How to Prioritize AI-Related Opportunities

This transcript was created using AI tools and is not a verbatim, word-for-word transcript of the episode. Please forgive any errors or omissions from the finished product.

Courtney: [00:00:00] In case you haven’t noticed, and frankly, I’m sure you have, but AI is everywhere. So, as an executive, how do you decide which ideas and applications get moved to the top of the stack? How do you strike the right balance between testing what might work and chasing too many AI opportunities?

And when does this permanent vacation that Elon Musk has been talking about recently start? Because y’all…I am not ready for that. Hi, I’m Courtney Baker, and this is the AI Knowhow Podcast from Knownwell, helping you reimagine your business in the AI era.

As always, I’m joined by Knownwell CEO David DeWolf, Chief Strategy Officer Pete Buer, and Chief Product Officer Mohan Rao. We also have a discussion with Eisha Armstrong, co-founder of Vecteris, about what it means to [00:01:00] productize and tech enable professional services companies and the role AI plays. But first, the news.

Courtney: Hey Pete, welcome to the show.

Pete: Hi, Courtney. Thanks for having me.

Courtney: The first story this week comes from Time. Elon Musk tells Rishi Sunak, AI will eliminate the need for jobs. Period. The UK Prime Minister interviewed Elon after the UK’s recent AI Safety Summit. And Elon’s prediction that AI won’t just eliminate some jobs, but will eliminate all the jobs raised plenty of eyebrows, mine included, Pete.

Pete, are you with Elon in seeing a world without jobs?

Pete: Do all jobs go away? On what timeline? I mean, I kind of doubt it. Do some jobs go away? Well, sure. We’ve seen backing up research on that for some time now. Do new jobs get created? [00:02:00] Well, surely. And I think the interesting question is… we get to a place where we’re being thoughtful and purposeful about as owners of technology, technology should own and what humans should own when it comes to the work? We will have a um, in the future with a gentleman named Christian Motzbjerg, who talks about this.

And I think the future is one where owns the work that it should own, the rote, the, the task oriented and even the, um, inference, you know, intelligence.

Courtney: Yeah.

Pete: And automation work. there’s role for, uh, a role for humans own the work of humanity, uh, where care and emotion, uh, make one’s contribution and the job better. I’d rather think about uh, purpose on where we’re headed with a mix of who owns what job as opposed to how’s it all gonna just shake out [00:03:00] in some Darwinian, um, uh, process over time.

Courtney: Yeah. And I would say for most of us, we probably, the idea of no jobs feels, I don’t, that’s too much time for me personally. I don’t know about those of us listening, but I think we all would agree there’s parts of this that are really enjoyable, uh, really enjoy doing the actual work. And so a world without that, Ooh, it’s hard, hard to quite get your head around how that would even be possible.

Pete: Yeah, and that comes up in the interview, um, where it’s, you know, it’s mentioned that, uh, work gives meaning and,

Courtney: Mm hmm. Mm hmm.

Pete: some amount of life satisfaction. And so, like, let’s use this occasion of technology to get to the optimal balance.

Courtney: Yeah, it’s good. Next up, watch out, Linda Yaccarino, because according to Fox Business, Mika has become the world’s first AI human like robot CEO. A partnership between the Polish spirits company Dictador and Hanson [00:04:00] Robotics has yielded the first ever robot CEO. The article says Mika was trained to represent Dictador and its unique values.

Pete, all the CEOs are wondering, Oh, I wasn’t quite thinking my job was coming up on the block yet. So should they be concerned about robots taking over their role?

Pete: well as happens sometimes through certain media outlets. The, uh, title of the article is just a little bit misleading. I think, um, Mika a little bit more of a, PR experiment.

Courtney: Yeah.

Pete: Necessarily, like, full on CEO running the business. But I applaud the experiment, nonetheless. Um, it’s all about humanizing, um, AI, and I think in both directions.

