How Empathetic AI is Creating Bionic Employees in Life Sciences? | Parth Khanna

Empathetic AI and the Future of Work in Life Sciences #aiinlifesciences #pharmaai #biotechbytes

Artificial intelligence is shifting how biotech and pharmaceutical teams operate in the field. Rather than replacing sales reps or medical science liaisons, the real power of technology comes from making employees better at what they do. In this episode of Biotech Bytes, host Steve Swan speaks with Parth Khanna, CEO and Co-founder of ACTO, about using empathetic AI to support a bionic employee workforce. Please visit our website to get more information: https://swangroup.net/ 

Parth explains how intelligent field excellence platforms give customer-facing teams quick access to clinical data, personalized training, and real-time coaching. They discuss why human connection, trust, and strict compliance matter when introducing AI agents into healthcare.

Key topics covered in this video:

• Why AI should augment human skills instead of replacing workers

• How field reps and medical science liaisons use AI agents to prepare for calls and learn faster

• The role of compliance, governance, and trust in enterprise AI adoption

• How human judgment and AI support work together to shape the future of work

Links from this episode:

✅ Get to know more about Steven Swan: https://www.linkedin.com/in/swangroup 

✅ Get to know more about Parth Khanna: https://www.linkedin.com/in/parthkhanna 

✅ Learn more about Acto: https://acto.com 

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#aiinlifesciences #empatheticai #pharmaai #biotechbytes #bionicemployee #healthtech

How Empathetic AI is Creating Bionic Employees in Life Sciences? | Parth Khanna

Transcript

Steve Swan [00:00:00]:
Join me next for a conversation with Parth Khanna, CEO and founder of Acto. Acto is an empathetic AI organization which helps biotechs with their launches and their field needs for their organizations. Parth really believes that AI is helping him and helping the world create bionic employees. Join us next to understand how he does that and what the organization can help do. Hello, welcome to Biotech Bytes. I'm your host, Steve Swan. And today I got the pleasure of being joined by Parth Khanna. He is the CEO and co-founder of Acto, which is helping to unite human intelligence with empathetic AI.

Steve Swan [00:00:46]:
Parth, thanks for joining us today.

Parth Khanna [00:00:49]:
Steve, thanks for, thanks for having me here. Very excited to talk to you today.

Steve Swan [00:00:53]:
Yeah, I'm excited to hear, hear, hear about what's going on with you. But before we get into all that, I just, what I'd like to do with my audience is just Let them get a quick intro on who I'm speaking with. So if you don't mind, give us a minute or two on how you got to where you are and the problem that you're trying to solve. We can get all into it.

Parth Khanna [00:01:12]:
Yes, Steve, thank you. Folks, my name is Parag Khanna, co-founder, CEO here at Akto, and we are an intelligent field excellence platform for life sciences. In so many words, Steve, we help customer-facing teams in life sciences, whether they're in sales, medical affairs, patient services, market access, make sure that when they're showing up in front of your customers, they're showing up as masters of the message. They're showing up as value creators. And how we do that is through, as you mentioned, building on strong human intelligence and supporting them with empathetic AI, i.e., AI and agents that understand the needs of the human. So that includes everything from training, certification, AI-based role play and practice, all the way to super agents that help field team members with pre-call planning, CRM logging, key insights, and helping them critical knowledge at their point of need. And Steve, we work with 12 out of the top 50 pharma companies with over 50,000 field professionals on our platform. How I got into this is a very interesting story.

Parth Khanna [00:02:31]:
My dad actually was a pharma rep in the '80s who ultimately became a medtech entrepreneur. And growing up, I did not want to do anything with pharma and life sciences and healthcare and actually went to law school to be a human rights lawyer. And lo and behold, my roommate in law school was a patent agent for BlackBerry. If you remember those ancient relics at this point. And just chatting with him over kitchen table conversations, him and I ended up inventing an early AI technology in 2013 based on natural language processing. And that got me into tech. And that was my first tech startup. I had a good run with it.

Parth Khanna [00:03:17]:
And around 2017, when the Sunshine Act was passed, and life sciences companies were struggling to get the clinical evidence story to their field teams and the customers, my dormant genes started expressing themselves and took my knack and love for tech and brought it into life sciences and started Akto and just been such an incredible run here.

Steve Swan [00:03:42]:
I guess, you know, Sunshine Act, field team, we're focusing on the sales folks, right? Obviously.

Parth Khanna [00:03:49]:
Yeah. Yes. Sales, sales, MSLs, um, key account managers, patient support specialists, case managers, all the folks that touch, uh, touch customers in different, different forms.

Steve Swan [00:04:02]:
Okay. And so the gap you saw or see is what? And then, then the gap we're filling, right?

Parth Khanna [00:04:11]:
Yeah, Steve, the gap has definitely evolved as the space has evolved. So when we first started back in 2017, it was more foundational, I would say. It's how do we just train and certify the army of field team members that we have? So you mentioned sales reps, for example, that were not used to selling on value or conversing with healthcare professionals on value. So how do we train them? That's how we started as a training platform. And then what we realized is that training is just one of the tools in the toolkit to make sure that those powerful conversations are happening. So we brought in coaching, practice, role play, and all those other aspects of making sure that the field teams are prepared. And then in 2024, Steve, as the AI boom was happening, we very quickly saw that one of the key areas where AI and agents are going to be able to add real value for life sciences companies is giving field team members an empathetic agent or assistant that can help them do their job more robustly. And that's where we got into Agentica.

