David Ehrlich is the CEO at the helm of Aktana, a provider of AI-driven support to sales professionals in the life science space. The technology helps reps deliver more value to the healthcare providers they serve while reducing typical costs associated with in-person sales teams.
Digital companies have always automated go-to-market processes, but it is not second nature in more traditional industries. In life sciences, the pharmaceutical or medical device sales rep has always played an outsized role in speaking with doctors, educating them about offerings, and driving adoption of new products.
However, it’s expensive to maintain an in-person salesforce. If each salesperson’s time isn’t optimized, the organization is leaving money on the table. And in a field like life sciences, that lack of optimization can have even broader consequences. When sales reps cannot reach the right doctors with the right solutions, patients may miss out on treatment options that can significantly improve their lives.
It’s important to note that you won’t hear David advocating for the end of in-person sales. He recognizes the valuable role salespeople play in building trust and maintaining relationships with providers. But there’s also room to fine-tune their approach.
That’s where the idea of human-machine teaming comes in. The best automated go-to-market processes, David says, allow the machines to do what they do best while freeing humans up to shine in their own way.
He provides an example of a sales representative working with oncologists. The sales rep has a fantastic second-line cancer treatment to offer, but for the doctor to find the suggestion relevant, the timing needs to be correct.
If they don’t have a patient on first-line treatment right now, there’s no need to explore second-line therapies. If they have a patient that’s completed first-line treatment with suboptimal results, the doctor has likely already found an alternative.
Aktana’s software combs through anonymized patient data and identifies doctors who are treating patients midway through first-line treatment that are seeing poor results. These doctors are most likely to need that second-line option right now, so this is the ideal time for the sales representative to approach them with a solution.
On their own, the sales representative knows the product, but they might not know when to talk about it with the doctor. Teamed with the machine, they speak with the right doctor at the right time, making the sale and (even more importantly) providing a viable second-line option for the patient.
Of course, people tend to bristle at the suggestion of incorporating AI into their existing processes. David shares tactics for addressing any potential hostility expressed by sales reps toward the program. He says the key lies in following these three guidelines:
- Give a recommendation. It likely won’t be well-received if the machine dictates what the salesperson must do. Instead, the person must have the power to accept or reject the suggestion.
- Share the reasoning. Remember when your parents used to reply, “because I said so,” when you asked why they were enforcing a rule? That tactic didn’t work then, and it won’t work now. Share the logic behind the suggestion with the person.
- Make it easy. If your sales team needs to open up another program to get the suggestion, they’ll likely just maintain the status quo. Instead, incorporate suggestions into the existing workflow to make it frictionless to adopt this new working method.
Even the best technology used correctly won’t deliver overnight success. David notes that it usually takes about nine to twelve months to see ROI after implementing the technology. During that time, there’s lots of testing and learning to fine-tune the AI and how your team interacts with it. As with most things in life, the more you put in, the more you get out.
Episode Transcript
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Rob Ristagno: Welcome to the CEO Campfire Chat, recorded live in front of a studio audience of leading executives. I’m your host Rob Ristagno. And today I have the privilege of introducing you to David Ehrlich he’s the CEO of Aktana, which is a very cool company that helps apply AI to the sales process in the life sciences space. Prior to running Aktana, he had senior executive roles at companies such as ParAccel, and NetIQ. He started his career at McKinsey. Welcome, David.
David Ehrlich: Hey, Rob, how you doing?
Rob Ristagno: Great. Thanks. We’re so glad to have you here with us today.
David Ehrlich: Thank you. My pleasure. Looking forward to it.
Rob Ristagno: All right, so, we’ll start this episode, like we start every episode: With a game of five questions, because CEOs just don’t have time for 20 questions. So let’s just get some rapid fire answers here. First thing that comes to your mind, just so we can get to know you a little bit before we dive deep into today’s topic, which will be automating go-to-market processes. Alright, are you ready, David?
David Ehrlich: The five questions, I’m nervous.
Rob Ristagno: Question number one, what is the vision for your company?
