinity for regulatory affairs. So, number one is the data layer. So, you can find all the global regulatory intelligence, all the good stuff that you can design a product, find a predicate or find a competitive landscape.
And number two layer is workflow automation. So, AI for medical writing, AI for literature research, AI for guidance analysis is almost like you have your AI intern that do a lot of manual and low value tasks for you.
And number three is we call that connect with agent where you have the AI agent that basically become your employee. Of course, we always suggest our users to have the AI like a human in the loop.
So, it's almost like instead of just an intern, as you feed your agents more and more data and train them about the workflow they can achieve. Like they can hit 80% or 70% or 80% of the capability of employee, which is quite shocking and quite exciting.
And most 80% of our customers are on stage one and two.
Like, like they can accelerate their regulatory submission, they can accelerate their regulatory strategy or prepare for UMDR and find the state of the art.
And they are one VP of regulatory affair who from the top three medical device company. He has a vision. He saw the AI coming. He claimed to be re reluctant techie but extremely ambitious lean guy.
So, what he has done is like he leveraged our data. He also wants to recruit us with like we still have our company, we love our company. Thank you. And then like he hired because he's rich experience and broad network.
He even poached the top researchers and engineers from the Google and Microsoft around the world. So, he spent two years lobby for the global community, build a global community, get the funding, get millions of dollars. He built his very own AI tool for regulatory and quality.
And so, like when and for the past, for the two years he's building his own AI tool for the company, and he traveled around the world to go factory to factory and meet people on the ground.
Like show me your workload, show me you’re like a paper stack and so on so forth.
So, like that and after the product that the first launch is 5,000 people log on completely.
So, which means in translation into like 5,000 people's work is automated.
Of course, they can use others like, and the next phase is like 12,000. So immediately 17,000 job is automated for quality.
Yeah.
Etienne Nichols: And the goal isn't really to remove those people or to replace their job. It's or, or is it, you know my, my expectation would be it is so that they can stop doing these lower-level admin tasks and just do 10 times the work that they previously did.
Michelle Wu: Yes, yes. So, they but 80% of the I, I, I, I'm a firm believer of 8020 rule. I think 80% of our work and time is spent on tedious manual tasks.
Only 20% like you know the podcast is the most like strategic test I have for today. But it's only like 20% of my time. Right.
So how, and the next thing is how they continue to upscale upskill those employees. At the same time the employees should have the motivation and urgency or even a little bit fair to advance their career.
So that's one extreme. I think that company that VP is like beats 80, 90% of the or like my customer. And I have another very interesting example is where a consultant, he loves our tool and AI so much and he's starting his own, her own consulting firm. She used to be, she is still a regulatory and quality consultant for pharma and medical device.
But now her majority, she's opening boot camps to teach people how to use AI tools.
Etienne Nichols: Oh wow.
Michelle Wu: And she even went further like she; she can build her own AI agent by using like, like she went really deep. She transformed from you know, regulatory consultant to a coder almost.
Etienne Nichols: Wow, that's awesome. Yeah. And just AI allows you to do that. I mean even without being having the coding knowledge. Unless she has built that as well. I don't know if that's in her repertoire or not, but that's impressive.
Michelle Wu: Yeah. I have never seen someone who is so committed to technology previously. She will, you know, we met at reps. She will be the person teaching like hey, here's how you do your 510(k) submission.
You know how you negotiate with the FDA. And now she's like here, here is like with zero coding experience you can build your own AI agent. And how do, how, how can a one-person regulatory quality consultant or like, like a professional with the help of AI build a 5 people team or 10 people team's capacity and deliver it.
That's like you know, science fiction for me.
Etienne Nichols: Yeah, I'm curious. So, with that being said and the fact that she is doing this and building out AI tools for more and more people, theoretically there's going to be a lot less of that admin work which could mean a couple different things. It could mean that the, the lower-level entry jobs have a requirement for a higher understanding of what you're doing.
So, I mean it's just the historical problem of we need experience even though it's an entry level job. So, the entry level people are like well how do I get that experience?
And all these different things in your mind for individual contributors.
What skills do you think are going to matter more than that hands on experience over the next two to three years? It used to be 10 years. Now I actually think we have to think 18 months at a time at this point.
Michelle Wu: Yeah, not going to lie is going to be a huge problem. She's not only teaching helping people to like the previous I call her superstar or super AI regulatory consultant. She's not only helping other building the tool, she's teaching people to build their own tools.
AI tools.
So, at the on the other hand, I also teach at Stanford and like I love student I still feel I'm a student at heart.
It is really brutal. Not going to lie. It's really brutal for junior people or individual contributors to find their finder footing to get into regulatory affair. When you said like the people's our customers attitude towards AI has done 180 or like even 2 of 180.
I also feel like the demand for regulatory professional has also been that been like that. For example, during COVID there's just not enough regulatory professionals like people are paying very high very salaries just to recruit talents and now they we have seen a lot of layoffs, a lot of reworks and either people are freezing hiring or they are just like somehow the role has been built.
