AI in Insights: Why Org Design Can Block Success (Full Transcript)

[00:00:01] Speaker 1: Hi, friends, I'm Catherine Korostow, and thanks for joining me today. I'd like to spend a few minutes talking about something that, in my opinion, is not getting enough attention in the market research and insight space, and that is about what the blockers are to successful AI deployment. I know many of us have been investing our time and brainpower and sometimes money into AI deployment, and obviously that can mean different things to different people, whether you're starting small using AI in your team for personal productivity gains or doing something that's a little bit more about changing how you actually conduct research. Whatever the deployment is that you're doing, whether it's modest first steps or bigger transformative types of steps, obviously we're all putting a lot of effort into AI right now, but there are blockers. There are things that anytime you're deploying any kind of important strategic initiative that can block you, and AI has some very specific ones. But there's one blocker that, in my opinion, is not getting a lot of attention, and I want to make sure that we're all aware of it, so I'm going to raise the issue today. It's a very unsexy blocker, but definitely something that can be solved. But before I reveal what that is, I'd like to share a little bit of background, and that is that all of us have been having this opportunity in the last couple of years to learn about AI. I've been very fortunate because I get to talk to a lot of different people who are using AI on the client side for internal uses of AI and market research and insights teams and CX research teams and UX research teams and various other departments. I've also had the opportunity to talk to a lot of people who are at market research agencies and other research suppliers about how they're using AI. I've also had the opportunity to do some AI work myself, just testing a lot of tools, and then also last year I did envision and develop an AI training tool called IDI Rockstar Pro that uses conversational AI. So I've had the opportunity to get my hands a little dirty, as it were. And throughout all of this, I get to talk to a lot of really smart people who have been early adopters, real thought leaders about AI. And one of the people I've gotten to speak to is Benjamin. So Benjamin Desangault, and if you haven't met Benjamin before, I definitely recommend you look him up online, check out his LinkedIn. He's done other webinars and things that you can see some of his thinking about AI and market research. But he really is a great thought leader in this space and has a really well-informed practical view. Now the interesting thing is I initially met Benjamin because he's an attorney who specializes in market research. And so I was talking to him about legal and ethical issues in market research, and we ended up having this really exciting conversation about AI. So Benjamin is the legal counsel at Market Vision Research, and I know many of you know Market Vision Research. It's one of the largest, most well-known market research firms. And so he has a really interesting perspective because he's at a very large market research agency that does innovative work, but he also works with clients. He's aware of what's going on with the clients and client situations. So he has a really good perspective on both sides of the desk, as it were. So we were recently talking about AI and other related topics, and we ended up talking quite in depth about two things. One was how can research teams get early wins with AI? And the other one was how do research silos make market research and customer insights inefficient? We know that there are a lot of organizations, and here I'm really talking about client-side organizations where there's a market research and customer insights team, a CX team that's also doing research, a UX team that's also doing research. There may be a separate voice of the customer research program. There are sometimes in organizations five or six different groups that are basically doing customer insights work with different lenses. And so we had a conversation about what that means and how that can lead to inefficiencies and whether or not there's opportunities to improve that type of organizational structure. Now, I know these might seem like two really different topics, but they actually do intersect and they have something to do with what may block us from being able to deploy AI successfully in our teams. So please enjoy these next two short clips to introduce you to these topics, and then we'll talk about that blocker.

[00:04:43] Speaker 2: There is a lot of real value, but I'm a big believer in the value right now being more on the smaller scale. I think we as a, not even just us as an industry, but like us as a society have set this really high bar that feels like it is success means total transformation of your industry. There is anything else. And when people see AI tools, they tend to think of it even if not intentionally in that sort of vein. Oh, this isn't a radical transformation of the research process. Eh, I'm unimpressed. But if we can give everyone access to tools that save 20 minutes every morning and 20 minutes every afternoon, you have saved the month of December. How would you like to spend the month of December, right? If that's spread across the year, maybe that means no one has to work later than five ever, no matter how busy you are. That sort of stuff is radical.

[00:05:41] Speaker 3: Those are radical benefits.

[00:05:47] Speaker 2: I also think that part of it is, it's back to the nomenclature issue, where something that I very much like is the British sort of counterpart of Intellus is the British Healthcare Business Intelligence Association. Business intelligence is just all those research types. So to a certain extent, I think just calling it business intelligence or calling it competitive intelligence.

[00:06:20] Speaker 1: And just a new umbrella that has all of it.

[00:06:23] Speaker 2: Yeah.

[00:06:24] Speaker 1: I love that.

[00:06:25] Speaker 2: Because there's still a lot of people that are really sticking to their guns on it's marketing research. Oh God.

