How AI Is Changing Strategy—and 4 Skills Leaders Need Next

Author: Harvard Business Review · Watch on YouTube

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Original language: English

Original video: How AI Is Changing Strategy—and 4 Skills Leaders Need Next · Channel: Harvard Business Review · 32:19 · Watch on YouTube

Topics: AI agents, Enterprise AI applications, Markets and economy, AI safety

Summary

Ethan Mollick argues that AI’s rapid progress and increasingly capable agents require leaders to rethink organizational goals, workflows, roles, and incentives—not merely use AI to boost productivity. He recommends an organization-wide approach combining leadership, a lab for ongoing experimentation, and broad employee access to frontier tools. Leaders should blend human and AI work deliberately, build capabilities such as deep and wide knowledge, taste, and agency, and stay adaptable as technology changes. AI brings major opportunities as well as risks, including cybersecurity threats, job disruption, and the danger of reducing people to checking AI outputs.

Key points

AI capabilities continue to improve, and agents can now carry out substantial work autonomously.
Productivity gains can be absorbed by existing processes; leaders need to reconsider KPIs and define better organizational outcomes.
Mollick’s recommended organizational model combines leadership, a dedicated lab, and broad employee access to AI tools—the “crowd.”
Leaders should experiment with AI themselves and accept that inaction also carries risk.
As AI takes on longer tasks, people increasingly need to specify, delegate, and manage work, rather than simply prompt and review each step.
Competitive advantage comes from designing processes that combine AI and human judgment at the right moments, rather than relying on full automation.
Four useful capabilities are deep knowledge, wide knowledge, taste, and agency—the willingness to act under uncertainty.
Organizations should explore future possibilities while using current tools, stop doing work AI has made trivial, and build ambitious new capabilities.
AI-enabled agents may intensify cybersecurity risks, so organizations should prepare for a more disruptive security environment.
Leaders should encourage beneficial AI use and mitigate harms, including job displacement, loss of meaningful work, and reduced learning.

Chapters

Start	Chapter	Summary
00:00	AI’s accelerating capabilities and the need for strategic change	Mollick says leaders should not assume their organizations are unable to change as AI continues to improve and agents become practical.
03:37	Leadership, lab, and crowd	He outlines an organizational approach built on active leadership, broad access to AI, and a team dedicated to experimentation and redesign.
05:13	From collaboration to delegation	As AI can handle longer tasks, people increasingly act as managers who assign work and shape teams of AI agents.
08:51	Human judgment and strategic decisions	Mollick describes how AI can make strategic decisions, while leaders choose which decisions to delegate and where human involvement matters.
13:32	Four capabilities leaders need	He identifies deep knowledge, wide knowledge, taste, and agency as useful capabilities in an AI-driven workplace.
16:04	Using AI for strategy and competition	Mollick encourages leaders to consult frontier models and argues that both large organizations and startups can benefit from AI.
19:44	AI risks and cybersecurity	He distinguishes risks from future or unguardrailed models from those of current guarded systems, while warning of growing cybersecurity challenges.
22:52	Strategic mistakes and organizational readiness	Companies should avoid basing plans only on today’s capabilities or outsourcing their understanding to vendors, and should build internal capacity.
27:40	Rethinking organizational goals and incentives	Mollick explains that AI agents make coordination easier, making it crucial for leaders to define better outputs and align incentives accordingly.
29:14	Employment, risks, and responsible leadership	The conversation closes on uncertain labor-market effects and the need for policies and leadership that encourage good uses while limiting harm.

Quotes

The idea that you can’t be flexible and compete seems to be overly baked in right now. — 00:00

So you need three things to succeed. You need the leadership team to actually lead in this. — 03:37

A conjurer is really about conjuring a team into existence when you need it. — 05:44

And then the fourth one is agency, the willingness to take action under uncertainty. — 14:32

You should be building at least one thing that is unbelievable and that would change the industry. — 24:56

Concepts and entities

Ethan Mollick, Alison Beard, Leadership, lab, and crowd, Conjurer work, Twilight factories, StrongDM, Anthropic, OpenAI, Claude Fable 5.1, GPT-6

Full transcript (original language)

