Brandy (00:01.979)

Hi, today I am joined by Mike Tomasko. He served as the VP of Pfizer Digital and has actually been, had been with Pfizer for 18 years. He's held many roles. is influential leadership roles in strategy and digital transformation, finance, business technology, you name it, Mike has done it. And today he is...

helping other organizations really transform through digitization and data -driven insights. So Mike, welcome. Thank you so much.

Mike Tomasco (00:40.44)

Thank you very much, Brandy.

Brandy (00:42.565)

Can you talk through a little bit about your career in specifically bio manufacturing?

Mike Tomasco (00:49.614)

Sure, you know, I started off with 10 years as a consultant at Accenture and within that 10 years, five of those were in the bio manufacturing world and all companies that were actually part of eventually part of Pfizer over time. And, you know, the journey was really interesting because there's always some sort of problem to be addressed throughout time. Whatever the technologies are, I always find that

Brandy (01:13.989)

Hmm?

Mike Tomasco (01:18.048)

A lot of what we do in this IT digital world is focus on how do we implement a project, sure, but as we do that, there's always something that comes up. There's always some sort of obstacle. And one of my core competencies over the years has always been problem solving. So I came up with a little mantra for myself and ultimately for the teams that I work with, like least to challenge them to think differently. But like, I really truly believe every problem is easy to solve.

until proven otherwise. And sometimes it's proven otherwise pretty quickly as we look at things. But oftentimes when you break things down into thinking about what's going on in the world of biopharma, when you get into manufacturing specifically, the processes are very complex. So you could get daunted by the problems that you face sometimes. I don't want to stop production. That's a big no -no when you put in a new capability.

But when you're looking at things, instead of being kind of paralyzed by that fear of stopping something, you have to look at it differently and say, how can I address this? How can I get around that? How can I break the problem down so it is actually easy to think about? well, if I don't put it in, it won't go down. OK, that's one thing. But how can we future -proof something?

so that it actually works well when it is implemented and then people can take advantage of it. That's something that over time has really been something to really think about when you get into this world, because it is complex, it's hard to understand. I was in it for over 20 years in actual bio -pharma world and still feel like I learn stuff every day.

Brandy (03:04.867)

Yeah, I would imagine. And things change so rapidly too, and trying to keep up with all the innovation and things that are coming at you, I would assume at a pretty rapid pace. know, kind of thinking about how you rose through the ranks of Pfizer and kind of ended up in the position that you were in, what were just some of the challenges that you were faced with that kind of brought you to the point in your career where you were

mostly kind of focused in on this data transformation piece.

Mike Tomasco (03:38.958)

I think it came down to a couple lucky choices perhaps, or maybe crazy choices when you think about it. But in any big organization, there's always a lot of people around, a lot of different roles, and constant change. I think every big organization I know, and Pfizer's no exception, the one constant was there's always going to be some sort of change. So over the time I was there, it was never a static, hey, here's your job and here's what you do.

Brandy (03:44.347)

I

Brandy (03:53.733)

Hmm?

Mike Tomasco (04:07.394)

There was always an environment that's moving and changing. So sometimes when that happens, I was a big fan of raising my hand to try new things. So there was one time where I raised my hand and I said, I want to go try to be on this strategy team that never existed before. we were, was myself and another person, we were given the opportunity to literally try to help create the future for, at the time it was a Pfizer global supply and the finance function. And

When you're paid to kind of stare at a blank sheet of paper for a while and do a lot of research as to what could be, you start to learn a lot about yourself and about how you might want to portray these things. And one of the things that I learned was I actually like doing them as well. So coming up with the ideas is interesting and takes a lot of thought and research and a certain mindset, but also implementing them.

is something that I had in the back of my mind as well, because it becomes challenging to have an idea or set of ideas that you give to others to go implement. And maybe they have other priorities. Maybe it's not the most important thing. We're not all aligned. it's that whole managing things through influence. And there's an inherent challenge with some of that. So along the way with that strategy role, there was an opportunity with another organization change that came up when things were moving along. And I actually suggested

that perhaps we need a team that works across all of our functions. So we have manufacturing, multiple quality functions, environment health and safety. What if a group worked across all that? And that was something that really sparked my interest because it hadn't been done before. It was kind of purposely a big change being introduced into the organization to try to do things differently and collaboratively.

Brandy (05:35.044)

Mm.

Mike Tomasco (05:59.342)

across the siloed domains and it really all came down to data and trying to surface data so we can do more with it. So that was definitely an interesting aspect of the journey.

Brandy (06:12.399)

Yeah, mean, introducing a completely new role to an organization that I'm sure had been operating in those siloed divisions for a really long time without having that cohesive bridge across the divisions. How did you get people to buy into that?

