The Rise of AI: Voices from the Frontlines

0:00:06.1 Producer: In late 2022, something remarkable happened that transformed our world overnight. ChatGPT burst onto the scene and suddenly AI became tangible, accessible, and powerful for everyone. In this special compilation episode, we're bringing together the most insightful conversations from our latest season, exploring the rise of AI. You'll hear from thought leaders like Fortune's AI editor Jeremy Kahn on rebuilding the middle class, marketing executive Michael Olaye on creative acceleration, and Tom Davenport on what real AI transformation looks like. Plus insights from numerous other AI experts including CDOs, academics, and tech entrepreneurs who are shaping our AI future today. We'll explore practical AI use cases, examine how data leaders can leverage these technologies, and discuss how the CDO role is evolving in response to AI's momentum. Whether you're a data professional, business leader, or simply curious about AI's impact, this episode offers perspectives from those on the front lines of the AI revolution. Let's dive in to how AI is transforming business and data leadership in ways we're just beginning to understand. 

AI as copilot

0:01:18.5 Jeremy Kahn: I think this is going to be a tremendously transformative technology and I think there's some really big positive effects. Particularly, I think we are going to see a huge uplift in labor productivity and I don't think we're going to see sort of mass joblessness from this technology. I think actually this is a technology that could enable people to sort of be lifted back up into the middle class. And one of the ways that would work is if you're able to create a whole series of AI co-pilots for knowledge work that are designed to help professionals complete certain professional tasks, things that they would do in their job, you know, co-pilot for lawyers, a co-pilot for accountants, a co-pilot for people who work in banking, for financial advisors, I think what you're going to see is that there are people who have less training and less experience and may not have the academic credentialing that's currently necessary to work in these professions be able to enter these professions with the help of an AI co-pilot. And the example I like to use here is sort of with accounting, where currently we do not have enough trained accountants in the U.S. There's a huge crisis, particularly for public company accountants, and there are states that are considering lowering kind of certification standards to deal with this already, and there's just a huge lack of people to go into the profession. 0:02:30.5 Jeremy Kahn: But I think what you can do is potentially take people who have a two-year associate's degree in bookkeeping and with an AI co-pilot potentially upskill them to the point where they could take on some of the public company accounting work that needs to be done. And I think it's true across most of the professions and a lot of knowledge work that the issue is not that we have too many people. The issue is that we simply do not have enough people actually in these professions. And at the same time, we have a lot of people who were squeezed out of the middle class in past decades who used to have good jobs sort of in manufacturing or sort of lower level service sector jobs who've been pushed out and often have ended up in retail and hospitality work, which is less secure and tends to pay less well. But I think some of those people with the help of AI co-pilots can upskill and sort of climb their way back into the middle class, which I think would be a hugely positive effect. So that's like one of the hugely positive impacts. 0:03:19.8 Producer: AI agents raise an uncomfortable truth. Could AI replace humans? Not yet, according to Paul Yacoubian, CEO and co-founder of Copy.ai, a startup that supports marketing and go-to-market teams with AI-fueled chat. Paul argues that replacing sales development reps, or SDRs, with AI could create a shortage of experienced salespeople. 0:03:44.1 David Chao: All right, SDR. Can it be replaced by AI?

0:03:48.7 Paul Yacoubian: No, I think you need the SDR. 

0:03:50.6 David Chao: Why is that? 

0:03:51.3 Paul Yacoubian: Especially if this person is calling another person, I think there's an element of trust there. Now, for some companies, some use cases, once the automated version of that, like with voice, gets good enough, I think that role gets reassigned. I think the highest and best use of that role is talking to as many people in the market as possible, and that's a great training ground to becoming a great sales rep and really understanding the market and really understanding the messaging. I do worry a little bit, if you remove that role, what's the next generation of sellers? Like, what's the pipeline of that talent? 