00:00 ETHAN MOLLICK: One of the most depressing things I see when I talk to boards of very large companies and to C-level leaders is a lot of them have come to believe they just can’t change. So I show them what AI can do. And they’re like, well, I guess we’ll ride this out. I’m like, that’s an insane answer. You have 100,000 people working for you. The smartest people in the world are working for your company. You have customers and resources. The idea that you can’t be flexible and compete seems to be overly baked in right now. [MUSIC PLAYING]

00:30 ALISON BEARD: Ethan Mollick, professor at Wharton and author of Co-Existence, welcome to the IdeaCast. ETHAN MOLLICK: Thanks for having me. ALISON BEARD: Yeah. So you’ve been closely tracking advances in AI and constantly experimenting with all the new tools. What is different today than even a few months ago in terms of how new developments should guide the way that leaders are thinking about strategy in particular? ETHAN MOLLICK: One is the exponential curve continues. AI just keeps getting better. There isn’t really a sign of that stopping right now.

01:00 And the second is that agents are real, which our AI tools can go off and do work on their own autonomously and do real process work, real important things. And that has become a viable approach to work in the near past. ALISON BEARD: It seems like a lot of companies right now are thinking of AI as really a productivity exercise. You’re automating tasks. You’re saving time. You’re reducing costs. How does a genuinely strategic AI transformation strategy

01:32 differ from that? What do you think companies should be doing now? ETHAN MOLLICK: I think it’s actually pretty urgent to do a few things. One is viewing this purely as a productivity tool gets you in trouble in a lot of different ways. People are getting ROI from AI. They’re getting ROI from that productivity gain. Productivity gains are real. The problem is that those productivity gains get eaten up by the organization, which is if you don’t change any part of the process, AI is mostly a single-player entity, and you end up with more of everything. So if you don’t change your KPIs, people can produce more PowerPoint. They produce more code. That’s not actually what you want. So leadership needs to decide what this thing is.

02:04 And the second is AI is getting very capable. And I worry that if our default is automation, we end up in a situation where we just keep handing tasks to AI and don’t think hard enough about where humans fall into this process. And that leads to a lot of danger as well. ALISON BEARD: Is your argument that leaders need to move from asking where can we use AI in this existing business alongside humans to a question that’s more like, how does AI change our business going forward?

02:34 ETHAN MOLLICK: At this point, we have ample evidence that AI can regularly do the kind of work that would take a human 2 to 18 weeks in a reasonable amount of time, a reasonable amount of money, with high success rates. That implies a lot of different things. It implies that what your organization does has to change. How you think about the boundaries between jobs is changing. I talk to C-level leaders all the time. And one of the first effects they see of AI, and we saw this also in our research, is that jobs start to blur. Coders do design work. Designers do coding. Product managers do everything. How do you start thinking about what the boundaries of your organization are?

03:04 What’s the product you’re trying to produce? Is it an interim product of a PowerPoint? What was the point of the PowerPoint in the first place? You start to run into really fundamental questions about what the goal of the organization is, how you accomplish that, what are your values for getting there. And they’re urgent. This isn’t theoretical, let’s sit down in an academic way. It is, your organization is already changing from the inside. What do you do about that? ALISON BEARD: So what are some best practices that you recommend to leaders who are grappling with these issues right now that need to start having those really urgent conversations, and not just have them, but make them progress

03:37 enough so that they can actually start making some real decisions? ETHAN MOLLICK: There’s some individual feedback. And there’s some organizational feedback. On the organization side, the model that I’ve seen works best is sort of like a Meta model, which is leadership, lab, and crowd. So you need three things to succeed. You need the leadership team to actually lead in this. That’s not just say, use AI. They have to make decisions about how AI is used, what the organization does with AI. How do we incentivize people? The crowd means giving people access to these tools. We’re at a point now where giving organizations inferior AI access because of either– just because you haven’t bothered

04:08 to improve the most recent models leads you into trouble. You’d actually become very diluted about what AI can and can’t do. And then you need a lab. You need people doing 24/7 work who are not primarily technical, by the way. There’ll be technical people there, but also organizational people who are thinking about, how do we restructure the organization around AI? They’re benchmarking what systems could do, building systems of the future, taking ideas from the crowd and ideas from leadership, and helping build them. So you need to be doing this as a whole organization response. At the individual level, the factor that I’m seeing that differentiates leadership that succeeds at AI use in organizations that succeed