Mike Tomasco (06:30.926)

Well, that wasn't easy. Every problem's easy until proven otherwise. Well, that one was proven otherwise pretty quickly. But honestly, what I think it came down to and what I really am happy over time that I grew into was becoming a storyteller. And storytelling becomes one of the most important aspects of how you lead a strategy function. You're painting a picture often of what could be

It's one thing to have a PowerPoint slide that states, hey, we're gonna go put all of our data in the cloud. Well, that idea itself was disruptive. we're gonna go create this thing called the digital operations center, because it's gonna work across functions and be a single pane of glass for lean manufacturing. talk about disruptive, right? Like as soon as you say that term, single pane of glass,

Brandy (07:11.035)

Yeah.

Mike Tomasco (07:27.31)

people get really confused as to what that means to them. forget our customers internally. They kind of understood, like, because they were asking for it. Like, I have to log into 15 different things to do my job. It would be a lot easier if I didn't have to do that. So if you could build some sort of abstraction layer that brought everything together, maybe we would be able to figure this out more easily. But for my own team, it was a bit disruptive in terms of

Brandy (07:33.157)

Hmm?

Mike Tomasco (07:53.624)

Well, if you weren't on the team that was physically building that aspect, that single pane of glass, the application that it was, you might think, well, maybe my job isn't as important. Maybe my siloed function isn't as important. But that couldn't be further from the truth, right? That wasn't the intent. Our intent wasn't to say, the quality assurance function is not important anymore.

It's extremely important. We need to focus on that, but we also need to focus on surfacing data and capabilities from it into other experiences to make another group's life easier. So if we only focus on that single customer and not look at the adjacent customers, like we're missing an opportunity to make someone's life easier. So I had this other kind of vision for the team is simplify processes and experiences to drive outcomes. If we weren't

Brandy (08:25.583)

Mm

Mike Tomasco (08:46.344)

answering that question constantly. We're probably doing the wrong thing. If I'm not making something easier, the process or the experience, and I'm not making an improvement to an outcome, like why am I doing what I'm doing? And it's kind of funny in a big company, you can look around at certain projects and say, well, that's not making it easier. Is it driving an outcome? mean, maybe that's okay. Are we consciously making that choice? Like, I think that's an important thing too, because sometimes you consciously make a choice.

something might be harder in the short term, but there's a bigger picture to it that then drives out maybe where the value could be. But it really does, as an individual on the team, like on a daily basis, you can say, am I making someone's better? And are we helping drive an improvement? That's really what it was all about in the digital transformation.

Brandy (09:17.093)

Picture, yeah.

Brandy (09:27.631)

Hmm.

Brandy (09:33.401)

Yeah, and painting that picture of what it will look like when everything is unified and people are importing information to help each other out. Just showing the beautiful end is really what people aspire to.

Mike Tomasco (09:46.606)

Well, I can tell you that one of the ways we overcame the cultural challenge with Single Pan Glass is we renamed it at least four times. Same concept, different name, different brand, different mark, like just try to change how people thought about it. We landed on composable experience.

Brandy (10:04.175)

Yeah.

Mike Tomasco (10:09.57)

So basically think about a framework, like a page, like a simple thing, like a portal. You could have little gadgets in there, Everyone can contribute from their domain of expertise a piece of that puzzle. You don't have to build the whole thing. And that was a human nature thing we were trying to get over with like our IT development groups of like, if they weren't involved in building the whole thing, they felt like they weren't important. But again, couldn't be further from the truth.

Brandy (10:10.105)

Hmm.

Mike Tomasco (10:36.704)

I need you to focus on your big main thing and then expose your information, data and processes into this other experience and work together collaboratively. But it was, that was a journey.

Brandy (10:48.057)

Yeah, that sounds like a pretty significant journey. And what was the end result? Like, what were some of the benefits that you were able to achieve and see after bringing that full circle?

Mike Tomasco (11:00.866)

I mean, ultimately it's a journey and we're talking about like a seven year transformation that's still ongoing, right? It's a never ending proposition, this digital transformation stuff. And I think it becomes more obvious that you need to simplify each persona's experience over time in the way that makes the most sense to your organization. eventually we got

Brandy (11:04.143)

Yeah.

Mike Tomasco (11:29.55)

the teams lined up for contributing to these things and then making the experiences happen became more seamless. But bringing in a user experience design group was another choice that I made earlier on in the program to help us drive out our understanding of what our customers needed. And this is what we kept hearing from the shop floor. Like I have to log in the 15 things every day to do my job. It might be easier if you built me something to get me through

Brandy (11:52.111)

Hmm?

