Thanks to the wonder of AI, enterprises have an opportunity to change at previously unimaginable speeds. Of course, what they do with this opportunity is entirely up to them. According to the authors of a new book, success may hinge on the way organizations are able to carefully empower “citizen developers” and then integrate their efforts with those of core data analytics teams.
Authors Tom Davenport and Ian Barkin appeared on a recent episode of our Alter Everything podcast. Their new book “All Hands on Tech: The AI-Powered Citizen Revolution” dissects the emerging role of citizen developers and looks at how the democratization of data science and AI use has impacted organizations. After a great deal of research, they both contend that non-data scientists can play a big role in unlocking the promise of AI.
Citizen Developers Step Up
The rise of the citizen developer is already happening but professional analysts in large organizations need not feel threatened by it. Quite the contrary, say Davenport and Barkin. They suggest that citizen developers can be “accelerators who transform both the future of work and the workers involved.” By reaching deep into the business functions of organizations and making data and analytics accessible, companies can scale AI’s impact exponentially. In that scenario, citizen developers are the agents of change that make it all happen.
Davenport and Barkin make the case that, while IT experts were initially the only ones capable of capitalizing on AI, experts from other domains – e.g., HR, finance and supply chain – have now joined the party and begun contributing in meaningful ways. So, what changed? The technology matured to the point where it not only became more capable but more accessible to the average person in the organization. Barkin points to the success of the low-code, no-code movement in helping accelerate that process. “You could speak your ideas into reality, and that just further pushed that race to that inflection point where anybody with an idea can start to use this technology to turn that idea into something.”
What are these non-data scientists creating exactly? Many are developing individual or departmental-level applications that track data for a particular department or building fairly small automations for a series of defined tasks. Davenport says the best-case scenario is that professional analysts will continue to lead mission-critical work while citizen developers will find ways to impact processes. “They’re not just throwing some requirements over the wall to the IT organization and waiting a few months to see something back because they’ve developed systems on their own,” he says.
This collaborative model now has a name, thanks to Gartner, who dubbed it fusion teams. Davenport suggests fusion teams are the way of the future. “People who are professional IT developers and non-professional business experts can work much more collaboratively than they have in the past. We saw a fair amount of that at companies.”
Both Davenport and Barkin are quick to point out the important role governance plays as data is made available to more people in the organization. They acknowledge the risks of democratization but insist that – with a healthy dose of governance, guardrails and guidance – chaos can be avoided and value can be multiplied.
What skills make for a good citizen developer? Barkin cites the importance of data literacy and system literacy along with having a general understanding of business processes. Beyond skills, he says, personality traits and mindset can mean a lot. “A lot of these grassroots innovators were seen as rogue mavericks who were going against the grain to even have the idea and to persevere and to pursue it. There’s an element of grit and perseverance.”
Gen AI and the citizen developer
When looking at how citizen developers will employ gen AI, Davenport simply points to the wonder of saying what you want and getting something back. “The question is, just how much expertise do you need to take that something that comes out of your prompts or even to create the right kind of prompts and get an app, a model, a website, an automation, whatever.” Results are likely to vary wildly, he says. “Some people will get in over their heads a little bit but it’s bound to accelerate the level and speed of digitization that companies can go through.”
One big lingering question for Davenport and Barkin is just how quickly large organizations will be able to leverage these tools to change and adapt on a dime. What stands in the way may have little to do with technology but instead revolve around the sort of reflexive resistance to change that exists in many companies. “You might have a fear of being obsolete or missing out,” Barkin says, but it doesn’t change the fact that your organizational constructs do not allow you to be constantly experimenting.”
The debate within enterprises may ultimately hinge on how much agency they are willing to grant people versus how much they entrench or fall back on compliance limitations and process considerations.
Growth in real-world applications
So are companies operationalizing AI and getting return on their investment right now? Absolutely, says Davenport, pointing to PWC and their use of gen AI to boost productivity. “A year ago I did a survey with AWS that suggested only 5% of large organizations had a production application. But now, in most surveys I see, it’s 15-20%. So it’s growing.”
Barkin acknowledges that some gen AI experiments will fail but he challenges companies to think creatively when assessing ROI. “Gen AI is changing the vocabulary. It’s changing the way that we look at how we operate, how we understand our data and how we organize.”
It remains to be seen but the most important by-product of this wildly dynamic moment could be reduced friction between the data side and the business side of organizations. Barkin is bullish on this. “One of the interesting comments we heard in our interviews for the book was that when you got a sort of a multifunctional team together in a room now, it was very hard to know who was from the ‘business’ and who was from the IT side because they were so fluid and comfortable with each other’s languages.”
Walls are breaking down, he says. “As we continue to do these iterations and experiments, I think we’ll just become more and more comfortable speaking to one another, which was half the battle.”
Alteryx and the role of AI-ready data
As technology matures – and teams find new, better ways of working together – data will play an outsized role in the expansion, adoption and scaling of AI. And platforms like Alteryx will provide important capabilities that provide AI-ready data while positioning companies for success.
Non-technical users who are experts in another business domain will more readily be able to turn their ideas into action. “Alteryx came up time and time again,” days Barkin, “as that catalyst for turning those good ideas into actions and actionable models.”
But, as mentioned earlier, it’s up to enterprises to jump in with both feet and Davenport says his research suggests not all of them are there yet. “There were some organizations that saw the benefit at a very high level and gave it all the support that it needed. But there were some who resisted at every turn. I think organizations need to realize that this is going to happen whether they support it or not and they might as well make it effective.”