04:40 and those that don’t is leaders who are willing to eat risk, who are willing to say, look, nobody knows what’s going to happen here. But unless we do active experimentation, we’re not going to know anything. And if you just let everybody in the organization have vetoes over AI use because it might be risky– it is. But not to do anything is risky, too. And that also means actively using it yourself. So leaders who don’t use it– the latest AI systems, who wait for reports from other people, who are waiting for training some week fall behind those who just are experimenting. ALISON BEARD: So I think the last time that you and I talked about this, you had this model for human-AI collaboration

05:13 that was either a centaur– you’re half using your human skills and then half using AI– or a cyborg, where you’re going back and forth between the two. Talk about how that is evolving to one that you now describe as conjurer work that you think leaders should not only be doing themselves as individuals, but also enabling in their organizations as part of their strategy. ETHAN MOLLICK: The biggest change we really look at the sweep of the last year was that in every independent study we have, basically, AI went from regularly being able

05:44 to do a half-hour of work, 45 minutes of work, to you can get 16-plus hours of work out of a single AI instruction with good chance of success. And that means you don’t walk next to the AI anymore. You don’t work back and forth with it in the same way you did before. You are actually assigning the AI to tasks almost like you’re a team you’re assigning. And it does the work. So a conjurer is really about conjuring a team into existence when you need it. It’s almost like writing an RFP for a project you need done, or SOP or any of the other standard TLA/three-letter acronyms that we use in business to assign work.

06:15 And you send that out. And the AI just does that work. That’s a very different situation than I prompt the AI, look at the results. I prompt it again. This is a very different, action-oriented field. ALISON BEARD: How does that change how leaders approach the structure of the organization, what it even means to be a manager? ETHAN MOLLICK: So everyone’s a manager, to some degree, right now. Everyone has AI to manage. And AI skills look like management skills. In fact, there’s a great study out of Anthropic that shows that the people who are the most successful coders

06:47 at the first pass of coding are actually managers, not coders, because they do a good job of specifying what they want. And the AI executes on that. So that is the big problem. Your organization– suddenly, capacity increases. And I think organizations that don’t have imagination are going to retreat to automating away the work. And automation is a well-worn playbook in the business world. I think it’s a recipe for disaster. It’s a disaster for all of us. If all of our work becomes automated or we just become verifiers of AI content, that’s not a good situation to be in. But also, it removes all the differentiation.

07:18 Leaders need to be building companies that blend AI and human work together with process and with culture in ways that accomplish things that neither could do alone. If you just move to automation, you’re going to have the same Claude-based company everyone else does. ALISON BEARD: Well, that’s an interesting point. So when we’re talking about strategy, we’re also talking about finding competitive advantage. So if every company has access to the same frontier models, where exactly can leaders differentiate their organizations from competitors?

07:48 ETHAN MOLLICK: That’s where the key comes in. You are probably not using AI enough inside your organization and not trusting enough. But you then want to bring people back in. We’re already seeing– the most extreme version of this in terms of organizational change are things like software dark factory. So dark factory, factory– all the work is done by robots. You don’t need humans in it. You don’t need any lights. Software dark factories– I know one company, StrongDM, that has been shipping product to customers. And there’s no human involved in that. Their rules are no human could look at code. No human could write code. It is 100% AI-automated. I think that’s a dangerous approach because you’re just

08:20 letting the code do the work. I think we need to think about building twilight factories, situations where the AI proactively reaches out to people and brings them in because they have variants and views because their process or expertise matters because their sign-off and approval’s needed or because they find the work interesting. And I think we need to start restructuring work around the idea that these systems really can accomplish many things, and will be able to accomplish more in the near future. So it’s integrating it with humans at the right places at the right time and process that’s going to give you a differentiator. ALISON BEARD: Where are the specific ways in which you think

08:51 that, as fast as AI is evolving, humans still have a role, and particularly leaders still have a role? ETHAN MOLLICK: One thing is you can’t really be in the loop in the same way you were. It was easy to be in the loop when you were– when it was a chatbot. It’s not easy when– I literally have projects where I have a swarm of 100 agents solving a problem for me. I have them report out to me like I’m a manager. So I am in the loop to the extent that they give me a PowerPoint about their progress and they show me demos of what they’re building. But that’s very different than I’m approving every action.

