← Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
5 ways to make your organization AI-ready
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- [00:00]
Steve Wonker
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iheareverything.com welcome to the Voices of Search Podcast. A member of the I Hear Everything Podcast network, ready to expedite your company's organic growth efforts. Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search podcast, Jordan Cooney.
- [00:43]
Jordan Cooney
Fewer than one in four companies have redesigned their workflows to capture value from AI, even as adoption accelerates. That gap explains a lot because most teams have AI, but very few have changed how work actually gets done. So productivity bumps and then stalls, not because the technology fails, but because organizations don't move with it. Old hierarchies, old incentives, old approval chains, all quietly rejecting new intelligence. I'm Joran Cooney, and joining me today is Steve Wonker, Managing Director at New Markets Advisors. Steve has spent more than a decade helping Fortune 500 companies to navigate AI transformation. He's advised companies like Microsoft and Meta, and he's the author of A.I. and the Octopus Organization. Today we're going to talk about why AI success depends less on tools and more on how teams are designed to think, decide and act. Steve, welcome to the Voice of the Search Podcast.
- [01:46]
Steve Wonker
Thanks for having me.
- [01:48]
Jordan Cooney
Really excited to be here. This is a topic that I think is radically underserved, not just in my industry, which is search and marketing, but just universally. I don't think enough teams, organizations and leaders are talking about how to design, organize, and set up their their teams for success. Maybe before we even get deep into some of the questions, would love to know how you got into this space and how you got into thinking about how organizations are structured and organized to be efficient and effective.
- [02:22]
Steve Wonker
So look, I've been in companies, I've been in startups and large enterprises where we had to create things that were new and had to do it really quickly. I've understood for quite some time that the way an organization is set up can be at least as determinative of the output as the underlying concept. So in my last book, the Innovative Leader, we looked at how organizations can operate in order to be more innovative, not just to create innovation, but in terms of their operations. In this book, we really wanted to bring AI through that lens to figure out how do organizations make the most of technology like AI. What we realized Very quickly is that it's, as you said, Jordan, it's not the technology itself. The technology is already quite good and it's getting a lot better every year. The organizations are changing a lot more slowly than the technology is. So we were trying to bridge that gap.
- [03:27]
Jordan Cooney
And you know, one of the things, the interesting concepts, not only that we discussed as we prepare for this, but within, within your book, is that most companies just bolt on AI technology to existing structure or, or process instead of actually thinking about redesigning or organizing differently based on how the technology can impact what actually happens. When teams just bolt on AI. Like I think so many of us, even in my industry, that's what we're doing today. Well, we create content or we do these technical assessments or we have the, we need this report for our boss and all we just do is just bolt on AI to do that without ever thinking or questioning what the report is, or should we be creating that content or should we be doing that marketing initiative or are we organized the right way to be effective?
- [04:19]
Steve Wonker
So here's some real company data from Johnson and Johnson. JJ recently tallied up the number of pilots for AI that they had going on in the organization and they had at least 900. There were certainly probably more, but there were at least 900 that the folks at corporate could figure out were going on. And then they tried to calculate the economic value that was being created by those pilots. 15% of the 900 were responsible for about 85% of the total economic value. So what you have is a lot of this magic AI dust that gets sprinkled on top of what people are already doing. It may create some efficiencies, it may not. It often creates more work on the back end than people think about because you have to attend to the data quality and the model outputs and the updating of the model. A lot of things that might not be normal software activities do become normal with AI. But regardless, if you're not changing the system of work, you're probably not creating a whole lot of value. You might be ironing out a couple points of friction, but it's a bit like when electricity came around. Factory owners at first just tried swapping out their steam powered machines with electric machines. And it took 35 years for Henry Ford to realize, oh, with electricity we can create an assembly line. And that was the huge unlock of productivity. Now with AI we don't have 35 years, but we need to be looking for those systematic changes. Like JJ found with its 15% that created by far the majority of the value.
- [06:04]
Jordan Cooney
And you know, I think one of the interesting things about your example and your story here with J and J is that there's just this small amount of these AI projects that yield an outsized impact. And is there a lot of, you know, success behind that because of how those teams operate? Is it about those teams specifically in the 15% that made the difference?
- [06:31]
Steve Wonker
So there's a couple things that are really determinative of outcome. Sometimes it's the use case, right. If you're trying to bring AI to the contact center, well that is a terrific place to bring AI. It is not only a lot more efficient, but customers are usually a lot happier with the result too. So great. But most of an organization isn't like that. So then what really creates the impact is if you don't think of it in terms of the current tasks that are done today and certainly not the current boxes on NORC chart, but what actually has to happen in an organization for a unit of work to be output? If you're optimizing pricing to a customer based upon their preferences for capex versus opex, what actually has to happen in the organization? And then how can you infuse AI into that process, not take it over completely, but to make it a lot more faster, a lot more responsive, often a higher quality, sometimes a lower cost process than you had before? Right.
