Management Consulting: Putnam Associates • Matt Riordan
Decision Quality in Low-Friction Information Economies
What happens when AI makes answers abundant, routine work cheap, and entire business models vulnerable to disruption?
Matt Riordan, CEO of Putnam Associates, joins Nic to explore how AI is transforming management consulting – and what that transformation reveals about competition, innovation, and organizational survival. Matt explains why the scarcity is no longer information, but judgment; why domain expertise and context may become even more valuable in an AI-driven world; and why organizations need to learn faster, not simply work faster.
They also explore the “pyramid problem” facing consulting as AI changes how people develop expertise, why companies that use AI merely to replace labor risk a race to the bottom, and why Putnam believes its future depends on combining AI with human creativity, experience, and strategic judgment.
The deeper lesson reaches well beyond consulting: when the environment changes faster than your organization, your ability to learn may be your most important competitive advantage.
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Nic (00:00)
Matt, welcome to UnNatural Selection.
Matt (00:02)
Nick, it's great to be on.
Nic (00:03)
It's great to see you and in full disclosure, you and I have known each other for 20 years almost. And so we're very good friends, but it's great to have you here because your career, especially nowadays, has just shown such a great progression from starting in, you you're early on as an associate and then moving your way all the way up to most recently CEO of Putnam Associates. And so I think there's the trajectory, the experience.
and how you've seen the industry evolve and the things that we're facing right now, I think is fascinating. before we dive into all that, I'd like to start with just the beginning question that gives you the chance to give us your context and why you do the things that you do. So with that, Matt, can you please let us know what need or impact drives your work?
Matt (00:51)
Thanks, Nick. Yeah. And again, thanks for being on here. It's been great to know you for a very long time and really excited to be part of this amazing podcast. And so the work that we do at Potnum and the reason I do the work here is really about a combination of being able to solve incredibly interesting and complex challenges. That's sort of the consulting nature of it. I think the fast learning, the problem solving element is something that
attracted me to the work, it attracts all, think, of us inside of the organization to do the work that we're doing. But the ability to do that serving life science companies, it really has a lot of meaning. It allows us to see that our end product isn't just the success of our businesses, that we're supporting an early stage biotech that's able to successfully
reach an IPO or an acquisition point or a large multinational pharmaceutical company that's able to say bring a new medicine to market. But it's really about the impact that those companies and the products they're developing have. We're able to a part, albeit admittedly a small part, but play a part in really advancing human health. And that's something that we're proud of and something that's great that we're applying our thinking and our effort and our human power towards solving some important problems.
Nic (02:07)
Yeah, guess, you know, the the audience for this show is very diverse. And so can you give us a sense of, you know, for the listeners outside of consulting and pharma, just a quick overview of what does Putnam actually do and how does management consulting shape decisions in life sciences and pharmaceuticals?
Matt (02:28)
Happy to do so, Nick. So Pundim has been around for more than 35 years. We support basically all of the major players in the biopharmaceutical space, as I was mentioning, from all of your top pharmaceutical companies to early stage biotechs to even some of the financial sort of VC and private equity companies that invest and sort of give them guidance. And really what we're trying to, where we sit in all of that is trying to help
those companies make better decisions. Those decisions could be at the front of the product life cycle around, know, what areas should we be investing in? And we help them understand where is their need? Where are things evolving? Where's there going to be the need for new medicines 10, 15 years down the road? Cause that's how long it takes to go from sort of program inception to actually making an impact to supporting the commercialization of those products and guiding them on sort of where to interact, how to interact with different healthcare systems to make sure that their products can be adopted.
to work with payer systems to make sure those products can be afforded. So we try and cover a wide range of advisory services inside that space.
Nic (03:31)
And just for clarity, so Putnam is also a life sciences specialist. You mentioned this.
Matt (03:37)
Yeah,
we're 100 % focused, all the team members here are 100 % focused in questions around the life sciences. Our heritage stretches back to some individuals who had actually left Bain and company, sort of, and the idea was to bring some of the strategy consulting disciplines, but to take that out of the C-suite and sort of the corporate reorg type questions and go more into sort of product or portfolio level decision making and really keep it focused in the life sciences. And it is.
I think the life sciences are an area where the stakes are so high to make decisions right. You don't get as many redos. The product life cycle, as I was mentioning, can be 10, 15 years long in development. So this isn't like creating a new software code where we might be able to get it wrong seven times before we get it right. These companies need to get these questions right off the bat. And so we, I think, play a pivotal role as a partner that can help them either provide analysis, provide insights, or provide support in helping them to make the right decisions.
Nic (04:33)
Yeah, it makes sense. part of the show obviously is innovation. The other part is composition. So if we think about that specialization angle, how does it play into working or even securing the customers and partners that you have when you might be competing with these mega firms like McKinsey and others? You you guys come in with a very razor sharp kind of expertise. They come in with a very broad one, but they do have departments within those. And so how does that play out?
Matt (05:00)
You know, I think as we talk about competition, it's a great question. And how we would answer that question in front of a client is you do have incredible depth and breadth of talent across a number of consulting firms, whether it's you know, the main, the Bain McKinsey BCGs, whether it's the Big Four, whether it's other players who are sort of more similar to us. I think what we try and bring and differentiate on is the experience that we have top to bottom. And so as you mentioned, if you're a consultant and a partner at
McKinsey, you're going to have people that are focused in the life sciences and they are expert and brilliant. The truth is the full team that they're bringing to it might have come from other parts of the organization that they're assembling. And so we have sort of top to bottom is expertise that is built and experienced in the life sciences and tools that we build that are enabling our consulting teams to really be successful in delivering for our clients in the life sciences. So it's all focused. It's a market, as I was mentioning it.
has long product life cycles, high regulatory requirements, high cost of getting things wrong. So we're able to, I think, thrive by having that expertise. And it's also about making sure that we're trusted, that we have relationships with Clive. They know the work that we're doing. They know the effort we're going to put in and what they're going get out of it at the end of the day.
