Let’s Talk AI: A Leader’s Playbook for AI Strategy, Compliance, and ROI

Artificial Intelligence is no longer a future consideration. It is becoming a business imperative across life sciences organizations. Yet many teams struggle with where to start, how to maintain compliance, and how to demonstrate measurable business value.

This webinar provides a practical roadmap for organizations beginning their AI journey. Attendees will learn how to identify the right opportunities, engage key stakeholders, evaluate risks, and build a decision-making process that aligns AI initiatives with regulatory requirements and business objectives.

Whether your organization is exploring AI for technical operations and compliance functions, this session will offer actionable guidance for moving from curiosity to execution.

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What You’ll Learn

Participants will learn how to:

• Evaluate AI opportunities using a structured decision framework
• Align AI initiatives with business goals and measurable ROI
• Navigate compliance considerations in regulated environments
• Identify and engage critical stakeholders across the organization
• Begin an actionable roadmap for AI adoption

Network Partners Group experts will share real-world perspectives on how to help Life Sciences organizations implement AI responsibly, efficiently, and compliantly while balancing innovation, operational excellence, and regulatory expectations.

Who Should Attend

This discussion is tailored to VPs and Heads of life science companies in the following areas:

• Operations and Process Leaders
• Executive Sponsors evaluating AI investments
• IT and Digital Transformation Leaders
• Regulatory Affairs Leaders
• Labeling Professionals
• Quality & Compliance Leaders
• Clinical Operations Teams

Speakers

• Christine Bonifield, Partner, NPG
• Matt Burger – Moderator – Partner, NPG

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About the Let’s Talk Series

Let’s Talk AI is a branch of NPG’s Let’s Talk series, bringing together industry leaders and NPG experts to explore how AI can be responsibly used for innovation and automation in regulated life sciences environments. The series focuses on sharing valuable, practical advice for VPs and industry leaders implementing AI solutions that drive ROI for their organization.

Overview

Artificial intelligence is no longer a future consideration — it is becoming a business imperative across life sciences organizations. Yet many teams struggle with where to start, how to maintain compliance, and how to demonstrate measurable business value. This webinar provides a practical roadmap for organizations beginning their AI journey. Attendees will learn how to identify the right opportunities, engage key stakeholders, evaluate risks, and build a decision-making process that aligns AI initiatives with regulatory requirements and business objectives.

Key takeaways

• Lock in your AI policies and processes today: they will morph as maturity grows, but they are the guardrails every downstream evaluation depends on.
• Investigate multiple AI tools with a single compliance lens, using a common scoring matrix rather than side-by-side feature bake-offs.
• Find your first AI opportunity in real work pain: repetitive, time-consuming, CAPA-triggering, or rework-heavy tasks, not in novelty.
• Treat the User Requirements Specification (URS) as the hero artifact: it turns excitement into a compliant plan you can validate, budget, and defend.
• Socialize the URS with cross-functional stakeholders and outside voices before you build; headwinds discovered now are far cheaper than headwinds discovered later.
• Make validation, IP protection, cybersecurity, and confidence-with-the-right-human-in-the-loop non-negotiables baked into the URS, not retrofits.
• ROI planning must include recurrence, revalidation, maintenance, and the cost of maintaining a validated state, not just development time.

Related Content

• Previous episode: Let’s Talk AI: Balancing AI Innovation & Compliance in Life Sciences
• Next episode: Let’s Talk AI: From Concept to Compliance — Building AI You Can Trust

Transcript

This transcript has been lightly edited for readability and preserves the substance of the live discussion. It is provided for general informational purposes and is not legal or regulatory advice.

00:00 — Introduction and speaker welcome

Matt Burger: Welcome, everybody, to Network Partners Group’s latest installment in our Let’s Talk AI series. Today we’ll be discussing the Leader’s Playbook for AI strategy, compliance, and ROI.

I’m very pleased to introduce Christine Bonifield, Partner here at Network Partners Group (NPG), where she helps life sciences organizations navigate complex labeling, regulatory, and operational challenges. Through her work with clients across the life sciences industries, Christine quickly recognized the wide spectrum of maturity, efficiency, reliability, and compliance that exists across labeling systems and processes. That insight became the foundation of her passion for helping organizations transform their operations and achieve sustainable results.

