Leading a Workforce Empowered by New AI Tools

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Welcome to the hberry ideacast from harvard business review. I'm alison beard. When any new technology comes into a workplace, you'll usually see the IT department and a few other early adopters experimenting with IT first. Eventually, though, as the tech becomes more user friendly, there's a tipping point where almost everyone could find a way to use it in their jobs. From the C suite to the back office. Right now, artificial intelligence and specifically generative AI is having that kind of moment. Once the purview of mathematicians, engineers and coders, AI tools can now be effectively designed and deployed by anyone who has some basic knowledge and training. But how do we as individuals figure.

Out how to do it most effectively?

How do managers both encourage and corral these efforts? And what strategies should organizations put in place to harness the power of what our guest today calls systems citizen development? Tom Davenport is a professor of information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and co author of the HBR article We're All Programmers now, as well as the book all in on how Smart Companies Win Big with Artificial Intelligence. Tom, welcome.

Hi Alison, happy to be here with you.

Could you start just by illustrating really.

Clearly this idea that everyone, me included, is or should be a programmer, a citizen developer. Now, how do you see that happening on the ground at organizations with people in non tech roles?

Well, you know, this is a trend that has been developing for a while. I mean, I think even my traditional area of analytics and big data and AI, we've seen these tools getting easier and easier to use and it's just point and click now for many interfaces and so on. So you didn't really need to have a lot of technical background. And then a couple maybe five years ago, you started to see these, quote, low code, no code tools emerging that would let you build small systems without really having a lot of technical background or needing to produce programming code. And then there were automation versions of that. The robotic process automation vendors introduced tools that were very easy to use and people could create their own kind of workflow automations. In citizen data science, we've seen great advances with automated machine learning tools that are quite accessible. And now generative AI really takes it to the ultimate level where if you can write an English sentence or whatever language about what you want, it can produce code or do a data analysis or do almost anything technical that you would like. And so I think the last barriers have been erased to non technical individuals being able to do almost anything that they would like to do with information technology.

So give me some specific examples of people who I wouldn't think of as being technologically focused in their roles putting these tools to work.

Well, one of my co authors on the HBR article, his name is Ian Barkin and I are working on a book on this topic. There are a number of people we found in large corporations who are starting to do this sort of work and having a big impact. So I haven't gotten permission to use his name, but there was a gentleman who was employed at Home Depot who had been doing a lot of spreadsheet work. He was in the area of forecasting and assessing consumer demand and looking at what that means for what kind of inventory they should have around the different Home Depot stores. And really a very time consuming job, basically lived on spreadsheets. And he discovered a tool, in this case it's from a vendor called Alteryx that lets you automate a lot of the data inputs and outputs and some of the analysis as well. And so he was able to produce a sales forecast in a few hours at most rather than the weeks that it took him previously. And Home Depot made a huge amount of money they cited well into the nine figures of extra income as a result. And he got employee of the year. Now as often happens, he's no longer doing that. He's now a consultant to help other people use these tools. So I suppose he's moved from being an amateur to a professional. But that's quite common. We've seen it at a number of other companies. Talking to somebody in LEGO recently who's done a similar kind of thing. I was talking recently to a fellow at BMW who I wasn't talking to him about citizen development, but turns out he heads that area as well. And he said, well, we're, we're training 80,000 people in Citizen development approaches, so it's really going viral.

Yeah, my worry when you were telling that story about the fellow at Home Depot is that he was putting himself out of a job. Now obviously he moved on to a much better one. But is that a danger associated with citizen development?

Well, there seems to be enough development around that anybody can do it and there's still plenty more to do. Almost every company these days wants to digitize and we have historically relied on IT departments to do that kind of work. But I think almost everybody has a story of asking for an application from IT and being told, I'm sorry, we don't have time for that, or sure, we'll, we'll get to IT within a year or two and nobody wants to wait that long. So I think this just radically accelerates the time frame in which we can get the technology capabilities that we, that we need. And there, there seems to be no limit to them.

Sue, is it your sense that sort of the average non tech employee is eager to jump in and learn how to do this, or do you think that most people need some incentivizing?

