recent

How AI-empowered ‘citizen developers’ drive digital transformation

Our top 5 ‘Working Definitions‘ of 2024

How to manage two types of generative AI

Credit: ioat / Shutterstock

Ideas Made to Matter

Organizational Culture

Who owns digital innovation? Who cares?

By

There’s little question that enterprises benefit from a focus on digital innovation. Gartner’s 2021 CIO Agenda Survey found that organizations that increase funding for digital innovation are nearly three times more likely to be leading performers than laggards as compared to their peers.

A question often comes up, though: Who owns the initiative?

It may seem to be a necessary discussion, as C-level technology, innovation, or digital leaders — or even CEOs themselves — could take the reins. However, a panel at this spring’s MIT Sloan CIO Symposium offered organizations succinct advice about debating digital innovation ownership: don’t.

“The debate happens at each level of the organization, and there’s no clear-cut answer,” said Brook Colangelo, executive vice president and CIO for Waters Corporation, a lab equipment and software company. The panel was moderated by Michael Schrage, a visiting scholar at the MIT Initiative on the Digital Economy.

“It lies between IT, digital, and business leadership,” said Akira Bell, senior vice president and CIO of policy research company Mathematica. “Traditional IT has to support the privacy and trust frameworks necessary to make that digital transformation work.”

“We cannot win for our clients or our colleagues unless we come together as a team,” said Gail Evans, chief digital officer of Mercer, an asset management firm.

Since enterprises shouldn’t debate who owns digital innovation, leaders are better off focusing their energy on creating a culture of innovation. Here are four tips for making that happen. 

Look beyond the technology

The CIO role has evolved over the last several years. Creating a technology roadmap for the company remains critical, but alignment with business strategy and customer expectations influences that roadmap more than specific internal hardware and software needs.

For Bell, this means knowing how Mathematica’s policy research helps clients make data-driven decisions and create value. In that sense, she said, digital strategy is in fact business strategy.

Evans agreed. Today’s IT leaders are exposed to a plethora of digital platforms and ecosystems, and it is incumbent upon them to evaluate these offerings as growth opportunities for the business as a whole. “We're talking about the evolution of the IT leadership and technology leadership to stop just thinking about technology,” she said. “We are business leadership.”

Be a city planner, not a traffic cop

For Colangelo, another sign of the evolution of the CIO role has been the transition from traffic cop to city planner.

The inward-looking technology leader was a traffic cop, managing enterprise software applications, networks, data centers, servers, devices, and so on. The shift to an outward focus requires big-picture thinking, Colangelo said.

“We have to start designing the streets, the experiences, and the roadmaps for the destiny that we're going to drive toward. We have to do that by envisioning where all of these things go and sequencing them and showing the entire business landscape,” he said. “Our digital strategy is not to just look at a list of portfolio and programs, but put our customer at the center, look at all the different delivery mechanisms that we offer to our customer, and then unify that in a frictionless experience.”

This requires thinking differently. Just as today’s city planners must consider issues that their predecessors did not — from electricity grids to decarbonization — the “city planners” of digital transformation must think beyond Gantt charts and waterfall development.

“We're going to work iteratively, think about how we drive different outcomes, and change that roadmap constantly,” he said.

Think about skills, not roles

While digital innovation has impacted the future of work for quite some time, the business world’s response to COVID-19 further disrupted this shift. Leaders, as well as the workers they manage, must navigate the rapid transition to remote work, the loss of routines, the return to office, and even the changing sense of self amid major personal and professional transitions.

Related Articles

Forget ‘fail fast.’ Here’s how to truly master digital innovation
How to master two different digital transformations
3 challenges to digital innovation and how to overcome them

Amid this disruption, Evans emphasized the importance of evaluating employees’ contributions based on their skills, not their specific role or title. Digital innovation brings “a whole new dimension” to designing the workforce, she said, and helping employees evolve their skills will let them bring new knowledge to their existing area of focus. “Our actuaries are now becoming data scientists,” she said.

Thinking about skills instead of roles also means organizations will have to “broaden the tent” of who is considered a digital worker, Bell said, adding that it’s likely to be most of the company.

“Our social scientists are coming out of graduate school, and they can program in Python,” Bell said. “The last thing they want to hear is, ‘Well, you’re not on the technology team, so you don’t get the keys to the kingdom.’”

Automate with care

That said, digital innovation initiatives must balance human and machine work, given that computers and robots can complete certain repetitive tasks faster than people can.

For Bell, an all-or-nothing approach is not feasible. Mathematica focuses on data-driven research that informs health and social policy. Without human oversight, bias in machine learning, whether in the data sets that are analyzed or in the algorithms that analyze the data sets, could lead to decisions that only widen equity gaps based on factors such as race, gender, or sexual orientation.

“It's really important for us to have the right balance of technology paired with a social scientist,” Bell said. That way, social scientists and researchers can use machine learning technology to inform their work, but the output of the algorithm isn’t expected to be the sole basis for a decision.

Evans described Mercer’s approach to automation as “test and learn.” The approach has two key goals: Improve the output of predictive algorithms and empower employees to solve business issues.

“When you teach new skills to the people who are doing the job today, they will help you to automate and find efficiencies that serve the greater good of the company and the client,” she said. “Our colleagues feel like they are valued, that what they bring is needed for the growth and success of the company.”

For more info Zach Church Editorial & Digital Media Director (617) 324-0804