AI: It’s still early days

0:04:30.4 Producer: Despite how powerful these models have become, our experts think that it's still early days for enterprise AI. In fact, most organizations that want to leverage AI simply don't have the right foundation in place. Wade Munsie and Ryan den Rooijen are two seasoned CDOs who have seen this fools rush in mentality play out time and time again. 0:04:51.3 Wade Munsie: The amount of performance or optimization people have got by using co-pilots to use your tools is great. Is it dangerous? Probably a little bit. People become a bit lazy sometimes. Lazy code is a real thing, and I had to have a lot of governance around co-pilots in my previous CIO role, but there is an upside to it for sure. I see value pockets everywhere, but I'm skeptical that we're trying to run a little too fast sometimes, and people are being sold the dream, but there's not a lot of AI running, or especially gen AI, running at scale in most organizations. In fact, very few from what I can tell. 0:05:33.1 Ryan den Rooijen: If you go to a lot of these organizations that are now talking about wanting to go agentic, you say, amazing, how are you doing the segmentation? How are you doing optimization? A lot of these folks haven't even started with the most basic tools in the toolkits, right? And it's kind of insane if you look at the amount of compute that's now required for some of these models. Like organizations that don't even have their customer data or product data or supply chain data or whatever, or even have, I don't know, standardized data definitions, right? They shouldn't be thinking about like hyper-sophisticated microservices making decisions based on all these different dynamic rule sets, because quite frankly, their datas don't even make sense.

AI use cases

0:06:13.8 Producer: Of course, this is not to say that very cool AI use cases haven't cropped up. Michael Olaye is a marketing executive who's worked with big name brands like Unilever and JP Morgan Chase. According to Michael, AI makes the creative work behind advertising much faster and easier. 0:06:30.7 Michael Olaye: But the half that I think is super exciting right now, which would help the second half later on, is building these internal work process tools, bringing efficiency, speeding up the creative thinking, speeding up strategy. And in our industry, in the marketing, technology, product industry, it's amazing because you can get from like initial concept to like ideation like super quickly. You can do stuff that would take weeks before and days. You can collaborate with people who have no technical knowledge on technical things. We have tools now that you can code by like verbally speaking natural language. We have tools that you can do design without having any design skills. So I think it's opened up a whole new side for agencies, consultancies, companies, but it's also opened a whole new side for a new economy of content creators. When you build anything with AI, having a human in that loop where we are today, having humans in that loop, checking that also, it's good. And I'm actually one of the people who truly believe AI is more of a companion than a replacer. 0:07:37.5 Producer: AI as a companion, not a replacer, was a recurring theme this season. Geraldine Wong, CDO of the digital Singaporean bank, GXS Bank, echoed this sentiment when she shared some of the AI use cases her team is exploring. 0:07:54.0 Geraldine Wong: Okay, so the general one that we have, like many others, is about chatbots. But how do we use the chatbot to first help internal customer service agents to do their job better, to retrieve information better so that they can answer the customers quicker, right? And this reduces the number of time and also the interactions that they have with the customers. I think the challenge there is actually about the updates of documents and products. Because as we are speaking, products are being released. And the nuances of each product details is also being updated frequently. So for customer support agents, it's very important for them to get the up-to-date information about the products. 0:08:30.6 Satyen Sangani: Are you using off-the-shelf models and supplementing them with RAG, or how are you developing these models? 0:08:34.6 Geraldine Wong: Yeah, precisely. That's what we are thinking. So that's the approach that we're taking as well. We're going to leverage off ensemble or hybrid of all these models that's out there, whichever is performing the best for this particular purpose. That's how we're using some of these models to do the chatbot. The second project is more from a fraud operations standpoint. In the banking industry, there's a lot of resource-intensive tasks that's happening in the back office, like operations when onboarding. There's a lot of tasks being to check on customer applications and on transaction frauds, for example. So we're trying to leverage AI, both traditional AI as well as generative AI, to shorten that process so that... My hope is to be able to move the first line of defense to the second line of defense. So more resources being placed on the second line, whereas the checker. And imagine the gen AI being the first line, which is what we call maker. And that would create higher value order work for the second level of people. 0:09:32.7 Producer: There's no end to AI use cases. Tom Davenport has worked closely with customers doing super cool things, like transforming photos of auto accidents into instant estimates for body work. 0:09:45.3 Tom Davenport: Yeah, and even eventually, i think inter-organizational, where you... As one company that I've worked with in the past, that is called CCC Intelligent Solutions, and they have created these ecosystems of property and casualty insurance companies and body shops and automobile parts vendors, so that when you crash your car, one, they can give you a quick estimate based on a photograph from your phone, but then they can orchestrate the whole process. And I said to them, you know, this agent thing is basically created for the kind of work you do. It'll be hard to sort of orchestrate all those inter-organizational ecosystems and the information flowing back and forth, and so I'm getting everybody to agree, but it would be really useful when it happens. 0:10:37.5 Producer: The big exciting takeaway. Most enterprises are sitting on heaps of unique internal data that's ideal fuel for innovative AI. Marketing visionary Michael Olaye has seen this firsthand. 0:10:50.5 Michael Olaye: Some clients do not know that they're sitting on gold. Like, they do not know that. They have tons of data that they've never done anything with, and then they kind of focus on the most simplistic things, media, SEO, social media content, website content. And then you come in and you're like, hey, we can help your customer service be more efficient by understanding how the data, how long it takes a call to go through. We can help you process products more better by understanding the transaction from seeing something online to going in store to buy it. It's looking at those data sets and seeing patterns or bringing them together to see journeys, that's kind of where the secret lies. 