- [07:41]
Jordan Cooney
I mean one of the interesting trends that I'm noticing not just in our industry but just universally around AI is there's so much innovation, so many new, I don't want to call them software companies but new AI utility companies that are very, very point solution.
- [08:04]
Steve Wonker
Right.
- [08:04]
Jordan Cooney
In your example, right. We're the AI customer service widget of X or Y or we're the, you know, the, the content marketing tool of A or B. And so we're seeing this like hyper focus and, and personalization almost to some extent in how AI tools products are coming to market. Is this a tools application opportunity or is it an organizational shift opportunity or is it both?
- [08:37]
Steve Wonker
Well, it is both to some extent. We tend to be skeptical about the long term potential of most AI only tools because look, what a software company does is only very partially about creating a tool, creating a bit of code. You have to fit it into workflows. You have to get people to change their behaviors, you have to get them to trust the system with sensitive data. You have to then tune the models and do a lot of stuff on the back end. So what you really are doing is changing your workflow and the tool is Only one part of that overall process. Oftentimes the big software vendors are just much deeper into an organization. So if they can be nimble, and that is a big if by the way, if they can be nimble and create these tools and bring them to market in AI appropriate ways, then they're the ones who are best suited to create the change. The tool is part of it, but again it's the assembly line, it's not the electricity itself. Let's think about the systemic change that has to occur.
- [09:51]
Jordan Cooney
Absolutely. I mean, let's talk about those systemic changes. One of the things that I, I hear a lot from different marketing leaders, maybe CMOs, VPs of growth. How do we get organized around AI initiatives? How do we put the right people into the right projects or the right efforts to execute new AI innovation? How do you advise those leaders and what direction do you give them to put the right people into these scalable solutions that companies are trying to solve with AI?
- [10:26]
Steve Wonker
So you are not going to have remotely the number of project managers that you would need to lead this. And sometimes they don't even have the right skills either. So that is not the sustainable solution. You can't do everything at once. If you did, the organization would break. So you have to prioritize what you're going to do and then you want to have people often volunteer to be AI champions in the organization and usually by the way, getting the right numbers is not an issue. You will have more volunteers than slots for championing. That's, that's a good problem to have that, that's fine. You need cross functional representation to think about workflows in a cross functional way. Right. Not the, the old style cones of an org chart which weren't really realistic to begin with and certainly aren't realistic when we're looking at changing how the work does, does happen throughout the organization. It's a much more horizontal approach. But you bring together that cross functional team with a champion who sort of functions a little bit like a product manager in the tech world and they're IT enabled but you don't need a huge investment by internal IT to support that. Then you can create some really focused change. And people have to be clear this is an agile process. That's one of the great advantages about AI. They can create things very quickly and see how it works and fine tune it. The work is going to be mainly not on the creating of the tool, but it's going to be on the embedding of the tool into the workflow and upfront Making sure that you're solving the right business questions, not just going around with a hammer looking for nails.
- [12:09]
Jordan Cooney
Yeah, absolutely. I think that's one of the great challenges that many of these organizations face is like, what are, what are you solving for? And understanding the solution that you're trying to achieve as much as putting the right people into the right roles. In your book, you talk about this concept of the octopus organization, and I'd love to just get your general sense of what does that mean for marketing teams. That's what we typically work with, right? Search marketing, paid marketing teams, content marketing teams. And how should they be thinking about this concept of the octopus organization?
- [12:43]
Steve Wonker
So we use the analogy of the octopus because it has a biology which is utterly weird to us humans. It is nine brains, it has a central brain, and then it has one brain for each of its arms. And that allows each arm of the octopus to sense and think and act quasi independently. And yet there's a nerve ring that connects all the arms and so each arm knows what all the other arms are doing. It's a wonderful analogy for how AI decentralizes intelligence and creates this fluid flow of information so the right people can get the right data at the right time. Now, for marketing, that means that there is less of a need for constant coordination and you can bring tools much closer into the business. So let me give you an example of a hospital system that we worked with recently and the marketing department within it. So they had often been pulled between all these different service lines of a hospital, whether it's, you know, obstetrics or cardiac or urology, whatever it might be. And they had to create different campaigns for them and then they had to actually get them to figure out what they wanted. So we have to start with what's the strategic objective here? And that was clearly that the marketing department focuses on the big strategic areas for the hospital. So the TV campaign for the heart service line is very important. The brochure for the urology department is not very important. So how could they then use AI to bring more self service tools to the stakeholders in the business who could create their own materials and oftentimes use that to figure out what it is they actually wanted? Because that's a lot of what marketing was spending their time on, to have that help the business figure out what they wanted. Now that doesn't mean that marketing abdicates its role. It still looks at some of the outputs and it coaches from the 80% solution to the 100% solution. But it doesn't have to do the 0 to 80, particularly in those areas that aren't a priority. That's, that's the octopus, right? You're devolving authority and you're enabling people to sort of know what's going on in the organization without getting super duper involved in it for each project.