Mike Tomasco (11:59.106)

different aspects of what I have to do throughout the day. And that team brought together a lot of research and development, walking in the shoes of people, trailing them around and understanding how they work and then mimicking how they work with digital technology. Making something simple, elegant and beautiful and easy to use is really, really difficult. You look at the end result, go, well, that seems obvious, but like it took us

Brandy (12:23.737)

Super challenging, yes.

Mike Tomasco (12:28.748)

many months and iterations and years to get it to the point where that obvious answer is what it was. It didn't start off that way. Sometimes they start off with much more stuff in it, and then the simplification comes in. As you simplify over time, things get easier to use and more straightforward.

Brandy (12:48.783)

Yeah, and as technology has advanced and AI is becoming more prominent, where do you see AI technology playing into the digital transformation?

Mike Tomasco (13:01.368)

think it always depends on what we consider AI, right? So any kind of analytics someone now calls AI. But I think the first thing to think about with AI is what's your foundation built on, right? Because AI sounds like a magical gift that was given to us, especially now Gen .AI. It's like super powerful capabilities, but to what end, right? Like, what am I going to make easier and all that? If I don't have a solid foundation in my

Brandy (13:04.089)

Yeah.

Brandy (13:24.667)

Hmm?

Mike Tomasco (13:31.288)

basic processes. I have a problem, right? So if I don't understand how we're making something in a unit of operation, if I don't understand what equipment is there, and what the process is, what the product is, who is working there and their skillset, if you don't understand all those things, there's so many other opportunities to focus on before you worry about AI, right? Because AI seems magical.

But in a practical sense, you can get more value out of making an investment in fixing your foundation. And with the eye towards, I know I want to move towards this AI thing, and you're probably going to start with basic math stuff first. So not really true AI, but just writing algorithms to mimic how a process runs. And BioFarma, the golden batch concept is a really popular one where you try to

create algorithms that can simulate what's going on at any given point in time in a batch. You can stream real -time data in there and try to see if and predict if I'm trending off or on where I need to be with my critical process parameters. And if I'm off, there might be interventions I can take that help to do that. So you want to surface that information to the shop floor worker. Well, this is where AI and then JAN AI becomes super helpful because

When you start to show a GEN -AI next best step to a process data scientist, they go, I don't need that. I can read the graph. I can read what the chart says. I know what has to happen. That person works in an office somewhere. They're not down on the line where this is happening in real time. So if you're able to now see the stuff in real time, start to predict how it's going to drift, now I need to know what the possible interventions are. Well, gee, if I have 20 years of quality historical data.

this other production data that's laying around that we've modeled in different ways. I can then use that with Gen .ai to tell me what to do next or suggest what to do next really is what it would be. And that becomes a super powerful tool. If you have your composable framework, you can pop that into right next to the chart. It'll tell the operator right there without having to log into 15 different things what they need to do. So over time, one of the things we talk about manufacturing all the time is

Brandy (15:35.663)

Mm.

Mike Tomasco (15:55.202)

the constantly changing workforce. the workforce time to competence is becoming difficult because the processes are becoming more technical, more challenging. People are switching companies all the time. So we need ways to help our operators come up to competence, you know, faster and then make their lives better throughout the day. So they want to stay and work with a company that's helping them.

Brandy (16:05.005)

more challenging. Yeah.

Brandy (16:18.405)

Hmm.

Mike Tomasco (16:25.176)

do things more efficiently so they don't have to do the busy work stuff. They're focused on actually making product because that's what they're there for. And then of course, making that of higher quality is like the holy grail. Like if I can get more out of my process of higher quality with the same inputs, that's really what you're looking for in a biopharma process. Same inputs, greater quality and more output, that's a win. Huge win.

Brandy (16:51.533)

Yeah, yeah, yeah, that's the point where everyone wants to be. That's the ultimate end game.

Mike Tomasco (16:56.3)

Yes. And that's where that AI stuff really on top of everything else can really help drive a difference.

Brandy (17:05.189)

So it sounds to me, Mike, that you're saying for companies who are interested in exploring how AI can help improve their systems, improve their processes, it's really doing a full audit of their current operations and fully understanding what's going on, making tweaks to things that maybe are not optimal, and then start finding ways that they can kind of bring in some generative AI tools to help make faster, better decisions.

Mike Tomasco (17:35.15)

Absolutely, and I think I can give everybody a quick cheat code for this if you're into video games the cheat codes or always a way to get to the end faster Go to the bioforum website and download the digital plant maturity model. It's available for free I along with many others in the industry helped create this back in 2016 It's now on version 3 with a lot of great updates in it. But basically what that

Brandy (17:44.826)

Yes.