How data leaders can leverage AI

0:11:26.2 Producer: So how can data leaders hop on the AI bandwagon? Raza Habib, co-founder and CEO of Humanloop, an LLM evaluation platform, reveals what success looks like. 0:11:37.6 Raza Habib: Deeply involve the subject matter experts, and they look at the data a lot. And so a concrete example would be, you know, one of our customers is a company called Filevine, and they're a contract lifecycle management thing for lawyers. And they have actual lawyers who are involved in writing the prompts, in looking at the outputs, in helping investigate them. And traditional software workflows don't have this very high degree of collaboration with a product manager or a domain expert during the development phase, right? They're there when they're speccing the project, they're defining the criteria, but they're not implementing it. They're not writing the code themselves. But with LLMs, they're actually involved often in prompt engineering, actually involved in creating the evaluations. And so that is something that's very hard to do with a traditional software workflow where you're writing your code in an IDE, and you're versioning things in Git, and you're doing evaluation via unit tests and integration tests. 0:12:25.8 Raza Habib: And so what Humanloop is, is it's an evaluation platform. There's one place where we're allowing you to score the models themselves, but also give you a place where the non-technical people can go in and tweak prompts, run evaluation reports, see the output, and collaborate with the engineers to improve the system. So it's a combination of the tools needed for evaluation and the tools needed to iterate on the prompts and other parts of an LLM application. 0:12:49.2 Producer: Analysts have echoed Raza's observations. Stewart Bond, VP at IDC, has coined a term to describe the need to keep humans involved in the creation of AI. 0:12:58.1 Stewart Bond: In fact, at IDC, we're seeing that people are going to be on the loop. They are no longer going to be in the loop. That's a huge distinction. And so people will be evaluating the outcomes of these models more so than being involved in making sure and providing the input to generate those outcomes. 0:13:26.4 Producer: That's a big shift. People moving from being in the loop to being on the loop, focusing more on outcomes than on inputs. But it also brings up an important question. How do we make sure that AI and data actually make a difference for the people doing the work? Ryan den Rooijen shares his perspective. 0:13:44.1 Ryan den Rooijen: And so I think, you know, the biggest kind of like data culture challenge is really how do we make ourselves relevant to the day-to-day of the employee, right? How do we make sure that if somebody is on oil rig or in a store or in a call center or on a trading floor or in a lab, right, they are going to do something different because of us. Because if they're not doing something different because of us, then honestly, like, we don't deserve to be here. 

Data leadership and systems thinking

0:14:07.6 Producer: The ability to see the forest and the trees is increasingly important for data leaders today. With his background in economics, Satyen points out that folks with skills in systems thinking are uniquely advantaged to wield data for success. 0:14:23.5 Satyen Sangani: And I think the data role has this one incredible advantage, which is that often data leaders are system thinkers. They're people who are trying to optimize the system, but they've been focused on the wrong system. What they're trying to optimize is the data stack within a company, what they should be optimizing is the system of the business. But people forget that. 0:14:42.1 Wade Munsie: Totally. I mean, I had this conversation with Brian only a few hours ago about systems thinking and about complex systems require people that can provide the connection between them. And that's the perfect role for a data leader. We talk in a currency which flows between systems, between complex systems. 0:15:03.1 Producer: That systems mindset is what sets great data leaders apart. But leadership isn't just about vision. It's also about being honest about the role itself. Wade shares a candid take. 0:15:14.3 Wade Munsie: When I first became a CDO, that was an amazing day. I loved it. And then I became a CDO at another company, even bigger, and it was, you know, after being a CDO at a couple of Fortune companies, you go, now what? You know, BAU, do I want to be worried about that BAU stuff? So that's when I took the leap into... To go to QuantumBlack and McKinsey. Well, does that necessarily tick the box in being satisfied? Some people. It wasn't for me. But then the CIO role came up and that was even different again. 