- [15:14]
Jordan Cooney
One of the, I love this example and one of the main themes that stand out to that for me, especially as you connect it back to the octopus, is control. Right? What, where and when you have control over certain aspects of your marketing work. Right. So, so in this example of building some, some content brochure or whatever it is for, for each of these parts of the hospital, I, I, I get the sense that leveraging AI, there's a world where you can have some centralized control like branding, color fonts, maybe even like, you know, history of the hospital versus this decentralized component of like what each of these departments or groups wants to convey or share with their patients or audience. How are you thinking about organizations and the controls that they should or shouldn't use and when AI is applied to that? I think that's where I'm really curious is like when does that actually matter to use AI to help you control or contain some of the requirements needed in these activities?
- [16:20]
Steve Wonker
That is a great question because as you devolve responsibilities, you create situations where people can run with it, but if they have to ask permission for everything, then you haven't solved any real issues. But look on the flip side, sales can now create its own marketing materials. Well, should it? In what situation should it, how should those be controlled? Usually you want to lay out what you can't do, not all of what you can do. Let me give you a totally non AI example. A pilot in the Air Force receives a very thick manual about what they are allowed to do with their plane. A pilot in the Navy receives a much thinner manual about what they are not allowed to do with their plane. Because in the Navy you have these very dynamic situations with carrier landings. Oftentimes it's much less pre planned as a mission and you need that flexibility. So we need to treat people like Navy pilots, not Air Force pilots.
- [17:31]
Jordan Cooney
Interesting. That's a great example. I mean, one of the things that I think about when we talk to leaders, right? Specifically leaders in organizations or even teams, is the ability to have the awareness, the judgment that AI simply can't make. I mean, AI can make judgment calls, but it's not going to make them correctly, which is two different things. Right, but in your example of what kind of Pilot, you should be. There's a judgment call there, right? Because there's a decision tree there of a bunch of people who've said, hey, here in the Navy, we just have to do things slightly differently than in the Air Force. And then thus our requirements are highly different. When you're working or coaching these leaders to understand these judgment gaps, what are some of the areas that you need them to focus on? What are some of the things that they should be seeing or reading or hearing from their organization to know that there's a distinction in how you create that control in the organization?
- [18:28]
Steve Wonker
You know, in the book, we talk about another aspect of octopus biology, which is that it is three hearts. It has different hearts for different purposes. And organizations need the same thing. They need their analytic heart, where things are very well governed by rules and you're very, very careful. So, you know, in the Navy, if you are on a nuclear submarine, you do not have a lot of flexibility. You, you don't want to get creative on the nuclear submarine. Right. And then you need the agile heart. So, yeah, the, the carrier pilot, they need more agility because all sorts of things happen up there that you can't just predict. And so you need to lay out where you're doing each, and then the other heart is the aligned heart. Where do you need to think about the system, both in terms of how it works together, but also in terms of how are people feeling and thinking? Right. I mean, there are a lot of emotions that are going on, on in an organization that starts getting infused by AI whether you're 10 people or 10,000 people, there's a lot of emotions. And so how do you pay attention to how that emotional arc is going to be playing out for people? That's an important one, too.
- [19:43]
Jordan Cooney
Yeah, that's super interesting. Can we just dig into that just a little bit in terms of how maybe certain organizations can collect data or information about each of those hearts and where they maybe need to prioritize or lean in one direction or the other, depending on the maturity or development of the organization.