Mike Tomasco (18:03.744)

model does is it says what does good look like across all the different processes in my facility on paper? What does it look like in a digital silo? What does it look like if we're connecting things together? What is a predictive world look like and then ultimately what does an adaptive world look like? That's the holy grail, right? That is level five. Like how do I get to that adaptive world? Some of that was meant to not even be possible, right? Or not possible in the industry because it's a striving mechanism.

Brandy (18:23.663)

Yeah.

Mike Tomasco (18:33.198)

There's also an assessment tool that comes with this and this isn't a sales pitch for the thing, but it's just, you can use it in any industry really, but like for a bio manufacturer, it shows you what good looks like with examples, right? Then you go in, as you mentioned before, assess yourself. Where are we at? And be really hard on yourself. We had plans that were very, very, very advanced and connected and even doing some predictive stuff with 25 year old technology.

So if I'm trying to make a business case to digitally transform them from where they are to where they want to be, you can't rate yourself as like, I'm already a level four, because who's going to invest in that? You're already a level four. We actually said, like, if you don't have certain baseline capabilities of the new future standard, you're a level one. Even though you have these systems, we're ranking you a one. So we were really harsh on ourselves.

Brandy (19:22.01)

Yeah.

Mike Tomasco (19:28.088)

so that we could justify the journey. So we had something with this story, how do I want to get from A to B to C? And then over time, not everything has to be at that highest level, but you pick the processes where you're going to get the most value, and that's where you focus on those higher level, higher order things. So manufacturing shop floor, lab operations, there's a lot of great things you can do with data.

algorithms, AI and gen AI in those two spaces to really make a difference.

Brandy (19:59.835)

Yeah, that's truly incredible. that was 2016 that you launched this with some other industry leaders. Okay. Okay.

Mike Tomasco (20:05.632)

It wasn't me, was his bio form as a group and its member companies. We were one of the member companies.

Brandy (20:14.735)

Got it. Okay. So a bunch of member companies coming together to inform this path for a lot of folks and kind of depending on where you're at, be able to do the full assessment, understand what your capabilities are currently and then ultimately where you want to be and how you can get there.

Mike Tomasco (20:20.674)

Yes.

Mike Tomasco (20:31.66)

I've even helped other industries adapt the model to their industry. It's really interesting because the base capabilities are there and yes, it's very bio -pharmacist specific, but you can use it in pretty much any industry.

Brandy (20:43.503)

Yeah.

That's incredible. Great. Yeah. So anyone that's listening to this can go check this out, see where you're at and see where you need to go. It's incredible. And it's updated, I'm assuming frequently.

Mike Tomasco (20:53.614)

Absolutely. Yep.

Mike Tomasco (20:58.794)

It's every year or two, it depends on the technology changes actually. When we released the first iteration, by the second year, we had to change it because everything that we had in level five that was impossible was now possible. So we pushed it down to level four and made level five harder to achieve. That's part of it too. It's a constantly changing journey, right? So we got to keep up with it.

Brandy (21:20.463)

Yeah, yeah. Yeah, and just kind of thinking about your own journey and how many different technological advancements happened throughout that time. What were some of those big moments for you?

Mike Tomasco (21:35.146)

I remember when I used to have to program in Visual Basic to write a formula in Excel. So when Excel came out with that little formula button, that was magical. Seriously, I don't think anyone understands. You used to have to write your own formulas, right? So like, yeah, game changers, like things that make life easier that we take for granted. I do think that just the ability to access

Brandy (21:41.304)

Wah.

Brandy (21:46.492)

That's a complete game changer. Yes.

Mike Tomasco (22:03.607)

data and do things with it is so much easier than it used to be. In the manufacturing world in particular, the ability to connect your equipment has always kind of been there, but it's been localized, right? So like if you talk to an automation engineer, they've always had access to this data and information, just nobody else has. And the other things that have come along of like cybersecurity threats are a huge problem.

Brandy (22:07.844)

Hmm?

Mike Tomasco (22:30.284)

and we have IT, OT, cyber layers, depending upon your company and how you're set up. When you're focused on your OT layer, don't just wall, it can't be a walled garden. We need a way to bring data out of there, up into the enterprise, so you can do some of this other modeling. And then ultimately, when you get to adaptive, drive it back down into that OT layer on the edge and run these algorithms in real time to drive change. That's...

been hugely significant even in this the last seven years the ability to do that has gotten so much easier than it was when we had started the journey.