Nic (06:18)
Yeah, that makes sense. And I've been on the biotech pharmaceutical side in my early career. And when you guys come in with that expertise of understanding not only the overall process, but each individual milestone in these, you know, whether you're going from discovery to development, from development to animal trials, and then eventually to clinical trials and so on, and even within the phases. And you can optimize for all those things all the way out to market and pricing and marketing and all that kind of stuff.
When you understand that process intricately, like you mentioned, there's a layer of trust that you start building with these people where like, know what, you guys get us. We're basically, we're the same type of person except that you guys are in a consulting firm. I work for Pfizer.
Matt (07:01)
Exactly. I think in the world of the life sciences, innovation doesn't happen in a moment. It's really a relay race. It goes from the early stage science to developing clinical assets to getting those launched, making them successful and adopted. So it's really more like a relay race. And what's critical is that baton doesn't get dropped in that relay race. And so we try and bring the expertise that helps to ensure that there is a common
understanding that the process makes sense all the way from start to finish.
Nic (07:35)
That makes sense. You just mentioned innovation. And actually, few minutes ago, you mentioned also tools that you guys developed. just I guess for the sake of completeness, what does innovation look like or what does it mean to you in the management consulting world? Is it innovation purely in the name of what you're doing with your clients? Or is there internal innovation that you guys are working on to make you more competitive, more viable, any number of things?
Matt (08:03)
It's a really good question, Nick. And I think it's, a strategy consulting shop, you think we are thinking about these sorts of questions all the time, but it definitely sometimes feels like we're a little bit of the parable of the cobbler's, know, children have no shoes. We don't spend enough time almost thinking about our own strategy because we are so invested in the success and strategy of our clients. But we are definitely in an environment that requires, you know, tremendous amounts of innovation. As I mentioned, I've been in this
industry for more than 20 years. And what we are experiencing right now is sort of the fastest pace of innovation that certainly that I've seen in that 20 plus year career. And even building on the concept of unnatural selection to sort of think about consulting as a species. I think what we're seeing is both the environment changing. And so those are elements such as the way we do work that we use tools like AI, the speed.
the level of specialization of the providers that are out there, as well as the client expectations are sort of all shifting at the same time. And so we're going through a rapid evolution, not just because we want to, or we're trying to think of new service lines, but because we have to, because we're being forced to evolve, to make sure that we're relevant for our customers, that we can have that impact that I speaking to before requires an ability to adapt and not only adapt at the historic pace, but really accelerate our
adaptation.
Nic (09:27)
Yeah, that makes total sense. And this is why I was so eager to talk to you about this, because you've been in this industry and with this firm for 20 years now. And so you've seen the progression. You've seen different technological waves come and go. We're old enough that I remember the
personal computer wave. And then eventually there was the internet wave and everybody needed a browser strategy. And then eventually everybody needed a digital strategy. And then eventually everybody needed an IOT strategy, whatever, right? And those things came and went and they seemed existential at the time, but they were just like, well, I mean, it's just new technology. It's a new medium by which you can communicate. Now we're actually at this inflection point with the AI wave, which I fundamentally do believe that it's very different. And it's very different. And I think this is where
it's going to hit at the core of what you do from my perspective is that humanity has been creating tools for millennia where every single time, whether we're talking about the hand acts through CUNY form tablets all the way through modern day technology, regardless of the technology, it's allowed us to extend ourselves and do something unique. But at the core, we were the authors of whatever the innovation was, and we were the authors of creating the product.
using those tools to do something. Now we've created a tool where there is a transference of authorship to another agent, AI. They can actually generate what seems to be like thought. It can do analytics. It can write sentences. It can do any number of things. And so now there's actually like, we're almost disrupting our own critical central piece of being the sole authors of how we are evolving as a society.
And that hits at the core of what you're doing because you guys are at this place of intelligence, right? You guys gather intelligence, you crunch the numbers, you do analytics, and then you generate intelligence and you generate strategic reports and so on, right? So now I feel like, to me, that's the essence of why AI is so disruptive generally, but why it's such a threat to the traditional pyramid structure of consulting.
in what you just talked about, which is like this massive environmental disruption that's happening around you.
Matt (11:47)
I think what we're seeing in our world, as I mentioned, sort of what we do is help clients make the best possible decisions. And what's happened with the availability of AI, widespread availability of AI is I think answers are very cheap now. First pass synthesis, good enough analysis are abundant. You can get that from any number of AI interfaces. What it requires us then to think about is how do we
continue to play above that? How do we both harness that power to make ourselves better at what we're doing, make ourselves more efficient at what we're doing, but more important than that, how do we use the time savings that come from using AI tools to elevate the way that we're thinking, the way that we're delivering answers? So the scarcity isn't information anymore, it's really about judgment. It's about how you apply that tool, that information.
towards a business objective, towards a scientific objective, towards a human objective at the end of the day. And so our ability to use those tools to help our clients both get the analysis done, but it's not about the analysis, it's not about the deliverable being a slide deck, it's about supporting the decision with confidence. It's about helping them understand how as an organization they're going to adapt to the new information we're sharing with them.
how they're going to put it in play. And the specialization required in the life sciences means that you can really get that good first pass through a lot of different AI tools, but they're not good enough yet to being able to think about how you synthesize a lot of different information sources in a way that's going to be, say, compliant with regulatory needs or is going to support the adoption of a medicine inside a complex healthcare system. So there's a lot of needs.