- [20:04]
Steve Wonker
So typically, organizations are really good at analytical or being really, really careful. They're not so good at being agile. And the bigger the organization, the worse it is at being agile. And then for alignment, it too often depends upon the individual, not upon how the organization really works. So that's an issue. I'd like to. If we focus in on the agile versus analytical, we refer to a story in the book about landscape architects studying playgrounds. So they started out with a playground with a big Slide in the middle and swings and you know, merry go round and hobby horses around the periphery, no fence. And they saw how did kids play in the playground? And they saw that these kids would sort of cluster in the middle of the playground around the really big equipment. And then the kids went away and the architects just changed one thing. They built a fence around the playground. Different group of kids comes in, how do they play on the playground? And the kids are using the full playground right up to the fence. And it shows that we all have fences in our minds. There's always the implied fence and usually that implied fence is closer than it actually is because we're risk averse about that stuff, whether we're kids or grownups. So you need to lay out the fence, paradoxically, in order to give people the freedom to explore all the territory that they have. Lay out the fence for where you're going to be agile and then beyond that you have to be analytical. Oftentimes the area for agility is a whole lot bigger than people think it is.
- [21:53]
Jordan Cooney
Yeah, absolutely. That's so interesting. And I love, I love the connection of that to how leaders should be thinking about helping their teams be successful here. Because it's not a, it's not a definitive component. Right. The fence can be moved or shaped or designed as needed, depending on the nature of the organization. But giving that clarity is immensely helpful in the direction of how teams can implement change, work process, or even the utility of AI. Another concept you brought up in the book is organizational antibodies. And this is something that I think is an interesting concept for our listeners. Can you tell us more about what that means and how our listeners should take insight from this?
- [22:37]
Steve Wonker
Look, just like our human bodies, organizations do not like change. They don't like transplants. They're going to react against it in a whole lot of ways, some of which are very obvious, some of which can be very insidious. Because AI changes so many aspects or should change so many aspects about how organizations work. It's those insidious obstacles that are often the greatest. Now they can be ones that directly affect the quality of AI systems like data quality and availability and freshness, or the quality of evaluation of outputs. But they can also be much more invisible about did people really change their work process or not or did they ship the work to another department? That's one we're often seeing with AI implementations. So I mean, the way to go about it is to focus on the business objective, right? It's not AI for its sake, we are going to reduce our cycle time by 50% or we're going to radically increase customization for a customer, whatever it might be. And then AI plays a role in that. There's usually other things that play a role in that too, if you can do that. It's less about the fear of the Terminator killing off jobs, but it's more how do we accomplish this. And then AI is actually a real accelerant of change that people actually want to accomplish in their organizations rather than some imposition by it or the board, whatever it might be.
- [24:14]
Jordan Cooney
You brought this up. And so I have to ask this because I think it's super important for not only our listeners, but technology at large right now. There's so much news of every single big tech company doing layoffs, cutting resources and, and they point the finger to AI, right? Like, you know that that's their escape is AI is the reason we're making all these cost reductions and we're just more efficient as an organization. My personal opinion, I'm just going to throw it in there is, it's just a, it's a bunch of crap, right? It's just, it's their use of, of, of AI to try to sell a PR stunt to the street and investors that they're just a better, more organized organization. And I think that this is very different. This concept of how companies are managing their P and L versus an antibody or something that an organization should stop doing because it systematically is not working are two very different things. And curious to know if you have any examples or opinions on the distinction of how companies manage themselves and manage their resources versus when they make decisions to stop or continue doing certain activities.
- [25:26]
Steve Wonker
Look, we've already seen some high profile announcements get walked back Salesforce, duolingo, they oh, we can eliminate all this stuff. And some of it, yes, I mean Contact center being a great example. There are others in the back office. But most organizations we've seen are looking for efficiencies, but not necessarily looking to chop a tremendous number of jobs. They're looking to deploy people on the most useful aspects of their work. So let's go back to healthcare. One of the most successful implementations of AI in healthcare is AI scribes that sit in silently when a doctor and a patient are talking. And rather than the doctor being focused on their keyboard during the whole consultation, the doctor can actually look at the patient and they can interact because it turns out they did not go to medical school to become typists. So what you get are better quality notes. You get those notes synthesized in with notes from the three other doctors that the patients saw in the past year or so so that you can get a better view overall about what's going on with the patient. There's a lot better communication between doctor and patient going on and the doctor isn't spending two hours at the end of her day catching up on her notes. So everybody wins. It doesn't mean that we need fewer doctors. It means they're actually practicing medicine a lot better. They're more satisfied, the patient's more satisfied. Yes, it's more efficient, but not in terms of chopping hints. I think most organizations are focused on that that we're working with. Yeah, there's a few where oftentimes from very on high you get this edict of can we save 25% of headcount on this function? But much more of it is how can we be better and faster and more focused on what actually matters?
