Relativity Representative (0:01)

Legal teams face more data and more scrutiny than ever. They need AI built for both. Relativity is the AI platform for legal work, delivering defensible AI that handles the tedious tasks so judgment stays where it belongs with you. Learn more@ relativity.com ideacast

Alison Beard (0:23)

how can your finance team fuel growth next year? Dive into Deloitte's Finance Trends 2026 report to gain insights from global finance leaders. Five top industries. Learn about their priorities, challenges and strategies to stay competitive. Visit deloitte.com US financetrends A new digital reading experience from Harvard Business Review is here. It's the HBR Interactive Issue. Swipe through pages, search each issue, and listen to articles with audio narration. The interactive issue is available now to all HBR Print Magazine subscribers not yet a subscriber to the HBR Print Magazine. Subscribe today@hbr.org interactiveissue. I'm alison beard, and this is the hbr ideacast. Harvard Business Review recently hosted the HBR Strategy Summit 2026, a day filled with expert advice and guidance from executives and academics alike, and we're sharing the highlights of the event in this special IdeaCast series. Today you'll hear a masterclass, an interactive lecture from HBS professor Siddal Neely about how organizations can drive successful AI transformation. You'll hear her explain the 30% rule, the minimum organizational change and baseline understanding of AI technology needed to drive real results. She'll walk through examples from Moderna, Domino's Pizza and Rakuten, and field audience questions facilitated by HBR Editor in Chief Amy Bernstein. Enjoy the episode.

Siddal Neely (2:19)