Will those tools evolve in some point in future do more of that? Absolutely. And we have to continue to evolve in front of that so that we can sort of not get out competed by those tools.
Nic (13:55)
Yeah, I think
Matt (13:55)
It's the companies that are focused on just summarizing. If all you're doing is synthesizing and summarizing, you're probably in the blast radius of these AI tools and you're not going to exist for much longer.
Nic (14:07)
That makes total sense. it's a, and you said a lot of things that totally resonate. think that, you know, again, for the people that may not be familiar with your work and management consulting in general. So let's say I am an executive in a big pharma company. I bring in Putnam because I have this API or some pharmaceutical agent that I'm trying to get out to market. And you guys come in and help me in part of that process of the whole process. When you guys come in,
I'm bringing you in because there's intelligence that I'm lacking that I think is going to help in moving this drug forward or killing it early, any number of different things, or maybe it's already been proven and I need advice on how to price it or how to market it, how to get it out to different segments around the world. So for that, you're going to bring in a team that consists of like very senior partners, middle management, lower level associates and so on, depending on the size of the project. But these people all have a function.
And there's an enormous amount of initial legwork and research that needs to be done, good gathering the data, reading the articles, pulling together the spreadsheets and so on. And then crunching through that, making decisions, rolling that up into higher level, synthesizing into decisions that the senior partners that look at, and they kind of do their work before it goes back to the executive and say, okay, based on this army of people that we put together, this is what we think and this is how we're gonna implement it, right? So that's a very high level. But now where AI comes in is,
it can do in four seconds what an army of associates might have taken a week or a month or longer to do because now it has access to all the data in the world immediately with infinite processing power. And so what you're saying is that a, that is disruptive because you have all these people that you've hired to do this work. They have a specialty, they're trying to work their way up in the rankings and they cost money. But in a way it also,
opens you up to maybe do less of that mundane work because it was very, very manual work that you kind of put your dues in as an associate to do. know, it's like nobody enjoyed doing those spreadsheets, but it opens those people up to do higher level thinking where now maybe it actually opens us up as a society to be able to start asking questions, more sophisticated questions of the data. So instead of looking for that low hanging fruit,
on how to treat some disease, maybe now we can look at things more around like cures, or maybe we can look at the multiomics and say, okay, before we were looking at genetic data, maybe we were focusing on monogenic diseases, now we can look at polygenic risk scores, maybe we can look at polygenic risk scores in combination with the environment or with your behavior and how these probabilities around genetics factors into the rest of what makes your healthcare. Those are much more sophisticated, much more complex questions that...
Pharmaceuticals are not necessarily in a position to be able to answer. So this opens up new opportunities for you, but the transformation is going to be very painful.
Matt (17:02)
It absolutely is. I'm sort of thinking of two ways that's relevant to the work that we do in sort of the broader consulting field and the work we do at Putnam. The first one is building off of what you mentioned of the work that we used to do. You'd have a team, many associates who are working and doing a lot of like the grunt work. And I'll date myself now by giving you a story of sort of what that meant for me. My first year to...
part of what we were doing, you we're trying to build intelligence on how physicians are using different products. So we would fax surveys to physicians to send us back and the fax machine was slow and I would be stuck at 11 o'clock at night sending faxes out. Do that sort of work anymore?
Nic (17:43)
So I
don't appreciate that answer, Matt, because you're younger than I am. So you just dated me in that process.
Matt (17:52)
Yeah, I think you just gave away a secret to your listeners there. But I say that because what we used to do there, that's how we would accrue and we get information through faxes. used to, doing the analytics, we used to have to set up an analytics platform where we sort of, before we even knew what we were gonna ask, we sort of would be running a cross tabulation of say 15 predefined segments against all the data. And that was sort of as good as the data analytics could be for the most part.
We started to develop new approaches. We use more sophisticated analytics. We were able to sort of be able to read and react more to the information that's coming back to us. What AI allows us to do is even more of that. We're able to rapidly bring sort of test large data sets. We're able to explore, just say surveys of physicians. We're able to merge that with real world data sources. We're able to query that data in interesting ways.
able to harness different methodological and analytical approaches. So what the skill has become is a little bit less of the brute force side of just you need to do it to, you need to be a thoughtful individual who really is considering like, what questions should I be asking of the data? How can this have the most impact? How do I bring in new information sources to confirm or deny what the recommended approach is? And the more
creative thinking allows for more robust answers, it allows for better decisions for our clients. Hopefully it accelerates innovation and hopefully it accelerates Putnam's ability to be a real strategic partner. And so I think that what we're seeing as part of it is there's always this evolution of the questions we're answering and how we approach them. And this is only accelerating that. But the other piece is what we would call the pyramid problem inside of consulting where, you the truth is you might have a team that has
several associates, have a couple of maybe senior analysts or senior associates, you have a project leader, a manager and a partner above that. And that pyramid works both in how you deliver work, but also is how you sort of, you're at each level, you have some people who stay at the firm and some people who leave and usually the main path is sort of is upwards and there's a pyramid that works. As we fast forward, you know, a few years from now, we probably don't need that same number of associates, we can use
AI tools, but that means that a couple of years after that, we're not going to have as many senior analysts. We're not going have as many project leaders. We're ultimately not going to have as many partners. And we don't think we need fewer of the advanced people. So part of the challenge is both making sure we have the right size of the organization, but also how do we make sure that we support the development of those individuals? How do we retain them better? How do we make Putnam an attractive place?
for them to build their careers in so that they stick around and sort of build that experience, the credibility, the knowledge, which is what when you add that to the AI tools is sort of the magic equation that unlocks value for our clients, unlocks value for us.