David Ehrlich: So our vision is to assist all brands around the world, ultimately. Well, right now it’s within healthcare, within life science, it’s to help them better connect with their customer population through all the various channels that they connect with. But in this particular case, in life science, it’s connect with healthcare providers, and add more value into healthcare decision making.
Rob Ristagno: Excellent. And question two, who is your ideal customer?
David Ehrlich: Our ideal customer is all the customers we’re currently working with. They’re, you know, they run brands at some of the largest pharmaceutical companies in the world. Their mission is to improve patients’ lives. And our mission is to help them work with the healthcare community to reach that objective.
Rob Ristagno: Excellent. Question three, somewhat related to the mission, what is your value proposition to your ideal client?
David Ehrlich: So ideally, we would help them deliver more value to all the healthcare providers at a fraction of the expense that it requires today. If you think through all the various channels that life science companies use to communicate with the healthcare community, the most expensive one is the in-person sales rep. We believe that there is a very important role for that in-person sales reps throughout the future, but it’s not kind of weekly dropping by and talking about, you know how the basketball game ended up last night. And that’s not what the healthcare community wants out of life science sales reps anymore. They want them to deliver value, to deliver something differentiated to help them make better decisions in their practice. And, you know, there’s a lot of different ways to do it that are a lot cheaper than having a sales rep show up every week.
Rob Ristagno: Excellent. And number four, what is the best part of being a CEO?
David Ehrlich: It’s a great question. I think the best part of being a CEO is watching the impact that your company can have on on people’s lives. And I mean, both employees, as well as your customers. And when employees tell you that they’ve never worked at a company, like their current company, the culture, the trust, the ability to have an impact. That’s, you know, incredibly exciting, and one of the most joyous moments that I experienced. And then when you see customers come to you and say, Oh, my God, I didn’t think I could ever do something like what I can do now with your solution with your tool, enabling me again, you get the gist, that there’s a lot of work behind the scenes. There’s a lot of frustration. There’s a lot of stress, but when you get people telling you that you’ve made an impact on their lives, that’s the best part of the job.
Rob Ristagno: That must feel incredible.
David Ehrlich: It’s the best feeling ever. It’s kind of like when your kids come to you, and they thank you for a role that you played in their lives. You know, to some extent your employees are an extension of your family, it’s your kids, you, you give them opportunities, you watch them grow, you watch them, take bold moves, hopefully, and sometimes fail and you watch them go through that learning process much like your kids do, when they fall down and scrape their knees.
Rob Ristagno: That’s a great analogy. Question number 5/5. And finally, what is one thing that is going to make or break the next 12 months for you?
David Ehrlich: You know, it’s been a difficult period of time going through this pandemic. And for our business in particular, it wasn’t just that demand fell during the pandemic, because if you think about, most of what we’re doing was to assist that sales rep channel, and sales reps were all grounded during the early parts of the pandemic. And nobody wants to invest financially into a solution for a field salesforce that’s going through a very unusual period of time. Now, the second impact that the pandemic has had is it’s really changed the market for us. And I think this is, you know, we will for the next 10-20 years, have academics come out talking about the permanent changes, that the pandemic led to, a lot of those are accelerations of trends that were in process anyway, with people resisting those trends. And then they thought resistance was futile, because, you know, everything changed, and they had to kind of adopt what they were worried wouldn’t work, and love and behold, those things worked. So for us, what it meant was, the manufacturers of pharmaceuticals, the med device started leaning on virtual or digital go to markets in a way that, you know, again, the industry had been talking about for many, many, many years. But people had only taken baby steps, they had to take some really big steps. And so and I’m returning to the question in a second, but when you get to those big steps, the entire thinking around go to market in life science changed. And it’s changed in a direction that we had thought it would much more omni-channel, much more AI-driven decision making. This was all stuff that we had been investing in for years, have been ahead of the market in our investments in those things. We’ve now just released the solution set for general availability, that that makes that all real. And for us over the next 12 months, what’s gonna make or break our year is the adoption and success of those products in the market.
