AI as a Strategic Operational Partner

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] How do you level up your AI game so that the technology is helping you work smarter at the operational level? And what impact will that have on the kind of work we humans will do? And should we expect an AI Roker to be delivering the weather forecast in just a few years?

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 this week by Knownwell CEO David DeWolf and Chief Strategy Officer Pete Buer.

This episode was recorded while our chief product officer Mohan Rao was visiting family. So we won’t be hearing from him this week. Don’t worry. This has nothing to do with the debate he and David had last week. By the way, if you missed that episode, you definitely want to check that one out. We also have a Q&A with [00:01:00] Tracy Spoenenberg about how a chief people officer thinks about AI and much more, but first the news.

Courtney: Chief strategy officer, Pete Buer joins us each week to break down the latest headlines in AI. Pete, welcome back.

Pete: Hey, Courtney, how are you?

Courtney: I’m doing good. You know, this week specifically is a really, you know, I’m looking for new ways to describe the news, but I think this is a really fun week.

We’re going intergalactic with our first story this week. It comes from space.com and the headline reads, AI chemist finds molecule to make oxygen on Mars after sifting through millions.

Now this AI chemist sifted through almost 3.8 million molecules it can make from the different elements that were found in [00:02:00] meteors from Mars. AI’s involvement here aside, the reason this is big news is that journeys to Mars will become much easier if oxygen can be produced once we get there. Pete, other than this being a really cool story, what’s the takeaway here for business leaders?

Pete: So I think the cool nugget, about the AI chemist is that it would have taken a human scientist 2000 years to find that best catalyst using conventional trial and error techniques. And it took the AI chemist only six weeks.

Courtney: Wow.

Pete: We’re hearing similarly fascinating stories about productivity gains and changes in the way that work can happen in the business.

Um, a couple examples I’ve heard recently have to do with, um, business intelligence reporting. In a large company, uh, you can have hundreds and hundreds of BI [00:03:00] reports chugging along in a disconnected way all across the business. If you wanted to make sense of them all together, it would take a team of analysts.

Hundreds, thousands of hours to probably find them in the first place and then sort through and make sense of them and try to divine somehow the top 10 things that mattered for decision-making AI can do that in literally minutes. I think the implication for leaders here is we have to step back and reset our sights on the art of what is possible.

Um, what are your crazy big data sets? What are your crazy big problems that you want to solve? And how do you turn them into rocket power for the business? Get ambitious about not just what your moonshot is, but what your Mars shot is.

Courtney: Yeah, I love that. That’s so good. So next up comes a story from Wired. Google DeepMind’s AI weather forecaster handily beats a [00:04:00] global standard. According to a paper published in Science, DeepMind beat the predictions from Europe’s top weather center. More than 90 percent of the time. And by the way, it has the potential to require significantly less computing power to create its predictions with one estimate, putting it at 1000 times cheaper in terms of energy consumption.

Pete, same question here. What should executives make of this?

Pete: And I think the frame on the answer is the same, is thinking… So differently about how work can happen in the future. Just step back and try to think about complex of people who are involved in building models, running models, divining forecasts, communicating guidance on forecasts, standing in front of the Map on TV, uh, talking about the forecast like [00:05:00] across the globe.

I can’t even begin to think about how many people are involved in. Imagine AI can outperform that mechanism 90 percent of the time. Fun questions start to arise. You know, what are the other use cases for, um, an application like that? is, you know, will Google have the new, CarPlay, forgive the crossover app in, uh, for ice truckers in Canada or for lobster fishermen in Norway, right?

Like, um, does this get us to a place where we can look at climate change in a different way and really start seeing our way to some clever solutions? It raises some serious questions to like what happens to all those people who are working on delivering the weather, you know, from models through the communication at the end what different roles could they be playing in that value chain of activity?