Nic (20:55)
Yeah, it's a difficult problem because like you said, a lot of that stuff in that lower middle layer can be now replaced by machines. so then how do you train? Because part of that grunt work builds pattern recognition, it builds expertise, it builds decision making and reasoning and so many different things that go into building middle management and senior management.
but you're now eliminating the chances for these people to learn those skills on the job, which is part of what they do. So then, you know, in five, 10, 20 years, you're looking at this cliff, which is like, how do we replace these partners that are going to be retiring when we haven't built that, that layer of people that are coming behind them, right?
Matt (21:42)
there's a huge amount of environmental change happening there. And I think there's been a lot of published research on the risk that in general use of these AI tools does not make people think more. It makes them think less. And what we have to challenge ourselves, and this is very much a internal talking point inside of Putnam is,
We don't use AI to replace our thinking. We use it to amplify our thinking. We don't use AI as the new work product. We use AI to empower our consultants to deliver better work for our clients. And the of the value that we bring is having a human in the loop in those AI systems.
Nic (22:18)
Yeah, I see that. It's in fact, I was going to ask you that question next, because if you're embracing AI, especially where you are, and I don't know if you guys are there already or on your way towards being kind of like an AI native AI first environment. if you are, it would make sense because now if we look at your clients, the big pharma companies, big biotech companies.
it's going to be years before their AI native AI, you know, first, right? And so you could actually be a very big bridge for them. That could actually last a full decade because I don't see these companies just turning overnight to kind of bring these modern technologies and start in a way empowering your associates to be able to now do higher level thinking that before they couldn't do. Now that said, there is something to be said for learning that lower level stuff, because as you know, as I do, you know, in this
part of our career, sometimes decisions or thoughts that you have draw on this knowledge of how the fundamentals work. That without that experience, you probably wouldn't have seen that pattern in the first place, right?
Matt (23:20)
Yep. And that's what separates the ability to really harness AI. With the tools we have now, and if we're being honest, there's still as much talk as there is actual product in a lot of different AI tools that are out there. In some ways, the AI tools that we have are about equivalent to having a really exuberant and eager intern. They can give you a great summary, they can do the research, but they always miss
something, they miss a contextual clue, they miss that sort of pattern recognition, they're pulling from resources that maybe are not quite specific to what is required to meet sort of the burden of proof for life sciences decision making for regulatory bodies or for communicating with patients or with physicians or other clinicians that are involved in the care continuum. So having that experience really is critical, but how we build it is a question and it sort of.
even just which path we want to take as a firm ultimately. As we're talking about AI is both an asset, but it's also a threat to consulting companies. And obviously one option is sticking your head in the sand and pretending it doesn't exist and that's not really an option. So there's basically two paths that consulting companies are taking. One is replace humans with lower cost AI automation. That's a play to allow you to grab greater market share, increase your margins and
you're basically using AI as a substitute for labor. But ultimately that becomes a race to the bottom. Your long-term viability of that model is, I don't think, very promising. Yeah, it's dead spiral, right? Yeah. Do you remove the repeatable elements to elevate the thinking, to reduce the time spent on those repeatable tasks, but still think about problem framing, judgment, creativity being a critical piece? And also the last piece of how do you translate
thoughts and ideas into actual organizational change. And that's a big piece of what not only we're trying to do at Putnam, but what we try and do actually with some of the companies that we're affiliated with as part of the broader INISIO organization that sort of focus in different elements of that, you know, of the overall commercialization spectrum and maybe about bringing strategy to life. And so there's almost like a symbiotic relationships that are developing in part because there's
know, these evolutions, the environmental pressures are forcing that and sort of valuing companies that can bring broader sets of solutions or not just the idea, but how you sort of come up with a better idea, how you bring that into action and how you actually deploy it to potential customers. And so we're trying to bring all of that together.
Nic (26:07)
And I don't know how Putnam charges, does this, I would imagine that this has had tremendous effects on your charging model as well. Like if you were charging on an hourly basis before, maybe now you have to transfer like on a project basis or something like that. Because, you at this point, it's like you've gone from, this is going to take 3000 hours to this is going to take 15 minutes, right? So you can't charge in hours anymore.
Matt (26:27)
Exactly. Well, yes and no. mean, to be honest, it's a challenge because our customers also want to sort of achieve some of those savings. So they're asked questions, right? If you're doing this using AI tools, know, is there a, you know, where do we see the savings? So it's not like we retain all of the benefits of that. And so what's happening is really a division of that value that occurs between the customers who get
cheaper, faster answers because their service providers are using AI and the service providers who are enabling and harnessing that AI. Obviously the cost increase we have to both pay for the utilization of different AI engines that we participate in as well as the tools that we build upon those engines that we invest a lot of time and energy. We've got an entire team of individuals inside of Putnam that that's their job is building tools.
that are really all native to AI platforms to help our consultants, help our clients answer questions. And that's something we didn't have five years ago and it's only going to grow for us over time. But we have to go monetize that if we're going to survive. If we're investing and expending our resources to do that, we have to find a way to recoup that. And we hope that through a combination of being able to charge for it, as well as being able to out-compete other firms that maybe aren't as investing as much, we're able to.
to be successful in that way.
Nic (27:54)
Yeah. And I think in a way it is going to boil down to your specialty, You specialize in the life sciences and healthcare space. Because I think AI, and I'm talking today, I'm not talking in 20 years, you for all I know AI will be so smart that they could do the work of a hundred PhDs better than humans could. But if we talk about today, there is a moat around the life sciences because number one is you're not going to disrupt
Necessarily pharmaceutical companies aren't going to go away. They still need to do all the requisite like analytical chemistry and animal studies and all that kind of stuff and generate the actual drugs and APIs and formulate them and get them out to market. So there's still a need for that that AI can't replace. They're also not going to become AI proficient overnight. And in fact, they may want to still outsource some of that work to other people. And so there seems to be like, although you guys are going through like very grotesque and kind of painful
metamorphosis, there's a need for what you guys do, even if it's in a different shape. And so I think there is a layer of that, which is protective to what you guys are doing because so it sounds to me like your reflection on what you're saying about AI, you're saying, yes, there are risks. There is a lot of painful transition. But in a way, it sounds like it might be also seen as an internal accelerator for Putnam that fulfills a need that is not going to go away for pharmaceuticals anytime soon.