Known for her practical expertise and strategic mindset, Christine excels at combining real-world experience with innovative approaches to solve industry challenges. She partners closely with clients to identify opportunities for improvement, streamline processes, and implement solutions that balance business objectives with regulatory requirements.

Christine has served as program director for FALCON Labeling AI — NPG’s AI-powered label verification and label data parsing tool — leading it through development and launch. Drawing on our deep labeling subject-matter expertise, project-management excellence, and dedication to quality and compliance, she helped transform a strategic vision into a successful, market-ready solution that advances efficiency and accuracy in labeling operations.

I’ll introduce myself: Matt Burger, also Partner here at NPG. My role today will primarily be to act as moderator. In my role as partner, I work with life sciences organizations when they find themselves at the intersection of growth strategy, compliance, and execution. I do this by helping connect what organizations are trying to achieve with how they actually deliver on it. I have over 20 years of medtech experience spanning QA, project management, labeling, and packaging projects — including numerous new product development projects, FDA consent decrees, and EU MDR transitions and remediations.

02:19 — Housekeeping and audience engagement

Matt Burger: A quick housekeeping note before we get started. First, we’re going to need your help by engaging in today’s chat and polls — this will help us understand where everyone is in their AI journey.

Second, everyone joining today will receive a copy of the recording. Everybody asked for it — I promise you’re going to get it.

Third, we’ll keep an eye on the questions tab at the bottom of your viewing screen. We’ll get to as many questions as we can in our Q&A session at the end.

Before I hand the microphone to Christine, I want to make sure you know about our other speaker series. NPG has Let’s Talk Labeling and Let’s Talk PPWR in addition to Let’s Talk AI. NPG is dedicated to giving back to our industry, and we’re proud to share industry best practices, knowledge, and skills through our free webinar series. Register for future episode alerts using the QR code on your screen.

If you’re interested in this type of information — or in learning more about how NPG has helped other clients — connect with Christine and me on LinkedIn. And now, without further ado, over to Christine.

04:19 — Different starting points on the AI journey

Christine Bonifield: Thank you, Matt. I’m pleased to kick off Episode 2 of the Let’s Talk AI series. In case you missed Episode 1, no worries — use the same QR code and you’ll get a link to previous episodes of Let’s Talk AI, Let’s Talk Labeling, and other series.

Let’s dive in with a little audience participation. As you’re answering, I want to thank you for the feedback you provided during registration — that feedback helped drive today’s direction.

What I’m expecting to see is that we’re all at different places on our AI journey — and that’s what we saw with your registration. Some people said they’re just getting started; others said they’ve been working on this for about two years. That makes creating a webinar quite challenging: we all have different needs, different demands, different resources, and different levels of comfort when it comes to AI implementation and AI use.

And I understand — because I’ve walked in your shoes. I’ve been at the “let’s do this, let’s figure it out” starting point, and I’ve had a tool in place for six years.

06:35 — The FALCON story and why today isn’t about the product

Christine Bonifield: Approximately six years ago, Network Partners Group identified a need in our industry and set out to find a solution. That solution was FALCON Labeling AI. FALCON is an AI-enabled label verification and label data parsing tool that reduces both time and risk.

You can ping me on the side if you want to learn more about FALCON, get a demo, and hear about the benefits it can bring to your company. But today isn’t about FALCON — it’s about our experience of developing FALCON, alongside our years of guiding clients through process improvement, digital transformation, and AI adoption. We have walked the walk, and we are honored to talk the talk with you today.

07:53 — Responsible AI as the moral compass for the series

Christine Bonifield: While we are on unique AI journeys, we do share one commonality: we’re all on an AI journey underneath a cloud of compliance. As members of the life sciences industries — regulated industries — it is our obligation to responsibly, and if you could see my script, in big bold letters, honor the way we develop and use AI. It is our commitment to our collective end users — our patients.

Throughout our Let’s Talk AI speaker series, we will continue to use responsible AI use in the life sciences industries as our moral compass. We will share our journey, our challenges, our advice, and our successes — both internally at NPG and through our work with clients. Some advice will be significant, like building a roadmap; other advice will fall into the tips-and-tricks category.

Our focus today will be on implementing AI as a tool. The guidance is transferable to implementing AI as an agent — everything will evolve and step up in complexity, particularly in your areas of validation, data integration, and data integrity. As the series progresses, we’ll dive deeper. If you’re ready to move faster right now, ping Matt and me and we’ll take that conversation offline.