09:23 And so leaders, actually– one of the outputs of AI is leaders are super empowered. I keep seeing organizations where leadership basically takes back control over their company because they can now directly ask the AI for the information they need. I have talked to people who are C-level leaders and who’ve actually diagnosed problems with plants that no one brought up to them because they could just literally query all of the information at their company’s disposal and ask questions and have the AI go off and do tasks for them. So it’s a really interesting change in the span of control if you set this up right, which lets leadership actually

09:54 think more about things rather than hearing things through three or four different layers of leadership. ALISON BEARD: So would you say then that AI agents are making strategic decisions on behalf of leaders and organizations with the right context given to them? ETHAN MOLLICK: They already are. If you have an AI agent that is doing any long-running work– Epoch, which is an AI benchmarking company– it was reporting that they could get regularly between 2 and 18 weeks of engineers’ work

10:25 out of a single prompt– Claude– for a couple hundred dollars. That involves tons of strategic decisions and judgment calls and creativity calls. They may not be your judgment or creativity or– but we can’t say anymore the AI isn’t making strategic decisions. It is. The question for you is, where do you want to make decisions, and where not? And just like any team, you can give those instructions. You can say, listen, I want you to come with the five best options, your defenses for each of them, surface those, pressure test them for me, and then deliver so I can pick between them, and this is the format I want them in,

10:55 or you could say, just go ahead and execute. You’re hooked up to code. Build a prototype for it and test it with a few people. Those are now options you have available to you. So some of this is reconstructing what we’ve already done with human. So what’s the process by using humans and AI together that I get a working idea that’s much better than ideas before? How does that get done much faster and better? ALISON BEARD: Where exactly, though, does a leader know where they add the most value and where they don’t? What are the points at which they should be involved

11:27 absolutely, every time? ETHAN MOLLICK: I think part of this is you have to make decisions now about what you’re– how to best accomplish what your company wants to accomplish. And you, obviously, don’t want to give this all up. You don’t want to give it up because the AI is not as smart as you and what you’re best at, because the AI doesn’t have enough variation, because it doesn’t actually absorb your culture in a deep way. Now, you can give some of that as context. But that’s part of process design. We do this all the time. What are the limits of your product engineering group versus your marketing group versus your legal team? We have to think about the same kind of process design here, which is, where are those limits and limitations?

11:59 I think the danger is that by skipping doing that, you either move to an all-automation world, where all those choices are made by AI, or you underestimate what these systems can actually do. So it is actually disciplined decision-making that gets you there. ALISON BEARD: And what are the things, the aspects of strategic leadership, that you don’t think can ever be delegated or outsourced? ETHAN MOLLICK: So I’m always careful about bright lines, where we say nothing. And it’s something– can be never done. ALISON BEARD: Never, ever. ETHAN MOLLICK: But these systems are jagged. They’re good at a lot of stuff and bad at stuff you wouldn’t expect. And that means, by the way, there’s

12:30 different things that jobs do. Think about coding. You are hiring coders to be the best at writing elegant lines of code. Then AI could write the code. So then it became about systems engineering. Could you see the bigger picture? Now AI is increasingly doing some of the systems engineering work. In some ways, it’s very upsetting if you’re a coder because the nature of the job is changing in ways that are good or bad all the time. And what you learned as craft no longer is. I think we’re going to see the same thing about leadership. Right now, your– maybe your judgment calls are about the details of pricing strategy. The AI might be better at pricing than you.

13:01 You won’t till your lab tests it for you. I can’t give you the answer in your industry or not. You’ll only know by experimentation. And you don’t have to do that all alone. That’s where your lab and your crowd comes in. Your crowd is surfacing use cases and values of AI. You don’t have to do this all yourself. But I think a big danger of companies is they think someone solved this problem for them, someone’s figured this out. We’re all faking it. Nobody knows anything. The AI labs don’t know anything. The vendors are all making this up. This is a three-year-old technology. So you have to seize control, to some extent, of this yourself to figure out what you want to use it for. ALISON BEARD: Are there capabilities

13:32 that you suggest your students, your MBA students, your kids, aspiring leaders that you suggest they develop because you believe they’ll be increasingly important in an AI-dominated world? ETHAN MOLLICK: So I think there are four things that are very useful. They’re not the only things. One of those is deep knowledge, which is the ability to actually understand a field really well. So we’re on this podcast. And you know from the beginning of my question or not, before I even say a few sentences, whether this is going to be an interesting answer or not–

14:02 I hope this is one of them– and whether you need to redirect things or not. That comes from deep expertise. It’s hard to make that judgment call if you don’t have the expertise. Similarly, in your industry area, you can look at a spreadsheet and know whether something is wrong. You don’t have to read those 400 pages of AI content because you know right away because you’re an expert. It’s one of the reasons why junior people are struggling now. They don’t have that expertise. Second thing you need is wide knowledge. As people start to cross field boundaries, knowing a little bit about many things helps you give the AI better feedback and ask for the things you want. The third thing you want is taste.