Uh, getting… Humans to feel more comfortable with technology, but also getting the technology to understand [00:05:00] humans better. So, pretty cool. If I go to the CEO role, set aside Mika and think about, um, how much and to what end it could be automated or AI enabled. If you think about the main responsibilities of the CEO, allocating resources, driving performance of the business, communicating with stakeholders, right? Investors, customers, employees. Every single one of those activities is absolutely made better by technology and AI. You know,

Courtney: Mm hmm.

Pete: to a wider net in terms of understanding what’s happening out on the front lines of the business. Uh, driving proactive insights. driving operational execution. Even, like, all of those are things that… would empower a CEO to be more effective. So, instead of thinking about it as pure automation, I think about this as maybe a co pilot

Courtney: Yeah.

Pete: of the technology. And so maybe rather than the CEO, it’s the chief of staff who should be [00:06:00] worrying about their job.

Courtney: Yeah, that’s so interesting. You know, when I watched this piece, it felt much more like maybe at best a co CEO, cause I noticed the current CEO has not resigned, uh, to Mika. yeah, it feels more like a PR move, uh, but an interesting one to think about nonetheless.

Thank you for your insights. Always so valuable. Thanks for joining us.

Pete: Thank you, Courtney. Love it.

Courtney: If you’re a CEO or executive trying to figure out exactly how and where you should be utilizing AI, you’re in a tough spot and frankly it gets tougher every day. Do too little and you run the risk of getting lapped or worse by your competition. Do too much and you spread your organization too thin.

I sat down with David DeWolf and Mohan Rao to talk about how executives should prioritize AI [00:07:00] related opportunities.

Courtney: David Mohan, good morning. How are you?

David: Hey, it’s great to be here. I’m doing awesome. Is it still morning? It doesn’t feel like it. Hmm

Courtney: It is, no, it’s actually noon. Okay, uh, but they don’t need to know that on the recording.

Mohan: But it’s morning somewhere.

Courtney: It’s morning somewhere. That’s exactly right. Okay, you two, today I really, I’m sure you get the sense of this, and I know everybody listening does for sure. But when it comes to AI, it feels like the opportunities, there’s something new coming. It is such a rapid pace that it becomes, I actually get really worried of how our business leader is going to be able to, uh, metabolize all of the different offerings, all the choices, the opportunities, they feel somewhat endless. [00:08:00] And so I wanted to have a discussion with you two today about how should we be thinking about the opportunities specifically with AI that we have and how we figure out which ones are the best to move forward and to implement into our organizations.

So, there you go. Can you, can you solve this problem? Because the fire, the fire hydrant is like cranked all the way open out here.

Mohan: Actually, it’s not that difficult of a problem if you think about it, right? So if you start from, uh, what can AI do for me, then you’re absolutely right. It’s a big problem. You don’t know where to start, but you’ve got to start with what are the biggest company issues that you currently have, and then start to think about How can AI help me with those issues? It could be on the revenue side. It could be on the customer retention side. It could be on the employee engagement side. It could be about operational efficiency. It’ll start from where the business problems currently are, and then ask the [00:09:00] question, is there a role for AI here in helping this?

David: Gosh, and it’s the age old problem, right, Mohan? We’ve been seeing this forever, right? When there’s a new technology that comes out, we all want to just rush to it and find a solution for a problem, but we’ve got the solution before we identify the problem, and so we end up wasting a lot of money. I think that’s such good advice to take it from the other way and not to get enamored with this technology.

Um, but instead educate yourself so that when there is something that you identify in the business that you can solve with it, now you can leverage it but not to force it. I, I think that is the number one answer. No doubt about it. that begs for me though, Mohan. Is this thing we’re so early on in this process of AI maturing that we’re still not sure.

What needs to be actually implemented by each individual [00:10:00] organization and which types of platforms and middleware and applications are going to be created with a that solve common problems. you were advising an executive who was identifying a real business problem and about to go build a solution, how would you Help them parse through.

Oh my goodness. Am I building something custom here? That’s going to be tomorrow’s product or platform that I can just buy off the shelf. And is this a waste of money?