I think it depends. I mean, if they are sort of technical heat seekers, then they're probably anxious to do it. They also, I think typically aren't quite sure what this will mean for their career path in many cases. And you've typically either been a professional IT person or you've been a non IT business person and never the twain shall meet. But now I think there are more and more hybrid kinds of roles possible in many cases. I think you do need a little bit of instruction in how to do this well, but I think there's a large population that is at least open to it with some instruction on the organizational level.

Do you see interest in and applicability of citizen AI development differ across companies, industries, geography? You know, does it have to be a company that's already focused on using AI or not?

I think it's bubbling up pretty much all over the place. As I mentioned, you know, consumer products companies like Lego, PepsiCo is another one that I've talked to, not historically known for their AI activity. So I think it is bubbling up in places that have not historically been heavily AI oriented. And maybe one could argue that the places where it's least common, the industries where it's least common, are those that are very sort of transactional in nature with their IT systems and where there's a fair amount of regulation. So you don't see as much of it in banking. For example, one of my co authors on the HBR article was head of data science at a big European bank and he's since become a consultant himself. But he said, you know, there was some opposition and you know, there probably should be. You don't want your basic demand deposit accounting system that keeps track of how much money you have in the bank to be generated all over the company in different ways. But I think there are lots of possibilities in almost every industry. I did recently talk to a very prominent technology company and they've recently introduced a number of tools for doing this kind of were both on the data analysis side and on the automation side and also quite big into generative AI. And I think they're right on the edge of chaos. They're not over the edge yet, but thousands and thousands of different dashboards being created of small automations, small departmental level programs and they're doing their best to try to maintain some control over all of this. But it is certainly challenging.

Yeah, so let's get into some of those challenges or pain points. What are the big ones that arise when people start either doing this on their own or with encouragement from their managers?

Well, I think, you know, most companies are not that far along yet, but certainly you worry about someone developing an important application on which the organization comes to rely. And you know, maybe it's not well built, maybe some of its calculations are not accurate, maybe that person leaves and hasn't documented the system very well. I mean one of the good things about generative AI is it's also quite good at documenting systems. Not just that, creating code. So, so maybe we'll see some help there. But if we become highly reliant on systems that are really quite critical to the success of the business that I think requires some controls, it requires sort of registration by some central group, maybe it maybe a review of the functionality of the program to make sure it's up to standard and so on. And some companies have done that. And we talk in the article about PwC's efforts at Citizen development and they have a group of people who were amateurs for the most part technically who have been charged with taking them into the digital age and digital accelerators they call them and they're encouraged to submit all of the applications they develop to some central hub and in that situation they're evaluated and if they work well and they appear to be of value to people around PwC beyond, you know, their own little groups or selves, they will make them broadly available and they even you know, pay a little bit to the, to the person who contributes IT depending on, you know, how much usage it gets.

So that's the role that the IT department plays in this new world. You know, oversight, quality control, that type of thing.

That is certainly one important role. And I think broad minded IT departments can play some other roles as well. They can provide a lot of the necessary training if that is deemed appropriate. And really there's a kind of a community development aspect of this where you encourage, not only encourage people to use these kinds of tools but also to share what they've learned and to get together in regular meetings and hear updates about the technology or hear what great things other members of the community have done. So I don't know who else is going to do that in general if it's not IT or you know, if we're talking an automation or an analytics oriented group. They may not be officially in it, but they can still sponsor some of those community development kinds of activities.

Yeah. And I imagine for those groups who have over the past decade been flooded with demand from colleagues to help them do all of this work, this will unburden them in some way. If they can figure out how to.

Do it right, there is that appeal. On the other hand, I think many IT organizations have been resistant to this for quite a while. They don't think amateurs will be able to create high quality code. They are worried about a citizen developed application being dumped on them and say, fix this up, it has a problem or update it or something like that. They rather develop their own code. I think only slowly, in some cases with reluctance, is the IT organization moving toward this view of the world. In some cases we've talked to a couple of companies where they got a new chief information officer and he or she realized, you know, this is the wave of the future, the way things are going. And so they started to open the, open the floodgates a little bit. But after, you know, many years of resistance.

What role should team leaders be playing, you know, to ensure that all of these efforts are happening effectively?