Rob Ristagno: Excellent. All right, a lot of exciting things and a lot more depth I want to go into because I’m really amazed by your your offering here. So the theme, again, for this week is automating go-to-market processes. Before we dive in David, just kind of paint a picture for us. What is an organization that is doing the best, they’re world class in how they’re automating their go-to-market processes? What are they doing? What are they benefiting from, you know, kind of what what does it feel like to work in this kind of organization?
David Ehrlich: You know, I’m not gonna answer the question that directly Rob. I’m going to talk around it for a minute. And you’ll, hopefully you’ll understand why, the audience will understand why as I do this, alright. So if I think about those companies that have really automated, the go-to-market, they’re digital companies, they’re like Amazon, right? They never had a non-digital go-to-market. And they really leaned into their digital go-to-market and they’ve done it exceptionally well. And they do, you know, very good at the omni-channel kind of cross-channel, you can put something in Amazon and, you know, if you want you get a call back from it. It’s just a very tightly run omni-channel organization. When you flip over to more traditional selling organizations, you know, it’s hard to find anything that’s truly automated. And for very good reason. What we have found is that by combining the human capability set in the marketplace that knows the customer base with what you can automate, you can end up moving the dial much more effectively than you can your automated solution. And so the challenge is less how do we do it without humans and automate this? It’s more how do we use systems to automate what is not in the comfort zone of a human being so you know, systems can go through and process tons of data and find aberrations to patterns and they can do that every night in a way that a salesperson the next morning, you know, it would take them hours, poring through different systems collecting data looking for those patterns, machines can do that a lot better. Machines certainly can automate the digital voter market in terms of sending out digital emails, changing website content when a specific customer goes to the website. But if you want that sales rep, if that’s still part of your go-to-market, want them engaged, you have to work with them very, very differently, you’re not going to automate your sales rep. And they’re going to resist any efforts to automate what they do. And for very good reason, they often know things about their customer base. And in real time, they can read body language in a way that a machine can never do. So I won’t say never, but at least right now, right now. Yeah. And so you know, taking advantage and really combining what is the human being best positioned to do and what is the machine best position to do and at least for us, in the state of the art today, the machine is best positioned to advise the human being, and you leave that ultimate decision up to the human being. And so what you end up with is rather than a fully automated go-to-market, you’ve got automated go-to-market some of the digital pieces, and you’ve got the automation supporting a human being in a call center or in the field, making better decisions because the machine is surfacing up to them, Hey, this insight, we just found, there’s an aberration to this pattern this customer just bought from your competitor, this customer’s buying pattern is increasing or decreasing or changing. And here’s what we know about that. But then letting that human being go in, ask the final questions to that customer. Like why, and start to find out what needs to be done about this. There’s a lot of data that’s never going to be in a machine that is required to answer those questions. And surfacing it to a human and letting them do that final detective work and use their judgment as to what the right responses can often lead you to the best kind of combination of capabilities.
Rob Ristagno: I like this this two worlds, the digital world can be more or less fully automated, but the real world AI is really an aid, an advisor, supporter, consultant to the human. And it sounds like actually leads to a better solution. A one plus one equals three type of situation, where you have the human plus AI to get better results than either could could do alone.
David Ehrlich: You know, I’m gonna mention and distract your readers to go elsewhere. But there’s a great on the Palantir website, there is a great article or blog. It’s somewhere in there. But it was written by Garry Kasparov talks about an experiment that Kasparov did back in the years when Big Blue was just starting to beat grandmasters at chess tournaments. And what he did was he pitted, a three-way contest, Big Blue, grandmasters, and then he took mid-range chess players. And he put together a PC-based chess move advising program. And it turns out that that PC-based program and the mid-range chess players beat the best chess players, grandmasters, and the best machine, Big Blue, pretty consistently. And his, kind of the “so what” coming out of this experiment was, if you can get the interaction model right, between the machine and the human, where the machine is doing what machines do well, humans are doing what humans do well, and that interaction model is homed to be the right one, you’re going to get a better answer than you will out of machines doing trying to do everything or humans trying to do everything.
Rob Ristagno: Gotcha, gotcha.
David Ehrlich: And it makes perfect sense, right? When you think about it, it’s divided, you know, division of labor into what each component does best. And then if you get the right interaction model, you synthesize them into the best solution.