Mohan: Oh, my goodness. There are so many threads to pull here, right? So you can, like we said, we can start with an updated business strategy, right? So you’ve got your baseline strategy and what does the strategy look with AI, right? So that’s a good place to start. And furthermore, you can then divide that into, uh, are my looking for new revenue opportunities?

Or are we looking for operational efficiencies and cost savings, [00:11:00] right? So that is a second frame that you can cut. Another frame you can cut is around organizing your data, right? So this is analysis only. I’m talking more data analysis, business analysis. organizing your data, prioritizing your assets and starting that out is super important, uh, because then that, that forms the requirements, if you will, for data desiloing.

And then, of course, as we said, and where Courtney started, there is the, uh, data infrastructure and data stack as well. So all of these have to be brought together. And the mistake that everybody makes is to think of this as a technology problem and then get behind that eight ball, but you really have choices.

You can look at this from multiple fronts. You have to look at this from multiple fronts and technology and the modern data stack is just one of those many problems to look at.

Courtney: Okay, I feel like I’m on repeat here, it’s like my question I ask every time the three of us are [00:12:00] together, but can you just break it down for me, really practical, what do I need to do next here? Because this is an important, this is actually in our readiness assessment, it talks about, you know, or asks do you have a system for reviewing opportunities and how you decide on opportunities?

We know this is. It’s critical for any business, but can you two break it down for me as I have this onslaught of AI opportunities? What do I need to be thinking about as an executive?

Mohan: So, Courtney, that’s an excellent question. So, let’s, uh, think about the biggest business, uh, painpoints that you have, that’s always a good place to start because you know that better than anybody else.

Then you start thinking about is, do I need to produce more revenues or am I looking for better operational efficiencies and scalability? That takes you along two different paths. And then you start looking at your data that’s required to… Form an algorithm or provide a co-pilot to your employees [00:13:00] and start doing data analysis.

That’s very important. And in parallel, you can start with all the data engineering aspects of modern data stack and all and those things. So there are at least three things. So start with your business painpoints start with your data analysis, and then look at your technology to come up with an overall solution.

David: The other thing I might just layer in is, um, creating a rubric, right? How are you going to decide? How are you going to prioritize what you do? Because we all know if you spread your dollars out and you make 15 bets, you’re probably not going to be successful. You want to get one, two, or three, um, probably one.

To start with, um, that you really nail. And so I’d encourage executives to go think about, um, do Do we want to make a small bet, right? Is it the size of investment? Is it the risk of that investment? Um, is it the impact of that investment that we’re going to prioritize? How did the three of those [00:14:00] interrelate and create a rubric around that of, Hey, we are looking for.

A reasonable size investment that is going to really drive us forward, but isn’t full of risk that we know we can solve those types of questions, I think, can be an abstract layer that you put on top of what Mohan is saying to really help guide the choice because we all know. Focus is about what you say no to, not what you say yes to.

So the more of these you can say no to so that you can pick that one that will really champion AI in the enterprise and drive that improvement, the better off we’re going to be.

Courtney: I could be wrong about this, but the way that AI has, you know, seem to bubble up from the bottom, you know, individual use cases, then maybe it’s easier to look at it as like departmental or like certain functional areas. I could just imagine a world where all of a sudden you as a CEO have all of your executives coming with their very own.

You know, pitch for, [00:15:00] we got to do this thing. This is what we’re going to do. It’s going to be amazing. Here’s the amazing ROI. They’ve, they’ve got the solution in mind, but all of a sudden you’ve got that across all these different functional areas and this becomes a big question.

Mohan: So ultimately, I think learning is going to come from doing. So with all of the inputs you as the CEO or the CEO are getting, you just have to pick one that you think is the best and just start doing it. Uh, rely on frameworks that are out there, but start doing it, pick one. And once that’s successful and you can demonstrate to yourself and to the organization.

That this is how you deliver on an AI project, then you can bring in more and more of those.