Well, I think that's probably the area where you're most likely to see the, you know, the motivation coming from. If you know, that Home Depot forecaster hadn't had a boss who was tolerant of that sort of technical innovation, then it probably wouldn't have been successful. At Lego we talked to someone who's a demand forecaster and I hadn't really thought about this previously, but it appears, you know, demand forecasting involves, typically involves a lot of different types of information that you're trying to pull together from across multiple systems, internal and external data and so on. And so it can be very labor intensive to pull all that information together unless you automate the process. And so it worked quite well at Lego as well to do that. Initially, the IT department was not supportive, but the head of demand planning was supportive of it. And that kind of team leadership role, I think is arguably the most important one in making this happen, both in terms of providing the encouragement and also ensuring that people are following the necessary control processes.

And at the higher level, maybe C suite leaders, for example, how did they Think about maintaining control over all of this AI development, if control is the right word.

You know, I think there's. So if we're talking about generative AI, there's so many different things that an organization can do with it. I was just looking at a survey that I did with the MIT Chief Data Officer Group symposium, the second annual survey sponsored by Amazon Web Services and data just came in. We have strong focus on generative AI. I think that software engineering was the third most desired use case or most focused on use case in there. But in another question, 16% said they were banning there was no authorized use of generative AI in their companies. I think in general that's a bad idea, but you know, it at least shows that an organization is paying attention to to it. I think almost every organization today should have high level meetings about a are there some ways that we can use this in our organization? B, what sort of risks and concerns do we have about its use? C, what kinds of policies do we need to put in place to make it more effective? And that's whether, you know, you're talking about generating code or generating marketing blogs or using it in customer support. Any of those things I think require some deliberation about what's the organization strategy and what controls does it need to put in place.

Are there any examples you could point to from organizations that are finding a good balance between keeping things open and nimble, but then also protecting against some of the dangers we've talked about?

Funny. Yesterday I got an email from the chief data and technology officer of a large marketing services company. They identified five or six key areas in which generative AI could be used within the organization. They said, in general, our philosophy is to make this a kind of decentralized activity. So we don't want to apply too heavy a hand, but we want to sort of see what people are doing. So they looked at a whole variety of use cases and said, what's the status of this particular use case? Is it in production? And there were very few of those that were. I think in the AWS survey I was just Looking at, only 8% of organizations said they had systems like this in production. Is it a proof of concept where we can see whether it works or not, or is it just an idea? So I think kind of inventory of things that are happening around the organization is a very good idea. And they, like many other organizations that I've worked with, have decided that the risk of using a public generative model is too high. So we need to do a deal with OpenAI or with Google or whatever provider they are using for these models and say we don't want our prompts to make it into your model. That solves a lot of the problems relative to intellectual property ownership that a lot of companies seem to be worried about.

Yeah, and you also mentioned training. So what types of training do you recommend or what are some examples of good programs you've seen?

Well, again, it kind of depends on what sort of citizen you want to empower. If, for example, it's generative AI, then you need to tell people what types of prompts are likely to be effective for producing code and how to interface with the needed data and other transactional systems that you may be working with. So that probably would be run by an IT organization or an AI organization or if your company has one, if it's automation. Typically we find that's being done more by some companies have, have specific automation oriented groups, but a number of them are primarily focused with process improvement. And you know, being a former process re engineering person, I'm very interested in the idea that, you know, we can produce code that will automate workflows within organizations. But in those cases you not only need to know something about the tools that are used, but also you need to know something about process improvement. But you know, you can save some real money that way. If it's data science that you're trying to empower the citizenry for, there is some sort of probably statistical training that you're going to make sure that people already have or you can provide it. You know, it's relatively easy to find a lot of that stuff in online courses these days, but you know, the software is now quite easy to use. And by the way, there's a generative AI version of that called Code Interpreter. It's a beta offering from OpenAI. Now that lets you do basically machine learning analysis with a very simple prompt. You're basically saying here, use this data set and here's the particular variable or feature that I'm trying to predict and tell me which variables in my data are likely to be good predictors and how good a job can they do. I did this the other day, a two line prompt for a little data set. Two line prompt gave me three pages of data analysis with a machine learning model applied to it. This is quite astounding.

How are you seeing companies identify the people who will become their citizen developers? Is it something that they offer this training to their entire workforce, a certain subset of the workforce, or is it mainly a volunteer army?