[00:06:32] Speaker 1: Yeah. Market versus marketing. I actually, that's one of my most watched videos on YouTube. And it's like, honestly, I think it's got something like 15,000 views now. I mean, it is crazy. I hope you enjoyed those two clips. I've really been enjoying my conversations with Benjamin and I really appreciate his candor and his directness about all of these topics. Now in my opinion, those two topics are not unrelated. And in fact, discussing them both at the same time in a conversation with Benjamin reminded me that in my many years of being in business and many years of seeing our research profession evolve and new strategic initiatives come about, that one of the things that is always a blocker that is often not planned for ahead of time is organizational structure. Yawn, I know. Not the sexiest topic in the world, but it is something that we know from both the market research industry in general, and just frankly, business strategy, MBA programs as well, that organizational structure can be a roadblock to any strategic initiative. And that absolutely applies to AI. Whenever you have any kind of new business initiative, it can have implications for the organizational structure. So for example, here I'm showing you, you have a new strategy, well, when you deploy a new strategy, whether it's about AI deployment or something else, there's typically going to be new KPIs, maybe you're going to have new standard operating procedures, probably new workflows. Wow, doesn't that apply to what we're all doing with AI these days? There might even be new employee feedback metrics, how you review and how you decide to promote employees can definitely be modified based on what the new strategy is, right? You're not going to reward and evaluate employees based on things you were doing five years ago. You're going to do it based on what you're currently doing and what the vision is for the company. So once we have that new strategy, whether it's AI or something else, there's going to be things that have to be done to turn the strategy into action. And a lot of this has to do with that change management of making sure, yes, we've documented what that new workflow needs to be. We understand what the implications are for our standard operating procedures, and this has all been thought through and approved. But once we even take those steps, then we have to take that moment to pause and reflect and say, okay, but what does this mean for the organizational structure? Because if you've got new KPIs, you've got new SOPs, you've got new ways of thinking about how employees are going to be evaluated for success, that does have implications for organizational structure. It may mean new team or department names. It may mean new definitions for what a team or department does. It might mean a change in who that team or department should be reporting to. Maybe a team that used to report to the CMO should be reporting to a different function now. There are a lot of different possibilities. Maybe you have to redefine what the skill requirements are for people who are on that team or in that organization. These are all aspects of organizational structure. And I know it's, again, not the sexiest thing when we're all having fun, looking at new AI-based tools, and it's really fun to look at the technology and test it and do experiments and pilots. But once we make the decisions about what we're going to do with AI, whether it's going to be the small wins that Benjamin was talking about, like maybe just starting with those personal productivity gains, knowing what we want to do, we have to take that pause and say, what's likely to deter our success? And if you haven't thought about organizational structure as a potential deterrent, hopefully this will give you a way to kind of think about this. And just to go back to a business school example, we've all read case studies, whether you went to business school or not, about what made different companies successful. One of the classic business school case studies is Kodak. At one point, Kodak had 80, 90% market share in the photography business. So they were the leader in cameras for decades. Then digital photography came around, right? Major new development, definitely disruptive, just like AI is disruptive for market research and insights today. What happened? Well, they had brilliant product developers and engineers. It wasn't that there was a shortage of great ideas there, but their organizational structure was very specific. It was extremely centralized and extremely hierarchical. And so even though they had the aspirations and they had the ideas, they were really slow to market and it cost them ultimately their position in the camera business. And so it's a great reminder that just because you have great ideas and great intent, if you don't have the organizational structure that's going to support it, it can derail it. In the case of Kodak, because their decision-making structure was so narrow, there was really just a few core people making all the big decisions because it was such a centralized organization that really hindered their ability to respond quickly and make fast decisions that would impact product development, which of course takes time. So it's a good reminder that yes, we do need to look at what we want to do with AI, but we also have to think about, okay, based on what we're going to do, what does it imply for our business processes, our workflows, our employees? Do we need to make a change to our organizational structure? So once we have our AI strategy, whether it's about starting small or whether you are looking to really get into AI quickly in terms of changing fundamentally how your organization plans or executes research or creates research deliverables, or really doing something transformative, maybe you want to really push the envelope in your team or companies planning to do things that really fundamentally change research methods. My experience here shows me that it's going to be really important in any of those scenarios to take a pause and think about, okay, this is great. We know what we want to do. We know how we want to do it, but is our current organizational structure going to be a roadblock? I hope you enjoyed this conversation. Again, I'm a real AI enthusiast. I've been really enjoying learning about it and getting involved with it, but I also want to have a practical lens on it and helping other organizations be successful in their deployments. If you enjoyed the conversation, please give us a like and a subscribe and feel free to add any questions or comments in the comments section. Thanks.