It’s an easy hop to get from that to business, right? Like, [00:06:00] think of the places where you wish you had better forecasting in the business, sales forecasting, customer retention, workforce planning. You name it. These are all places that would benefit from superior forecasting. And imagine if you had a system that was right writer 90 percent of the time, how awesome would that be?

Courtney: Really awesome. That’s what it would be. I would certainly take that. I hope today that as you listen to these new stories, that if nothing else, they help you dream a little bigger because that’s what these stories do for me. Pete, thank you so much for joining us today.

Pete: Thank you, Courtney. Love it.

We talked on episode eight about the different altitudes of AI in business. Today, David DeWolf and I dig deeper into that concept. We look at why moving from the execution layer to the [00:07:00] operational layer should be top of mind for pretty much any executive leading a company today. Let’s go ahead and as David says, double click on this topic. Hey David, welcome to the show.

David: What’s up, Courtney? How are you doing?

Courtney: I’m doing good. Today I actually, let me go back in time a little bit.

David: Okay.

Courtney: Weeks ago, we had an episode on the altitudes of AI and how it’s going to impact business.

And we kind of laid out those different altitudes, but we kind of just covered at a high level.

David: Mm hmm.

Courtney: That episode, I was just itching for us to go into more depth about what you called the operations altitude of business. And so

David: Okay.

Courtney: want to do today. Are you up for that?

David: Okay, a double click. Let’s do it.

Mm

Courtney: So obviously the layer underneath this, just to give some context in case you [00:08:00] missed that episode is execution.

And

David: hmm.

Courtney: A lot. What you’re seeing today, individual usage of AI and that coming online. The next layer of that is operations. David, do you want to just give an overview of what we mean?

David: Yeah.

Courtney: altitude?

David: Well, let’s look at both of these altitudes because I think the compare and contrast between the two of them really, really matters,

Courtney: Yeah.

David: mentioned it. It is the execution is the automation of the knowledge work itself. And I think of this as production, right? So,

Courtney: Yeah.

David: For example, um, we hear use cases like software engineering, right?

I can be a more productive software engineer because I’m leveraging AI to help me write code. Right. That that is pure execution, pure production of work, and I can be more efficient and more productive in that. Um, content creation is another one, right? Can you go write your first draft of your blog post leveraging [00:09:00] AI?

Absolutely. You can. Will that make you more productive? Absolutely. It should. Right? So we see these use cases of production. Now, we’ve also seen it move beyond a human being, just a single person, to small team-based settings as well. Medical research—pharmaceutical research is a great example of this.

I’m not sure there’s a single person doing that research. I’m not an expert in the field, but I imagine a small group of researchers that are able to accelerate the work they’re doing because they’re able to iterate a lot faster on these different combinations of molecules and chemicals or whatever they do, right?

It’s over my head, but I can imagine it. I can see it, right? So that is the execution layer. And what you said is true. It is where the vast majority of focus on applying AI to the enterprise is right now. Okay. We see use cases, especially in sales and marketing. Um, you see the business development case of writing your cold email for you, right?

[00:10:00] Um, and, and there’s some great results coming from that, That said, if you step back and you think about the other work that happens in the enterprise, the brutal reality is if you go talk to anybody, one of their biggest complaints is I don’t have enough time to do my job to actually do that making me more productive in that is not the problem the problem is all of the cross functional collaboration.

How do I get these different aspects of the business to work together? What are the workflows between them? How do we take? Individual, discrete work and combine it in order to drive operational results. This is multifaceted processes and workflows, collaboration, reporting, meetings, all of that type of work that happens in the enterprise and my hypothesis is that the bigger impact from AI In the [00:11:00] enterprise will be from applying it to these cross functional situations in how we work together, how we actually manage and run our businesses and how we combine these different, areas of the business into a cohesive machine that produces results.

That’s what the operations are. And, um, It’s about directing the work and and figuring out how we do the work as much as it is about getting the work itself done. And that’s the operational component.