I Think it's mainly volunteers. There are some companies, I mean JJ has a series of criteria that they apply to you. Once you volunteer, do you have any experience with data, are you technically adapt, et cetera? Some other companies will take anybody who comes along and figures even if they drop out, it'll advance the cause of citizen development if they get at least a little bit of training. There are some organizations that have certifications. I talk, I do a lot of work with Deloitte and they have certification of citizen developer, citizen data scientists and so on. You have to go through certain levels of training to get to each level. I think it depends in part on what you're trying to accomplish. Are you open to a broad democratization of these capabilities or do you really want to be a lot more targeted about it and a little bit more careful about who's doing the work?

You have talked about hours saved, money saved and you talk in the article about how important it is to track these measures of a value creation. What are some of the effective ways that you see companies do that? When you, when you say that this company has saved X hours of productivity or this company has saved X amount of dollars, how did they track that? How did they measure it?

Well, yeah, and by the way, I think there's a big difference between the minutes and hours saved. I think it was at and T that said, oh, we'd say 13 million minutes from our automations and my immediate question is well, what are people doing with all that freed up time?

I think about that Home Depot guy too, like when he automated his job. What happened then?

Yeah, I think in his case he moved on to other aspects of production forecasting and got more detailed about the different product categories they would would use it for. But I think in general it's not a good idea to just look at hours or minutes saved without a sense of what people are doing instead. And so I think the smart organizations. I was talking to an individual at PepsiCo lately who is in the finance organization and sponsoring a lot of citizen automation efforts in particular and he said every one we try to figure out what's the economic value of it? Is it costs avoided somehow? Is it higher that was prevented as a result, not too many people seem to get laid off as a result of these things yet. But that could be another source of savings. Is it making better decision that's yielded more sales in a particular domain. So has it increased the bottom line or the top line or reduce cost? The various categories of economic benefit you can say and I think that's the only way you can really justify a lot of these activities. And typically you want somebody to certify that and not just have the IT or automation or data science people do it, to have the finance organization be behind it as well and kind of certifying the outcome.

Yeah. And so if you're the person who's say, working for a small or medium sized company that's, you know, very far away from the tech sector and you want to try this, do you talk to your manager about it? Do you experiment with it first and then show the results? You know, as we talked about earlier, is there a danger there that you then just put yourself out of a job?

Yeah, I mean, I think employers don't own 24 hours of our time. So if you want to explore these capabilities on your own time, I don't see that there's anything wrong with it. But I wouldn't take it very far. After you demonstrate to yourself that it's a workable solution for improving your own productivity, I would talk to my boss about it. And I think I'd say that with generative AI, if you're going to use it, you should inform your teacher, your boss, your whatever the stakeholder is that you're going to use it or it's probably not going to be good for your career in the long run. I don't really know. I haven't done enough code generation with generative AI to know how obvious it is. But I read a fair number of term papers that have been created with it and I can identify them pretty easily now.

Yeah. Okay, so last question, big picture. Does this totally change digital transformation as companies have previously thought about it?

I think it does in the sense that, okay, you can have a small group of IT professionals doing your digital transformation or you can have, I don't know, 50, 60, 80% of your people working at it. Obviously you're going to get a much faster transformation if you have that democratized approach. And we don't have too many examples yet of really that broad scale adoption, but it is happening at that vendor that I mentioned previously. I think it's going to be happening at BMW when they finish training. There are 80,000 people in it. So I think the new digital transformation is going to have to be a lot more democratized than the previous version was.

Well, terrific, Tom, thank you.

I came into this conversation a little.

Bit scared about having to become a programmer, but it sounds like really exciting development for all organizations. So I'll get on board. Thanks so much for talking to me today.

Thank you.

That's Tom Davenport, professor at Babson and co author of the HBR article We're All Programmers now, and the book all in on How Smart Companies Win Big with Artificial Intelligence. We have more episodes and more podcasts to help you manage your team, your organization and your career. Find them at hbr.org podcasts or search HBR in Apple P podcasts, Spotify, or wherever you listen. This episode was produced by Mary Du. We get technical help from Rob Eckhart. Our audio product manager is Ian Fox, and Hannah Bates is our audio production assistant. Thanks for listening to the HBR IdeaCast. We'll be back with a new episode on Tuesday. I'm Alison Be.