But now I've stepped back into data and in a new role now, I'm back to leading a data transformation. But I've been very honest with them saying, this doesn't mean you need a CDO. If I do my job right, you probably won't need a CDO at the end of this. And that's okay. And I think we've got to be really honest with ourselves about where we're going and going into a job, because I'm sure people will be listening to this and go, well, Wade's written all of that stuff out there about the future of a CDO, but now he's gone back into a CDO role. It's like, well, no, I haven't actually. I've gone into a data transformation role, which, if I do it right, will mean there is no need for a CDO. 0:16:28.0 Ryan den Rooijen: So in theory, the ingredients are there for you to have that much, much, much broader scope of impact. But it's something that people need to consciously acknowledge, to Wade's point, there needs to be maybe a bit of an awakening, and people have to reflect on, huh, do I see myself as a CDO or do I see myself as someone who's going to deliver that transformative impact using data? And those two things are absolutely not the same thing. 0:16:53.0 Producer: Todd James, former CDTO of Kroger's retail data science group, 8451, echoes this sentiment, saying data roles will move back to the CIO. 0:17:02.7 Todd James: I think we're going to get more and more generations of young leaders coming in that are already more equipped with an understanding of how to apply artificial intelligence. We'll see that become part of the business. So based on partially where I see the puck moving, the Wayne Gretzky, I skate to where the puck's going, I think what we're seeing with the education programs and how some of these people are building their careers would indicate that. 

And two, even what I've seen when you've got your teams that are closely embedded with the businesses, they open up career paths for those technical leaders into the business, which opens up the possibility to think differently about how you're structured. Now, what does that mean? I think data goes back where it was, and it's more of a foundational element. It sits within IT, probably underneath the CIO, and they're focused on governance, they're focused on data operations, and it's a good role, but it's a different kind of role. 0:17:55.6 Todd James: Now, is that tomorrow? No. But I think transitionally, that's where we're headed. I mean, if we're still 10 years out, we don't have companies with heads of marketing, heads of operation, heads of supply chain, heads of financial product that understand intimately how artificial intelligence and analytics can impact their business, I think those companies are gonna be in trouble. 

The future (and limits) of the CDO

0:18:17.1 Producer: We encountered a range of perspectives this season on how the CDO role is changing. While some leaders think the CDO is growing more vital, others like Tom Davenport argue the role is on its way out. 0:18:29.1 Tom Davenport: I have a bit of disagreement with my co-author of this article, I just already mentioned him, Randy Bean, who believes that chief data officer role is very critical. It's critical that it's a C-level role. It's critical that it's a business role rather than an IT-oriented role, and I just don't think that's very feasible. I've done another survey of senior tech people in organizations, and I was very surprised to learn that they think there are too many of these C-level tech roles, and that causes a lot of confusion in organizations. When you have a CIO or chief data officer, chief digital officer, we don't even have a unique acronym for digital and data officers, the chief technology officer, the chief analytics officer, blah, blah, blah. So I think a better future is if you have a very business-driven executive who gets along well with other senior people in the organization, then you can have one person who's likely to report to the CEO and oversee all of these things. 0:19:38.1 Tom Davenport: Many of the functions would continue as before, they just might not be called CDO or chief digital officer or whatever. And there are a number of those already happening, particularly in financial services. They manage all of those tech functions, and in many cases, even things like customer service or operations in addition, and they're very much a part of the senior management team. So I think that kind of senior spokesperson for all of these issues can really have a big impact on making the culture more data-driven. 0:20:15.1 Producer: IDC's Stewart Bond, however, thinks that the rise of AI has made the CDO even more important. 0:20:21.9 Stewart Bond: Well, certainly the role of the chief data officer is growing from what we've seen. And the number of chief data officers that exist out there in the world is growing. And we actually have a completely opposite perspective. Every year IDC publishes our futurescapes, and our futurescapes are essentially 10 predictions for different areas of the markets going forward into the following year. So these are our predictions in 2025 and beyond. But we actually believe that by 2028, 60% of chief data and analytics officers in the G2000 companies will rival the CIO in terms of influence on enterprise spending and technology. I'm not saying they're gonna have the same budget, but they're going to have the same influence. There is no AI without data. And there are very few organizations that have their data AI ready. And what the CDO can do, what the office of the CDO can do with the processes, the policies, the technology that it can bring to bear, can really help the organization get their data AI ready. 0:21:52.2 Producer: One key thing working against the CDO, lack of role clarity. Wendy Turner-Williams, former CDAO of Tableau, argues that executive functions like security are much easier to validate because risk talks. 0:22:06.8 Wendy Turner-Williams: The CISO's world is very, very clear, right? They're all about risk. And risk talks, right? Like you could be a CEO and not understand things like threat detection or things like how do you do a king ring process or king ceremony or all this other stuff. You don't need to know that. What you need to know is that you're not getting the SACS certification or you could be fined XYZ by a breach or that a breach will be on the news, right? That's very, very clear. The data role and the AI role is not that clear when you talk about the problem sets or the monetization opportunities or the impact on the company overall or how you enable like a strategy back to like we're not... CDOs and even CIOs, we're not very good marketers of ourselves, right? We get so... We don't communicate very clearly and many of us tend to ignore the actual business strategy and just focus on the day-to-day operations of the data and that's never going to get you any type of real focus or invite into the big table in regards to the strategic conversations. 0:23:25.7 Producer: That gap between data work and business value is one of the biggest challenges and it starts with how we define the role itself. Consultant Taylor Culver calls this a definition problem. 0:23:37.7 Taylor Culver: Yeah, I think that data leaders have a definition problem, which is kind of ironic and if you look at a finance professional, career path is pretty straightforward. Data professional, no way. So a CDO has a different challenge. So when they come in, they have to immediately go on the offense, which is this is the data strategy, this is my job, this is what we do, this is what we don't do, here's our metrics for success. If you don't like this, please fire me because it's going to be a giant waste of everyone's time if we continue down this path, right? And those are hard conversations executives need to have.