Matt (29:20)
No, it's not. And as I said, there's multiple paths that companies could take. It may sound like the obvious path is to take the second path, but that isn't obvious for every firm. think we are, as you alluded to at beginning, we're a smaller player. have offices across the globe. We've got hundreds and hundreds of consultants, but we're still much smaller than some of these other players. So we've got, I think, an ability to continue to differentiate on
the knowledge that we bring, the strategic depth, the thinking end of it. And that's really what we're trying to do and differentiate on. I think there's a bright future for us. I think there's plenty of opportunity. I don't think we're ultimately asking the question of, know, will consulting survive in the life sciences? It's really like what consulting firms or which consulting firms will survive in the life sciences when we have these environmental pressures, you know, working against us. And, one of the things we haven't talked about, which is almost maybe the most obvious piece of this is
as those organizations, our clients, biopharma organizations are adapting to AI, there's a lot of work involved in helping them build those capabilities. And that's something we're doing. We have an entire practice that's focused on data analytics and AI strategy. And really it's the work with our clients of how they get ready for deploying different AI tools.
Some of it's the real nuts and bolts work, the pieces of building the data sets and the data architecture that is clean enough that you can actually query it in a way that gives you good results. I think on the R &D side of BioFarma and on the manufacturing side of BioFarma, they're light years ahead, honestly, of where the commercialization piece is, probably because they're so disparate in terms of the resources and data that's available, it's not clean.
it's not comprehensive. The concept of big data still exists and has existed for a while, but there still is a lot of barriers, a lot of challenges in creating those really pristine data sets that you'd like to be able to utilize to answer some of these questions. So there's still pockets of knowledge everywhere and we help in some cases bring that together and help our clients answer questions using it.
Nic (31:35)
Yeah, that makes total sense. at least modern day AI is really good at using data as input and generating some kind of a synthesis out of that. But it's still not exceptionally good at generating new knowledge or new data, right? And so there's still the whole part of like, where's the new data coming from? You know, it's not like we have infinite data that covers everything. We just haven't had a chance to go through it. There's still
plenty of stuff we don't know about genetics, plenty of stuff we don't know about proteomics, plenty of stuff we don't know about healthcare in general. And so somebody needs to do that and AI is not gonna replace that anytime soon.
Matt (32:15)
No, it's not. And that's certainly a limitation. And we think about AI, like we're generally talking about large language models, and that's definitely a limitation to the way that those AI models are supporting clients and supporting decisions here.
Nic (32:30)
Yeah, and you and you mentioned also just data, know, it's, your synthesis is only gonna be as good as your data. And we know that in pharmaceuticals, like there's a lot of disparate data, very different, contextually different data, a lot of dirty data. And so it's still gonna take a lot of work to go through these different silos and cleaning. mean, that's been an endless problem in pharmaceuticals. Maybe AI is gonna help and make that easier, but that's still very big problem. They have so much data.
but so much of it is like fairly useless because of the former words or the structure of which is stored.
Matt (33:03)
Exactly. And so we definitely see a role in supporting clients in that. In general, going back to your question around sort of moats, we think about like what's Putnam's moat or what is a winning strategy consulting firm in this area's moat? It's about having domain depth and sort of understanding the area quite well. It's about having the experience of actually not just understanding the right answer, but how you put that into place. So having the experience with commercialization, say with market access,
with medical leader engagement. What are the ways you actually do that? So it's not just about developing a great summary or a great pitch, it's about how do you actually deliver that in a way that's going to win. And then the last piece is making sure that the way we position ourselves is at the sort of bespoke problem solving end of things versus the highly repeatable part of the business. Those parts are gonna be continually commoditized. So we wanna stay in front of that. And so that's sort of as we think about our moat.
relative to competitors and how we sort of protect what we're doing at Putnam. But the way we really do that, like what the most important thing for us now at Putnam is, as we go through these pressures of evolution, of environmental change, is really like learning velocity. And it's the ability for us to continue to adapt. I talked about before the fact I'm not doing the same work, or our associates are certainly not doing the same tasks that I was doing as an associate.
We are always adapting. That's one of the things that makes work fun as a consultant is you're always learning about something new and you're sort of being challenged to be smarter than your client is on something in pretty rapid order. So the ability to be a fast learner has always been a character trait, I think, of successful consultants. But what's happening now is not only do we need fast learners inside the firm, we need the firm itself to be a fast learner. We need to be thinking about how do we respond to
signals in the environment and how do we figure out if those signals are working or not working or how do we adapt to them. When we do have a win, we need to be thinking about how we deploy that rapidly across the firm. We've created platforms internally for sharing AI innovations. We encourage people to be building AI tools. And what's interesting is I'm definitely humble enough to know that I am not at all the sharpest person when it comes to AI tools inside our firm.
That's gonna come organically through so many of our individuals, some of our younger employees who have come up and probably had more experience playing with these tools, maybe in their high school or college courses, maybe was sanctioned or not, but they're very sharp on these concepts. And what I need to do as a leader is think about how do I create an organization that encourages that innovation? And when the innovation occurs, how are we making sure we're creating platforms so that
we can rapidly disseminate that information and we can not be too beholden to the ways we've always done things. We can be open to, okay, here's a new way we're gonna pilot delivering something, we're gonna test it, we're gonna see if it works. If it works, we're gonna roll it out and we're gonna evolve. so hopefully we are evolving faster. I think we are going through these periods of evolution, but if we can be an organization that learns faster than other organizations, that's gonna be a competitive advantage for us. As much as any of those other elements of our sort of
Note.