14:32 You want to be able to pick among things. And that could be company taste, too. What strategy feels like the kind of strategy our company would do? Of these many designs the AI came up with, which is the right one, selecting among many options? And I think we can develop taste. And then the fourth one is agency, the willingness to take action under uncertainty. And I think that’s a little bit harder to teach. We try and do that. I’m an entrepreneurship professor. But I think giving people the leeway to engage in experimentation is important. ALISON BEARD: How are you encouraging the old guard of leaders who aren’t accustomed

15:03 to such uncertainty to become more comfortable with it? ETHAN MOLLICK: I think there’s a danger of people overstating the generational aspect of this technology. It certainly was true that digital natives were a thing. You didn’t grow up with internet. There’s a lot of weird stuff about the internet you don’t understand as an internet user. It doesn’t work the same way with AI because AI doesn’t have the same innate background, it’s a single-player setup at this point, and because it rewards expertise. In our early studies with some of my colleagues at Harvard, MIT, and University of Warwick, we found that BCG consultants with less experience

15:33 were worse at using AI. And there’s other studies that showed less expertise results in worse AI outcomes, including from Anthropic itself. And so I think one of the dangers you have is you’re like, oh, I need these– there’s some group of people who gets this. Nobody gets this. So as a senior leader, you actually have the best tools available because you have that expertise and judgment to make decisions about things. And you probably have uncertainty, too. If you’ve been around for 20 years or you’ve been through financial crisis, you’ve been through wars– this is going to be a time of a lot of change. That doesn’t mean that you’re inflexible because you’re

16:04 a senior leader. In fact, often, the best senior leaders who got where they are are good at deciding how to make changes. ALISON BEARD: Have you seen companies or leaders that you know really successfully use AI in strategy development itself? ETHAN MOLLICK: I’ve talked to C-level leaders at very large funds who’ve done this. I have spoken to senior leaders in large financial services organizations, certainly in software. Yes, there are people doing this all the time. And in fact, I would argue you’re probably making a mistake if you’re not getting a second opinion from the AI.

16:34 The models are very smart right now. It’s just if you’re getting medical advice, absolutely see your doctor. But every piece of evidence is– you should absolutely be feeding that into the AI system, too. I would do the same thing with legal advice. Listen to your lawyer first. But why not get a second opinion? Second opinions are cheap and– right now. And the best models– and again, I can’t emphasize enough you need to use one of the frontier models, which at the time of this recording, that means Claude Fable 5.1 and whatever it’ll be in the next couple of days and GPT-6. And maybe Google comes up with a– with a new model again.

17:04 But you need to use one of these frontier models you have to pay for. And you should be asking it questions. The only way you’re going to find out what this is good or bad at is by using it. So by all means, use it for everything during the day. Take its opinions. Decide where it’s dumb. That’s a useful set of information for you. ALISON BEARD: When it comes to strategy, does AI– do you believe it ultimately favors large companies with more resources and data and those experienced leaders that you were talking about, or does it allow much smaller companies to compete with capabilities

17:37 that they couldn’t access before? ETHAN MOLLICK: Both of those things. One of the most impressive things I see when I talk to boards of very large companies and to C-level leaders is a lot of them have come to believe they just can’t change. So I show them what AI can do. And they’re like, well, I guess we’ll ride this out. And I’m like, that’s an insane answer. You have 100,000 people working for you. The smartest people in the world are working for your company. You have customers and resources. The idea that you can’t be flexible and compete seems to be overly baked in right now. And I’m a business school professor. I understand we talk a lot about how difficult it is to survive disruption and how much inertia companies have.