Parth Khanna [00:05:28]:
And so today we call ourselves more full-stack intelligent field excellence.

Steve Swan [00:05:34]:
Hmm, that's pretty cool. So, your AI, Models, agents are essentially looking at what the gaps that these folks have in their delivery, and you're trying to help them. What do I wanna say? Train that weakness, help them make that weakness stronger so that that's no longer weakness for them. Am I hearing it right?

Parth Khanna [00:06:02]:
Yeah, Steve, that's a great point. So if you think about so much of AI is based on context.

Steve Swan [00:06:07]:
Yeah.

Parth Khanna [00:06:07]:
And the question that we asked is, what if the context was actually what the human needs? And that gave birth to this notion of, hey, let's make agents empathetic to what the needs of the humans are. So imagine if I'm a sales rep who's gone through his training and I really struggled around the clinical data and my clinical fluency is just low. Then imagine an agent that has that know-how and has that understanding So when I'm going to that agent and asking it to, for example, summarize clinical papers, it's not just throwing a book at me. It knows that, hey, Parth actually struggles with these clinical concepts. Let me break them down more intricately or more slowly versus another area where I may have just accelerated through my onboarding and an agent might just gimme an answer. quickly, be brief, be bold, be gone, and I just need to deliver that message. So, it's interesting how I've seen the conversation around AI take 2 forms where there's this one school of thought, which is around replacers, where their ethos around AI is, hey, how do we maximize efficiency and bottom-line savings in an organization? And the other school of thought that I prescribe to is around augmentation. How do we build agents and super agents around humans that orbit the human, always understand what their needs are so they can help humans do what they do best, which is human connection, strategy, judgment, taste, all of these brilliant things that 4 billion years of evolution has— has endowed us with.

Steve Swan [00:07:54]:
Well, so it's funny you say that because my, lots of my CIOs that I talk with, right? They talk about AI, but the way that they frame AI is to supplement, like you just said, what we do. And a few of them have called it, you know, creating the bionic employee, right? You know, just kind of brought out the best in that individual and where their weaknesses are.

Parth Khanna [00:08:18]:
Yeah.

Steve Swan [00:08:18]:
completely supplement it, you know, and, and make that weakness. I don't wanna say paper. I, I keep trying to, it keeps coming to mind, paper over the weakness, but you're not, you're, it's actually real. You're not just creating a false, you know, paper over a hole. You're, you're, you're actually filling that gap, you know? Yeah. Which is awesome.

Parth Khanna [00:08:35]:
Steve, I, I love this notion of a bionic rep. It's, it's interesting. The, the, so I've let, I've spent the last 18 months very deep in the throes of agentic AI and deep in product builder mode, if I may. And one of the biggest transformations that has happened in my thinking, Steve, has been when I started out this journey around for us to build these agentic products, I had a much more simplistic view where if you take a person's job description, for example, and you say, okay, this one pie chart, let's say, represents the work that needs to be done by the human. And on that pie, you take a slice off and you say, okay, these are the maybe dull and difficult or heavy data processing tasks that the agents can perform. And you take the slice out and you almost delegate it out to the agent. Steve, now my thinking has transformed, which actually I think in my belief is the right way to think about it is actually not a pie. Pie that gets split in how agents and humans work, but it's actually 2 concentric circles where at the center of it is the human intelligence and cognition, and agents actually operate at the periphery and at the edge of human intelligence.

Parth Khanna [00:09:53]:
So where your thinking stops is where agents pick up. And what I mean by that is, let's say you're a chief medical officer looking at a bunch of evidence in front of you and you're trying to articulate the claims. from that evidence. What agents can do is they can chomp through and process so much more data than a human mind could. And that's where I love your notion of a bionic human being. So imagine a world where agents in real time are parsing this data. They have their tentacles in different systems of record, getting that insight, and then they're serving that up to the human where the this chief medical officer, they uniquely possess the sympathy of having sat in front of patients, maybe with a rare disease, looking wide in their eyes and seeing what it actually might feel to have that disease and bringing that judgment and taste. And it's not just someone senior in HQ, like a chief medical officer.

Parth Khanna [00:10:57]:
The same is true for Roles where there's high consistency and control needed, like a sales rep, where agents can go perform things for the human and bring it to them. And then we exercise our human judgment that's at the center. So this whole notion of 2 concentric circles where agents pick up where the human cognition leaves off, I think it changes the discussion from a zero sum, right? Look, everyone's thinking about future of work right now. But it changes the discussion from zero sum to more additive. How do we make the pie bigger versus humans competing against machines, which I think is just a reductive conversation.