- [27:32]
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- [29:00]
Jordan Cooney
Yeah, and in your experience when it comes more to specific programs, initiatives may be related to AI and the rollout of that in organizations or even just more broadly other types of organizational initiatives. How does this octopus model help you make better decisions as to when to stop doing something? When to kill a project or an initiative or an effort. And to your point, that doesn't necessarily always mean reduction in headcount, but it certainly could mean reprioritizing or shifting focus.
- [29:39]
Steve Wonker
We like to think about innovation in general, not in terms of you're running a startup, but you're running a venture capital firm. You need to have a portfolio of initiatives and you need to be absolutely ruthless about killing the zombies, because there get to be a lot of zombies. And the bigger the corporation, the worse they are typically at killing the zombies. We, we came out with a paper a while ago that had the ABCs of AI adoption. A is AI Phi the present. So sure, if you can get a 5% efficiency gain from implementing some easy set of tools, yeah, okay, by all means, do it. We're not saying don't do it, do that. B is become great at experimentation, which unfortunately is a letter that many organizations have ignored. So that means being very clear about what are you trying to prove or disprove. How are you capturing learnings? What are the pre and post measures that you can actually assess? How are you going to disseminate those learnings? What criteria do you use to kill things? All things that venture capitalists do very well, which a lot of big organizations do not. And finally C is create the future. So that's having those half dozen or so initiatives that are really fundamentally transformed by AI do that and then move on to the next half dozen and the next half dozen. You can't do the whole organization at once, but you can do it in prioritized tranches.
- [31:13]
Jordan Cooney
Absolutely. And that sense of focus, I think is something that I haven't really seen in a lot of organizations. And largely, I think there's this rush by leaders and leadership in organizations to just try and do everything. If we try everything with AI, something is going to work or stick. But there's no system process. Going back to the octopus analogy, then there's this nervous system that connects across the entire organization. So if you change an AI model or process of work in one organization, it's going to have an impact at some point throughout the whole company as it pertains to leadership.
- [31:51]
Steve Wonker
Sorry, George. So we talked a while ago about Johnson and Johnson and how they found that 15% of their AI projects created 85% of the value. What that led to was a much tighter governance process of chartering a much smaller set of AI projects made sure that they had the value. Now, we need to keep something in mind here. JJ is the biggest healthcare company in the world. And they don't have the resources to spread Amongst all these 900 projects. If they don't have it, then I'm betting that most of our listeners here don't have those resources either. So yeah, we need to be a whole lot more critical about where we're focusing our time.
- [32:37]
Jordan Cooney
That's right. And I think leadership is the core behind prioritization, right? At the end of the day, that's where decisions are going to be made as to what you start or stop in, in why you invest in the new experiments or experiments as an organization. And, and fundamentally, I think leadership is, is at a kind of crossroads because of AI being involved in their own lives. Right. AI becomes a, a crutch or a tool for leadership. And you argue the opposite. You, you share that with. We shouldn't necessarily think of AI as this leadership fulfillment or leadership tool, but that leadership just needs to be more strong around clarity and decision making. Tell us how leaders should be more clear about making decisions in this AI era. What are some of the key similarities or common ground that you see amongst leaders that are defining for their organization successfully today?
- [33:42]
Steve Wonker
So before I co wrote AI and the Octopus Organization, I had a book called the Innovative Leader and that had a different ABC framework for leaders that really wanted to make their organizations more innovative. A was aspire, you need to have clear aspirations. B was build actual mechanisms to be innovative, not just create ideas, that's the easy part, but to implement the ideas. And C is cultivate the culture and operations that are needed to change things in a sustainable basis. Most organizations could benefit from a lot of thought around each of those letters. Aspirations are often too vague. They don't help people make choices, they don't really prioritize, they're not measurable. So there need to be clear aspirations around AI usage. B, building the mechanisms to actually implement the change. It's not just about saying here's the tool, but what are the suite of products and services, ways of working that are going to be offered to people so that they can actually make these changes in a thorough and thoughtful sort of way. And then C, how do you cultivate these changes over the long term? I was talking about the book recently with the CTO of a really big insurance firm and she's a very thoughtful person and she said, you know, what I've realized is I've got a lot of people in my organization and so many are hired for their technical capabilities. And yet everybody in my organization who has direct reports needs to become a change manager. This affects everybody there's no longer the high priesthood of change management in C suite. And that was such a simple and profound thought that I think a lot of organizations haven't fully embraced yet. But they need to. Everybody becomes a change manager.