Rob Ristagno: Excellent. I will say we’re not at the trivia portion of our show yet. But we actually, one of the questions was who lost to the computer in chess? So you already got one of the questions, right. You brought that up. Okay. So let’s talk a little bit talking about the machine is picking up on patterns and advising. Just give us some more you gave us a few examples. Maybe your customer bought from a competitor? What are some other use cases? What how else are our sales professionals using some of the insights from from your product or other similar products that provide this AI advice?
David Ehrlich: Yeah, so again, like we’re talking about pharma, or med device sales rep selling into the healthcare community. We can find, you know, when you go through the data, well, I’ll give you an interesting example. So if you’re a pharmaceutical company that’s selling a second line cancer therapy for a particular type of cancer. Your mission then is to find the doctors that have patients on first line therapies where those first line therapies aren’t working. They aren’t giving a sufficient solution to improve the healthcare of the patient. There may not be very many of them around the country. And oftentimes these therapies are pretty expensive, but want to target in to that doctor that’s taking care of that patient at the time that they are looking at the results and going, “Huh, I need a second line therapy, the first line therapy is not working.” If you show up when they don’t have a patient, it’s a wasted go-to-market. If you show up before the patients on that first line therapy, it’s a wasted go-to-market. So one of the things we do is we can pull in diagnostic test data, or therapeutic test data, anonymized, but with the ATP that wrote the script to that test as part of that dataset. And so we don’t know who the patient is, but we know who the ATP is, and we can look and go, ah, the antibodies aren’t, or you know, the antibody levels aren’t what you would expect if the first line therapy was working, or tumor’s not shrinking at the rate it should be. We’ve seen that in the test data, we would get that test data, and we can surface it to the sales rep saying, “Hey, one of your doctors has a patient that is not responding to first line therapy, they’re probably within three or four weeks of you know, prescribing the second line therapy, now’s the right time to go to them and start showing them the different efficacy rates of the different therapies.”
Rob Ristagno: That’s amazing.
Audience Member: That’s powerful.
David Ehrlich: So that’s one, I’ll give you one more example. Let’s say that a primary care medicine is tends to be very, you know, price competitive compared to other medicines in the same category. And oftentimes, different insurance companies bais the patients towards different medicines. And let’s say our customer or client gets a big formulary win in a city, and they’ve just gone from silver to gold in terms of the reimbursement rates, wouldn’t it be great if the system behind the scenes could look through all the doctors in that area that have patients in that insurance program can then assess those doctors and say which ones write prescriptions based on affordability. And by the way, which ones are predisposed to our medicine, when all things are equal, and have more than 20% of their patients on one of these therapies, like let’s say on a competitor’s therapy. So that is great opportunity for that sales rep. Now to go into the doctor and say, “Look, I know you have a lot of patients on my competitor’s therapy, we just became a lot more affordable and know you love our therapy more than theirs, want to let you know any of your patients on X insurance plan are now covered by gold.” And that could change a lot of prescribing behavior right there. So these are just a couple examples of in the first case where you can actually get a better medicine to the patient through educating the doctor at the right time. And in the second case, it’s just moving market share, which every one of our customers wants to do.
Rob Ristagno: Yeah, excellent. And to earlier point sales reps need to be providing value to be getting in front of anyone these days. So this is this is kind of two sets of benefits there. Not only you’re getting the sale, but you’re actually providing a lot of useful information.
David Ehrlich: And you know, the sales reps, they want to move market share. They want to provide value right through in talking to doctors, doctors want to help patients, and pharma reps at the end of the day really want to help the doctors help patients.
Rob Ristagno: What about this point that came up earlier about sales reps, maybe not being super open minded about bringing computers. I’m sure there’s the natural human tendency to feel like your job might be at risk, and no one wants to be automated. So how do you get them bought in? What’s sort of some secret secrets there to get sales reps excited about AI rather than maybe feeling a little bit threatened?