Brandy (23:05.401)

Yeah, that's, that's absolutely incredible. And just kind of seeing that transformation take place and, just being able to really wrap your arms and utilize the data. Cause I think that that's really been, it's taken a lot of time, I guess, just thinking about all the data that's coming in that we have access to and then figuring out how you're going to organize the data and then use it.

Mike Tomasco (23:30.99)

Well, let me tell you a story about failure. So one of the things that you could do is decide early on in this journey, hey, let's throw all of our data into the cloud. That was a big trend, right? And we'll figure out what to do with it later and we'll do that. Well, that doesn't always work out the way you expect it to. without going into any details, let's just say the first time we tried that, it wasn't great. The second time we tried it, we focused on

Brandy (23:44.463)

Yeah.

Mike Tomasco (23:59.798)

a use case driven approach and reusable data sets, I guess the right word. So we chose three sets of data, a subset of our ERP system, a big chunk of our quality assurance system, and a bit of our lab system data. Those three systems together were able to spawn all kinds of amazing

applications like analytical driven applications that were driving efficiencies, value, insight, replacing people's spreadsheets. Because when you start to connect those things together and you understand your cross -functional processes, one of the things that happens in a manufacturing plan is in a weekly or daily type huddle, everyone comes to the meeting with their own set of information, right?

Brandy (24:53.967)

Mm -hmm.

Mike Tomasco (24:54.634)

It's their own spreadsheet that they got from somewhere and their spreadsheet is right. No one else's spreadsheet is correct. But when you normalize all the stuff and bring it together and then build an experience that they can share, you can track exactly where something is at in this process, exactly what is holding it up. And you can create actions against that to go drive it out if that becomes the most important priority.

that stuff starts to become a game changer. And we started to do that as part of this lean production system where we were rolling out paper -driven processes, much like a Toyota would have years ago, right? And you measure with visual management how you're performing and you talk about it. You take actions, you look for continuous improvement opportunities. There's also the concept of standard work. You do X, Y, Z to go.

build something, but it also takes this amount of time. Well, when we were rolling that out, this was 2016, it was starting to get put in place and it was a journey over time. One of the things early on then was our digital transformation program was born at the same time and in parallel. And we were on a plant tour one day and the team was very proudly showing us all of their metrics on their magnet board and stuff like that.

somebody's hand hit the magnet board to half the magnet fell off. you're like, do you know where this guy goes here? The other thing that I noticed is like, you couldn't read anyone's handwriting on those little magnets, like they were all fuzzy. And then people were walking around with clipboards collecting like action items and stuff. then meanwhile, you're all gowned up in like sterile environments. You're like, I what is going on here? So we learned from that very early on that

Brandy (26:41.221)

Yeah.

Mike Tomasco (26:47.156)

something could be digitized here, like something could be simplified and experience could be made better so we could drive these outcomes. This, we created a thing called the Digital Operations Center, which basically is the digitization of lean manufacturing and it all the different components. And I still remember this one time, cause there's a lot of, a lot of change management issues with this. Don't, don't get me wrong. Like it was a big change to move into the digital world from a physical.

Brandy (27:02.697)

Mm

Mike Tomasco (27:14.604)

world, everybody understood the physical world. go to the system, I print it out, I post it on the wall. This became a, how do we collaborate through a system to have these conversations? And it's, it's really taken off and grown and is still used every day today. But I remember a continuous improvement specialist pulled us a silent day and said, thank you so much for what you're doing. You have no idea how you've changed my life. And this is the making someone's life easier.

Brandy (27:15.066)

Nah.

Mike Tomasco (27:43.202)

Their job was to go into every suite and with that clipboard, go gather the action items and prepare them for the meeting the next week. Well, that all became instantaneous because it was all in the system. So that was one of those immediate benefits you get is that that person can now focus on what their real job is, which is doing analytics around why things are a problem. So they're trying to do continuous improvement, not just go around collecting stuff. If you fast forward another year or two,

Brandy (27:54.885)

Yeah.

Mike Tomasco (28:12.418)

those analytics get automated, right? Because now we're collecting them automatically and like, you know what the problems are, so you know what the algorithms are you're looking for. So then they can focus on running a project to make the improvement. Then if you fast forward a little bit, you move into predictive world, not only are you collecting these things and showing how you're doing, you can start to predict when things are gonna go wrong. You can prevent them. That was the key that.

Brandy (28:25.902)

Hmm?

Mike Tomasco (28:38.752)

really people did not understand when we started to digitize things. The ultimate goal wasn't just to just put stuff on a digital piece of paper, it was actually called digital paper at one point, but it was to get to the point where you could start predicting outcomes and preventing bad outcomes from occurring and then layering in all the other really hardcore manufacturing analytics along with that stuff. And now with GEN .AI, you've got that little co -pilot helper telling you what to do next.