Courtney: I believe in that episode, we even mentioned, you know, that it was important for executives to have the awareness of this next level because you need to be building your organization to get ready for that type of altitude of AI in your business. What do you think the steps are get there, you know, to be ready as maybe we’re seeing some progress towards [00:12:00] that altitude.

David: Yeah, I think one of the things we can do is just be aware of some of the tools that are starting to merge that are out there that are operating at that level, right? Um, I think the best example that I’ve tripped across is six cents.

Courtney: Mm

David: cents

Courtney: Mm

David: All about intent data in a marketing funnel, right? How do you identify the individual buyers companies that are interested in making a purchase and are out there doing the research to do that.

I think of this as operational because it’s not about doing something more efficiently or effectively, um, in and of itself, it’s about gathering the information, gathering the data we need to make more deliberate, better decisions and to drive the action of the organization, right? So if I have this intelligence from six cents saying this company, this company and this company are in market buying your services right now.

I should [00:13:00] be able to direct the organization to be more effective, to be more efficient without getting together and pouring through data myself, without holding a meeting to do it, without having data scientists need to climb. The tool itself is just telling me these three folks are in market. And now I can direct my sales team to go prospect to them and to go work the funnel in that direction that’s operational in nature.

And I think it’s a good example of where your question, how do we get to work? What do we start doing? Start looking at tools like that. That is the next generation of software, software that is coming out and actually helping us make those types of leadership decisions of where do we spend our time?

How do we prioritize our time? Right? I think then going from there and starting to apply that to our business. Here’s the brutal reality. Besides six cents, there aren’t a lot of tools out there, right? Really compelled a while ago. I saw in the news come across that HubSpot had just a company in [00:14:00] the client data space.

Courtney: clear,

David: clear, but right. My mind just started going crazy thinking about what are the possibilities here now? How can they inform? From the way we manage our client base in different ways. And I’m sure because it’s a data business that they are thinking something around AI, um, HubSpot is a tool that’s already, um, managing workflows and processes.

Can they get smarter about how they do that? And can that leverage us as business leaders to be more, um, effective in how we direct our teams and manage our teams and, um, and how they target their time. Right? Um, so that’s another example, but there aren’t a lot of examples out there. And I think the challenge for business leaders is to start thinking like this before it becomes the norm.

Because if you start thinking about leveraging the data and the A.I.

So besides those two examples, right? Six cents. And then the potential that maybe HubSpot’s [00:15:00] doing something fascinating here. I can’t actually think of a lot of examples here aren’t proprietary solutions in organizations that are just on the leading edge, right? The perfect example is Amazon, how Amazon runs their supply chain, right?

They’re so connected to the customer and to the market and where demands coming from that they’re able to automatically adjust their supply chain to optimize for revenue and profit and getting products into the hands of customers, right? That is how all of our businesses will work. And I would look at those types of examples and I would start to think about what are the areas where we can’t find the signal in the noise that we don’t even think about using computers to solve, right?

That this is a human process that is leveraging manual knowledge work in order to direct how we do work and where we put our time and effort. It’s in those situations [00:16:00] that I think we’ll have more and more use cases emerging over the course of the next year.

Courtney: So, David, I’m curious, you know, obviously there’s been a lot of fear around AI and certainly we have to be responsible and how we weighed into this technology, but I think a lot of the fear has been driven around this execution layer, I think, as we move up to that next altitude, I’m just wondering if some of that fear evaporates, and actually we get the reverse of people getting to do more of the work they love, and I’m just curious to get your take on that.

David: That’s that’s a fascinating conclusion because I’ll be honest, I’ve been thinking about it the the exact opposite way. Um, you make a great point if it empowers people to do more of what they love, and it is actually taking away what they don’t like. That would be a positive on the flip side. I think a big thing that folks are concerned about is the robots were ruling the [00:17:00] world, right?