David: And I think looking at the fact that you have an embarrassment of riches of ideas is actually a good thing. What

Courtney: Yeah.

David: in the market is the opposite.

Courtney: Mm hmm.

David: A hammer that’s looking for a nail, and I see actually a dearth of use cases. [00:16:00] Um, so I think it’s probably actually an advanced Organization.

If you look at our maturity assessment, one of the dimensions is knowledge and strategy. That knowledge is do executives actually understand AI to be able to come up with those use cases and say this is where it could be impactful.

Courtney: Hmm.

David: The answer to that is yes, and you’re being forced to choose and prioritize; kudos to you.

I think you have a more

Courtney: Yeah.

David: than the average that is out there. And yeah, like Mohan said, pick one and get going so that you’re staying ahead because you’re going to learn more by experimenting.

Courtney: So what I really proposed, if that’s you, congratulations.

Good on you. Well, David, Mohan, thank you for being here today.

David: Thank you. It

Was great to be here.

Courtney: If you’re enjoying AI Knowhow, I think you’ll be interested in our free AI readiness assessment. In just 10 minutes, you’re going to get results that [00:17:00] really can be a game changer for helping you evaluate where you stand today when it comes to AI. And you’re going to get some customized analytics on the areas of your business you need to focus on to make sure your organization can truly take advantage of the many benefits of AI.

Go to knownwell.com/assessment today to get started on your AI journey. Pete sat down recently with Eisha Armstrong, co founder of Vectoris. Eisha is the author of two books on how professional services companies can productize knowledge work.

She shares some keen insights with Pete from both real world experience and a recent roundtable on AI that her company held with 10 B2B leaders from a variety of companies

Pete: Eisha, welcome. It is so great to see you.[00:18:00]

Eisha: Good to see you too, Pete.

Pete: For those who have no reason to know, Eisha and I go back… Way back. couple decades together at C. E. B. I think.

Eisha: Yes.

Pete: Um, I want you to know I’m of your biggest fan boys. I brought along a couple exhibits to share and we’ll have an opportunity to reference these.

You’ll see I even have Pages

Eisha: Ah,

Pete: been referencing, uh, product ties.

Let’s start with with first principles. Why productize? Why is it so important?

Eisha: Yeah. So certainly this year, a lot of companies that we’re talking to are concerned about the threat of generative AI in particular to their business. How might they be disrupted if they don’t? Start thinking about how to use it both to standardize the delivery of services or think more creatively about, um, again, how it might [00:19:00] be disrupted to them.

But, um, before, you know, people woke up on, you know, whatever date in December it was when everyone woke up to generative AI, uh, and started thinking about it, companies were interested in productizing because it helps scale, and ensure more consistent delivery of their services. So, if a company is, uh, concerned about, uh, finding enough people, uh, to do their work, productization can help, uh, do more with fewer people.

Uh, if a company is concerned, again, about consistency of delivery, uh, if they’re trying to improve, operating margins, uh, productization can also help. Uh, so there are a number of reasons why a company may be interested in it. Some of it could be, um, the opportunity or the threat presented by new technologies, but others could just be wanting to gain more efficiencies within their business.

Pete: [00:20:00] If you’ve grown up in a services business model where until today or until recently, everything has been bespoke and you pride yourself on the bespokenness of what you do. How do you get over the hurdle of digesting the notion of productizing something that is otherwise in your reference? So very special.

Eisha: Yeah!

Pete: of its besmokeness.

Eisha: Yeah, you bring up a great point. So the first thing that we coach the companies that we talked to is you have to think differently about how you create value and realize that the way that you create value is not by unlocking, time with a particular expert. But documenting that expertise and your intellectual property and delivering it to your customers to solve the problem.

So thinking differently about how we create value is a big part of it. And then the fun part starts, which is if you can standardize and tech-enable some of the more [00:21:00] routine components of your work, then you get to spend more time on the fun things. Uh, and this is what we’re seeing with the. The companies who are really applying AI in their work is that it’s making their, the the jobs of their professionals more rewarding because they can spend more time talking to clients or, solving those really creative ideas that, uh, generative AI hasn’t, uh, seen before.