We're living in a good time for manufacturing. I boot.

Brandy (29:12.515)

Yeah. And it's really incredible, the journey and how it affected each person and really just freeing people up from busy tasks to do critical work, which is the work that people want to be doing. And you talk about folks jumping to different companies because they can, and perhaps the situation that they're in currently isn't...

flexing that muscle that they want to be using and allowing them that opportunity to take the things off their plate that's just weighing them down and busy work and letting them do the work that they really want to do. It's huge.

Mike Tomasco (29:52.462)

Well, the other thing that really changed like the whole IT digital industry, I'll just hold up my iPhone, right? Like it took a long time, but the expectation for experience is now how my iPhone just works. And I don't have any training for how to use my iPhone more than sometimes maybe a little help tip screen for things that are new, right? Like how, why are we not building industrial manufacturing or?

quality assurance capabilities that are that easy to use? Why are they so complicated? And it's a great question, because I think all of the software makers out there are on some sort of journey of their own with their products and where they're trying to go in layering and capabilities and making things easier. But one thing that I think most of them don't understand is how important openness is. So in our manufacturing world, let's just look at that with some blinders on.

Our plants had come from, plants fluctuate all the time. When I left, were 35 plants and they came from, always say, like 50 different legacy companies, right? Like there's a lot of mergers and acquisitions and the equipment was the equipment of the day. You start to get a little bit more standardization as you move up the layers, but like the equipment's different everywhere. Even if you're making the same product in two places, the equipment is different.

the characterization of how a product and material flows is different. It's not the same. You have to adjust for that. what our software, especially on the industrial side, the software manufacturers or hardware slash software, they don't seem to understand that it's a heterogeneous environment. We don't have a plant that has a single set of equipment from one supplier where we have their factory of the future. That doesn't exist.

unless you're building something new and you make that choice, which even today people don't make that choice because they want the bioreactor from this group and they want to get XYZ from another group, right? So we need platforms that are open. Oftentimes, as long as we can get to data through APIs, we're good. But like, I don't necessarily need someone's...

Brandy (31:46.629)

Hmm?

Mike Tomasco (32:11.614)

sensor on their pump and their as a service cloud analytic for that pump. Like that doesn't help me because I don't have more than like three of those and I can't replicate that to be thousands of other pumps that I have. I need to be able to write that algorithm myself so I can do predictive maintenance or something. I'll buy your algorithm maybe right but like I don't need your as a service thing right and I think that's something that a lot of

Brandy (32:29.37)

Hmm.

Brandy (32:37.722)

Yeah.

Mike Tomasco (32:40.632)

the software makers don't understand. Be an open platform.

Brandy (32:43.853)

Do you think that's shifting? Do you think they're seeing the challenges there?

Mike Tomasco (32:48.85)

I am starting to see more of that, especially with startup companies. mean, they just start open from the beginning. We're an open platform. It used to be the walled garden, like the big equipment manufacturers. And I think they're starting to see it as well, but it takes time to change, right? You've got all the history. So oftentimes the smaller companies have an advantage in that. And of course the pure play tech.

Brandy (32:56.688)

Hmm?

Brandy (33:06.137)

Yeah, yeah.

Mike Tomasco (33:14.978)

people like Amazon Web Services, from the get -go, that's just how they're built, right? They are an open platform that you can connect things into and do stuff.

Brandy (33:25.081)

So did you find it really challenging then to incorporate different software technologies because it wasn't conducive to what, I mean, did you have to build everything from scratch essentially?

Mike Tomasco (33:31.49)

Done.

Mike Tomasco (33:37.446)

No, well, it depends on what year we're talking about, like integration between systems is one of the most difficult things to accomplish consistently. How many years have we been hearing about like the magical service bus that's just supposed to be plug and play with XML? Like the stuff is there. You still have to orchestrate it all.

Brandy (33:47.653)

Hmm?

Mike Tomasco (34:03.81)

The really good orchestrators are very expensive. So then you look at open source orchestration, there's different ways to do it. depends on what your goals are. Do you want to be a group that's buying everything and configuring? Do you want to build some of the stuff because you have the technical capability? I think it really just, it depends. There's a lot of different ways to do it, but it is a phone. It's like data has to move. It has to move in close to real time, especially from equipment.

Brandy (34:07.81)

Yeah.

Brandy (34:23.47)

Yeah.

Mike Tomasco (34:32.574)

that connection is difficult because it's so heterogeneous. Same thing, the lab is even worse. Lab instruments and connecting is extremely complicated because everything's bespoke to the instrument and whatever that manufacturer wants to do and how they want to hold you beholden to using their instrument, their cloud -based thing, which might be fine for that one application, but we also need the data somewhere else. So you typically do have to build some sort of

orchestration capability to move data around.

with WhatsApp.