09:45 — Tip 1: Investigate multiple AI tools — and lock in your policies

Christine Bonifield: Here’s your first tip: investigate multiple tools or AI agents to meet your needs.

When creating this slide deck, I prompted an AI agent to create an image illustrating responsible AI in life sciences under a cloud of compliance. This is the image it generated. Not bad, right? It checks the boxes from my prompt. It’s okay — but not great. Truthfully, I was a little underwhelmed.

So I prompted a different AI agent using the exact same prompt — I literally copied and pasted it. Same prompt, different agent, different result. That matters. As we discussed in our first episode, there are many opportunities to implement AI into our daily work, including many not shown on this slide. The possibilities are exciting — and the possibilities can be a bit scary. Our goal for this series is to feed your excitement and reduce your fear.

Matt Burger: Chris, I’m going to jump in for a second, because it feels like every single day there are new AIs that pop up, and so many different options to implement AI into our work lives. It can feel really confusing or overwhelming. I’ve felt confused or overwhelmed about how to use AI. Where do we start?

Christine Bonifield: Great question, Matt. I strongly encourage the first step to be: lock in your company AI processes and policies. Here’s a hint — you’ll hear me say this on probably every episode I host, because I feel so strongly that it’s important. Your AI policy may morph as your AI maturity grows, but lock in something today, or tomorrow, and update as you go.

Before we move on — did you notice anything interesting with this slide? That’s a new way to spell the word “guidelines,” isn’t it? When I prompted an AI agent to create an image about AI policy, it did that beautifully — I thought. And then I looked, and that misspelling jumped off the screen. My attention-to-detail radar was blasting. This is why we need to be diligent in our regulated industry — about data integrity, about validation, and about compliance. This is what I call a money slide when we talk about the human in the loop.

I promise we’ll have a future episode where we dive deeper into the human-in-the-loop conversation. I’ll also introduce you to the human in the loop-de-loop. Okay — I made that term up. But I predict human in the loop-de-loop will become part of our common vocabulary in the months and years to come. I’m thinking I should look into trademarking it.

Matt Burger: I love the term “human in the loop” — and “human in the loop-de-loop,” which you should probably trademark, because people are absolutely going to steal that. But let’s stay on track. We’ll come back to that another time.

13:58 — Tip 2: Find AI opportunities by solving real work pain

Matt Burger: Let’s say we have AI policies in place — or at least in the works. I want to know how to implement AI in some way or another. What are the next steps?

Christine Bonifield: Great question. Knowing that you want to — or need to — implement AI can be exciting and downright scary. The possibilities are truly endless, and knowing where to start can be very overwhelming. I encourage you to take a deep breath and look for opportunities to make your work easier.

My original slide for this topic was a mirror, because I think of this as a time to do some self-reflection. Ask yourself: What are your needs? What are your pain points? Where do you have repetitive, time-consuming tasks? What are your CAPA trends? What about your complaints? What part of your job do you simply not want to do — those tasks that always go to the bottom of the to-do list day after day? Are there areas in your process where rework is triggered, or where you’ve had to put in intermediate approval steps just to mitigate rework?

Look at AI implementation as your opportunity to simplify or improve your day-to-day work life. Give yourself permission here — permission to dream, to innovate, to create, to be excited.

Matt Burger: Chris, I think that really resonates with a lot of folks. Who wants to do repetitive tasks, the grunt work you have to do every day because you just have to do it and there are no other options? That’s really where a lot of folks are looking at how AI can help — eliminating repetitive tasks or taking away the mundane work that doesn’t provide any real value.

16:23 — NPG Blueprint Strategy Sessions as an entry point

Matt Burger: We added this slide because we wanted to include a little PSA. NPG has what we call Blueprint Strategy Sessions. It’s an opportunity where we facilitate a conversation with you — whether you’re a client or you bring in other players from your organization — as a strategic reflection and process-improvement discussion. It’s an opportunity to identify “where could we?” With respect to AI, these sessions are incredibly impactful for identifying opportunities to implement AI tools.

If you want to learn more about how we run Blueprint Strategy Sessions, and the value you can get from a session customized for your organization and needs, ping Christine or me. Back to you, Chris.