Steve Swan [00:11:39]:
It is. And that's where it's, it's devolved to recently, right? You know, it's just, it's, uh, I don't know. Again, that's why I kind of like the idea of the bionic employee, right? It's helping us. be better at what we do. And I think that a lot of the folks, when I talk to 'em, you know, they, even the leaders, you know, it can help us, they say, you know, do like you just said, some of those mundane tasks that we don't want to do. Or, you know, the chief medical officer that you just talked about, right? He or she can have AI running in the background and it's grabbing data from so many different sources that your team can't even do. And It's putting, it's feeding it up to you, right? Like, here you go. This is what we got.

Steve Swan [00:12:21]:
And it's, it's doing it constant and doing it in real time as opposed to, hey, did you guys get this together? Hey, did you girls get this together? Whatever it is, you know? Um, so now you've got something pulling it together automatically and it's just whenever you want it, it's right there, you know? And that's making your life as that person that much easier and that much more up to date and that much more concise and, and, and, and You know, plugged into exactly what you need to be plugged into, right? You know?

Parth Khanna [00:12:48]:
Exactly. And Steve, rolling that forward, so if you map on, imagine a time, an x-axis where you have a chief medical officer and then you have, let's say, your field medical director and then your MSL. What's interesting, another very interesting finding that we've had is that Firstly, what's true for each of those roles, while they're different, is this notion of augmentation or bionic human, as we talked about, and that changes and varies, right? So for a field medical director, for example, someone that is about to have a coaching conversation with an MSL, it's hard for that person to go through the CRM, pull the reports, pull a field coaching guide that their medical education team or L&D team may have given them, go to page 55 and say, okay, this is a— maybe I'm the first-time manager, right? And go into the right paragraph and say, okay, this is how I should have a crucial conversation versus what you can have an agent do is go parse through the records on the CRM, determine where the MSL that is reporting to you or a team of MSLs, where do they stand relative to their KPIs?

Steve Swan [00:14:02]:
Yeah.

Parth Khanna [00:14:03]:
reference the right field coaching guide and find the paragraph and say, hey, this is how it's suggested by your L&D team you should address any competency issues or performance issues. That is a perfect manifestation, Steve, of your— this idea of bionic, because you've now augmented the cognitive capacity of this field medical director. And same thing for an MSL who is preparing for a scientific exchange. For them to go through all the clinical papers, go through all the CRM records, parse it together, stitch it together, is just— not only is it arduous, but let's just face it, how good can a human being get at that? And that's where I love the idea of we don't have to compete with the machine. The machine's there to help help us be better and help advance the value that we want to create for the ATPs.

Steve Swan [00:15:00]:
Yeah. And they all, you know, everybody's able to take advantage of that as long as you know how to take advantage of it. Right. So, so you put this system together, right. And you're selling it to all of these different organizations, these different companies. And is it all around, like you just talked about, right? Is it all around the actual science and the field force? Does it get into different areas? Tell me a little bit about that.

Parth Khanna [00:15:21]:
Is it?

Steve Swan [00:15:21]:
Or can it kind of help any group as long as you put the variables in properly to your tool?

Parth Khanna [00:15:28]:
Steve, great question. And this was one that we grappled with. And if you're a builder right now, you've been just given this amazing toolkit, right? The palette that you can paint with is just wider now. So I think we all need to ask some very important questions around should we focus? And as an organization, what we get excited about is post-market commercialization. So if you're L-18 months from PDUFA all the way to late-stage therapy that's about to come off the patent, in that journey, Steve, is where there's room for both efficiency and effectiveness. And effectiveness happens more on those frontline roles, right? Let's, for example, MSLs, sales reps, key account managers, where they're actually interfacing with the customers. So we think that the right set of KPIs for measuring agents and AI in those roles is effectiveness. And then the closer you get to the home office, so to speak, where there's higher degrees of judgment, you're not striving for effectiveness as much.

Parth Khanna [00:16:42]:
But more so efficiency. It's how much of the, to your point, roadwork can we compress, or we can just improve the quality of output. So, one of the areas that we're hearing a lot about is how do we reduce agency dependency, where if there's a new launch coming in, teams just want to be able to do more, have more capacity in-house to be able to deliver quicker with agentic support, it's not a notion of completely gutting out the agency model, but having more internal capacity to respond quicker and at least a set of messages and materials that can be created in-house. So to answer your question, Steve, where we focus is on that commercialization journey. Right now we're doubling down on frontlines, making them more effective with super agents. And of course, underpinning with training, coaching, role-play practice. In our labs in stealth mode, we're working on agentic systems that go more upstream in the strategy and content creation work that we're excited to bring to the market next year.

Steve Swan [00:17:51]:
That's cool. That's fun. So with each customer that you sign on or that you bring on, is there a lot of customization that needs to take place or is it— or not?

Parth Khanna [00:18:01]:
Steve, if you asked me 3 years ago, I would say it is, it's a lot of heavy lift. Now the beauty and elegance is agents can actually help create agents. So for example, one of the ways that we've designed the system is you upload the job description of the human that you want to support, And the agent, the super agent gets so much of its context around how to help that human from the JD itself. So that's empathetic AI in action, right? 'Cause you're saying, you're telling the agent, hey, your reason for existence is to make this human better. And we did this really brilliant case study with Curex, Curex Pharma. Shout out to them and congrats 'cause they just got acquired as well. Been in the news. We're very happy for them.

