Tech is not just about tech – it’s about everything else

The same applies when organizations try to adopt new technology—new tech. It’s about much more than the technology itself. New tech can mean many things—and right now, artificial intelligence (AI) is front and center. But when it comes to organizational use, there are many commonalities across different types of new technology. If we lose sight of this amidst the hype, we risk overlooking one of the most critical factors for success.

Let’s lace up those shoes.

 

How do we get started with the new technology?

When an organization aims to adopt new technology, it’s rarely the technology itself that’s the problem. It’s often the people, the culture, the processes, and how we understand the task at hand.

Technology adoption isn’t just about buying new equipment. It’s not simply that the new “AI models can do all sorts of things.” Technology won’t transform your organization on its own—it requires much more: hard work, persistent learning, and curious exploration. As Mollick puts it regarding AI adoption:  

“To get organizational gains requires R&D [research & development, red.] into AI use and you are largely going to have to do the R&D yourself. I want to repeat that: You are largely going to have to do the R&D yourself”¹. 

We can’t expect the major tech companies to fully understand—or solve—the specific challenges we face in our own organizations. We need to roll up our sleeves ourselves.

But how do we approach it? There are several strategies, and Mollick highlights two overarching ones across organizations: “the crowd” and “the lab.”

 

The crowd: Let everyone run from the start

Imagine handing out race numbers to the entire organization at once. All employees—regardless of title, experience, or department—are invited to experiment with the technology.

This strategy emphasizes broad participation: rolling out the tech across teams and encouraging each employee to explore its possibilities. It creates a culture of discovery, ownership, and knowledge sharing.

You start running as one large group, with community spirit, curiosity, and countless small experiments eventually lifting the organization to a new level.

 

The lab: Focused training in a test environment

At the opposite end of the spectrum is “the lab” strategy. Here, a small, specialized group takes the first steps. Those who have trained the most—or are simply “born with strong legs.” This team spends time developing, adjusting, and fine-tuning the technology before broader implementation.

The goal is to work out the problems, identify the most useful features, and create clear guidelines for the rest of the organization. It’s like having a few seasoned runners test the course, improve the shoe fit, and design tailored training programs before the rest of the team hits the track.

 

Technology adoption: Maturing both the tech and the organization

Regardless of whether you unleash everyone at once or let an elite group pave the way, some basic conditions must be met. The technology must be mature, stable, and scalable. The purpose must be clear, the data must be sound, security must be in place, and ethical considerations must be thought through—each a major topic in itself.

The same applies to the organization: Are employees ready to use the technology? Do they have the understanding, the skills, and—most importantly—the courage to change their habits and work methods?
Do they have “data imagination”? It takes creativity to find new running routes instead of always sticking to “around the lakes” or “the forest path” depending on where you’re from.

It’s about creating a culture where technology is taken seriously while also allowing room for play and exploration. Leadership must support, guide, and invest in learning and communication. They don’t always have to lead the race—but they must run alongside. And the organization must prepare for technology adoption as a process, not a one-time event.

In practice: A combined approach

At Epinion, we’ve chosen a combined approach. We have our own lab—a Data Science Innovation HUB —that tests, adapts, and learns from new technologies like large language models (LLMs). At the same time, we apply the crowd approach by inviting the entire organization to experiment, ideate, and refine applications.

So, about that new running watch? If you want to track your pulse or whatnot, it might be necessary. But if it’s simply about getting out the door, it starts elsewhere—with a pair of running shoes and a partner who supports you. A joy in running, and a community to run with. Otherwise, you probably won’t get very far.

Want to learn more? Get in touch with Allan Toft Hedegaard Knudsen.

 

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1: Mollick, E. (2024): One Useful Thing, oneusefulthing.org. 

Allan Toft Hedegaard Knudsen
SENIOR DIRECTOR
atk@epinionglobal.com