17:15 — The question spiral: from excitement to paralysis

Christine Bonifield: Great call-out, Matt. Now back to today’s discussion. You’ve found an area to implement AI in your work. You’re excited — and you’re scared. You’re not an AI expert. Or maybe you are — and even if you are, you have many steps to navigate and questions to answer.

How will I validate this? Will there be a return on investment? How do I protect our intellectual property (IP)? What will it cost? Is this a one-and-done project?

Let’s say you’ve identified a task that would take three months of effort to complete. You know AI could help do the task — but it’ll take a month and a half to develop your plan and for the development team to create your AI tool. Is that worth the investment? Or should you stick with the status quo? Well — maybe it isn’t worth the effort, so you look for something else. But what if this task comes up every single year? Maybe the AI tool you build could then be modified to meet a very similar task. These are the types of questions you should start to consider.

One question leads to another, and to another, and to a slide full of even more questions. Do we develop this in-house or find an outside developer? Is there an off-the-shelf option already available — and if so, is it validated? How do I know the data is correct? How do I protect our IP? It all becomes head-spinning.

We could fill the rest of our time compiling question after question. And honestly, this is where people get stuck. They spiral through a sea of questions that trigger more questions, and they become paralyzed. They don’t move.

19:32 — URS as the hero: turning ideas into a compliant plan

Christine Bonifield: But do not fear, my friends — our hero is here to save the day. Our URS hero.

In all seriousness, creating a User Requirements Specification (URS) is the next step for you. It is a powerful and essential tool to help transform an idea into a foundational document that outlines what you expect your AI tool to achieve.

We want you to be excited. We want you to move forward with your idea and vision. Creating that URS will force you to begin articulating your vision — putting it into words — and it will force you to begin answering some of the questions from those previous slides. But I want to stress: begin answering those questions. You certainly won’t have all the answers.

20:36 — Socialize the URS: better together, and headwinds matter

Christine Bonifield: Next: socialize your vision. I’m a firm believer that we are better together. Talking with others allows you, one, to practice vocalizing your vision. What is often clear in our head is often muddy to others — and the more we talk about it, the more we start to put our vision into words. Then you start to add the ideas of others, and the wheels really start to turn.

As you vet your vision with others, you might start to hit headwinds. Have you ever noticed how happy people are to pick apart your ideas? This may be hard to hear — okay, it will be hard to hear. Getting negative feedback about your idea is difficult. But I challenge you: this is valuable. Take it in. It will enable you to better define your URS — or it may identify headwinds that you really can’t get through, or aren’t worth getting through. Headwinds like that cause you to hit the brakes. That happens, and it’s okay. It’s better that it happens now than later in the process.

22:05 — “If you build it, will they come?” — the FALCON socialization example

Christine Bonifield: But let’s assume your vision is really getting tailwinds. You’re excited. Others are excited. And it’s time to ask that question the image begs to be asked. Matt?

Matt Burger: Yep. I’m going to paraphrase from Field of Dreams here: that classic line, “If you build it, they will come.” But let’s say — “they will come.”

Christine Bonifield: Most of us hear it as, “if you build it, will they come?”

Matt Burger: Exactly.

Christine Bonifield: When we decided to build FALCON AI, we socialized our vision with our clients. As consultants, we have a pulse on the industry — we engage with what’s happening across it. We were able to lean into our clients to gain their thoughts and opinions. We talked to cross-functional leaders from some of the top-five global life sciences companies. We talked to startups. And we talked to companies everywhere along that size continuum. It was imperative that we had this feedback. It influenced our URS.

I’ll be honest — we thought our ideal client was going to be the startups and small-to-medium companies. It influenced our development, because FALCON AI is actually being used by the big guys as well. Getting that feedback was valuable to us.

We also — keep in mind, I shared this in our first episode — we were sticking our neck out to build this. Efforts like this are normally not cheap and not quick, so we made our business case. We had to know that if we built this, the industry would come.

23:54 — URS and the business case: from excitement to organized decisions

Christine Bonifield: Now back to your journey. Your excitement is growing. You have tailwinds pushing you on. You really want to build this. But let’s put it into business terms: if you build this, will it be used? Can you justify the investment?

This is where we return to our friend the URS hero — and introduce our new friend, the business case. We also return to the questions we glazed over earlier. Some questions will fit into your URS. Others will fit into your business case. And some might need to be set aside until you figure out where they go. Regardless — they need your attention. It’s time to dive deep, get organized, and get serious.