Parth Khanna [00:18:59]:
So one of the use cases there, Steve, was for their field sales teams. When they went and spoke to specialists, they were getting a lot of complex questions and the answers resided in different systems of record. So what we helped, what we co-created and built with them was almost imagine a Bumblebee i.e., a super agent that can go from flower to flower, suck up the nectar, process it, and bring it to the human. So let's say if I have a meeting with an OB-GYN and they ask a tough clinical question, and now the answer is in PubMed, my approved marketing assets, and my training materials, this super agent that has the necessary guardrails and context because they understand that role That not only are they pulling from one place, but across different systems and processing that information in a way that it's compliant. Steve, I would be remiss if I didn't say this. If I am one of your listeners right now, I'm driving to my nearest Starbucks listening to this, my first instinct, and this is with a lot of folks, is, Man, this sounds great, but how can we trust it? How do we bring this into our organizations? And one of the biggest barriers to climb is compliance. So—

Steve Swan [00:20:25]:
Yeah, I was going to go there, but you went there. Go ahead. Go.

Parth Khanna [00:20:29]:
Yeah, Steve, I would actually love to hear your thoughts as well in the conversations that you're having. What role is compliance and trust playing? And I'd love to share my thoughts, obviously. Yeah.

Steve Swan [00:20:40]:
Well, I mean, you know, compliance is a big part of all this, right? Because how do we, you know, when we talk about, let's just go back to an example you used 2 minutes ago. You know, if you're looking in, I'm just gonna use the simple term of HR, right? You know, let's say you download a job description there and let's say you ask AI to help you find that person, right? And let's say AI starts screening resumes for you. You know, I went to an HR conference a year ago about this and they were talking about how They have to, um, be able to prove that, you know, Parth, who was rejected by AI with the same resume as Steve Swan, who was— Steve opted out of AI, right? The human rejected Steve. From an audit perspective, we gotta be able to say this is why they both were rejected. Or, or if Parth gets pulled off the pile by a human, why wasn't Steve pulled off the pile? You know?

Parth Khanna [00:21:34]:
Yeah.

Steve Swan [00:21:34]:
What is, what ha— you know, so it's a lot. There's a lot of, a lot of moving parts there, you know? So it's tough right now for, for these, for everybody to, it's an employee, right? AI's an employee. So we gotta, at a certain level, treat it like that. And, and when we're doing our QA on it, you know, lots of times HR needs to get involved too. So as we're putting our checks and balances on this new AI model, what would AI say if it went left? And what, I'm, I'm sorry, what would HR say if they went, if it went left or if it went right? You know? Um, So it's, it's, I don't know. It's so big. It's such a big issue. And when I think about it, my brain just starts melting down, you know? Um, and, and I'll be honest with you too.

Steve Swan [00:22:15]:
I think that I'm very, very busy this year, right? As an executive recruiter. And I think it's because of AI and the reason being, not that I'm finding, not that I'm looking for folks with AI, but I think AI has taken I think it's taken on a lot that it's hard for it to swallow, right? So it's writing the descriptions, candidates are using it to write the resumes, then, then it's screening the resumes. So it's kind of checking its own homework, if you will, you know?

Parth Khanna [00:22:44]:
Sure.

Steve Swan [00:22:45]:
Um, and so the managers are calling me and saying, dude, I don't know what to do. I don't know what to do. I got a lot of resumes. They look great. But when I talk to folks, you know, it's clear to me that AI wrote 'em. So I'm not sure exactly. What's real and what's not. So I need you, a 28-year veteran, to get me through that, you know? So I'm really busy this year helping them weed through that anyway.

Steve Swan [00:23:05]:
But from a compliance perspective, it's, it's, it's a tough, it's a tough balance, you know?

Parth Khanna [00:23:11]:
Steve, you touch on a very important point where, because the content creation capacity across the board has increased, that means that there's just being more put out in the ether, right? Because at the end of the day, look, these are large language models and language or ability to generate ostensibly good language has been democratized to a point where it's everywhere. And it's interesting if we think about, we were talking about customer engagement. Let's put ourselves in the shoes of our customers' customers, which is healthcare professionals. How are they feeling in this environment of— because now, Steve, what you're experiencing—

Steve Swan [00:23:52]:
Yeah.

Parth Khanna [00:23:53]:
where there's been this tidal shift in more stuff and there's more that's being thrown their way as well and now they also have open evidence and more tooling, AI tooling. So, in that world, how do we differentiate our brand in a sea of noise? And going back to the frontline team, Steve, I was talking to someone just over coffee and they're like, well, what is the future of a sales rep going to look like in pharma? And is that going to get automated away because now doctors can get the information on the phone? And my view on that, Steve, is that is a very, very short-sighted view. And here's why, because in this sea of information, what actually will make your brand stand out is the human connection.

Steve Swan [00:24:46]:
Yeah.