So I think that for data people, they love data, but for everyone else, it's a pretty obtuse, boring and complex topic, but the business needs it to do well, right? So it's kind of a wishy-washy engagement. The challenge is that a lot of data leaders try to teach business people how to think like data people, which I think is a huge misstep in our space, right? 0:24:32.5 Taylor Culver: Rather, I think it's more about how do you enable them to do things that are ultimately going to get them where they want to go without having to teach them every specific detail along the way. So a big thing a data leader can do because they can work horizontally across the business is align different functions to a common problem, right? By just kind of bringing clarity to, hey, these six people are talking about the same thing, really, right? So like I said, it's a lot of active listening and kind of bringing clarity and alignment to how people think about problems within the business. The data leader has to act like a CEO without the budget or resources or influence or power. You know, it's a very handicapped role. And the other thing is that data leaders generally have a technical apprenticeship, and then when they get to that director, head of, product manager, VP level, their job is no longer technical. It's 100% political. 0:25:24.5 Taylor Culver: And first off, a lot of data leaders are in technical fields because they don't want to work with people, right? And they're faced with an enormous challenge. Now, not every data person is afraid of this problem, and they're willing to go at it, but it's a big mindset shift. And you really need to drop the data at that point and really just focus on people, problems, and needs. And that's very, very difficult. 0:25:49.2 Producer: Luckily, Tom Davenport has seen what it takes to transform raw data into competitive advantage. It comes down to great leadership. 0:25:56.9 Tom Davenport: Sure. Yeah, I mean, it's not rocket science. It's having senior people who are interested in analytical decision-making, hiring people who can do the work, you know, the day-to-day work of analytics, both the data management and the data analysis. By the way, you know, all these things are true about AI as well. I wrote another book a few years ago called All In on AI, two years ago now. And it's basically the same set of ideas, only about AI. I think because many people are not oriented to analytics or AI, devoting a lot of attention to upskilling them and helping them use these tools more effectively in doing their jobs and ultimately having some sort of unique data that is proprietary to you that will really differentiate you because ultimately, you know, data is the fuel of analytics and AI, and if you don't have something distinctive, you're going to have the same models that everybody else has. 0:27:01.4 Producer: While we've explored AI's practical applications and leadership implications, there's another crucial dimension to consider, the ethics of data itself. Chris Wiggins, chief data scientist at The New York Times, reminds us that despite our quest for objectivity, human choices shape every aspect of data work. 0:27:19.8 Chris Wiggins: Yeah, the tension that I try to get students to wrestle with is the tension between claims of objectivity and the reality of subjective design choices. So, the nature of the subjective design choices will be very different from constructing and publishing a table to publishing unstructured text or just publishing all of Shakespeare's work and putting it on GitHub, say. There's not so much work into the structuring of the data, that's true, but there's other places with all sorts of subjective design choices that go into the creation of what will eventually become a product. So, people who do data, like people who work with data professionally, know that they're constantly making subjective design choices. In fact, arguably, that's why they have jobs, right? It's because these people are constantly making wise decisions about how to make sense of data. And yet, we have centuries of rhetoric in which, once something is reduced to a number, it has become objective, right? 0:28:11.3 Chris Wiggins: Because we conflate the objectivity of, let's say, 1 plus 1 equals 2 or other statements of logic, we conflate that objectivity with the objectivity of a narrative in which a number appears, or a product which relies on a data-empowered algorithm and because there's data empowering it, we somehow imbue it with claims of objectivity, forgetting that there's innumerable subjective design choices. And again, anyone who does data professionally knows that those subjective design choices are being made. Arguably, that's why we remain employed. You mentioned something about the politics of gathering data. So, yes. So, all those subjective design choices have politics, even before you start doing any mathematics, just when you've made the decision of what data to keep and what data to throw away. It's well known that these things have politics. And again, by politics, I don't mean ever relating to voting. I mean ever relating to power. When we choose different quantifications of things that we see in the world and we wish to turn into mathematics, those things themselves have politics behind them. 