18:10 But it’s also a very weird decision because you have all the resources, because who’s going to figure out how to use AI in your organization is going to be people in your company. Experience has returns. The startup doesn’t have any of the same barriers you do. But they don’t have the experience anymore. And that experience matters. It’s no longer a 20-something knows everything about this. The more experience you have, the more you know about– in AI in your field. And everyone’s using the same AI models. It’s not like the startup you’re talking is going to have a better model than Claude. So I worry a little bit that people are giving up. On the startup side, it’s never been a better time

18:40 to be a startup company. Startups are all about that you’re the top 0.1% at something. And everything else you’re terrible at. And you hope that doesn’t destroy your company. Now we have a tool that makes you good at everything you weren’t good at. Now, it makes that stuff all feel a little bit similar to each other. But that’s OK. I’m somebody who when I was in my first startup company, we had to do payroll. And I didn’t realize you could pay a payroll company a couple cents per payroll period to do taxes for you. So I would do taxes by hand in Excel for every person in the company. It took, like, eight hours every week before I realized a better way.

19:11 AI will just tell me I didn’t have to do that. It could take care of it for me. There’s so much that it could help solve. So it’s accelerating everybody. And we haven’t talked enough about the individuals in your company. They’re also accelerating. The question is, what do you do with that? ALISON BEARD: I have to ask about all the fearmongering now that’s going on about unleashing agents in your organization and having them create chaos or start doing whatever they want to without listening to what leaders want. So tell me your reaction to all the recent news

19:44 with OpenAI and Anthropic calling for maybe a slowdown in AI development. ETHAN MOLLICK: So I think we need to differentiate between future model capabilities, the unguardrailed version of models that are being tested now, and current capabilities. So I think there is enough smart people worried about future model development, and we’re still in this exponential curve, that if they’re worried about a slowdown, government action, or other action to slow– to pace the frontier and to figure out what the right thing to do is and give people time so that we are in the loop

20:15 as deciding what happens seems reasonable. What form that takes is up for grabs. How important that– how risky it is really is up for grabs. But there’s no reason not to have some– to have policy to prevent existential risk. I think we’re in for a cybersecurity environment that is apocalyptic in the near term. You’re going to have– now, it might be solvable. Defense could beat offense. But I think we’re in a– for a world where when we get unguardrailed models, cybersecurity is going to be a real issue. Within organizations using guardrailed models,

20:47 there just isn’t the same kind of risk. So I think we need to differentiate between those things. ALISON BEARD: But how should organizational leaders be thinking about that environment, which you said might become apocalyptic for rogue agents from outside thwarting their competitive strategy because they’re stealing information or causing disruption? ETHAN MOLLICK: I think that you should think about it not so much as your competitors unleashing things on you

21:18 to cause trouble. I think it’s going to be that you’re going to have to deal with security environments where agents for many, many reasons are going to be attempting to penetrate your security. I think that when I talk to CSIOs– OpenAI pulled 20% of their staff off of research and 20% of the resources to hardening their systems. I have talked to leaders of large financial institutions who are pulling 50%-plus of their staff and trying– everyone trying to figure out what to do. At the same time, defense has increased. The amount of patching you need to do

21:48 has increased, which also creates problems. So we don’t know how this goes through. But I think we’re going to have a very disruptive period where the cybersecurity environment is going to get intense. And that will look very crazy from the outside. I think that thinking about how you’re going to deal with that now is really important. I think you’re probably underestimating how difficult that situation is going to be based on the experts I talk to. I am not a cybersecurity expert. So I don’t think it’s so much attackers bothering you as much as every part of your system exposed to the internet is going to be a target for agents,

22:20 maybe accomplishing completely random, benign tasks. If I ask an agent swarm to price products, they might– it might decide to really get good pricing for Christmas because you wanted to buy a sweater. I really need to get inside of this wool mills systems because that’s the only way I can find out the pricing I need and relentlessly attack that wool mill. That wasn’t a malicious attack. That was a– I just need to find the price per wool that you have in the futures contract market. And I become obsessed with this. So it’s going to get very complicated. We’re dealing with alien technology, to some extent.