Have these big bad machines directing us to do things. And when you start thinking about operations, what you’re really talking about is how we design our work, how we coordinate our work, how we direct our work and all of those start to feel a little bit more like Big Brother robot looking over our shoulder, and I think that’s exactly what people are afraid of though I will also say I think a lot of the fear is around the fact that it’s totally uncertain and unseen right now because there has been so little work done in this area, and so you don’t see specific solutions once we start to see specific solutions people will begin to trust it because it’s not a nebulous idea it’s specific.

It’s there, but there is a big change management initiative and effort that is needed to get there. And I’m going to start leading on your insight. I think it’s interesting if we can position this as allowing people to get back to the work they [00:18:00] love and do what they are uniquely qualified to do. I think there’s a big upside there.

Courtney: Great, David. Uh, glad I could help

David: Yeah, good insight.

Courtney: the episode today.

David: It’s great to be here. Thanks so much, Courtney. Take care.

We’ve mentioned it a few times on this podcast, but just in case you missed it, we have a newsletter. We keep you up to date on all the things like our latest podcast episodes, articles you may have missed, and just in general, keep you informed on what’s happening with AI and business.

Courtney: So if you’re interested and want to stay in the know, go check it out and sign up at knownwell.com. Tracie Sponenberg is the chief people officer at The Granite Group. She’s a self described HR rebel who is passionate about the people experience, generative AI, and all things [00:19:00] HR tech

Pete: Tracy. So great to have you on the show. Thank you for being here today.

Tracie: Thanks for having me, Pete. I’m very excited.

Pete: As you know, the focus of the program is on AI and we tend to like starting just with some grounding in where are you seeing AI affecting your business? Can you share with us what you’re seeing happen at The Granite Group?

Tracie: Yeah, I think it’s a couple things. I think it’s the business or a few things, the business, the industry, and then my industry or my, the human resources field or the people field. And, and my industry overall is distribution and we tend to kind of lag behind in technology. Um, but that’s rapidly changing.

In fact, I was part of a conference a couple of months ago that was, heavily focused on AI and innovation. So we’re starting to see it creep into even my company and starting to have conversations with companies that can help us use AI to move further and faster. And in the field of human resources, I think sometimes we also lag [00:20:00] behind in technology, but we’re seeing more and more practitioners adopting generative AI and in some way, AI in general has been around.

In our field and for a long time, but, uh, generative AI, um, but still not enough. I still want to see more people using it.

Pete: Let’s talk a little bit more about, um, AI in the HR context or the people context. are you seeing AI affect, change, alter, make better people strategy in general?

Tracie: So the way I like to think of it and the way I like to use it now, and I think there’s a lot of potential, but there’s still a lot that it doesn’t do. Um, you know, I really want a digital partner. I really want that, that Microsoft copilot that I can’t yet get because we’re not enterprise level. But I really want that digital partner that I can explain.

Hey. I want to do X, Y, and Z and do it for me and I think we’ll get there. I think we’re not yet there. But where [00:21:00] I like to use particularly ChatGPT or, you know, other generative AI tools is really like a thought partner and really to work out problems and to do brainstorming. So, you know, if I’m trying to come up with a people strategy and I have the basics of an idea, I can throw that into ChatGPT and get some additional ideas.

Now, it’s not everything, um, but it’s something, um, and it can be used in any way from, from helping set the strategy to working on, um, you know, communications and conversations to, you know, really, there’s no limit to really everything. I think that the one point that we’re really, really careful on is not to put any sensitive data because we don’t have a corporate subscription.

So, um, I think the analytics with AI, I’m, I’m super excited to explore that, but I’ve only just dipped into that.

Pete: I have a personal [00:22:00] preoccupation or concern that companies aren’t doing enough. To get their people up to speed and also to plan for a future where so much of their people’s work can be automated. You know, there’s statistics that 65 percent of all work can, you know, be done by machines instead of people.

Your take? Are, are, are companies ahead or behind in, in planning?