And I saw like you had a podcast talking about the job market with one of my favorite people, Ilana.
And I think the, the challenge not going to lie. The challenge is there. So, number one the company will not need as many junior people as before. And number two is like junior people want to stand out in the quality and regulatory field.
They need to recreate the game. So previous in the previous generation like a lot of my customer who are the VP of regulatory affairs they get ahead by spend just spending time have multiple reiteration with the FDA and multiple years of doing the work.
Now that luxury is gone. Giving remote work, giving the job market and technology that we're facing today. So, for junior people to stand out in their career, they definitely need to leverage AI and try to build their own new master of learn and asking questions.
I remember when I first pitched to one of the medical device companies, the director put out a thick book like this and was very, and I was very proud that he can recite the whole title 21, you know, line by line.
That's with.
With. That was very impressive back then. But with modern technology that skill is irrelevant because I can just type in ChatGPT or Google or Nyquist guidance and get anything, ask any sophisticated question I want for guidance.
So, in some sense knowledge is commodity. Now what's the previously regulatory consultant or regulatory professionals stand out by their knowledge?
Now with knowledge being with AI level the field, I think the capability is for people who can ask high quality questions. And what is behind high quality question is the clarity of mind.
And also, the second point which is very counterintuitive of our field because most of regulatory people are very humble, you know, very reserved and they don't, they don't, they kind of, you know, held their cars very close to their chest.
In the new modern world, especially for junior people to stand out. They need to have courage to ask questions, to speak up, to come up with new ideas. If they just follow the, the old game in the new time, I think they're just going to have a hard time in their career.
Etienne Nichols: Yeah, there's no more just sitting back waiting and learning, you know, from the sidelines.
Michelle Wu: Yeah.
Etienne Nichols: You mentioned, you said you would if you were advising a young RAQA professional, you'd highly recommend that they start leveraging AI that. I think that's good advice. I wonder if you could go one layer deeper and tell us what that even means and looks like in your mind.
Michelle Wu: Yeah, yeah.
Like before I answer that question, I want to tell you why. And remember when I started out I said I'm a data nerd and I'm a tech nerd.
So, before this I have done a lot of research.
LinkedIn data shows the professionals regardless of jobs and I intentionally eliminate the data of engineers because that's a very, you know, outliers. But for knowledge workers, like if you are in clinical, if you're regulatory or whatever, for any knowledge workers or white-collar workers, the AI literacy will help you earn 56.8% of salary premium.
Etienne Nichols: That's a lot.
Michelle Wu: Yeah. And you have like 30% more chance to stand out in resume screening. And quite frankly now a lot of companies use AI to screen your resume, right?
Yeah.
Etienne Nichols: You need to be able to use AI to get past their AI.
Michelle Wu: Yeah. And I have a very heartwarming, like a story to give the young professionals like or anyone who want to get ahead of their career, like some courage. Now it's the end of year, we need some sunshine and some confidence.
So, one of the, like our user, we, we provide company academic access to a lot of institution who teach regulatory science and regulatory intelligence. And so, one person got the job from Abbott and her hiring.
This is her first job and her hiring manager said she beat 500 applicants.
So, for one junior regulatory role, there's like 500 people competing against the same role.
And like, of course she is like a rock star by herself. But the fact that she's very creative, she has the AI literacy, she wants to guide her team how to adopt AI and use the AI, make her shy in her resume and in her job interviews.
So, like besides just doing the work, her leadership, her hiring manager felt like she can add more value to the team.
Yeah, yeah, yeah, yeah.
Etienne Nichols: That's, that's awesome.
Michelle Wu: Yeah.
And then back to your question, like how can people really leverage AI to get the most out of their career? We talk about the good things, like, you know, you can actually earn a lot of money and stand out in the job career.
And also, there is also scary things.
According to Anthropic's research, 700 occupations will be either automated or augmented by AI.
And surprisingly, not surprisingly, regulatory is among 1,700 and automation ratio range from 21% to 15%.
So that means like if people do not know how to use AI to make them more efficient and do things that AI cannot do, they are going to not have a job.
Etienne Nichols: Yeah, yeah. Because they're only going to be able to do 80% of the job that someone who is utilizing AI. I guess if those numbers are. If I'm applying those numbers correctly.
Michelle Wu: Yeah, yeah, yeah, yeah, yeah.
I see like the data.
Data nerd. You shine with the data nerd inside of me.
And I also. And also, it means like. And that's the research done this year with large language model become more like a more powerful.
I think the ratio which is getting higher and higher. So, the scope and the scope and content of regulatory affairs will be redefined or expand.
And also, for people, they need to outsmart AI or outdo AI. Otherwise, if they just compete against AI based on memorization Speed is a losing war from the start.
And back to your question. There's a couple of ways that people can really get ahead with AI, and we have seen from our 200 customers result.
I think one of our champions. She started as just a senior manager. She's now the youngest VP in public company. I'm so proud of her. What she has done right is first she, she asks the permission of it. Of course you know, you need to have the permission of it. Do we have any AI tools like what are the resources and what are the guide rails?
