The AI Advantage: Building Better Roads, Not Faster Horses

By Darren Person, Chief Product & Digital Officer, Cengage

My fourth-grade math teacher, Mrs. File, used to tell our class, "You're not always going to have a calculator with you."  She was right for the world we lived in. She just couldn’t have imagined how quickly that world would change.

Today, I carry a smartphone in my pocket, which means I have a calculator with me everywhere I go. We still teach math, but  the tools have changed what people need to learn, how they apply that knowledge and what they are capable of doing.

I think about Mrs. File often when people ask me about artificial intelligence (AI).

The biggest question isn't whether AI will change the way we work. It already is. The more interesting question is  whether organizations are preparing their people to take advantage of that change.

At Cengage, we've learned that AI isn't just another technology rollout. It's an organizational capability. Building that capability requires more than choosing the right tools. It requires investing in people, creating the right infrastructure and giving employees the confidence to experiment, learn and share what works.

That investment is already making a measurable impact. Today, more than 2,100 Cengage employees are actively using ChatGPT Enterprise, with nearly 90% adoption among enabled users. In the past month alone, employees exchanged more than 190,000 AI-assisted messages and created more than 1,000 projects, reflecting how quickly AI has become part of the way we work.

AI Is Changing Expectations

 The most significant shift created by AI may be the expectations surrounding it.

Employees increasingly expect AI to eliminate repetitive work, organize information and help them focus on higher-value tasks. Leaders expect teams to move faster  and produce better outcomes. Customers are changing, too. They increasingly expect information to be conversational, personalized and available  they need it, rather than delivered through static reports or one-size-fits-all experiences.

Research reflects that shift. The Wall Street Journal has reported that business leaders are shifting the conversation away from whether AI will replace workers and toward how it can reshape work and increase productivity. At the same time, the Pew Research Center found that many Americans remain cautious about AI's growing role in the workplace, even as adoption continues to increase.

Those changing expectations require organizations to think differently. AI isn't simply another application to deploy. It's becoming part of the infrastructure that supports how employees work, how decisions are made and how customers experience a business.

Building Better Roads, Not Faster Horses

One of my favorite analogies is the difference between faster horses and better roads. Organizations can deploy AI and simply move faster in the same direction they've always been going, or they can build the foundation that enables entirely new ways of working.

We've intentionally focused on the second approach.

From the beginning, we believed successful AI adoption had to happen from the top down and the bottom up. Leadership established the vision, governance and responsible use guidelines needed to create trust and clarity. At the same time, we wanted employees to help shape how AI would be used across the business.  The people closest to the work are often best positioned to recognize where AI can remove challenges and create value.

Rather than making AI available to everyone at once, we introduced it in phases through a network of Change Champions across the company. These employees received early access to AI tools so they could experiment, identify meaningful use cases, surface questions and bring what they learned back to their teams. Their feedback didn't just help colleagues adopt AI with greater confidence. It helped shape how we expanded access, developed training and strengthened governance.

 The result was a shared learning model. Leaders set the direction, but employees helped define what success looked like by discovering practical applications that improved the way they worked.

That spirit of shared learning inspired AI in Action, a recurring feature in our company-wide newsletter, where employees share how they're incorporating AI into their work. The examples look different depending on the role. Designers use AI to overcome creative roadblocks so they can spend more time refining ideas. Product marketers use it to synthesize customer insights and market research, allowing them to focus on strategy instead of organizing information. Sales and change management teams use AI to prepare for customer conversations, tailor communications and turn notes into actionable plans.

One example that stands out comes from our K–12 content development team. Brooke Jones, a Sr Content Developer for National Geographic Learning, has been working with her colleagues to build AI-powered workflows that support copyediting, assessment review, and fact checking. Early estimates suggest these workflow changes could save tens of thousands of dollars once the team is enabled to implement them in production, while also reducing weeks of manual effort. More importantly, those efficiencies will create additional capacity for higher-value work, giving content teams more time to focus on instructional design, classroom experiences and creating engaging content for educators and students.

What stands out isn't that everyone is using AI the same way. It's that the most valuable ideas are emerging from across the organization and spreading through peer learning. That wasn't something we could mandate from the top. It happened because we invested in the infrastructure, governance and culture that gave people the confidence to experiment, learn from one another and continuously improve.

For me, that's been one of the biggest lessons of our AI journey. Technology alone doesn't transform a business. Building an organization that's prepared to learn, adapt and evolve with the technology does.

Future Focused

Everything we've learned internally is shaping how we think about the future.

As customers increasingly expect more personalized, conversational experiences, we're applying many of the same principles to the products and services we build. That work is ongoing, but it starts with having the right internal foundation. The governance, infrastructure and learning culture we've built internally, positions us to innovate responsibly for the educators, learners and institutions we serve.

Like many organizations, we're still learning. The technology evolves almost daily, and expectations evolve with it. That's why I don't think of AI adoption as a project with a finish line. I think of it as a capability organizations must continue to develop over time.

Mrs. File couldn't have predicted that we'd all carry calculators in our pockets. She also couldn't have predicted that we'd one day carry AI with us wherever we go. Technology changed what we needed to know then, and it's changing what organizations need to build now.

For us, the lesson is clear. AI doesn't create business value on its own. People do. Our role as leaders is to build the infrastructure, culture and confidence that allow people to use AI in ways that make our business stronger and better serve our customers.

The organizations that succeed with AI will not only help people do yesterday’s work faster. They will give people the foundation to imagine and build entirely better ways of working.