David Ehrlich: Yeah, so the first thing again, all go back to Kasparov’s kind of story, you have to think about that interaction model. Number one, the interaction model needs to come across as not telling the rep, “Hey, go do this now.” It needs to be more along the lines of, “Here’s an idea, a suggestion or recommendation.” And by the way, you sales rep can say yes or can say no. And your compensation doesn’t change whether you say yes or no we’ve had some of our customers come to us and go we really want reps to adopt this. So we’ll start compensating them if they, you know, perform according to the suggestions coming out of your system. And we always wave our arms, it’s like no please don’t do that. The data that we get back as to whether a sales rep thinks it’s a good idea or not a good idea is really important for the learning that our system goes through. And at the end of the day, if you tell a sales rep, we’re going to compensate you on accepting the suggestions, which you’re likely going to get is a bunch of acceptances without the rep actually doing that activity, or at least without thinking it’s the right activity to do because you’ve paid them to do that. So you know, getting the interaction model right where it’s surfaced up as, it’s here to help you make a better decision, and you are empowered to make whatever decision you want, that’s one. Second is you have to trust the sales rep. If the sales reps going to make that decision, you need to give them the reasons behind the suggestion, it’s not good enough to say go visit Dr. Smith. You have to say go visit Dr. Smith, because Dr. Smith was on the website last night looking at adverse impact papers, it means Dr. Smith might have a patient having a negative side effect, you need to get to Dr. Smith within the next 24 hours or call them or email him to see, you know, can we do something to help this patient do we need to get the patient registered is having an adverse impact, etc, etc. So it has to be a suggestion has to be optional. It you have to give them the reasons behind it. And I think the last thing is you got to make it easy. It’s got to be in workflow. It can’t require them to go and log into a different system. I mean, we’re all busy, sales reps are busy, we all have limited time. So to the extent that these suggestions in what we describe as suggestions and insights to the extent that they pop up in workflow, I’m gonna get a lot more acknowledgement, you know, interaction with that suggestion?
Rob Ristagno: Makes sense. What about companies who want to make the investment here? How do they go about building the ROI case?
David Ehrlich: You know, the first thing is a lot is going, you know, ultimately, all of our customers want to see sales lift.
Rob Ristagno: Yeah.
David Ehrlich: Okay? But the path to sales lift is not an easy one. The first thing is, when we implement it actually has to kind of function. So the first set of KPIs that we always look at is, you know, is the data getting in? Is the data getting out in time? Is it published in time? Is it the right publishing to the right messages, the right reps? So, is it working?
David Ehrlich: The second is, does it create behavioral change? Because if it doesn’t create behavioral change, then you’re not, there’s no chance to have any impact. Well, to go back to the early parts of this campfire chat, for the fully automated digital channels behavior change easy, you tell a machine to do something, it goes and does it. For reps, it’s very, very different. And you can watch and you can see where the reps really pick up. And if they haven’t, there’s a whole lot of change management that needs to go into it, helping the reps understand that this is really a tool that’s there to help them not replace them back to your prior question, etc. And the reps have to feel like their knowledge and insight into their marketplace is reflected in the configuration of the engine. Okay, and we do that through a set of workshops, where we really depend on the reps to help us understand what does the competitive landscape look like? What are the most important things to be acting on? Which use cases are going to move the dial the most? And there’s an interaction between marketing and sales that we catalyze as the upfront piece of that.
David Ehrlich: So you’ve got a system has to work, behavior has to change. And then all we’ve really done at that point, is get a strategy to be executed with much higher fidelity. If it’s not a good strategy, you’re not going to see lift, and you’re not going to see ROI. And a lot of customers are looking and saying, Well, heck, if your system works, and I see the left, it’s like well, all my systems, all our systems gonna do is help you execute your strategy with much higher fidelity, and then help you learn what parts of that strategy are working and not working. Yeah, that’s, that’s when you got to get iterative, you got to be looking and using the system to identify and learn for those areas where your go-to-market’s not working, what signals are we seeing of a different go-to-market that would be more effective, and you have to iteratively kind of modify that go-to-market in order to achieve those results. But ultimately, it’s sales results. And that can often take nine to 12 months to see. And the best way to see it is to hold out a control group.