So do not do things that AI, it does not allow you to do. And second, she's very dedicated to spend just one hour a day or one hour a week to learn and constantly try.
I think Bill Gates has a great quote. People always underestimate what they can do.
You know in five they, they always overestimate what they can do in five years. And they're estimated we can what they can do gradually and every day.
Etienne Nichols: Yeah.
Michelle Wu: And so, she has come up with different use cases.
They are not like rocket science use case. Some of it's very simple like oh I can auto augment a table of analyst is using Nyquist or I find a new interesting competitor or news from Nyquist.
And over time she built her credibility. She becomes the AI person for her little team. And then she led the team. She was very generous. She hosted lunch to learn, she teaches her team, so it becomes a center of AI excellence.
And because they things do not happen overnight in the second year her AI skill gets so much better. They were able to do a product launch to reach agreement or product launch regulatory strategy from the start to finish within one week.
Leveraging our tool and does not account the time spent of cross function communication alignment with marketing, alignment with legal, alignment with the sales team. So, it should be she, she set the company record and, and from that like her career just took off and every like you know, reps did an interview for her. She was featured and she's so aspiring for so many regulatory professionals, especially young women because through her the, the, the future young leaders that they can say like oh even though I'm about to graduate or like I'm just, especially as a manager, I can be her one day.
Etienne Nichols: Yeah. I mean to your point young people now like you said, if knowledge is commoditized knowledge, something you had to gain over years and years and years, now it's at your fingertips.
It's really a question of what are you going to do with it? How are you going to apply it. And I like as you were talking it made me think of something about how she found those use cases and you said it's not rocket science the use cases she found but it sounds to me like the difference in how she applied AI and how some other people are thinking about AI.
So those other people may be thinking oh AI is going to take my regulatory job or how do I apply AI to regulatory. It's too big. And she took those bite sized chunks that got really specific.
I have one thing I've got to do in the next half an hour. Can AI do it for me? And so, you're very specific and I think that's what's helpful.
Michelle Wu: Yeah, yeah. She really blended like a lot of the, you know when I talk about predicate search and, and like gap analysis it sounds like very generic, but her beauty is she applied into her day-to-day job.
I want predicate search for you know, this particular patient with this particular mode with this particular metal at this particular country that is magic to her team and her organization.
Etienne Nichols: That's awesome. If I had one last question to ask it would be I guess, do you have a message that you have just to the general MedTech industry.
Any last piece of advice or pitfall to avoid and it just anything at all as far as, or a call to action. Go check this out. Whatever it may be, Any, any last pieces of wisdom.
Michelle Wu: I think just like start small, don't start small like every day or every week, just take 5 minutes, 10 minutes, no more to build your AI literacy because this will not only benefit, you know, your work but really, you're doing this for yourself to advance your career. Not only AI is going to eliminate a lot of the tests like people do, but it is also going to be your multipliers. Think of the VP that he just built his own AI startup within a public company. That's just quite incredible.
Etienne Nichols: Amazing. Yeah.
Well thank you so much Michelle. I really appreciate it. This was a fun conversation. I know we could go on for hours and I know you had you would, it would be worthwhile conversation, but we have to end somewhere.
So where can people go to find you and learn more about what you're doing?
Michelle Wu: Yeah, so they can come to like they can find me on LinkedIn like Michelle Wu at Nyquist AI or they can find us from our website.
We have. We are not only just selling the solutions, we are also selling the way of work and way of organization.
So, there's a lot of complimentary, you know, videos and framework thinking, prep thinking SaaS leadership pieces is almost like BCG McKinsey level of thought but just dedicated to regulatory and quality so people can check it out.
We also have a very cool product like Nyquist Connector and plugin that we are rolling out in beta versions, and we plan to launch during the JP Morgan. So, if you ever are interested to be the pioneer or have front seats, please contact me and I will like, you know,
offer a complimentary access.
Etienne Nichols: Yeah, I may have to do that myself, but you know, don't mean to put you on the spot while we're recording. But anyway, no, this is really, really cool. Thank you so much Michelle. I appreciate it.
We'll let you get back to the rest of your day and hope you have a quick recovery and get back to 100% soon.
You're, you're quite stoic coming on today, so I really appreciate it.
Michelle Wu: Thank you. Thank you, Etienne and thank you for creating this platform. Thank you for, you know, seriously the source of conflict and wisdom, like while I'm just like lying in my bed recovery.
And I think your work has really touched the souls of thousands of thousands of people.
Etienne Nichols: Well, thank you so much. I really appreciate you saying so.
Those of you who've been listening, thank you so much for listening to the Global Medical Device Podcast. We hope this has been beneficial and we will see you all next time.
Take care.
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The Global Medical Device Podcast powered by Greenlight Guru is where today's brightest minds in the medical device industry go to get their most useful and actionable insider knowledge, direct from some of the world's leading medical device experts and companies.