Tracie: Probably behind. I mean, it depends on how you look at it, right? Probably behind. You know, I think that years and years ago, we were looking at automation, particularly in warehouse automation and manufacturing, you know, AI and was coming for the more entry level jobs, the physical jobs. It’s the deskless workers, right?

And we’re like, okay, that’s appropriate. And now we’re seeing AI come for the office jobs and we’re going, oh wait, wait a minute, wait a minute. We better slow down, which is appropriate to pause and think. But I think, The biggest thing I think we’re behind on is we need to be prepared and we need [00:23:00] to be prepared with ethics and governance and, you know, making sure that we remain human.

Um, that’s where I think that we really need to focus.

Pete: I think we feel the same way and it strikes me that this is a moment in time for the chief people officer for the CHRO role to elevate and take a stronger position in business. Do you see that happening too?

Tracie: Yeah, I think, you know, there’s this is like a days long podcast that we could explore and but just to kind of sort of break that down briefly. Um, you know, I think that there’s two potential barriers to doing that. And that’s. There’s a lot of barriers, but two main barriers. That’s the, the CEO and or the leadership team not allowing that to happen.

And the second one is the CHRO or CPO themselves not, um, you know, taking what they’re given and running with it and [00:24:00] being bold and brave and stepping forward. And, and I tend to get some flack when I talk about that, but that’s true because I was that. that CPO before. Um, so I think it’s a tremendous opportunity.

I think that there are some organizations that don’t allow even that role to be present. Um, and that there are some people that aren’t ready for, um, what is out there. And so I think this is a tremendous opportunity for CPOs to get in and dig in and learn about things that may be really out of their comfort zone.

And, you know, didn’t know anything about AI. I still am a novice, right? But I learned and I learn new things every single day. And that makes me so much better at my job. And I listen to people who are much, much smarter than me all the time and know that I’m never the smartest person in the room.

Um, instead of being scary is actually refreshing because I know I always have something to learn unless I’m alone. If I’m alone, then I’m definitely the smartest person in the [00:25:00] room.

Pete: So we we have our audience is leadership teams, you know, in the executive crew. if I’m a if I’m a leader on the team trying to think about either myself as a CPO, how to, how to get, get smarter, get readier, or as a leadership team broadly, how to bring our, our people strategy and acumen up, Where do you go for information? Uh, how do you do your learning?

Tracie: right? My biggest source of learning is my community. So others around me who, um, I didn’t network at all. You know, we used to be, I’m still very introverted, but I used to be like a hiding in the corner, kind of didn’t step out ever, really traditional HR person. And I transformed over the past several years.

10 years or so, um, but my community and as I developed and built that networking community, that’s where I learned from. And, and, um, things like the, the, the platform that used to be known as Twitter and, um, [00:26:00] LinkedIn are really helpful because I like kind of quick hits. I have a number of newsletters that I like to read, um, and using AI itself and using tools like generative AI to teach me.

So if I need to know something, um, I can go there as well. And conferences and I do a bit of speaking now and I learn every single time that I do that from, from others, but primarily it’s from, from other people.

Pete: Thank you so much, Tracy. It’s been a pleasure speaking with you and look forward to having continued conversations.

Tracie: Thank you, Pete. Great to be here.

Courtney: That’s it for today’s show. Don’t forget to go to knownwell.com to sign up for our newsletter and stay informed on what’s happening. With AI and business. Also, be sure to like, rate, and review the show wherever you listen. And, if that’s on Spotify, leave us a comment in the Q&A [00:27:00] portion of the app. We will actually give you a shout out here on the show if you do. As always, we like to ask one of our favorite AI chatbots to share their take on the episode’s topic. So, hey, Claude, uh, welcome to the show, buddy. What do you think about how companies can move from the execution to operations level when utilizing AI?

And now you’re in the know. We’ll see you next week with more AI news round table discussions and interviews.[00:28:00]