I suspect that if we all shared our email file-organization method in the chat, we would not find any two systems that look the same. The same is true as we start to walk through these questions. As we shared earlier, our AI journeys are at different places. So as we navigate, I encourage you to pull out what works for you. Start to create a skeleton of your URS — and a ghost of your business case.

Keep in mind, we won’t be able to answer all the questions today. But this will get your thoughts going, and your questions will generate more questions specific to your unique URS.

One note: as we walk through these questions, you’ll hear me say “how will you” or “you need to.” When I say “you,” I don’t mean you personally — I mean your organization needs to make sure these questions and steps are built into your URS and your business plan. You don’t have to have all the answers. You don’t have to have all the solutions. In reality, your IT department is not going to want you to create their cybersecurity plan — but they will be thrilled to learn that you’re making room for it in your development steps.

26:32 — Non-negotiable #1: validation and bringing quality in early

Christine Bonifield: Let’s dive in with the non-negotiables. Think of these as your go / no-go items.

First up: validation. This is huge, and we’ll only start to scratch the surface today — my focus is to build validation into your URS. Bring your quality team into the conversation today. Validating an AI tool is a huge hill to climb. The more complex your tool is, or the greater number of AI components within your tool, the more complex your validation will become.

We will have a future episode dedicated to validation — I promise it’s coming soon — and we’ll dive much, much deeper. We’ll also share NPG’s validation approach, and we’ll talk about the human in the loop, and probably the human in the loop-de-loop, on that episode.

27:42 — Non-negotiables #2 and #3: protecting IP and cybersecurity

Christine Bonifield: Moving on: protecting your IP. I don’t need to convince anyone in life sciences that this is a need, not a nice-to-have. Ensure your URS includes this protection.

Here’s another side tip: having a paid subscription — meaning you pay for your AI agent — does not automatically equal protected IP. Please check your contracts and see how they are handling your information. It’s a biggie.

Cybersecurity — we’ll go through that one fast. This is a need. Make sure it’s in your URS.

28:35 — Non-negotiable #4: levels of confidence and the right human-in-the-loop

Christine Bonifield: Now let’s talk about levels of confidence. You can see the smile on my face — it truly is a fun topic. You might even use it as a conversation starter at your next team dinner.

I know you’re thinking this should be a no-brainer: we want 100% confidence in the results. Did I forget we’re in a regulated industry? Of course not. Being compliant is what makes this topic super spicy.

Are there instances where the confidence level could be 90%? Maybe 80%? Actually — maybe. Depending on your end cases and what you’re using the tool for, you might be able to have a 90% or 80% confidence level. I know you think I’m crazy. However, depending on your validation requirements — it always comes back to validating this, doesn’t it? — you may be able to have less than 100% confidence. But I caution you: if you proceed down that route, I highly recommend you have the right human in the loop as part of your process.

29:48 — Build, buy, or license: how your URS shapes vendor selection

Christine Bonifield: Let’s take a look at how you’re going to start to build your AI tool. Some companies have policies limiting AI development and AI use to internal, company-controlled agents only. Many other companies do not have that level of sophistication yet — you need help, either internally or externally.

This is where your URS is critical. If you’re vying for internal resources to build your tool, you have to give them something they can build off of. If you’re working with an outside vendor, you will need clarity as well — you’re going to need to ask them for a budgetary quote at some point, and they won’t be able to give you one if they don’t understand what you really need. There are also off-the-shelf options that may be a consideration. Your URS will help you with vendor selection — that’s what the graphic here represents. It helps you get through the sales pitch and get down to: does this check the boxes of what I really, really need?

That leads to your next consideration: if you get something off the shelf, or a tool that’s already pre-built, will you access it with a license, or engage with it as a service? Service versus license affects your validation. Remember when I said we vetted and socialized our FALCON idea with industry partners? We intentionally built FALCON AI as a service rather than a license opportunity.

Finally: use your URS to create your vendor selection matrix, or your decision criteria.

31:55 — Source data, infrastructure, and the human infrastructure

Christine Bonifield: I’m going to pull the plane up a little and go through a couple of these slides more quickly.