Parth Khanna [00:24:47]:
is that someone that is trustworthy that walks in through the door and says, hey, let's help, let's make sense of the data. What kind of patients are you seeing right now? How can I help? How can I be there to service you and help you sift through the noise and be a true value creator? It's the same reason why these companies are calling you, right? It's like, hey, this is too much. Help us make sense of it. And I just think that the human connection and field teams now have a greater role than ever to be the signal in the, in this noisy world.

Steve Swan [00:25:20]:
And I think we got 2 things right there. Number one, I think the companies that are, that are actually using the AI to create the bionic employee, as opposed to using the AI to try and replace the human, that, that, that model's not working. That's what I think I'm feeling the effect of. The companies that are now saying, okay, We'll give the AI agent or whatever it is to the 22-year-old as opposed to the 37 or 50-year-old person that's been doing this for a long time. Uh, well, you know, we're not going to create the superhuman. We're going to replace the human. Well, that's fallen on its face in my opinion, right? Right now. So what we've been talking about all along is the, the, the supplementing, you know, what one already does in their workflows.

Steve Swan [00:26:05]:
And to make sure that they get to that elevated level, letting AI do those mundane tasks. But I think some people try and shortcut it and say, okay, yeah, you know what it can do mostly? Uh, it can, it can do some of it, but you still have to have that, that, that, that, that trained professional. And the second part of that is if too many of these companies do what I just said, we're now going to lose the training ground for the junior level person to get elevated to seasoned person later on in their career, which worries me for my kids, right?

Parth Khanna [00:26:37]:
Yeah, exactly. Steven, it's interesting, even for the non-mundane tasks, for the more complex tasks, going back to that 2 concentric circle notion, agents can do so much for the humans. And I think what we need to all think about is these are 2 types of distinct intelligences and they're meant because we built it, we invented it. It is both for ethical reasons and commercial economic reasons. They should work in tandem, right? And I love what's coming out of this conversation of this notion of a bionic life sciences worker. And then the question, if you're a leader, is going back to what we were chatting about, how do you trust that this, you know, the Iron Woman or Iron Man suit's not going to start having sparks flying out and smoke coming out of it. And I've spent a great deal of time talking to folks about this topic, Steve. In fact, we just did a roundtable series in 10 cities, North America and Europe, very intimate, 3-hour-long discussions with over 100 leaders.

Parth Khanna [00:27:50]:
Um, definitely hit my air miles, uh, highest threshold this year.

Steve Swan [00:27:54]:
But, uh, that's why you got sick. That's why you got sick. Too much flying.

Parth Khanna [00:27:59]:
Exactly.

Steve Swan [00:28:00]:
Air, right?

Parth Khanna [00:28:00]:
Steve, nothing an espresso shot can't fix. And let me come to this one. Agreed. And Steve, what came out of that is very actually interesting where what is trustworthy first and foremost depends on where you go on the spectrum of HQ to field. So, for example, what is a trustworthy agent for a chief medical officer will look very different from a field medical director to an MSL. That's one premise. However, there's this one unique universal equation that came out of it, if I can geek out here for a second. It's that trust equals compliant plus capable.

Parth Khanna [00:28:45]:
And what I mean by that is if you think about an AI or an agentic system, even in your life, that is capable but not compliant, i.e., let's say ChatGPT. So it's highly capable on its face, right? Nothing built around ChatGPT. If I just go and use ChatGPT, start asking questions, It's quite capable. It'll give me answers every single time, but not all the answers will be approved or compliant. There's no guardrails built around it. So I am unlikely to trust it because it's highly capable, high on capable but low on compliance. Now conversely, if you have something that is highly compliant but very low on capability, so think of You know, if you go to a pharmabrand.com website, there's like a popup chat window that comes in. You ask it a question, it's like, sorry, I can't help.

Parth Khanna [00:29:42]:
I can't help you with that. You're going to do that 3 times and you're like, dude, you're wasting my time.

Steve Swan [00:29:47]:
I'm out. Right, right, right.

Parth Khanna [00:29:48]:
Of course. So capability and compliance become 2 vectors that go into a trustworthy agentic system. And then depending on who you're looking at. on that HQ to frontline journey, what is capable and what is compliance, there's more nuances. And we distill down compliance piece because that's so important into governance, observability, security, and testing, or GHOST as an acronym. And then in capabilities, what we think is important sub-factors are Is it role-specific, i.e., does it center around the human? Does it understand the needs of the human so it can be more capable? So imagine an agent that knows Steve Swan is going to just be more capable. And then the last thing that may be valuable to our tech and CIO audiences, we think that a multi-agent architecture is just dramatically more superior than just a single agent architecture. So a role-based multi-agent architecture where you can measure the value, where there's metrics built in, is really high-end capabilities.

Parth Khanna [00:31:12]:
But yeah, happy to add more examples and color that in, Steve, but that's a great example that we came up with. After those routes.