Nic (36:25)
Yeah, I mean, I'm just going to emphasize a few things that you said there because it really hit home. mean, first of all, you're exactly right. mean, the consulting world, by the very nature of what you do, you have to be flexible. You have to be malleable, right? Because you could be working on an oncology problem one month and then in six months later, you could be working on a rare disease or some other area completely. So by the very nature of what you do, you've got to go into it. First of all, you probably attract people that like that variability to begin with.
You're not going to attract a double PhD that knows exactly what molecule and that's what they're always going to do. You're going to attract people are like, that seems kind of cool. Like a whole bunch of different projects and I get to learn every six months or every two months or whatever. So by that nature, you actually already have a culture of intellectual curiosity. That's like, okay, we have the people with the right mentality. What we need to do now is accelerate our level of internal process evolution.
because maybe the way you went about your work before was fairly similar. The domain of the work that you were doing was one thing, but then you have a methodology that's worked for Putnam. There's probably a methodology that works for consulting in general. You guys have perfected it to work for you. That's gonna have to change because now you have to plug into AI, it requires different skill sets, different number of people and all that stuff. But you already have that culture mindset, which is like, we just do cool new stuff.
Now, the other part of that equation though is, and you said this earlier, that you guys, not just Putnam, but the consulting world in general, has done less internal innovation because obviously you're so focused on the projects at hand. You sometimes there's just no time because everybody's allocated 100%. There's no time for internal innovation, internal kind of piloting. But you're saying that has to change in a way now because now you have to be able to, you have to be playing with these technologies in order to change your process.
And so I think those things are coming together. So I was hearing again, an optimistic note, which is like the industry is not going away, but that doesn't mean all the players are gonna survive. The players that are gonna survive are the ones that think about this in a way that's like, okay, there's this existential risk. There's an environmental pressure that's happening. And the ones that adapt the quickest and embrace that are the ones that are gonna win.
Matt (38:44)
Yeah, that's absolutely true. think we think that the, you know, the advantage right now isn't in sort of being able to produce rote tasks or even just exactly what you know. It's how fast your organization can learn, whether you can really out-compete in that evolving environment. You can continue to be a leader for your clients and you continue to develop new ways of working so that you're staying one step ahead. And it's never going to be static. The truth is it never was static. We may feel like, okay, we're going through more innovation right now. And the truth is we are.
but it's going to continue to remain that way. We'll have to manage through it in the future. Once some of the services we're talking about to provide now become more automated, we'll have to find the new roles. And that's, an interesting challenge. It's scary in some ways, but it's also a great opportunity for us to be continually thinking about it's one of the things that I think does attract the people that are inside of this firm and other similar firms to Putnam, people who really like jumping into those problems, trying to solve them, trying to come up with creative solutions.
And if the work changes as a result of that, if there's more thinking that revolves around being creative, that evolves around connecting dots from disparate sources, that seems exciting to me as much as it's a threat and a challenge to adapt. It really makes the work just as exciting, if not more exciting than it has been over the past 20 years.
Nic (40:02)
Yeah, and I mean, it's got to be exhilarating too. Like if you're taking it from this perspective and puttnum is embracing this new technology, everybody who you're leading has got to be seeing that and be like, OK, it could be scary, but we're like riding this wave, right? I mean, we're like at the very edge and doing something. And so that's actually really cool because you're pioneering something. you know, if we turn our attention a little bit for this last segment towards your customers, right? So biotech.
How's that world, biotech and pharmaceuticals, how are they faring? Are they being, they've faced over the last 10 years or so big market pressures. There is a lot of pressure to bring down prices of drugs and all that. They're still blockbusters though. And so because so much of your work revolves around your clients, what does that industry look like now? Where do you anticipate it changing over the course of the next five years or so? And what does that mean to the work that you're doing?
Matt (40:56)
It's an environment and I think our clients are operating an environment that, at least in the past year or so, has had an extreme level of uncertainty. And this sort of separates out AI or any of other backdrop, but I think there's been a lot of elements related to the administration in Washington that relates to regulatory concerns, changes of sort of the normal operating ways of doing business.
a lot of challenges, a lot of questions that are being asked. trying to make sure as an organization, Putnam is ready to answer some of those questions. But I think the overall arc for where biopharma organizations are is now at a point of asking a lot of questions about how do they unlock productivity at scale. And so that might be a question for R &D team might be around, how do you increase the way that you're identifying new molecular entities, new proteins,
How do you evaluate those faster if you are thinking about bringing your clinical operations? How do you increase patient enrollment? How do you decrease noise? How do you think about playing in global markets in a appropriate way that's going to develop data that is of regulatory quality in all of the markets you want to enter? And then on the commercialization angle, how do you use these new tools, data, teams to be able to have an impact in the most efficient and effective and fastest way possible?
You mentioned blockbusters, Nick, and I think there's plenty of blockbusters that took a long time to get there in the history of pharma. And I think now we're in environment, there's a little bit less patience for that. There's an expectation of faster launches, of faster support for products. They want to see them scaled up more quickly. And so the challenges for biopharma leaders, for the executives that we're working with is how do we ensure that we're getting the launch right?
first time, there's even less patience to sort of recalibrate, readjust, make things work over time. It's about building elements around getting the right evidence. So both for clinical needs, but also for payer bodies, you know, building that in early in the process, it's thinking about the way you're going to execute and making that not just a afterthought of how you bring it, but also how do you actually as a business, how do you as an organization.
build platforms for execution that are competitive advantages. And so in these ways, whether you're thinking about the evidence you develop, whether you're thinking about your strategy for market access, whether you're thinking about how you execute, those aren't just tactical questions now, those are actually the strategy of organizations. It's we're going to be better at developing evidence early. We're gonna be better at securing access for our products. We're gonna be better at deploying repeatable execution platforms.
and making those best in class. That that's going to be your strategy as much as picking the right molecule in an oncology indication or dealing with a new type of renal disease, whatever it might be. That's historically where pharma has focused, picking the right assets and bringing them to market. I think there's now a focus on how do you unlock value through having productivity at scale across R &D, clinical ops, commercialization.