Brandy (35:06.351)

Yeah, I mean, that makes complete sense. mean, we did touch on this a little bit already, Mike, but I'm just curious if you have anything to add additional to add here. What do you think the big next frontier in bio manufacturing will be?

Mike Tomasco (35:21.422)

boy. So the big next frontier. Well, I mean, we're going to have to say AI is going to be involved in some way, right? Like as we get more and more into innovation in the industry, the AI has to be applied in the R &D space. just, it's been around for years, big data. It has, but it hasn't really fully paid off yet. But I think with this wrap,

Brandy (35:32.239)

Hmm?

Brandy (35:50.128)

Yeah.

Mike Tomasco (35:50.99)

constant rapid change that we've seen in last two years, there are going to be tremendous breakthroughs driven through AI -driven discovery of new entities, whether they're biological or chemical, whatever they might be. That's going to be fundamentally important. I think in the operations space, if you have your foundation solid and you've really focused on that for a while,

Then Gen .AI becomes a bit of a game changer, I think on efficiencies and automation of capabilities. But you have to do it very purposely. You can't just kind of throw a Microsoft copilot in the mix and say, summarize my meeting minutes. Like that's fine. And great actually, it's pretty awesome what it does. But in the manufacturing context, you want to be able to consume.

Brandy (36:24.432)

Hmm?

Mike Tomasco (36:46.062)

all your historical information into these things. So then you really can do some magical type things with it for a user. The problem with that is the cost and how much it costs to operate those things. Like if you want to bring in 20 years of quality data into a GenAI, you know, because an LLM on its own is great for writing sentences and things, but it's not incorporating your knowledge and your data. So if you start to incorporate your own information, these things get

Brandy (36:59.045)

Yeah.

Mike Tomasco (37:15.554)

very big and very expensive quickly, but there's a potential huge payoff on it as well. I guess.

Brandy (37:18.555)

Sure.

Brandy (37:22.837)

Yeah. Yeah, they also become very intelligent, right? But it's just putting all of that infrastructure in place.

Mike Tomasco (37:26.714)

Yeah Well, you can start to move into causal AI so, you know Using your first principles algorithms and things get you so far The concept of the gen AI layer in King can start to also you can interview your experts and you can have a standard template for asking them questions for how they solve problems all different kinds of things they do that intrinsic knowledge that's in intangible in people's heads and

Brandy (37:45.125)

Hmm?

Mike Tomasco (37:55.916)

You can kind of get it out through interviews and incorporate it into the machine and teach the machine how to go with, you know, math and science, historical information that we can all learn from. You can teach it from books as well. Like you can teach it to be like a university student. Then you can layer in your experts and how they actually do things and how those individuals, your high performers are super efficient. And then you can get it into the hands of people in regular plane.

plain language. Wow. Right. That's amazing. It's also going to take a while. It's going to cost a lot of money, it's possible today. So, I mean, there are a couple of companies that are out there doing these things and it's pretty fascinating to watch.

Brandy (38:34.277)

Yeah.

Brandy (38:43.801)

Yeah. And I think too, from a talent perspective, I would imagine that the experts and the people that are, that we want to be tapping into to answer some of these questions, there's probably very few of them and perhaps they're leaving the industry. Like the shortage of talent in that space, I would imagine is.

Mike Tomasco (39:02.764)

I think we have about five years to capture it, I think, because we're starting to see people are retiring. It's just the natural way of life, right? You don't want to work forever. So eventually you retire. And the younger people coming into the, let's say the shop floor lab space, they don't have as much experience. They need that apprenticeship to really learn from the job itself.

Brandy (39:07.556)

Yeah.

Mike Tomasco (39:29.09)

There are ways to try to curate that, by the way. You can use virtual reality training to help bring people up to speed. So we did a little bit of that in a few of our plants during the COVID times to help bring people up to competence faster. And also so you don't have to take down any of your production lines so you can teach people virtually. Now those things will really take off once you can actually use your hands.

Brandy (39:54.063)

Yeah.

Mike Tomasco (39:54.446)

The little clicker things aren't so great. So like once you have to go pick up a vial and you just click it and you can throw it or like, you know, it's the same as clicking, picking up a person. Like it's, it's not really effective, but we were playing around with haptic feedback gloves and stuff like that, that are incorporated into that technology will be there pretty soon. And that'll make the experience real. Like, so you can squeeze a vial and break it in a nice way. That's a problem. How do you clean that up? But those are all training things you can do that are so much cheaper than

bringing them into a production area and making a mistake.

