In conversation with Tom Davenport and Ian Barkin

Welcome to the second December issue of Further's Own the Unknown? LinkedIn newsletter which means that we’ll offer a recap of our recent discussion with the coauthors of All Hands on Tech: The AI-Powered Citizen Revolution, Tom Davenport and Ian Barkin . Further’s Data Science Principal Keith McCormick interviewed Tom and Ian on December 10th. Twice monthly, we share what we’ve learned from these thought leaders. In January we’ll be introducing our next thought leader, Matthew Lungren MD MPH .

The contribution of All Hands on Tech to the conversation

Keith kicked off the conversation by asking Tom and Ian if they saw one of the major contributions of All Hands on Tech was that it brought topics like citizen data scientist and citizen automation together for the first time. Keith set up the question, by mentioning that citizen data scientist, as a term, has been around for quite a few years, and citizen automator is less common. While we didn’t discuss the implications until a bit later, these different types of citizens might require similar enablement and governance strategies.

“Is citizen data automation a common phrase?”

Ian:

No, not really, but it was our focus on being specific and crisp with our vocabulary that actually had us exploring a whole spectrum of citizenry throughout the course of our brainstorming and research. There were a lot of citizen types that we thought of, and then ended up in the cutting room floor to reduce complexity. But ultimately, the question was, okay, how would you use this technology? It's either to replace APIs and create better integrations between applications, which felt a lot like robotic process automation space, so we called that Automator, or automation. There's other areas of automation where people were building sort of more comprehensive applications of capability. So it was a tool that helped you with supply chain logistics and shipping of goods or onboarding of employees. That wasn't a glue between apps. That was as close to an app as you could get. In fact, they were apps. So that was where we left citizen developers. And then citizen data scientist, which is Tom's world.

Tom:

And in that space (data science), Keith, I mean, you're right that it was identified a long time ago, but I don't think it has really taken off even as much as citizen automation has, because historically, at least, in order to do it well, you had to have a pretty strong statistical background, and I think AI has changed that automated machine learning changed it before generative AI came along, and then generative AI has made it even easier. I mean, it still helps, obviously, to know something about how the statistics work behind a particular data science model that you're creating, but you don't really need to know it anymore. And if it's a non business critical or life critical kind of analysis you're doing, if it's something for marketing or something like that. Who cares if it's not perfect, most post marketing initiatives are not very well personalized or tailored anyway.

“Me” projects versus “We” Projects

Keith asked about a distinction that the authors have sometimes used, “Me” vs. “We”, to differentiate between projects that have been associated with less risk, and projects that while associated with greater complexity and risk also represent greater opportunities.

Ian:

In the early days of automation and even today, many initiatives start as what we’d call ‘me’ projects. These are individual efforts—projects that happen because someone cares about their work and sees a way to do it better. They might not ask for permission, and often they don’t wait for formal approval from IT or leadership. Instead, they use tools like low-code or no-code platforms, sometimes even buying software on their own credit card or within their department’s budget.

But here’s the thing: these ‘me’ projects are often the seeds of transformation. They let individuals solve problems within their domain, quickly and without much risk. These projects can build momentum, encouraging people to experiment, test ideas, and see results. That’s the first step in creating a culture of innovation. However, real organizational change happens when those projects scale—when you move from ‘me’ to ‘we.’

Tom:

Moving from ‘me’ to ‘we’ projects is where the complexity—and the opportunity—really lie. A ‘we’ project crosses boundaries. It involves collaboration across teams, departments, or even external partners. For example, you might have a finance team working with product, or sales coordinating with supply chain. These projects are inherently larger in scope and more challenging because they touch multiple systems and often deal with regulatory considerations.?

But they’re also where the big wins are. That’s where you’re solving enterprise-wide problems, not just individual pain points. The good news is that platforms like the Microsoft Power Platform, or other low-code tools, help bridge these gaps. They make it easier for teams to collaborate, share insights, and scale solutions. Still, you don’t get to those successful ‘we’ projects without encouraging the smaller, scrappier ‘me’ efforts first. Organizations need to create environments where individuals feel empowered to start small and then provide the structure to scale those ideas into something bigger.

Tom and Ian on Gen AI

Tom:

Generative AI is redefining the way we build and develop. Most Python programmers already rely on libraries and tools like GitHub Copilot to accelerate productivity. Soon, generative AI will handle everything from start to finish, better than a human could.

Right now, it’s the case that experienced developers benefit most because they can spot mistakes, but that won’t last. Generative tools will eventually make programming from scratch obsolete, making the process faster and more reliable.

Ian:

Generative AI enables group collaboration in ways we haven’t fully realized yet. Right now, you can prompt yourself for various things and share links, but collaborative discourse and design are just on the horizon. That’s going to redefine teamwork. Generative AI is so straightforward—it lets you use natural language to articulate your needs, acting like an Esperanto of sorts.

This closing of the chasm between technical and non-technical folks will boost the success rate of digital transformations, which often fail due to communication gaps. As those gaps close, we’re going to see a new era where ideas, no matter how technical, can move forward seamlessly.

Upcoming interviews and events

If you haven't done so, follow Further here on LinkedIn for a steady diet of news, events, and interview clips. Our 2025 events are right around the corner. For more about Matthew Lungren MD MPH , our January guest, you can check out his Linkedin Learning course about Medical AI. AI ethicist and author of Responsible AI, Olivia Gambelin has agreed to be our February guest.

Coming up soon in February, Keith, Nicolas Decavel-Bueff , Kristy Hollingshead , and our recent November guest, Donald Farmer , will all be at TDWI. You can check out the conference at https://tdwi.org/events/conferences/las-vegas/home.aspx.