Rob Ristagno: Ah, yes. Well, I like this framework. I think it should apply to almost any sort of change management program. It sounds like go in realistic about what has to happen. It has to work technically be able to change your behaviors. You’re gonna have to tinker and experiment and iterate, and then you get the results. I really like that, because you can imagine stage gates at different metrics that you could track along the way to make sure you’re on track. Without people, you know, turn it on, plug it in, and I want to be at 10% lift tomorrow, like that’s not realistic. But you want to make sure you’re you’re measuring the progress along the way toward that, that lift.
David Ehrlich: And like a lot of things in life, if you lean into it, you’re going to get more impact. If you lean into the change management, if you invest in the iterative kind of strategy evolution, you’re going to get a lot more out of it. And, you know, we’ve had customers that believed if they just turn it on and walk away and don’t pay attention, they’re going to get the kind of impact that other customers get. And that’s just not going to be the case. The more you put in, the more you get out, like anything in life.
Rob Ristagno: Questions from the audience?
Audience Member: Is your AI system capable of giving you advice on what information is missing? And/or it would be helpful? Because context is a very interesting concept.
David Ehrlich: Yeah, and in fact, we, you know, we call our engine CIE contextual intelligence engine. And, you know, yes, at the end of the day, our engine, depending on the use case, and the strategy that the customer has, the more data we get, the more we know about every single healthcare practitioner out there. And the more we’re able to operate. But for specific use cases, like if you’ve got a use case that I just described on, you know, side effects, if you’re not reading what every customer coming to the website is pulling down, and notating that and plugging that into our system, we can’t plumb that use case.
Audience Member: Can’t do it, right.
David Ehrlich: That’s right. And, and what’s really interesting, if you look around the world, the quality of data, and the abundance of data is very, very different. In the US, you can get sales data down to the doctor. That data does not exist in Europe, it does not exist in Asia, it really does not exist outside the US. So you can do some things in the US that you can’t do elsewhere, because the data is not there. But then one of the use cases of our solution is to have your sales reps go collect the data that you need to arm certain use cases. So if the data is not out there, if it’s not purchasable, if it’s not downloadable, you can then have suggestions to tell all your sales reps go and progressively ask the following 10 questions so that we can fill out the dataset on on the healthcare community.
Audience Member: That’s brilliant.
Audience Member: David, could you maybe estimate, like, what the percentage, of the field staff that is actually back out there deployed, versus pre-pandemic now that we’re sort of coming out of it? And is that number going to possibly keep creeping up? Or are we at kind of a new normal?
David Ehrlich: No, it’ll keep creeping up. And, you know, it’s different depending on where you are in the world, the US is starting to come back. Japan has been back for about nine months back in the market, those sales reps were grounded initially. And then over time, the the community in Japan was really, the healthcare community was saying, we need the sales reps back. But it’s a very different culture than it is in lots of other parts of the world, where the pandemic is still racing out of control reps are still grounded, where the pandemic’s largely in control reps are back in the field. So I think we’re not quite at the post pandemic new normal yet. But that new normal will be a different new normal, and it will be a lot more digitally enabled. And we’ll be asking reps to kind of play a role within a broader ecosystem rather than operating on their own.
Rob Ristagno: Nora do you have a question?
Audience Member: I do, I do. Thanks for calling on me. You know, it’s really interesting, David, to hear you talk about this stuff. Because I have been teaching, I teach a lot of different courses. But my most popular one by far is called the neuroscience of sales. So I have worked with so many sales teams in so many industries, including plenty of healthcare companies. And what occurs for me as I listen to you talk about sales, team resistance or sales rep resistance is that the vast majority of sales reps I’ve worked with, I mean, the way the value proposition of your company occurs for me is that this is a way to get rid of the most tedious, most annoying parts of the sales process that everyone complains about.
David Ehrlich: So that makes me really happy because that’s exactly how we want sales reps to think about our solution and respond to the solution. It’s not there to disempower them. It’s not there to tell them. It’s there to empower them, to enable them. I mean, this is all about, you know, putting information at people’s fingertips. One of, there’s a question about the name of our company, Aktana. And it really comes from a combination of actionable and analytics. So act-ana, but it’s not A-C-T-A-N-A it’s K. Why? Well, our first customer opportunity was in Japan. And there’s a word in Japan called Katana, which is the word for a sword. And if you look at our logo, all of the cross bars on the A’s come to a point, they look like a sword. And it all comes back to wanting to take that full weight of the organization, all the knowledge, all the experience, and deliver it at that point of interaction with the customer.