Steve Swan [00:31:19]:
Yeah. I mean, so, I mean, I haven't come up with any models, but, you know, when I listen to my CIOs talk about, you know, um, AI and where they're using it the most, right? Where they're seeing it the most, where they've had, I don't know, I don't want to call it the low-hanging fruit, right? But maybe that's what I'm talking about. Um, and, and the bionic employee where, where it's been most effective there. Number one, they talk about, and I think we all know this, customer service, right? it helps those folks move those calls around or automate those calls. Number 2, marcom, marketing communications, you know, real easy, helping to put all that stuff together. And then the final step, which kind of confused me a little bit, and I'll tell you why, um, developers, right? I know, like, I have a kid that she, she went to school, data science, can develop. The, the, the part of her job she dislikes the most is the actual hands-on coding, right? But she has to interact with business. Right? Understand what they want built.

Steve Swan [00:32:13]:
Then she has to get whatever agent to write her code. She still has to know how to code. She has to code review. You can't trust everything, right? Blindly. Still has to do the code review, has to make sure it built what the business wanted, goes back to the business, says, hey, did we build what you wanted? But then also has to make sure it fits in with the technical architecture of what's going on. So, and the reason why that all confuses me is because it almost seems like it's replacing Or helping to not replace, but augment the developers, the hands-on technical folks, which we've already moved offshore, which is, you know, we're paying $7 an hour for that. So I, I don't know. That's the confusing part to me.

Steve Swan [00:32:52]:
Maybe I'm missing something. But anyway, those are the 3 areas where, um, my guys are telling me, guys and girls are telling me they've, they've made the most headway with AI and where they really see it, you know? If, if, you know, one CIO said to me, you know, if someone comes to me and says, we're using it in our accounting department and maybe we'll be able to, uh, uh, save, you know, a quarter of a headcount or whatever, you know, that kind of thing. He's like, there's no ROI there. What are we doing? You know, we can augment those folks, which is great, but, you know, we can really augment the folks in the customer service, the marcom, and the development world. Then we're making a difference, you know? So yeah, that's, that's where we're seeing it.

Parth Khanna [00:33:30]:
I, I love that. And I want to use the developer example and Bring it back to life sciences.

Steve Swan [00:33:35]:
Yeah, do please. Love this.

Parth Khanna [00:33:38]:
Look, we're a tech company, so we live and breathe exactly what you shared every day. And about 9 months ago, my CTO had this massive breakthrough around something that they're calling spec-driven development, where a developer can just do so much more using agents, right? Like Claude agents, et cetera. So what's interesting thing that has come out in tech through our work there is that, you know, going back to that bionic employee where you have the human cognition and then around the concentric circles, you have agentic AI around them. In tech, Steve, what it ended up looking like is we concluded that we needed a new role to define what the work now was. So instead of just a developer, you're almost having this hybridization happening of development and product management together. And there's a new role that we had to create, which is called product engineering. So it's, and another thing that you touched on, which is really powerful, Steve, is when building becomes easier, what is left, or what is more important rather, not left, what is more important is what you build. So your taste and judgment becomes important.

Parth Khanna [00:35:00]:
And taste and judgment is rooted in human connection, empathy, sympathy. Because you, if you're my customer, I'm sitting across the table from you, looking at you in the white of your eyes and saying, this is what I think Steve needs to solve his problem. That becomes more important. So It's almost this new hybridized role that gets created, which is product engineer. Now bringing it back to life sciences, there are all these bionic humans that are going to pop up everywhere, everywhere, and these concentric circles will pop up on the screen. So my guidance to leaders listening to us today is if you haven't opened up your team's job descriptions in the last 12 months, it's probably time to go into Workday or find wherever the hell that they're stored, dust them off, because that's the important conversation around future of work. What is that augmented role going to look like? What are the things that are important? For example, let's say if pre-call planning is an important part of a sales rep's territory management, then let's emphasize on that and how are we going to support that? Meanwhile, data hygiene on CRM, one of the things that Steve, you were saying is road tasks can be supported now. So maybe an agent supports on CRM logging.

Parth Khanna [00:36:25]:
So what is the ripple effect on the job itself? And we think that the JD is just, in my opinion, Steve, this is a hot take. It's one of the most important artifacts. in an enterprise organization right now, because that becomes a central focal point where the leader and the contributor can sit down and say, all right, we gotta think, rethink through this together. And that's also where the frontline managers have a very crucial role to play.

Steve Swan [00:36:55]:
Well, so I'm gonna bridge the gap here for you. I've been thinking about exactly what you just talked about, and I was in Boston Oh boy, was it a month and a half, 2 months ago when I sat at the PwC office in the seaport with 50 other CIOs. They invited me up. Um, it was 2, 2 CIO groups and it was all about AI, Anthropic. That's all they use, right? So, um, we talked about AI and, and where it is and where it's going. And there's obviously, as you know, and you run into this every day, Some board of directors are totally on board. Some board of directors are saying no. Some CIOs are completely opposed to their board of directors.

Steve Swan [00:37:37]:
Some of them are aligned with them. You know, we got it all. It's all over the map right now. What they say is missing. So I do a lot of work with, with these companies and a lot of what's come to the forefront over the last several years is the days of, you know, the IT people keeping the lights on. That's, that's the MSPs are doing that now. That's over. Right? So now you need the folks that understand the business value of IT.