- [35:46]
Jordan Cooney
I mean, and that leads then probably to this belief that the way we use AI is not only within these massive system wide or organizational wide ways of changing or doing work, but it probably even can, can impact the way we integrate or collaborate in very small micro moments. Right, so back to your example of the doctor patient note taking. I mean as, as a, as a manager or a new manager you can simplify your note taking of one on ones and create processes within you and your employees that are much more efficient and scalable and useful than, than, than you sitting there taking notes and sending an email. That's a bunch of follow ups with your, your employee. Simply because we have the technology and AI to do that and make that much more scalable. So then there has to be a belief right in this octopus organization that these changes with AI are not just macro but micro in many instances. How do you see those micro moments unfolding and are there any examples of where that's been really impactful to organizations at large?
- [36:59]
Steve Wonker
A lot of attention in AI rollouts has been on the bottom up process. Let's give people these tools, see what they do with it and have things bubble up. And oftentimes there's not been the follow through to actually ensure that things do bubble up. And so the micro changes never stick or never become widespread. That's, that's real waste. You need both, you need both the big let's create the future, let's think across these different workflows, bring together a cross functional team to do so. And then you need to have people have access to tools so that they can make the micro changes. Sometimes the micro changes are actually really easy to do and they're going to be very impactful, but they haven't happened. I was in a workshop a couple weeks ago with an organization that does a lot of field sales and the head of North America for this organization said wouldn't it be great if we could have sales reps when they leave a customer dictate a note about the customer interaction rather than waiting five hours for them to get home and type up something really abbreviated salesforce? Well yeah, that would be great. And actually we have the technology to do that, that exists right now, so we should just get that done. And yet I think the people on the ground didn't know that they had the authority to do that. And the head of North America, I hadn't thought about it at that granular level, but I mean, that was something that we could have done two years ago with AI. That's not that hard. So we need that as well. It's like, okay, let's set the business objective, maybe let's save sales reps time. Let's ensure that they can focus actually on customer interactions and not on their keyboards. Let's be more responsive to customers. Let's ensure everybody in the organization, not just the sales rep, understands the customer's context. Okay, well one way of doing that is pretty darn simple of getting accurate real time notes rather than this abbreviated stuff after the fact. So yeah, we need to open up that especially around the business priorities. That doesn't necessarily create the 900 pilots, but it gets people saying oftentimes in very low hanging fruit ways what can be changed. And then you know, if you might pilot that in one territory, for instance, and then you can very quickly scale those things out across the organizations. That's part of what becoming great at experimentation involves.
- [39:46]
Jordan Cooney
I love this concept in this example of a sales team. I've, I've managed sales teams in the past and these can be groups of individuals that have a lot of fear around change. Right. And they also have a strong belief that a lot of their own knowledge and their own ways of working is some special power that they have that no other salespeople have. And so that confidence is necessary to be a successful salesperson. But they can also be a detriment to the overall success of the organization or, or the company at large. And one of the concepts that hits me as you share this example is psychological safety. How do you ensure as a leader, as a sales leader that if I'm capturing these audio notes, this isn't going to become a recycled thing. That then becomes my coaching or my quarterly review or my annual review process, but more of a tool to help better understand clients, customers and prospects. Where and how do leaders create that psychological safety component to the way they implement these kinds of work changes and AI utility at large in organizations.
- [41:03]
Steve Wonker
So this is why cross functional teams are so important. If you're going to do that. Having it involved, probably marketing, certainly sales ops and you know, sales management, maybe other elements of an organization like service. They should all be involved in thinking through what actually is going to be useful. Create the mandate, then figure out the tool. I experienced an organization recently that did the opposite. They rolled out a sales coaching tool that would listen in on every conversation and then provide notes about the conversation and tips about what could be done better in the email. And, you know, very, very rapidly. That went right to the. The trash folder. Nobody wants coaching on every single conversation that they have. My goodness. You know, it's, well, certainly not going to make people more efficient and it's going to make them paranoid and everything else. Now, listening to something over the course of a week and giving people a sense about how they're doing, recognizing that sometimes the tool doesn't have all the context. It doesn't know that you've known this customer for five years. And so, yeah, you start out asking about the daughter's dance recital. It's not weird if, you know, if you've been to a dance recital before with the customer. Right. So knowing where the tool fits in and having enough humility about it, that is really important, whether we're talking about sales or whatever else. But just saying, oh, here's this bright shiny object, now go use it. Oh, boy. There are a lot of disasters that are on that road.
- [42:52]
Jordan Cooney
Absolutely. I mean, one of the questions or topics we discussed prior to us recording was kind of the diagnostics that you use or the process you use when you're talking to a new CL client or partner, and that there are three diagnostic questions that you ask and that helps you better understand and reveal where an organization might be at. Can you share with our listeners what those questions are and how? That's a more useful approach than just a standard checklist of asks.