Nic (44:15)
Yeah, that makes a little sense. and I guess, you know, it's worth unpacking a little bit about the world of pharmaceuticals because it's such a black box for so many people. And, know, I've been in it for a long time. I helped start a few biotech companies. So I understand that process. I understand the people. And at every stage, people are trying to do the right thing, whether you're an analytical chemist or, you know, somebody who's running a clinical trial.
But from the people that don't know it, it's a black box and it seems almost sinister because like all these pharma companies are trying to keep us down or pricing things high. What people don't understand is for any given drug, and I don't even know the stats because it keeps changing, you know, it could take 10 years or more. You mentioned just now how long it takes for these blockbusters. It's not like somebody came up with it yesterday and all of sudden it's making $10 billion a year. It's like, you know, for that one drug, there were probably hundreds of thousands, if not millions of experiments that went into that.
10 years ago, somebody came up with a particular like disease area to start doing research. You have endless amounts of people doing like the raw research, trying to figure out like, what do we know about this? Eventually start designing molecules, whether in silico or doing it in the lab, testing those against like Petri dishes and all kinds of things to see like how these things react. In the process, every single stage, you're losing hundreds of thousands of different experiments. This molecule didn't work, this didn't work. this one looks interesting, but it's not great. Let's try it in different ways.
And now you go through progressive stages of filtering things out, losing a lot of stuff, using a lot of money in the process. And then you go from discovery, you go through the development, you go through animal studies, you go through clinical trials, even within those air phases. In clinical trials, have pre-clinicals, you have phase one, phase two, phase three. At any one of these things can fail. So now you have hundreds of millions of dollars or billions of dollars that have gone to that point that could just go poof overnight.
And if you're lucky enough to get past phase two clinical trials, which is where a lot of these things go to die, now you get to phase three and scale up. Each one of these costs more and more billions of dollars because now you've got to do more and more people across more and more health systems that are charging you through the nose. And then eventually, if you're lucky, you go through a process where you take this out to market and start making money. And so then when people see like, wow, you're charging.
you're making $10 billion off this drug, it's like, well, yeah, but it costs like $5 billion just to get it out there with tremendous pressure and no guarantee that it was gonna work. And so what you're saying is the reason why I'm bringing up this entire process is because at every single one of these stages, there are improvements in efficiencies to be gained. And so if you shave off a month from the discovery process, or if you shave off,
half of the experiments that failed during some developmental process, or if you shave off a day from the clinical process, all that equates to millions or hundreds of millions of dollars. If you can now start adding up all those efficiencies across the different stages, then that could equate to billions of dollars in revenue because now you get out to market faster, you start selling the drug faster and so on. these, you're saying is tiny little efficiencies that every single one of these stages can mean.
enormous savings or revenue generating potential for those companies in the long run.
Matt (47:29)
It can, it can. And what that means for the pharma companies is potentially, you know, some, you know, it's incremental revenue, as you were saying, it's time to market advantages. But what it, the more important thing is what it means is you're going to have the ability for products to get out to market faster, to tailor those products to patients better, to sort of communicate with the healthcare authorities or healthcare systems.
in a more, in a way that's really meets the individual needs of those systems. And so they can be more confident in their adoption of new medicines. And that means the clinicians have more options in front of them and our patients, our, you know, our family members ourselves have new options in front of us. And, you know, there are certainly no shortage of stories of missteps of things that have happened from pharma companies over the years. And that's true of any industry, but I do think at it.
there are a lot of people who really are in this industry, the life sciences industry overall, that care really about these questions around human health, around outcomes, and really are excited about the ability at the end of the day that their work is contributing to better outcomes for patients.
Nic (48:45)
Yeah. in, you know, in theory, I guess, by using these new technologies, you can eliminate some of the uncertainty that happens because even once you get a drug through clinical trials at the end, you could have adverse events, all kinds of things. Even once it gets out to the population, it's still like a living experiment because you couldn't have tested every human being under every scenario. So now you start finding things out that happen out in nature that it's like, wow, we didn't anticipate this. So you might be able to prevent some of that because now you can use AI to do better analytics. But then
The balancing part of that is that now you might start getting to more complex science, right? The multiomics, you start looking at cures and things like that, which are fundamentally harder. So now you start looking at harder science, you have more data. So it still balances out. It's still a very complicated space. And maybe what you guys bring to the table in addition to the efficiencies is also a level of confidence. Cause one of the things that AI is not that good at today is, and this goes back to the experience that you and I have and having done a lot of low level work in our fields.
When AI gives me an answer, there are plenty of times where I'll read the answer and I'm like, that doesn't sound right. Whether it be technical or scientific or business. And it's like, it's only because I did that at some point in my life that I'm like, no, this seems often it's going to take me in a wild goose chase. Part of what people need to build up these expertise for is to be able to identify that. But then maybe what you do to pharmaceuticals is whatever your report is, because Putnam stands behind this, you give them a level of confidence.
in what this report is. It's like we're standing behind this. This isn't just one of your middle manager layer people in pharma writing a command script and getting an answer. We've done the research and we validate all that went into this recommendation.