22:52 ALISON BEARD: What’s the biggest strategic mistake that you see companies making right now as they’re trying to figure out what AI means for their business? ETHAN MOLLICK: There’s two giant ones. One of them is fixating on the ability of AI right now. I have this list I have of what the hot thing is in AI. And every three months, it changes, and everyone goes all in. All of these things are because we’re anchoring on what the capabilities of AR are today. So the hot thing that has been recently is everyone’s spending too much on tokens. Well, guess what? Once you start thinking about multiplayer AI, where many people in your organization are using it,

23:23 token costs become more reasonable. All the AI companies are now reducing the cost of models and having models use dumber agents to do work that are cheaper. Everyone went all in on cost was the main issue. That may or may not be the concern a few months from now. I can’t tell you. It depends on your organization and the choices you’re making. So I think there’s a danger in fixating on what you have now and satisficing based on the systems and capabilities you have today and not realizing that we’re still in the exponential. That is by far the number one problem. The second problem is trusting that other people have got this nailed down.

23:53 Every vendor you talk is also making this up as we go along. Every consultant you talk to does not have 17 successful cases. And if they have successful cases, it’s for GPT-4, a model that’s obsolete and no one would use for anything anymore. And your phone’s model is more powerful than that. So you have to build the capacity to make your own solutions in-house. That doesn’t mean write all your code or anything else yourself. But it does mean building that capacity internally. ALISON BEARD: So let’s talk about that first problem or mistake that you see. How do you teach yourself to embrace

24:25 the idea of exponential advancement and be ready for where the puck is going? ETHAN MOLLICK: So to take an idea from Harvard– it’s from Mike Tushman and company– it was the idea that you would need ambidexterity. You need to be able to do two things at once. You need to be able to exploit what’s happening today, but realize you’re still exploring. That’s why the lab is so important. Without your own internal, flexible team that’s building impossible things for the future, you get stuck. So there’s a few questions I ask organizations whenever I talk to them. The first is, what have you stopped doing as a result of AI? AI has made something in your organization trivial.

24:56 What is the trivial thing that used to be really important, but is no longer important? You have to stop doing it. AI just does it. Second thing is, what are you building that’s impossible? You should be building at least one thing that is unbelievable and that would change the industry. If you’re not building that, you’re not being ambitious enough. Even more so, you should be building something that doesn’t work yet because you’re betting that exponential curve will keep growing and at some point, it will work. And then the fourth question is, do you have leadership, lab, and crowd in place? So I think if you have those four questions answered, you’re in much better shape for being ambidextrous. ALISON BEARD: Yeah. And how do you staff your entire organization

25:31 with the talent that’s able to execute on everything you’re talking about? ETHAN MOLLICK: It’s also a really hard question. Everything from psychological safety to how do we build the right structure– all of that now is– you’re cashing all that in. If you have a good structure and a good organization and a good culture and the right reward systems, people in your organization are discovering use cases. And they’re telling you about it. If you’re not, then everyone’s hiding all their uses. And nothing you can do can change that because they’re scared if they tell you, they’ll get fired or they won’t get respected or they won’t get rewarded. And why would they ever tell you about how they’re using these systems? So you need expertise in your organization

26:03 finding this and people willing to talk about that. And that comes from having a good culture, structure, reward system, openness, good management. Those are more urgent than ever. ALISON BEARD: You talked about the first questions that you ask leaders when you’re beginning to advise them on how they should be managing this. As you get down the line and they face challenges, what are the most common hurdles you see to prevent them from fully moving forward into this new world of AI-led strategy?

26:38 ETHAN MOLLICK: I think that your danger is in a lot of organizations making this a normal technology. There’s a desperate desire to make this like any other technology that we can swallow and will turn into a– will take a– turn into a project that has X dollars associated with it and as a– and it was six-month RFP process and then an eight-month discovery process. That is not going to work. You’re going to experiment. You’re going to have to fail. Nobody has answers to your question here. So if you’re trying to do this as a completely de-risked thing, you end up in a position where you end up

27:09 replicating other large technology projects. And that is going to be obsolete the moment you launch it. And you haven’t built the dynamic capability to change what you change. Making sure that you stay hungry for the future is the real problem here, and keeping up with that exponential. And I only have so much good advice. It’s a hard problem to solve. ALISON BEARD: Yeah, absolutely. So it’s not an AI adoption or even transformation plan. It’s really revolutionizing the way you think about what your business is designed to do? ETHAN MOLLICK: The one thing I’ve gotten most wrong about AI

27:40 in the last year is I thought agents would be harder to pull off than they are. I thought you had– to organize teams of agents, I thought we’d need to use all of our organizational design knowledge. And it turns out we don’t, that there are 100,000 agent swarms that are self-organizing. I didn’t expect that to happen. But it makes sense when you think about the reason we have organizations and they’re complicated is people have limited knowledge. People have limited– have different interests in mind. They have conflicts. They don’t have the same goal. AI agents share all those things. So suddenly, coordination is not as hard as we thought the problem was going to be. Coordination turns out to be an easier problem than we thought.