Hi everyone. I'm delighted to be here today to talk about why AI means radical change. And what I'd like to do in the time that we have together is talk about what it takes to adopt AI at the pace that makes sense for you, for your organization, and for your industry. AI is everywhere. People are talking about AI, AI, AI, and even agentic AI, agentic AI, agentic AI. Some of it is hype, Some of it is real. The job that we have in all of our organizations is to figure out how do we get beyond the hype and start moving at the pace that makes sense for us now. First of all, I'd like to talk about the 30% rule just to orient us on how we should think about AI and how much do we need to understand as individuals, as leaders, but also as entire workforces in order to make advancements in AI? The 30% rule is actually a proportionality that says that we all will need a minimum technology and change capability threshold in order to contribute to a future which has data, algorithms and AI as part of them. And the 30% rule says you don't need to be a programmer, you don't need to be a data scientist, you don't need any of those things, but you need data baseline understanding, like the 30% of the English language that most global employees have to master if English is not their native language. So one of the ways to get beyond the hype is to have some baseline understanding of what AI is and what AI is not. The reality is AI is not new. It's been around for a very, very long time. And in fact, flagship pioneering has captured for us four innovations or four waves of innovations in AI. The first one actually started in the 1950s. It's called the cybernetics era. In the cybernetic era, that's the period where scientists at Stanford, mit, in the military were trying to use biology or engineering, say, can we actually have machines behave in ways that have human elements? And can we actually have machines? These were very rule based, feedback based, behave a bit like machines. These are the early periods of robotics, actually. And then you go to the 1980s, the 1990s. This era is called the trained expert era. And this is where machines were attempting to replicate human decision making in specific domains like medicine, like engineering, and really relying on rule based programming and databases in order to simulate expertise in those fields. This was an effective period, not wide adoption, but we saw a big leap in AI innovation. Then you fast forward to the 2000s. This is a period where machine learning came to be. We started to be able to learn from the abundant data that were coming in and computing power were present and machines started to learn and to adapt. And this was kind of the early period of computer vision, visioning, natural language, processing, all of the things that got us to today, the 2000 and twenties, generative AI, with of course the release of ChatGPT in 2022, and increasingly agentic systems. This is where the new technology called the transformer enables us to create new content, text, language, video, audio, big wave that has dramatically shifted the pace of how AI has been developing. But AI is not new. That's really important to understand. Now I offer two definitions of AI that we need to think about. One of them doesn't exist yet. One of them is very much in our world. The one that exists is specific AI. General AI doesn't quite exist yet, but I will define it for you because computer scientists and philosopher of AI actually always talk about these two elements. The one that doesn't exist yet. General AI, you could think of this as the Terminator, where you have these systems that are human likes, they behave like humans, they act like humans, they make decisions like humans. Doesn't exist yet. Piece of me hopes they never do. What truly does exist is what's called specific AI or narrow AI. This is where AI performs specific tasks, much like large language models or facial recognition or voice recognition. Okay, now what do we know? Many of the Companies in the 2000s, like Meta, used to be Facebook, Apple, Amazon, Google, Netflix, they've been deploying specific AI at science scale in the last 15, 20 years. So we've actually been very much exposed to this specific AI at play in our world. So today, AI enables scale. We can serve millions and even billions of people very quickly. Speed in decision making, in operations and scope. We can do so many more things now that we have data and we can be creative at how we get to solutions. And I'll give us some examples about this in a little bit. A little bit of groundwork before I do is around what I just described in terms of scale, speed and scope. What are the ways in which these are operating? Well, There are the three Ps, predictions, pattern recognitions, that's the facial recognition example I gave you. And automation, a fourth one that's now taking hold more and more. The fourth P is production with agents, and I'll define that for us in a little bit. Now, there are three vectors of value that we need to think about with AI. And this is why we say AI means change and radical change. The first one is we need to make sure that we have products that people want to use with features and functionalities that make sense for our world today. The second vector of value is network value. We want as many people using our products and services, so we can not only expand those who have value from what we have to offer, but also in order to innovate. And that final vector of value is data. Data data, both internal to our organization and external data as well. And so a flywheel of AI. As you think about what this means for us and how we need to change for this, is that the more data we have that we can harness to serve our customers, our stakeholders, or even super serve them, the better the algorithms can become or the models that we use, the better the services so we know how to customize and personalize and the better the services. People use our services more and more. That leads to more data, more data, better algorithms, better services, more usage, more data. That's really the flywheel and this is where innovation truly comes through and often specific to our stakeholders, how we can super serve them. Now, the impact of AI has been quite divided. On the one hand, we know some are really leading the way with AI. We also know adoption has been a challenge and I'll talk about that as well. So the impact of AI inside of organization and there's so much data today to show that people who are using AI, particularly generative AI inside of organizations, are seeing a boost in their productivity. The data, if you look at the many of the studies that have been conducted at the Harvard Business School and way beyond in many other places, a one hour task with AI used to take up to three or four hours without AI. So it's really accelerating what people can do and how they can do them, including by using tools that typically used to be done manually. So if we think about sales and marketing, marketing is an area that's really been pressured and changing in the face of AI because of all the video and audio and all the images and all the content creation that's now much faster to do in finance or even fintech, the legal environment, hr, engineering, customer service. All of these areas, when done well, are really booming, boosting creativity and redefining the nature of competition. Which means if a firm is using AI and another is not, it becomes obvious on the external side. We have many examples, right? Some examples includes companies like Moderna early on. And this mindset shift that we need is captured in this quote by Stephane Bunsell, the CEO of Moderna. We're a technology company that happens to do biology. And what we know from Moderna early on, during the COVID days, they had only 800 employees. Pfizer had 100,000 employees. Both companies were critical in the production and the delivery of the COVID vaccine in 2021 or so. You see the differences in scale. Domino's, We're a technology company that happens to do pizza. Domino's has had quite the storied run for many years in terms of its performance because they've put technology and now AI at the heart of everything that they do. Rakuten is another example of a company. I happen to sit on the board of Rakuten and I can tell you Rakuten, when it announced its AI strategy, that's called aionization. Imagine the term. Not the easiest term to say aionization, but you can imagine what that means. And to put a much more specific point to this, the aionization strategy at Rakuten was to achieve triple 20 business growth and what that meant was 20% increase in marketing productivity, 20% increase in operating productivity, and 20% increase in client productivity or revenue. And this was a mandate for the entire organization to pursue and interpret in the right way, depending on the services that were the services that were functioning for each of the various businesses that Rakuten has. It has an ecosystem. And within months the results were staggering, particularly since this was an organization mandate plus the 30% rule for everyone. And here's some of the things that the company saw. One is 77% decrease in marketing cost in about four months for those who had mobile phones. Rakuten mobile phones we saw an increase of 50% in the e commerce side. And the adoption of AI was pretty massive, particularly because of this mandate and the 30% rule. Over 25,000 custom bots created internally within the company in a very decentralized way empower people, equip people, and then they begin to manage their workflows. Another 800 agents as well. Another example I can give you is what was implemented called AI semantic search. So if you need to make a purchase on any e commerce site of any kind, you typically say Amazon, you typically would go on the dialog box and you say, huh, maybe it's sneakers. I want sneakers, women's sneakers. Maybe I'll even add a size. Maybe I won't. Maybe I'll add a color. Or not. I hit enter and then I see what comes out. When you embed AI, specifically an AI semantic search like Rakuten did you put in I'm going to a music festival. It's a work event, it's a family event, it's a date, whatever it may be, it might be a rainy day. You know, my favorite color is red. What do you recommend? And the system would recommend a full outfit. This simple innovation has led to to increase in Gross merchandise sales 6.5% here. Gross merchandise sales. If your gross merchandise sales is in the billions, imagine what this number can do. So this is the kind of thing that leads us to say this is about change and this is about redefining the nature of competition.