Brandy (40:26.779)

Yeah, that's incredible. That's truly incredible. I guess this kind of moves into my next question here for you as we close out this conversation, but thinking about folks who will be entering this space, what is the piece of advice that you would give to someone just starting in bio manufacturing?

Mike Tomasco (40:44.598)

You know, I think there's a few things, actively managing your career is really important. Deciding what you want to be when you grow up at every stage. That's what I always say. Whenever I mentored anybody at any level, it was always the first question is like, well, what do you want to do? And if you don't have an answer to what do you want to do, I can't help you as effectively on how to manage that. my advice would be,

Brandy (40:57.53)

Hmm?

Mike Tomasco (41:13.4)

Figure out what you want to do. And at each stage of your life, it might be different. When you're first starting out, raise your hand. Raise your hand. I still raise my hand to do things because that's how you get known and recognized. Like you do your day job, that's fine. But when there's the extra thing, I can help with that. Or I have some free cycles, I can help with that.

And then you get to know more people, they get to know you, and then when something pops up, they think of you, hey, that person who raised their hand, I wanna work with them. You build a reputation of being somebody who can help do things. And that's the most important thing when you start. You wanna show up and help people do things and try to make a difference. So I think that's what it comes down to, make a difference for your team, for your company.

for your customers. In our case, the customers are patients, right? So if we can help do things that drive better outcomes for patients, like what else do you want to get out of your life, right? Like it's great.

Brandy (42:18.767)

Yeah. I, Mike, that's what you, that's how you built your career is by raising your hand and putting yourself in positions that sometimes didn't even exist. Now you raised your hand to, yeah. It's incredible. It's incredible. And, know, it's, it's interesting because especially in this industry, you know, you talk about making mistakes and failing.

Mike Tomasco (42:24.301)

Yes.

Mike Tomasco (42:29.484)

That was the fun part.

Brandy (42:45.861)

This industry is particularly hard to fail in because it's so critical. The work is so critical. But you have to in order to push innovation and continue to grow.

Mike Tomasco (42:53.154)

Yes. Yeah.

Mike Tomasco (43:02.528)

You know, I do find that there's sometimes a drag on innovation and trying to drive these new things and it's fear. You just brought it up. There's a fear of everything. fear to make the wrong decision, fear of failure, fear of spending too much money. Well, the way I look at it is I used to have a sign on a little saying on my whiteboard that said, do something.

Brandy (43:11.962)

Yeah.

Brandy (43:16.558)

Hmm?

Mike Tomasco (43:31.628)

And if that doesn't work, try something else. Because the act of not making a decision is a decision. And you're slowing things down, you're becoming bureaucratic. You can drive a lot of things pretty quickly with a little bit of information. You have to be confident that you're gonna succeed and if not, you're gonna pivot and change until you succeed, right? Like that's what iterations are about.

That's what we want to do. We want to drive these innovation things, but a lot of us in leadership positions are afraid to fail. We're afraid to stand out because we're ultimately afraid we're going to lose our job. And I think that that fear really holds the human potential back. And if you try to build a culture and environment where that's not part of it, you can do a lot of crazy things that are awesome.

Brandy (44:18.862)

Yeah.

Brandy (44:27.92)

Hmm.

Mike Tomasco (44:28.396)

That's what I always try to do on my teams is build this environment where you can go try and do things. I still remember sometimes people would say, Hey, I'm thinking we should go do X, Y, Z. And it just sounds like a good idea. It just makes so much sense. Like do it. Like, why are you even asking? Like that's a great idea. We'll do it. But that's a different leadership style than maybe some other people have. if someone brings me a good idea.

Brandy (44:45.637)

Yes.

Mike Tomasco (44:54.23)

and it's better than my idea. do, to be fair, I do usually fight it first. I'm like, wait a second, my idea. But once I get over myself, right, and their idea is better, I'll go champion that idea because it's better. And it makes us all look good. And I think that's really what it comes down to is trying to make a difference and trying to find those ideas that make a difference and fostering a culture in which everyone's just trying to make a good, positive difference. And by the way,

We've got to have fun when we're working together. So that's a core competency as well for the younger people. Have fun in your work. Enjoy who you work with. You spend more time with them than your own family sometimes. So enjoy it. You might as well.

Brandy (45:24.813)

Absolutely.

Brandy (45:34.731)

yep, no, that's truth. I really love that, Mike. Thank you so much for talking with us today. Really enjoyed this conversation. And hopefully we can have you back for round two.

Mike Tomasco (45:50.712)

Sounds great, I would love that. Thank you so much.

Brandy (45:52.923)

All right. Thank you.