- [43:31]
Steve Wonker
So you could think about AI in three buckets. What won't humans do? They're not going to take notes after every meeting and summarize what happened, and then distribute those notes to every attendee. It's just not going to happen. What shouldn't they do? Doctors should not spend half of their time with a patient looking into a screen, typing on a keyboard. That's not a good use of their skills. And then what can't they do? We did a workshop recently with a big organization where we had 20 different tables, about 120 people all together in the room. Each table, rather than having a flip chart of what they were going to do, recorded the whole interaction that they had. And then we were able to take those 20 notes, get an AI transcript, put it through a couple of different models, not just one, by the way, and then figure out what people committed to what issues they had. We could get a really detailed summary of that whole meeting the same day, not just doing summaries of a flip chart. But very detailed and nuanced and accurate without that flip chart getting in the way and a whole lot faster. That's something humans just could not do before AI. So can't, won't, should. Those are the three things you need to think about in terms of the full potential for AI in an organization.
- [45:04]
Jordan Cooney
I love that. And this is such a great example of how to kind of organize the ways of approaching innovation in your workforce or in your teams. Do we spend too much time looking at the things that we can't do? Do we spend way too much energy trying to solve those areas? Because we simply are, you know, incapable of turning around, you know, an instantaneous set of feedback on a workshop session. That used to be something that was in a, you know, week after email summary that came out, but now you can provide live right there in person. That is an amazing, you know, transformational way of learning and growing. When you're paying for a workshop or a session like that. It clearly is a great example of a can't becoming a reality. But my question for you is like, as organizations and as leaders, do we spend too much time and energy trying to solve those things when we should be looking at the shouldn't or won't areas that we want to solve in organizations?
- [46:07]
Steve Wonker
I actually think it's the reverse, Jordan. I think people don't spend enough time thinking about what can't we do today. So let's take market segmentation, right? So a lot of, well, too many organizations don't have a real segmentation. They have a product segmentation or a buying segmentation, which is not a real segmentation. Leave that aside. People get to a segmentation and typically you've got maybe four to five segments because beyond that it just blows everybody's circuits. You cannot remember all those segments. You can't act on it. If you look at a really AI infused marketplace, like digital advertising for instance, or even in a mobile game, a game you're playing on your phone, a sophisticated gaming company might have 50 or 100 different segments and they can do that because it's the machine that's then acting upon the segments. It knows what offers to send you, how hard to make things right, how much it should push you to engage or not, depending upon how you're playing and how you typically buy things or look at ads, whatever it might be. Now we have the ability with AI to move a lot more organizations into that world of the 50 or 100 different segments, but extremely few organizations are actually thinking about, about that. I think where they're focused is on what humans shouldn't be doing. Where are, what are the tedious and frustrating things that people are doing today? Now look, by all means we want to get rid of those, right? We can use AI to get rid of those. So I'm not saying that's bad, but if that's all you're focused on, you're thinking about making the world as it is a little bit more efficient. You're not thinking about really transforming formative types of change.
- [48:06]
Jordan Cooney
No question that is true if all your energy is given. There one interesting kind of follow up to that. And just in your experience within this model, how are general leaders thinking about the outcomes of these areas when these three questions are discussed? You know, if, if this example of, of something that we shouldn't be doing, you know, taking these notes and capturing them and then kind of synthesizing them, that this is a task that's routine for a doctor that should just be managed fully through AI as an example, like how are teams measuring that this was a good use of implementation, that this was an impactful thing to our organization, that this actually save time, save energy. There's always critics and I'm sure that even in a hospital with your example, there's going to be the 10 doctors who say my human written notes are simply better than the robots. And so how do organizational leaders entrust that these areas of shouldn't or won't do are useful outcomes and a measurably different impact to the organization?
- [49:28]
Steve Wonker
It's a great question, Jordan, because I think in most cases they're not. And that's an issue. Now in the case of AI scribes, we worked with an organization where they actually, they did that. They measured the quality of doctor patient interactions by things like how much does a patient remember what a physician told them. So there was a very clear pre and post. What was the rate of physician job satisfaction? How did that change? So there were very clear metrics that indicated that they should go proceed. But what you want to do is have the business objective defined and then the pre and post measures. So let's go back to our marketing department. There was a market department we worked with recently that wanted to have its cycle time for creating marketing campaigns, clear business objective, so then it could disaggregate what was creating the friction today, what was creating the, the long cycle time that it had and then look in a pretty granular way at whether AI was decreasing that or not. Now by the way, AI doesn't uniformly create efficiency, right? So it Might increase work in some areas, but they could also then look at the net of that. Right? Yeah. Okay. There might be a couple people who lose out over here and we've got to make sure they're resourced appropriately to deal with the increased work. But there were many other people who had decreased work. So you have to look at the whole system of change.