Steve Swan [00:38:01]:
IT for business sake, not IT for IT sake. What that's given birth to is what we call the business partner role, the art of the possible, bridging the gap, the product engineer, the product managers, that hybrid person that you just described is exactly the business partner. What no— and this is now, now this is Steve Swan brain going. What Steve Swan's been thinking about lately, and nobody's come to him and said this because he's, I'm stockpiling these candidates, is AI business partners. Nobody's come to the conclusion that they need the AI business partner, which is half business, half AI, because then what they're going to do is they're going to go in, they're going to sit with the business and say, okay, you want to do X, Y, and Z. Okay. AI is not going to handle that. But what we can do is this, business.

Steve Swan [00:38:48]:
Okay, IT, this is what you got to build for business because this is what we just talked about. I know you can't build it this way, but you got to build it this way. So somebody who knows both languages so well that they can bridge the gap, that product engineer or however you want to phrase it. Again, IT business partner slash AI is what's needed right now. And that's where the gap is because things are falling through the cracks and both sides are going like this. And you need that middle, that middle piece. And nobody's come to that yet. Some people have articulated to me that I need X, Y, and Z, and that's what they've asked me for.

Steve Swan [00:39:25]:
And their HR departments have sent them through 40 different interviews. One CIO came to me and said, I've done 40 interviews with people and I can't find what I'm looking for. And I've placed business partners with this guy in different capacities, you know, whether it's corporate systems or commercial or whatever. But on the AI side, his AI team, his AI team. His HR team won't let him yet come to me and say, okay, I got, I can solve this problem for you, which I can. They just haven't come to the conclusion yet that that's what they need. They, they're looking for something. They haven't put their finger on it.

Steve Swan [00:39:55]:
And the HR team's not, not, not, not locating it, but it's, it's a business partner in AI. And again, I'm stockpiling those because I got a sense that once everybody comes to that conclusion, these guys are going to start going like crazy, you know, into different companies.

Parth Khanna [00:40:10]:
So Steve, I just had a burst of neural activity hearing that.

Steve Swan [00:40:16]:
No, that was your espresso, man. That was your espresso.

Parth Khanna [00:40:20]:
Espresso and great conversation. I, I just, uh, that's the nirvana, man.

Steve Swan [00:40:24]:
Yeah.

Parth Khanna [00:40:24]:
Steve, you know, it's interesting in what you're saying is this whole construct of people, process, and technology now converging. into one because of AI. And these constructs existed because there were limitations in what we could do. There were systems limitations, people limitations, process limitations, and we almost always treated them as distinct. But in this AI business partner role that you're describing, someone that can speak the language of people, process through workflows, and technology with model behavior and agentic AI converging into one, and they become the orchestrators and quarterbacks to actually percolate down. There's a difference between what can be done and what is actually being done. Anthropic actually, Steve, I don't know if you saw, it was all over my LinkedIn feed. They put out this report around model capabilities and actual adoption, and there's a huge There's a huge delta there.

Parth Khanna [00:41:31]:
Uh, and this AI business partner role, which I love that, uh, that can bridge that. And it's almost like your chief people officer and your CIO probably need to spend more time than they would have a couple of years ago.

Steve Swan [00:41:43]:
They do. And they, they're gonna get through AI, those 2 groups, departments are, in my opinion, I'm gonna probably be outta the business when this happens, but They're gonna be one department. HR and IT, um, are gonna be one group, one area, one department, 'cause they're, they're, they're hitting each other a lot for better or for worse, you know? Um, the, the issue that we're gonna run into with these IT business partners for AI is that a lot of them are super technical. That's not gonna fly on the board level.

Parth Khanna [00:42:18]:
So, yeah. Yeah.

Steve Swan [00:42:19]:
They gotta be able to talk human like we're talking about here and Um, I'd say I could find a lot of business partners that are probably pretty good at what they do and could probably get through this. I'd say I'd put 30% of them, maybe 25% of them, not 50% of them in front of a board, even for a 200-person company, I wouldn't, you know.

Parth Khanna [00:42:37]:
Yeah. And Steve, that's where this whole intersection of technology and human, that's why we, we love the phrase, uh, empathetic AI and yes, because the human part and AI is the tech part. And that was why I'm rocking this Team Human jersey today.

Steve Swan [00:42:53]:
And, uh, I love this. I love this. This is good stuff. Well, thank you. This has been great, man. I love talking like this. Anything I got, I always have one final question I ask my guests, right? Which I'll ask in a minute. But before I do that, I just want to see if there's any conclusion or anything that you think we should hit at the end before I get to that final question.

Parth Khanna [00:43:13]:
I would My final thoughts will be to all the leaders listening to this. The trust is a universal currency in bridging that gap between what agents can do and what is actually going to get done. And trust is not a monolithic variable. There's a lot of things that go into trust, and depending on where you are in the commercialization journey and which human You look at, observe. It's almost that in quantum mechanics, Steve, Schrödinger's cat. Based on the observer, the behavior will change. So based on who you're looking at, whether it's the chief medical officer, field medical director, or an MSL, what trust means evolves. And I think there's very important discussions that all CIOs need to make is, do we have the organizational capability to build that trust, not build agents, but build trust, or should we look for partners? And that also then forays into AI business partner type roles and future of work.