What is the source data for your tool? If you’re using data, is it accessible? Is it reliable? When we assist our clients with system implementations, we use the term “garbage in, garbage out” — it’s a consideration for you. And a bigger consideration, possibly, if you’re using external data: how do you know it’s reliable? I promise you, your quality group is going to want to know.

Do you have infrastructure in place? We’ll go deeper later, but this circles back to your non-negotiables — cybersecurity and IT protection. You need to make sure anything you bring in checks those boxes.

And what about your human infrastructure? Do you have the right — and I strongly emphasize the word right — humans in the loop? What is your AI literacy? What’s the AI literacy of your team, and what level is needed? Will AI agents have a spot on your org chart? We’ll dive much deeper into the people factor on a future episode, because this is way too big for a single slide. We’ll discuss culture and change management, which are critical to the success of your AI implementation journey.

33:46 — Governance: version control and a maintenance schedule

Christine Bonifield: Rounding out this part of the conversation: I want to encourage you to consider some governance in your URS. Build a plan for version control and a maintenance schedule. You do not want to pour all this time and effort into building an incredible tool and then have it become obsolete. Additionally, you don’t want to spend all of your time updating and revalidating your tool over and over. You need a plan — there’s a middle ground, and you have to find it.

34:21 — Two takeaways: you are not alone, and be excited

Christine Bonifield: Let’s take a deep breath. We’ve covered a lot, and we have so much more to chat about. If you have one takeaway from today, I hope it’s that you’ve learned you are not alone. We’ve been on this AI journey, and we’re still on an AI journey. We are happy to be your thought partner and your guide.

A second takeaway I hope for you is: be excited. AI can truly improve your work life, and it can be reasonably used in our regulated industry. We need to be aware of the possibilities and the challenges — but together, we’ve got this.

35:06 — RAPS Convergence and a look ahead

Matt Burger: Thanks, Chris. I’ll hopefully land the plane from today’s conversation. Before we get to questions, I wanted to share that the NPG team is going to be attending and presenting at RAPS Convergence this year. We’d love for you and your colleagues to come meet us in person. We love learning from everybody in industry — what your journey is, how you got into this industry. If you’re intrigued about AI or regulatory affairs — because it is RAPS — happy to have any and all conversations. Please don’t be a stranger. Stop by the booth. Come to our speaking engagements. We’d love to see you there.

35:51 — Q&A: I’m not an AI expert — where do I start?

Matt Burger: Now let’s move into questions. A really solid question came in earlier: “I’m excited to implement AI, and have been strongly encouraged by management to implement it — but I’m not an AI expert. Probably an AI novice, probably an AI newbie. What advice do you have for me to even get started?” What do you think, Chris?

Christine Bonifield: That’s a great question, and I was so appreciative of the transparency when I read it earlier — you’re in really good company with others. I recommend you start by increasing your AI literacy. You’ve already taken the first step: you’re here, attending today. Keep attending — other webinars, podcasts, whatever you can find. But also start reading.

On the screen are my most recent reads. I do suggest, if you’re at the novice level, hold off on the Pascal Bornet books — those are the two on the diagonal. They’re good books — they’ll challenge you, you might even learn some new vocabulary — but they’re big, they’re long. I think the audio version of each was around 15 hours. They’re better for readers who are further along.

I also suggest you don’t just read AI books. The photo on the right is the bookshelf sitting behind me — you’ll notice it’s filled with leadership books, change-management books, innovation and motivational books. Especially change-management, leadership, and culture books. These topics are going to be critical to the success of your AI implementation — probably more so than even your AI competency. So take some time and start reading. It’ll get those juices flowing, I promise.

38:12 — Q&A: Will AI eliminate the work of experienced validation consultants?

Matt Burger: I’ve got a couple of questions in the chat I want to get to before we close out. First: “I’ve got 23 years of validation testing experience as a consultant. My concern is that AI will eliminate the opportunities for me to continue to do said validation.”

Christine Bonifield: I love that question — and that’s why you saw in the last slide we’re talking about culture and change. There is a really sincere fear of, if AI comes in, where do I sit? Where do I fit? And this is part of the human-in-the-loop piece.

Our FALCON AI tool is label verification. I’ve got an incredible team of label specialists who do an amazing job on attention-to-detail tests — they are my label verifiers, and I would put them up against anybody if there was a competition. And then we have FALCON, which seems a little conflicting. But I’ll tell you what — the tool and the humans work together, and they’re the right humans, which is what you are. You can’t just put any person in there, because that’s where you get the human in the loop-de-loop.