- [50:57]
Jordan Cooney
I love this. This is, this is a great moment to transition into our lightning round. Steve. So I'm going to ask you five questions from, from, from our episodes here, and I think we'll probably be ending on one of the questions around measuring how this impacts organizations. But to start off, what's the biggest mistake leaders make when rolling out AI to their knowledge teams?
- [51:24]
Steve Wonker
They think about AI as technology and not changing the system of work. Put tech at the table, but look at it as the whole system. You need to change.
- [51:36]
Jordan Cooney
Absolutely. And on that system, just give us a little bit more clarity as to what those areas of focus should be in terms of team structure, organizational structure, measurement, for example.
- [51:48]
Steve Wonker
Right. So get people together, cross functionally and understand how are your business objectives being suboptimal now without AI and how might AI improve that? Whether it's customization of an offering or the cycle time you take in making a proposal to a customer, for instance, how is AI changing that? That then gets you more right side up, thinking about what your objective is, not the nuts and bolts of technology and how that might be achieved.
- [52:19]
Jordan Cooney
Love it. What's one sign an organization isn't actually ready for AI change, no matter how many tools or people or resources they have?
- [52:33]
Steve Wonker
So usually to get the most out of AI, you need to have some decent data and you need to have governance of how AI tools are being used. If you don't have good data, you don't have good governance guardrails or systems, then you probably want to attend to some of that first. There might be a few things you can do with AI, but it's certainly not ready to get the full potential until you have those, those predicates in place. Place.
- [52:59]
Jordan Cooney
Yeah, Absolutely love that. Especially the piece around governance. I think that's one piece that a lot of teams overlook. Where do marketing teams underestimate the human side of AI transformation?
- [53:15]
Steve Wonker
They need to think not just about the adoption of the tool, but what then happens to the flow of work, to how people interrelate, to how people might meddle with each other, given that they have more visibility about each other's data, to how decision rights get changed, to the cycle time of decisions, to Write the just the process of it. You need to have a weekly or a monthly steering committee if people can have real time visibility into what is happening at various people's workflows. So you need to think big before you then zero in on how to particular tools get used.
- [53:54]
Jordan Cooney
Yeah, absolutely. What's one belief about productivity that AI is quickly breaking or changing?
- [54:04]
Steve Wonker
I think AI is breaking the belief that it actually created initially that it was going to wipe out a ton of jobs. Yeah, there are some jobs that are going to be affected, just like the word processor effect typist jobs. But look, I've got a word processing capability and I'm still gainfully employed. So I think we need to think about what we really want as companies. It isn't necessarily just to chop heads, it's to be more productive, it's to get higher revenues. So let's think strategically about it and not narrowly in terms of how can we get rid of 50% of jobs. Even the companies that have made those big announcements are often quietly walking those back. Yep.
- [54:50]
Jordan Cooney
In three years, what will companies regret not changing sooner?
- [54:55]
Steve Wonker
Oh, that's a good one. I think they will regret treating AI as something that the IT department needs to roll out. This is fundamentally different than a lot of IT tools that have been rolled out previously. It's a very different cycle of development and usage and evaluation and upgrading throughout the organization. It is not just an IT change, it's a business process change. And oftentimes you're not just investing in it, you're investing in capabilities, you're investing in a transition of the way people work. So they need to get HR at the table for, for instance, and oftentimes it's not. They need to really be thinking about the big picture and not just by the way, in terms of how they operate, but who are they in the marketplace. AI can transform what your value proposition is. It can transform your channels, how you go to market. Many companies have not yet been taking those steps and they really need to.
- [56:01]
Jordan Cooney
That message of team empowerment is this great one for us to wrap up this episode of the Voices of Search podcast. Huge thank you to Steve Wonker, Managing Director at New Markets Advisors, for joining us. If you'd like to contact Steve, you can find a link to his LinkedIn profile in our show notes or on the voicesofsearch.com you can also visit his company website, newmarkets advisors.com if you haven't subscribed yet and would like a daily stream of SEO, content marketing knowledge or podcast feed, hit the subscribe button in your podcast app or on YouTube, and we'll be back in your feed every week. Okay, that's all for today, but until next time, remember, the answers are always in the data.