Rob Ristagno: Before we jump into our next segment, David, how, if you want to learn more about Aktana, where should they go? What should they read? What could they do?
David Ehrlich: Well, you can go to our website, Aktana.com. And you know, there’s got to be an option to kind of click on something and say, I want to learn more, and we’ll contact you. If that doesn’t work. I’m David at Aktana dot com. Just email me and I’ll get you in touch with the right person.
Rob Ristagno: Excellent. All right. Now let’s move into the Campfire Games segment, which you already got, you’re already going in in the lead because you already got a question right that you didn’t even know I was going to ask about Garry Kasparov. So, we did some research, we found some fun AI-related trivia. So we have three more questions, you already got question number four correct. Question one. The first ever artificial intelligence conference was held in 1956 in Hanover, New Hampshire, on the campus of which college.
David Ehrlich: Hanover, New Hampshire. How long do I have to answer this?
Rob Ristagno: The 30-second Jeopardy.
David Ehrlich: Yeah, I have no idea.
Audience Member: Dartmouth? Dartmouth.
Rob Ristagno: Got it. You got it. Yeah, yeah. Next question. Which Middle Eastern country has actually granted formal citizenship to a humanoid robot called Sophia?
David Ehrlich: That has to be Israel?
Rob Ristagno: That’s what I would have guessed, but it’s actually Saudi Arabia.
David Ehrlich: Really?
Rob Ristagno: Yeah. Yeah. All right. And last one, according to a research project done by Omdia, what is the expected market size for artificial intelligence software going to be in 2025? I’ll give you some multiple choices here. 22 billion, 51 billion, or 126 billion?
David Ehrlich: I’ll go for 126 billion.
Rob Ristagno: You got it. That’s right. 22 billion was last year. 51 billion is the expectation for next year. And then 126 billion is the forecast for 2025. All right, this was a lot of fun, David, very excited about your product. And a lot of things you said apply even if you’re not in life sciences, or even if you’re not in AI, I think a lot of the principles that you shared with us can help all of our listeners no matter what problems you’re trying to solve. So thank you so much for your time. I learned a lot.
David Ehrlich: Rob. It’s been really fun. Thank you. Thank you listeners.
Rob Ristagno: And now it’s time to check in with Joe Galvin from Vistage to hear today’s CEO Data Point.
Joe Galvin: Thank you, Rob. Today’s data point is 67%. That’s the number of CEOs in our Q3 CEO Confidence Index that said that the talent scarcity issue is preventing them from operating at full capacity. It’s a real challenge for leaders when they can’t get enough people to meet the opportunity that they see in the market. We see people increasing salaries, providing hiring bonuses, 51% are investing in employee development. Why? Because they want to improve the productivity of the people that they have. But also retain and attract people based on that, and 48% have shifted their recruiting strategy. As organizations move to work from a hybrid environment. They’ve now opened up the hiring field, in terms of people they can attract, while at the same time, their employees are also able to work for organizations that are no longer within their geographical commuting limits. So it creates a real challenge on both sides of the fence, as leaders seek to ensure that they get the talent they need to meet the growth they see.
Rob Ristagno: Thanks a lot, Joe. Appreciate it. And for more research from Joe and his team, be sure to check out vistage.com Vistage Worldwide is a membership organization for leaders to refine their skills, make better decisions, and get better results. In fact, there’s over 24,000 members right now. Vistage has been around for 60 years and Vistage members outpace their competitors by growing 2.2 times faster than them, so you can do it too. Again, Vistage V-I-S-T-A-G-E dot com.
Rob Ristagno: Thank you, Joe. And thanks once again, David. That’ll do it for this episode of the CEO Campfire Chat. I’m your host, Rob Ristagno. For prior episodes and bonus content, be sure to check out CEO Campfire Chat dot com. See you next time around the fire.
