Making AI experiments work

Experiments can provide useful insights into the types of tools organizations should invest in. However, it’s easy to get too excited about promising results and jump headlong into opportunistic use cases. What might seem like a quick win could turn out to be a drain on time and resources.

To avoid falling into this trap, it’s important to set objectives ahead of time. Is the organization looking for increased top-line growth? Greater productivity? What are some growth areas that need to be improved? By how much and by when? Focused experimentation leads to better results than unstructured inquiry does.

The AI Maturity Index bears out this claim. Businesses that have created and aligned on clear strategies and goals are more likely to be Pacesetters than those that haven't.

Experimentation demands the right tools for the job. For AI initiatives, this means providing developers with robust AI platforms and tools, access to an organized enterprise data catalog, evaluation tools, and policy documentation. This empowers teams to accelerate the pace of pilot programs.

AI is not just about incremental optimization. It’s also about driving step-change progress in business processes.

While organizations often have in-depth knowledge of their own processes, bringing in third-party partners can offer fresh perspectives on key challenges. This kind of strategic collaboration can unlock innovative solutions that internal teams can’t achieve on their own.

Once an idea looks promising, the next step is to thoughtfully integrate the new tool or technology into the user workflow. This enables teams to determine whether the experiment can deliver value for the enterprise. If it does, teams can quickly scale and iterate, maximizing the impact of successful pilot projects. Before scaling, establish the exit criteria for the pilot so there is clarity on how to effectively scale.

Many businesses have overlooked this step. Just one-quarter of businesses have surpassed the pilot stage of AI implementation maturity, according to the AI Maturity Index. And only 23% have identified opportunities to automate routine tasks and workflows. Taking that extra step to deliver results at scale is critical for getting the most out of a given experiment.

When it comes to implementing AI solutions, efficiency is often synonymous with value. But safety and regulatory compliance are just as valuable.

Any culture of experimentation must be grounded in strong governance. Not all use cases are legally or ethically appropriate, and they may not align with organizational objectives. That’s why it’s essential to maintain a centralized repository of AI use cases and ensure they’re reviewed and approved by an independent internal team. This oversight helps ensure alignment with the company’s AI policies and mitigates potential risks.

The most advanced organizations understand that innovation thrives in an environment where employees are not just permitted to try new things, but encouraged to do so—even if, and especially if, they don’t always succeed. The path to AI maturity is paved in trial and error, informed risk-taking, and continuous learning from both successes and missteps.

Fostering an experimentation culture means giving teams the psychological safety to explore bold ideas and the support to treat setbacks as valuable data, not dead ends. That’s how Pacesetters stay ahead: by moving forward with purpose, learning at every step, and embracing experimentation as a competitive advantage in the age of AI.