The Hidden Barrier to AI Success

Think back to the last conversation you had with a friend or colleague about how they are implementing artificial intelligence, measuring its use, and tracking success.

Chances are good that if their company is using AI tools, and if anyone is trying to quantify usage, their chief technology officer or IT function has led the rollout . The company is likely measuring success by Microsoft Copilot logins or the number of customers engaging with the AI chatbot on the website. Chances are also good that some team members and clients are using the new tools with gusto, while others are fighting the technology tooth and nail (or just ignoring it and hoping it goes away).

If that scenario sounds familiar, then the company in question is probably in the throes of an AI rollout, but they are falling short on AI adoption and struggling even more with AI integration.

Here’s why: Rollout is easy. Rollout is simply a matter of making the tools available and accessible.

But adoption and integration are harder. Adoption requires willing and eager participation bolstered by an expanding and innovative mindset. Adoption involves building capability in your workforce and then putting it to use. Integration takes capability even further. It happens when AI becomes “part of the way we do things.” All things. All day.

So, what can you do to ensure that your organization doesn’t fall victim to adoption and integration failure? Start by re-envisioning your AI technology rollout as a change management opportunity. Providing access to the new tools is necessary but insufficient for successful integration of any new technology. True success requires access, ability, and appetite.

Of course, there is a reason so many rollouts focus primarily, or even exclusively, on access. Access is an extrinsic characteristic. As the word implies, making the tool available or accessible is the only requirement for achieving success in this dimension.

Ability and appetite, however, require diving into the skill set and mindset of the employees (and the clients) who expect to not only access the technology but leverage it effectively on a regular basis as they complete their work or use your company’s products and services.

That raises the question: If adoption and integration are human capability problems, why isn’t L&D more involved? When skill set and mindset are the focus, L&D must be involved.

To illustrate this point, consider the following scenario. Eight years ago, one of our organizations asked itself: What does success look like for our employees? After a bit of hard truth telling, it became clear that goal accomplishment was the key measure of job success.

And in some cases, it was goal accomplishment by any means necessary. The how didn’t matter as much as the what, and in the worst instances the how didn’t matter at all. We, as L&D leaders, asked ourselves: Do we want to continue to be a workplace where brilliant jerks succeed and receive rewards for their behavior? The answer was no. That began our journey to change the culture, including redesigning our performance evaluation program.

We could have looked at the redesign as a technical launch, because in many ways, it was. We needed a new tool that would capture and document every employee’s performance, enabling managers to have conversations about role success and future readiness.

We could have created a new performance evaluation form that asked people to document one or two additional performance criteria. We could have made a check box that asked managers to ensure that they spoke with every employee about the importance of being a strong communicator, partner, and collaborator. We could have made performance forms that included all the behaviors we wanted to see and left it at that.

Our teams would have had access, but those solutions completely ignored ability and appetite. The project would have failed.

While giving access is an important step, it is not the only step necessary when you are looking for true adoption and integration of a new tool, work process, or organizational structure.

Instead, our L&D team took on the project with enthusiasm. We gathered groups of people to discuss the existing performance evaluation process, why it wasn’t working, and what we could do differently. We listened, educated, co-invented, and we co-created. Through a two-year process, we built a way of measuring work success that still measured goal accomplishment, but also valued job mastery, collaboration, continuous improvement, and belonging and inclusion.

Our transition to a “No Brilliant Jerks” culture was successful because of the groundwork that happened before the formal launch of the new technology tool.

Now, we know that direct equivalence in the context of AI will be difficult to achieve simply because of the speed at which AI is moving. State-of-the-art capabilities and tools for AI are changing so rapidly that a complete governance framework could quickly become obsolete.

Still, there is a lot that that TD professionals can learn from that example as we think about the culture change necessary for successful AI adoption and integration across any organization. There is still plenty of room to create guidelines that will inform a nimble and evolvable policy structure that combines adoption with the mindset and work process shifts needed to truly capture the usefulness of AI.

By implementing the following steps, your L&D team can be a powerful partner to CTO and technical leaders at the leading edge of AI integration. Expanding the cadre of employees ready to make the most effective use of agentic tools will help your organization move from AI rollout to AI adoption and integration.

1. Build clarity around what leaders expect. Establish proficiency tiers with observable behaviors to help employees visualize and understand their leaders’ expectations. Then tie the behaviors to proficiency assessments, efficiency metrics, quality scores, and error rates. Organizations that are new to any of those dimensions can begin simply by measuring the areas where they anticipate AI tools to drive the most change. Having internal benchmarks in place creates the ability to assess success by looking beyond logins or activations of AI tools.

2. Identify and leverage your successful early adopters. With proper measurement capabilities in place, add incentives to reinforce the new norms that the company wishes to see by leveraging a train-the-trainers approach to upskill employees. Look beyond managers to find AI leaders up and down the organizational hierarchy.

For managers, consider incentives that will inspire experimentation with AI tools so they can quickly become comfortable using the technology directly, and then showcase managers who are modeling AI adoption and integration.

In parallel, build safe-space work environments for individual contributors where peers can experiment and share, thus creating opportunities for knowledge gathering, knowledge transfer, and practical application. Doing so ensures all employees can capture and assess visible wins that highlight the power of leveraging AI as a tool in all employees’ toolkits. Source those stories from across the organization to increase the chances of sparking creativity.

3. Identify knowledge gaps and fill them. Creating the right role-based pathways for AI ability and appetite requires a depth of knowledge and insight that is core to the L&D function. L&D leaders can partner with technical experts to assess AI tool acceptance and integration across the workforce and assist in creating compelling learning opportunities and assets. Tailor the materials by function and seniority to ensure that AI moves beyond “a tool I can use or not” and instead becomes a smart tool that individuals are excited to use to perform their work more efficiently and more effectively.

4. Think holistically, not just top-down or C-suite. While the specific training needed will likely vary across organizations and functions, one element that seems poised to be universal is the need for individual contributors to incorporate task breakdown and delegation skills into their repertoires. As agentic AI becomes increasingly common across functions, individual contributors effectively become managers of their own agentic teams. Fortunately, that shift in mindset and skill set from individual contributor to manager has been core to most L&D functions across industries for decades. Use that prior knowledge to ensure even entry-level workers have the skills they need to manage their new AI teams.

5. Reward the behaviors you wish to see. Implementing multiple and varied methods for recognizing and rewarding adoption and appetite reinforces the integration of AI tools across an organization. Building on the metrics already developed to drive clarity, L&D leaders can continue to source and socialize success far beyond the early adopters.

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