Your knowledge and skills cannot be replaced. If you think about bringing in an AI as a junior consultant, they can do a lot — but they can’t do it all. Your expertise and knowledge cannot be replaced. Your job might look a little different — you might not be doing as much of the grunt work, or you may be verifying some of the results — but we still need you. And I will make that case all day long. We still need people who have the knowledge and skills, especially in our industry. It can’t go away.

Matt Burger: I’ll add a little cherry on top, Chris. I’m not going to name the company — everybody can hop on Google or use AI to track down what I’m saying. There was a very large automobile manufacturer that let go a lot of senior engineers, and at the same time was implementing AI to take on some of that work. Flash forward: things went bad. Quality went down. The organization recognized that they needed to bring a lot of those senior engineers back, because something wasn’t right. The implementation wasn’t right. The human-in-the-loop element was removed. And you can probably connect the dots that they didn’t validate it — at least the way they needed to — and they downplayed the tribal knowledge those senior engineers had.

Chris, I think you’re spot on: this is an evolution of what jobs will look like — it’s not necessarily a replacement. Some jobs will be impacted more than others, but it’s pretty hard to beat a seasoned engineer and the knowledge they have between their ears.

41:44 — Q&A: Number-one change-management book to read

Christine Bonifield: I popped into the questions. Dawn, that’s a great question — what would be my number-one, most-recommended change book to read? Switch is probably one of the first ones, and it’s one of those tried-and-true ones.

Oh my goodness, I’ve read so many, and I try to pick something out of each of them. Dawn, let me think about that a bit and I’ll get a list together — I’ll shoot an email off to you. Great question. And I’ll say, in many of the books I read, you look for those little nuggets of change and pull those into your portfolio. That is a great question.

42:34 — Q&A: Can you provide a hypothetical URS example?

Matt Burger: Brian’s asking, can you provide a hypothetical example of what a URS might look like? I think that’s a tricky one — I don’t have a slide made for that one, Brian. Let’s talk through it.

Christine Bonifield: Yeah, we can. You build your success criteria in order. Like, for me — I’m playing with the idea of a new car. I’m the shortest one; everyone else in my family is between six feet and six foot seven. So a criteria for me is: is the backseat legroom big enough for somebody to sit in the back seat besides me? Because I’m tired of sitting in the back seat.

You start to build out your “I need this, I have to have this” — that’s where those non-negotiables come in. Then you pull in your nice-to-haves. I kind of like red, black, and silver for vehicles. My husband has a baby-blue Mustang — don’t love it. Those are nice-to-haves, but not needs-to-have. You start to build out: what does this need to have in order for you to say, “this is what I want”?

And it’s going to look different depending on the complexity of your tool. Many of your companies have their own URS templates. We do a lot of system implementations and support our clients with that — and I’m surprised sometimes when I see people skip over the URS part of the process. Start there. It is a really critical piece of building your process out.

44:32 — Closing thanks and how to keep the conversation going

Matt Burger: Christine, I don’t see any other questions. It’s time to land the plane. I’ll pivot to closing out today’s discussion.

First and foremost — thank you, Christine. You put an obscene amount of time and effort into this. Thank you for sharing your industry expertise and insights today. If you want to hear more from Christine live and in person, please reach out — or visit her at RAPS Convergence. Another plug.

Second, thank you to today’s audience for joining, participating in the polls, and putting in questions. We recognize these conversations are really beneficial to industry, even when we have to keep things generalized. Should you have specific questions or a desire to set up time with our subject-matter experts, please scan the QR code.

One ask from the NPG team: tell your friends and colleagues about these webinars. The more people who attend, the easier it is for us to keep hosting them at our current cadence. We do this because of experts like you who attend, who take an interest in our topics, and who provide input into what the next topic should be. Don’t be quiet — let us know what you want to hear about.

The final item: if you still have questions we didn’t get to, or if you didn’t want to drop it in the chat because other people would see it, please reach out to connect with Christine or me. We’ll find some time — we’ll dive into it one-on-one.

With that, I’m going to close today’s conversation on Let’s Talk AI. Take care, everybody. Thank you for choosing to spend some of your day with the NPG team. Be sure to connect with us — and we will see you next time. Talk soon.