So.

Pete: So let’s say I’m an executive and I’m running a services business, and I’ve just been convinced by your incredibly compelling argument. the first thing I do? How do I start the journey toward tech enabled productization?

Eisha: Yeah. So a couple of things that we’re seeing that seem to work. Uh, the first one is finding those people in your organization who are truly passionate about it, uh, and finding a way to harness, uh, that passion, uh, and getting them involved in either hackathons or tournaments to think about how might we be able to apply [00:22:00] this new technology?

The second thing is to get out there and talk to your customers. Find out where are their pain points so that as you are making investments in productization, you’re making investments that align with the things that are most urgent and expensive for your customers, as opposed to just. And then the third thing is to also look internally. So productization is not just to create something that your customers might use, but it may also be like I referenced before to enable your professionals to do their work more effectively. Um, so looking at where are there opportunities based on the new technology to automate standardized, uh, work that doesn’t need to be done by people.

Pete: My understanding is that recently you held, a session with some B2B executives talking about the impact of generative AI, [00:23:00] uh, on the business. Can you share some, uh, of the juicy stuff that came up in that session?

Eisha: Yeah, yeah. No, happy to. yeah, we had a group of executives, I would say, at different levels of maturity, uh, who were there, uh, some who were just starting to think about how to apply AI in their business, some who have stood up, um, completely, uh, new business units, uh, kind of designed to disrupt their core business, uh, using generative AI, and then other ones that were somewhere in between.

Um, and one of the key themes that came out, especially as we talk about how to get organizational wide adoption, is to use what they were calling kind of a human led AI approach. and that meant a couple of things. One, being very careful about how they’re talking about the use of AI in the business.

So rather than saying it’s, you know, it’s going to do this job that used to be done by a person talking about it as a a co author or a co [00:24:00] pilot or even like the sous chef who comes in before the the chef comes in to finish the meal I thought that was really nice. So more of a partner rather than a replacement and I think that that language is is important to think through especially again is as you think about those culture change and organizational change challenges.

Pete: A sous chef makes things far more digestible.

Eisha: Yes, exactly. And again, more fun because then you, you can spend your time rather than, you know, chopping vegetables, uh, actually sauteing the meal, for example.

So yeah, so that, that was one thing that came up when you talked about kind of a human led AI approach. Uh, and then the other one was just the upskilling that’s required.

And, and this is something we’ve been talking about from day one is the importance of the leadership team leading by example and really using these tools themselves, taking, you know, [00:25:00] classes or however best they learn, um, to understand the technology and make sure that their digital acumen again, they don’t have to be digitally fluent, but they have to be literate, is, is at the level that they expect other people in their organization to be.

Um, and you’d be surprised at how often that doesn’t happen. So that upskilling is, I think, the other side of that human led AI approach.

Pete: So insightful. Eisha, thank you so much for your time. I feel like we’ve kind of only scratched the surface, and so I hope we’ll have a chance to do this again one day.

Eisha: Yeah, happy to talk anytime, Pete. It’s great to see you.

Pete: Great to see you, too. Take care.

Eisha: Thanks. That’s it for today’s episode. Thank you as always for listening and don’t forget to rate and review the show wherever you listen. Seriously, if you would [00:26:00] take like two minutes today and do that, it would be really helpful for us to continue to let more and more people know about this show.

Courtney: Also want to remind you that there is a free AI readiness assessment on the Knownwell site, where you can go right now to find out how to get prepared to meet the moment with AI.

Before we go each week, we like to ask one of the popular AI tools, what it thinks about this week’s episode. So ChatGPT, let us know, how should business leaders be prioritizing AI related opportunities?

 

Courtney: Now you’re in the know, and we’ll be back next week with more AI news, roundtable discussions and interviews.

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