Predictions for AI: What’s next?

0:29:15.4 Producer: As AI continues to evolve at breakneck speed, what does the future hold? Our experts offered fascinating predictions about where the technology is headed. Jeff Chou points to increasing specialization as the next frontier in AI development. 0:29:30.8 Jeff Chou: I think where the next world is... Where the world is going is like specialization. And obviously NVIDIA benefited from that massively, right? Like GPUs, et cetera. And so I see a lot of really exciting innovation in terms of like, for example, if LLMs continue to dominate, what does a chip that only does LLMs look like? I think that's where the next frontier in terms of like big hardware breakthroughs is specialization. 0:29:56.8 Producer: While many are focused on explosive growth and innovation, Tom Davenport suggests a different trajectory, one where the AI landscape may contract before it expands again. 0:30:08.4 Tom Davenport: My guess is that there will be a lot of consolidation. I mean, AI has driven... In particular generative AI, has driven a lot of that pro version over the last several years. And I think we're starting to realize that generative AI, while amazing and powerful and incredibly useful under the right circumstances, is not going to be able to drive our economy the way it seems to have gone over the past year or two. Like some of these analyses that say to justify the amount of money flowing into the AI hardware sector alone would require between $600 and $650 billion in savings on the part of the companies that use it. 0:30:57.4 Producer: Despite potential consolidation ahead, Sanjeev Mohan draws an important parallel to another technological revolution we've all lived through, the internet bubble and its aftermath. 0:31:08.3 Sanjeev Mohan: When the dot-com burst happened, people's attention moved away from the internet. And they're like, oh my God, that was such a bad dream. We've moved on to other interesting things. But the technology never stopped, innovation never stopped, and we came back with something that did my entire tax, entire US tax code. I mean, it was fascinating to see with the mobile, with social media, all the changes. Same thing's going to happen to AI. We may have detractors, we may criticize it, we may point its deficiencies like hallucination. It's going to get better. Essentially, the trajectory is even faster than anything in the past. So I think next year, when we are talking again, if we are getting ready to meet up at Gartner Data and Analytics Summit somewhere in the world, you and I will come with our agents. That's how fast it'll be next year. That's my prediction. 0:32:10.6 Producer: With all this excitement around AI's capabilities, the conversation inevitably turns to AGI, Artificial General Intelligence. But what exactly does that mean? Satyen asked Tom Davenport about how close we really are to machines that can think like humans. 0:32:27.5 Satyen Sangani: First of all, what does even AGI mean? Because a lot of people have different definitions for what this sort of fully automated intelligence is. And then the second is, how close do you see us being based on your survey of the actors that you talked to? 0:32:40.9 Tom Davenport: Yeah, well, I think it's quite ironic that we have all these conversations about when we'll achieve AGI when we haven't even defined human intelligence very well. So I think it probably plays into the hands of the vendors who want to claim, yeah, we're just about to hit AGI. With such a poor definition, it's relatively easy to say that some aspect of it has certainly been achieved. But I believe that we won't hit AGI with generative AI alone. I think we'll need something that is able to make sense of the world better than a predictive word model can do right now. I don't know exactly what that is. There's people with varying philosophies. It seems unlikely that we'll go back to logic as a driver of AGI, but something closer to human intelligence in which young children can make better sense of what's going on in the world than even the most capable generative AI model. 0:33:48.6 Producer: The jagged, uneven development of AI capabilities creates both excitement and confusion. Chris Wiggins explains why some AI functions surprise us while others disappoint. 0:34:00.1 Chris Wiggins: I'm not bullish on the singularity arriving anytime soon, but I am an observer that the thing that we call artificial intelligence is quite jagged in that there are parts... There are things that you can ask a computer to do where it does it much better than you expect. And then there are other things that you ask a computer to do and it does much worse than you expect. And that's been true for decades. Part of what leads this longstanding feeling of elation and despair about what computers can do and what computers can't do is this jaggedness, that there are some things that we don't think a computer can do and it does this and we're all either elated or terrified. And then there's still the other things that you think a computer should be able to do and it just does not do it. And that front is quite jagged and evolving all the time. 0:34:43.3 Satyen Sangani: Yeah, and if it can do this completely surprising thing, then of course there's a whole bunch of trivial things that it ought to be able to do. And so a lot of people underestimate those somewhat trivial tasks and therefore get to a place where they're like, of course we're going to be able to get to this end state faster. 0:34:56.3 Chris Wiggins: That is the jaggedness of artificial intelligence and an underappreciated aspect of the sentence you just said is surprise, which is that we're always constantly doing this gap analysis between what we think should be possible and what is possible. But it involves our own subjective sense about what should be possible. So the surprise is really a function of our own norms and what we expect. 