Parth Khanna [00:44:19]:
And if anyone wants to chat over an espresso, I'm right here with a stupid cup.

Steve Swan [00:44:25]:
Because trust is built in the IT department. It's kind of, it's a maturity model, right? So security, reliability, right? Enablement. Then the final level is that business partner level. And they're not going to talk to you about any of this if you're not doing any of this. I mean, let's block and tackle first. Let's do the fundamentals. Let's, let's, let's get the grounders and get them over to first base, you know, before we try and get a double play, right? So they got to trust you first before, you know, you got to get the fundamentals down. Otherwise you're not getting to the AI conversation.

Steve Swan [00:44:55]:
You're, you're now an executor. Business partner thing. That doesn't matter, you know, because we don't, we don't care about that. We know, we know what we want. Well, you, you think you know what you want, but are we capable of it? Or is IT or is AI, you know, whatever. So anyway, I'll get off my soapbox there.

Parth Khanna [00:45:10]:
And Steve, uh, on that note, we have an AI trust report coming out, which is conclusions from the roundtable. So I would love to share that with you and—

Steve Swan [00:45:19]:
I'd love to. Yeah. And we can, can we attach that to this?

Parth Khanna [00:45:24]:
Uh, I depends on when we, when we launched the podcast, the report's coming out in August. But Steve, what I was, I was going to offer up is this was so amazing. Once the report is out and, uh, we can come back and discuss the report.

Steve Swan [00:45:36]:
Sure.

Steve Swan [00:45:36]:
Do it again.

Steve Swan [00:45:37]:
Yeah.

Parth Khanna [00:45:37]:
I'd love that.

Steve Swan [00:45:39]:
I'd love that. Okay. Final question. I ask this of everybody. I don't know if you watched any of my other podcasts or not, but you would have had to watch them all the way through to get this final question. I love music. I'm a music guy. So my wife and I, we go to see live shows and we see different concerts and things.

Steve Swan [00:45:54]:
And I always like, you know, from a personal perspective, like to get a little personal spin on folks, right? So I like to ask the same question about music and, you know, their thoughts around music and such. So my question is, any live show, band, any point in your life, right, that you've seen that you would say is the number one concert you've seen, or number one band, or number one actor that you've seen? Live at all in your life that you would say, yeah, that was my favorite. That was good. That was fun.

Parth Khanna [00:46:24]:
Man, I love that question. Especially I did a self-benchmarking. My musical intelligence in terms of producing music is very low, but it's very, I'm a 9 in terms of consuming and enjoying music.

Steve Swan [00:46:38]:
Good.

Parth Khanna [00:46:38]:
So Steve-O. And hey, I'm from Toronto, so I have to rep here. So Drake is huge in Toronto. And, uh, I attended his OVO festival after Toronto won the Raptors, Raptors won the championship.

Steve Swan [00:46:55]:
Yeah.

Parth Khanna [00:46:56]:
And, uh, he performed God's Plan, which, uh, which is amazing. It's one of my favorite songs. And also probably why I'm in life sciences is probably God's Plan because I wanted to be a human rights lawyer.

Steve Swan [00:47:08]:
Yeah, that's cool. Good. So Drake. Yeah. Awesome. That's good stuff.

Steve Swan [00:47:14]:
I've got to chime in on that question. Go Eagles at the Sphere in Las Vegas.

Steve Swan [00:47:20]:
When did you go to that?

Steve Swan [00:47:22]:
Uh, 2 years ago.

Steve Swan [00:47:24]:
Yeah, I didn't. I went to see Dead and Company at the Sphere and the tickets were expensive enough, but the Eagles were about 4 times more expensive. So I wasn't doing it.

Steve Swan [00:47:32]:
Well, and you know what? We were lucky. We got on right when the tickets went on sale. I mean, they were still expensive, but when we looked back later, They had like doubled or tripled what we paid.

Steve Swan [00:47:44]:
Yeah. Yeah. Yeah. See this part, this question brings them out of the woodwork. Even people you didn't know were here chiming in, you know, it's great. It's, it's so funny how, how, I don't know, music's just, it's the, seems to be, I don't know, the international language that everybody's got, you know, a different taste, but it's, it's, everybody wants to talk about it, I think, you know, and we love music, my wife and I, so.

Parth Khanna [00:48:07]:
That's amazing. It's cool, Steve. I, I also love closing on this note because it's like you said, it's such a human experience to uniquely enjoy that music and have those memories flood your mind and the emotions. And, uh, it's, it's such an interesting time. We're talking about AI and all this tech and we talked about bionic humans and just, uh, closing on this note of being human. I love that.

Steve Swan [00:48:32]:
Well, and it's totally because You know, Sherry, you were in the Sphere with, with, with, you know, I forget how many people are in the Sphere, but every single person had a different experience, right? Um, you know, everybody's view of that, everybody's understanding of what took place was totally different, you know? And by the way, the Sphere, Parth, if you haven't been there, it's, it takes your music experience to a whole new level. You never want to see anything again. So it's pretty cool.