Matt (50:27)
That's exactly right. think we've spent a lot of time today, Nick, talking about what's changing, but a lot of stuff also isn't changing in how we approach the work that we do. Trust is still the currency, especially in these regulated high stakes industries. think context still wins. mean, domain expertise, it matters more, honestly, with these AI tools versus less, because you need to be able to interact with them and understand where there are missing pieces. And at the end of the day,
even if you have these AI tools, at least for the foreseeable future, humans are still the ones who are making the ultimate decisions about what gets put into play, what doesn't get put into play. So understanding like the human side of adoption of new ideas and concepts is an integral part of any sort of advisory service that we're providing.
Nic (51:14)
Yeah, makes total sense. just turning now, if we just project forward, you've had this career path, like we said before, going from initial associate, moving your way up through the racks, that consulting pyramid structure that you talked about. Now you're a CEO. Congratulations for that, by the way. This is recent, I think, in the last six months or so, right?
Matt (51:36)
last year. Yeah, so been about a little over a year in role now.
Nic (51:39)
Okay. So if we think about essentially everything we just talked about, how quickly this industry is changing, how quickly society and business is changing, and if we have people from high school or college listening to this thinking like, what the hell am I supposed to study? Like, how do I make myself viable? And I don't expect you to have a full answer for this, but just from your vantage point, just having knowing the management consulting
world and how things are kind of evolving at the pace that they are. What kind of advice would you give people as far as like where to specialize or what to do to prepare for a world that's changing so quickly?
Matt (52:24)
I can only answer that for consulting, at least from a career perspective, and there's no major that is consulting. When I started in my cohort, there were individuals who had everything from an economics degree, which is what I came in with, which is maybe more classical, to individuals that had a art history degree. And some of those individuals were brilliant and just as good, if not better consultants than I was.
What the work has required is thinking and interest in learning and ability to be creative. I think those are only being amplified now. And so, yes, I think having familiarity with AI tools and being able to operate in that evolved environment is critical. Just like when I was entering, having the toolkit of Microsoft Office was a valuable thing for a new consultant to be able to enter the market now.
You know, now there's new tools that we want people to be familiar with and we're evaluating in our new hiring processes. But what still is the most important thing is people who can really think creatively, who can connect the dots, who are interested in using these tools to help them think more, to solve interesting problems, to push thinking beyond where the normal limits would be. Those traits are the ones that were separated.
individuals early in my career, it's what's going to continue to separate individuals. So don't think there's necessarily a singular path. I do think there's some degree risks of over-specialization, which can be a detriment to those creative thinking processes. So I think still there's foundations. If you're a liberal arts major out there, all hope is not lost. There's potential opportunities for you in the future.
Nic (54:11)
OK, and now if could just finish off with a question that really zooming out, if we think about your career and you're nowhere done with your career, you're obviously still very much at the peak of your career. So but if you can project forward into some time in the future where you can pause and reflect on everything you've accomplished, whether it be things that you've accomplished today or the things that you anticipate doing over the next decade or so, what would be the best case scenario where you might be able to
find fulfillment and satisfaction over the things that you've accomplished. What does that look like or what are the things that you're still working on and you're like, this is what I want to be known for?
Matt (54:50)
As a consultant, think one of the most important things you have to be comfortable with is not getting the credit. So the work that we do is not, you people don't know Putnam because Putnam developed a drug or launched a drug successfully. They know the large multinational corporations or they know the drugs that, you know, either they see on TV or probably perhaps more importantly, the ones that sort of helps your mother avoid a breast cancer recurrence. So these are the things that I think we
live for that we enjoy being a part of, even if it's a small part, as I mentioned. So for me is that I sort of take a step forward and think back, like, what does success look like for me individually? It's being able to help this organization continue to evolve, to be able to help build a team and an environment and a culture that continues to learn and adapt, that continues to expand our ability to have impact.
on our clients and never really loses sight of the fact that the reason we're doing all of this is, you know, obviously we all have jobs, we need to be paid for what we're doing, but we could do that in a lot of different ways. And there are faster growing industries than the life sciences, if we're being frank, but the reason a lot of us are in this, reason I'm in it is we can look back and point to drugs that we know maybe wouldn't have made it past the, you know,
gate seven test of the financials inside the organization because it didn't look like you have a good enough business case for further clinical development. can sort of, we know we've had our hands dirty in that process of ensuring that these products are commercialized or helping make sure they're more successful or getting in the hands of people faster than they otherwise would. And so if we can help very incrementally build an organization that has a lot of those really small differences, a month here,
you know, increase in the speed of uptake for a new cancer medicine or a new heart failure medicine by six months or 12 months faster than it used to happen. Those have human impacts. Those are patient lives that are affected. Those are families that have more people, you know, around the table at Thanksgiving. We're playing a small part again. I'm not, you know, a lot of humility here, but if we can do that, if we can build an organization that continues to do that, does it at an even greater scale, you know, I'll be quite satisfied with my career.
Nic (57:07)
Well, definitely a lot accomplished so far. I can't wait to see all the things you're going to work on in the coming years. You know, the management consulting world is one of the key areas that's being disrupted. I happen to be a part of the other world, which is software. You know, that's also a big area being disrupted. And so we're actually seeing this in real time. And so it's incredible to hear your perspective on the side of innovation, but also
how to change an organization and how to adapt in a world that's changing so quickly and how to have a perspective there and thinking about what you need to do today to stay competitive. But with all that, Matt, it's been great. It's been a pleasure knowing you all these years and thank you for being on Natural Selection. Next time we do this, it should be over a drink.
Matt (57:47)
All right, Nick, thank you so much for having me. It's really an honor to be a part of this. It a wonderful conversation. So thank you and thanks to all your listeners.