Career advice

0:35:16.8 Producer: For data professionals watching this AI revolution unfold, the big question becomes, how do I adapt? Raza Habib and Chris Wiggins offer practical advice for those looking to build skills in this emerging field. 0:35:30.6 Raza Habib: So I think the good news is that I don't think it's rocket science or that complicated, and I think people will be able to pick up the skills relatively quickly. The bad news is that I don't think that the playbook has been written fully yet or kind of the knowledge is all out there. I think it's early enough that a lot of companies and teams are still learning as they're going and figuring things out, and the workflow is to a certain extent still being invented. So I would say that the way to learn it right now is, I mean, one, get your hands dirty. So it's not hard to go and grab some models and build prototypes and build things yourselves. There's very active Discord forums. There's a lot of people in online open source communities who are building things and sharing knowledge. There's podcasts like this one. I run a podcast as well called High Agency that has exactly that purpose. The reason it exists is to try and help people learn these skills. 0:36:20.7 Raza Habib: But I think the reality is that right now we're still at the stage where you learn by doing. Go do it. Get involved in online communities. Go to meetups. Build things. Listen to the content that's being created by others who are doing it. But yeah, I don't think there's a textbook you can go buy. There's a couple of courses. I think people are starting to produce stuff, but it's very early. 0:36:38.9 Chris Wiggins: I mean, one piece of advice to any student is simply to keep a close eye on where that jagged edge is between what computers can and cannot do. Another thing for anybody in the workforce is to see that disruptions often aren't from technology replacing people's work, but from people who run companies changing where they invest. So the threat that a computer can make a particular economic sector no longer economically viable is enough to make that economic sector no longer economically viable. If nobody's going to invest in that anymore, if nobody's going to go into that field, that can be self-fulfilling in a way that the technology itself did not drive, but rather the way that managers and investors responded to that threat can have an order one impact. 0:37:25.3 Producer: As we wrap up this season, a few clear themes emerge from our conversations with industry leaders. First, AI isn't just another technology trend. It's fundamentally changing how we work. With AI co-pilots potentially uplifting workers across countless professions. But success requires the right foundation, solid data infrastructure, clear business objectives, and thoughtful human oversight. Second, the role of data leadership is evolving rapidly. Whether the CDO position grows in importance or merges into other functions, what matters most is having leaders who can translate between technical possibilities and business realities. And finally, while we're all excited about AI's potential, maintaining a balanced perspective is crucial. As Chris Wiggins points out, AI development is jagged, surprisingly powerful in some areas while frustratingly limited in others. The challenge for all of us is to identify where AI truly adds value in our unique contexts. I want to thank all our incredible guests who shared their insights throughout this season. Their diverse perspectives help us navigate this rapidly evolving landscape with both enthusiasm and wisdom. Thanks for listening.