When breakthrough technologies emerge, organizations often turn to specialized innovation teams to drill down and experiment new applications.
But as technologies like artificial intelligence and cloud computing have evolved at an unprecedented pace, that model has begun to break down. Innovation is no longer confined to a single department—it’s become a company-wide imperative.
Amid this strategy shift, intelligence gathering has emerged as a defining competitive advantage. Rather than relying solely on broad industry trend reports, organizations are prioritizing targeted research and internally driven insights to guide modernization efforts.
As Christopher Miller, Lead Emerging Payments Analyst at Javelin Strategy & Research, detailed in the From the Lab to the Lead: Analyzing Successful Changes in Innovation report, companies embracing these innovation trends are not simply chasing the latest technology wave. Instead, they are building the organizational foundations needed to capitalize on technological advancements both today and in the years to come.
The Innovation Maturity Process
Miller identified this culture shift after conducting extensive interviews with innovation leaders at large financial institutions. He found that responsibilities such as landscape monitoring and forecasting are no longer confined to specialized innovation teams. Instead, these functions are deeply embedded within product and business-line operations.
As a result, initiatives such as forecasting three- to five-year market trends have become enterprise-wide responsibilities.
“You can summarize it as simply a maturity process,” Miller said. “We saw and continue to see widespread digital transformation programs. This isn’t the same thing, I want to be clear, but companies have been talking to their employees about digital transformation for more than a decade now.”
“While those programs have sometimes met with mixed success, the fact of the matter is that the employee base in 2026 comes with different skills, variances, and expectations than the employee base did in 2006,” he said. “The result is those items that once were defined as unique, special, and different are set aside, and innovation functions can now be more readily expected of the employee base as a whole.”
Gathering Relevant Intelligence
Alongside the shift toward distributed innovation, advances in technology and employee capabilities have made intelligence gathering increasingly internalized.
“In many cases, what you had were third-party firms providing external insight for sale in a subscription model,” Miller said. “If everybody read the latest report about a given topic from a large popular research firm, then that’s not a differentiator. You either have that information or you do not, but that point of view is not a differentiator.”
“What we’ve seen is that companies are building their own intelligence gathering organizations,” he said. “While they still use third-party points of view, they leaven those points of view with lots of other sources of information.”
This approach enables organizations to build tailored ecosystems that combine insights from internal research, strategic partnerships, venture investments, and relationships with startups and universities. As these intelligence-gathering functions have become more comprehensive, they have evolved into the differentiator organizations have long sought.
“If I am the only company in my line of work that is invested in a certain portfolio of startups, that is a unique and differentiated source of information,” Miller said. “If I own the same companies as everybody else does, it is not. There’s a line here—it is not functional, it is not inevitable that this would work out to be a differentiator—but as it happens, that’s been one of the points.”
The Innovation Mindset
These evolving approaches to innovation have increased organizations’ ability to deploy emerging technologies at scale. This capability is especially critical given the breakneck pace at which technologies like AI continue to evolve.
“I’ll use an example of something that I’ll call the innovation mindset,” Miller said. “The Lean Startup model of developing, which often was deployed by innovation areas, is important to this. It’s the idea that you should find low-cost, simple ways to test out if value can be achieved by doing something.”
“The two-year-long pilot, where you study and the burden of proof is to demonstrate that there’s value there before you begin the process, has in many cases been replaced by an understanding that you can run short-term tests—seven days, 10 days, 30 days, whatever the case would be. It’s to find out how it works, what went right, and what went wrong,” he said. “And that notion that mistakes are learnings that can be used to improve outcomes has, in many cases, been institutionalized in a way that was not true 15 or 20 years ago.”
Despite the advantages of distributing innovation functions across an organization, the model also presents challenges. Rather than leading modernization efforts directly, many innovation teams are now responsible for consolidating lessons learned across the enterprise and disseminating best practices—a difficult task in a fast-changing technological landscape.
While communication gaps and organizational siloes remain obstacles, businesses that embrace this mindset will be better positioned to adapt to future challenges and opportunities. This includes the ability to optimize what is arguably one of the most transformative technologies to emerge in recent years: AI.
“That is its own kind of differentiation,” Miller said. “AI isn’t just a thing that you drop on anything and—presto, change-o—you have success. You have to define business problems. You have to understand all these standard tactics. The fact that business leaders have increasingly assumed this responsibility situates them well to take advantage of AI in a way that might not have been true 15 or 20 years ago—and not just to take it, but to do it more quickly.”