28:13 And as a result of all of that, suddenly, you can get things done with AI in a way you didn’t expect. So the problem becomes now in the leadership side, what do I want the organization to do? And if your KPIs and other material are all baked around the idea of human outputs, you don’t want 100 times more PowerPoint. You don’t want 100 times more code. You want different outputs that are better. So you need to sit back and think about what those outputs and what those goals are and be aiming to maximize those goals. Otherwise, you have internal things and you end up just producing 100 times more code, 100 times more PowerPoint.

28:44 If there’s one lesson I learned from my MBA program before I went– got my PhD– is that people do what they’re incentivized to do. And you need to think really hard about the incentives in your organization. So the incentives in your organization are minimize the impact of AI, use AI as productivity tool to do more of what my KPI tells me I should do. It’s not transformation. ALISON BEARD: Let’s zoom out and talk about a few bigger picture issues. First, obviously, there’s a fear that this will lead to massive unemployment. So what do you see as the future labor market

29:14 in this new AI-led world? ETHAN MOLLICK: That is the biggest question and debate among economists about this topic. And we don’t have easy answers. You can easily theorize that the junior market gets squeezed because junior employees don’t have the expertise. Why use them when you can use AI? And there’s some indications of that. But there’s also some indications there may be more hiring because now you can use coders to do things that– organizations didn’t have coders– now can write their own code. So you can start insourcing. And now everybody’s doing their own marketing firm.

29:45 So maybe you lose jobs at the large centralized firms. But inside the organization, jobs grow because there’s more demand. It’s really hard to know. You can imagine– and the AI labs, to be clear, are aiming for the jobpocalypse. They want AI to replace all human labor. That is their explicit goal. I think there’s reasons that, A, they’re– might be wrong about this, and B, we don’t want them to win. So we shouldn’t let that be the future that we get. But that is their goal. So I don’t know what the outcome is in the long term in terms of employment, per se, like what the impact on jobs

30:16 is going to be. And nobody does. So we’re watching that closely. And everything is very confused right now in terms of the indicators. We don’t really have an answer. ALISON BEARD: As a technology advances so quickly and we don’t even know what’s going to happen with it– as you said, you have to be developing your strategy dynamically. You have to be prepared for change. You have to experiment and fail. You have to be ready for exponential advancements. How do we get better at predicting

30:46 not just the opportunities, but also the really terrifying risks? ETHAN MOLLICK: Things are accelerating quite quickly. I think it is hard to balance those things out. I think that this is a general-purpose technology. It’s many different things at once. There are existential risks associated with it, potentially. There are certainly near-term risks. I worry a lot about, again, automation of jobs instead of augmentation of jobs. I worry about the people being taken out of all the interesting parts of their job

31:17 and their only job is verifying the results of AI content, which is– and be fired if it’s wrong. It seems like a terrible way to live. I worry about impacts on our information environment. At the same time, I’m incredibly– and I’m worried about, as an instructor, that people are cheating with AI, and then they don’t learn as much. At the same time, we know AI is also an incredibly good tutor. So it can transform teaching for the positive, just like it can for the negative. I think that it’s a great freeing aspect for people who are stuck that they can get more stuff done before. We certainly know it gives good medical advice,

31:48 legal advice, things like that. Still, consult your doctor and lawyer. I’m not trying to tell you not to do that. But I think that there is– it’s a mixed bag. So how do we react to that? We react with policy to encourage good uses and mitigate bad ones. We encourage it by being meaningful about how we’re using these systems and not doing them blindly. We do that by, as leaders, making choices about how these systems are used and working in concert with our team and our organizations to make sure that we’re not disempowering people en masse. And I think good leadership will start to show through in these kind of circumstances.

32:19 ALISON BEARD: Well, Ethan, thank you so much for wrestling with all these really tricky issues with us. I’ve learned a lot about AI from you over the years, and especially a lot from your new book. Thanks so much for being with me today. ETHAN MOLLICK: Thank you for having me. [MUSIC PLAYING]