In November 2022, OpenAI brought ChatGPT to the public stage. The democratization of this new technology sparked the greatest disruption since the advent of the internet.

For the first time, AI was easily accessible to everyone — no computer engineering degree required. It put an exponential amount of data into the palms of business leaders and lay people alike, allowing them to realize the potential impact of AI on our everyday lives.

And they didn’t waste the opportunity. In fact, according to a 2024 Harvard Kennedy School study, 39% of Americans aged 18-64 adopted generative AI (GenAI) within two years of its launch — a stat that was nearly double the adoption rate of the internet (20%) and personal computers (20%) when they were introduced to the market.

This led many enterprises into a phase of experimentation and POC development in 2023. Some even began to scale use cases across the organization, exploring the possibilities of this transformative technology. Those at all levels of the organization sought to tackle quick-win use cases that boosted productivity and drove efficiency. They began looking at ways to bolster their tech stack, so that they could unleash even more AI-enabled use cases.

As engineers continued to experiment with new capabilities into 2024, there was no shortage of ideas for automation. But business leaders wondered how they could implement all of these ideas — at scale — and fast.

All the while, few business leaders realized that they were sitting on one of AI’s most powerful enablers (and barriers) ... people. 

Interestingly, 49% of our respondents self-selected that their companies were advanced — and an additional 5% called themselves disruptors. But, do businesses truly know what it means to be advanced in this era of AI?

Do they understand that it goes beyond having access to the right technology — that it’s about having the processes, people and culture in place to leverage the data and technology effectively? Are they focusing on use cases that have the potential to create wide-ranging impact on the business?

What we found was surprising. 

Of a list of 10 possible goals, “improved productivity” and “greater operational efficiency” topped the list of enterprise goals for their AI-related initiatives — no matter their AI maturity level. “Improved customer experience” was a distant third while “revenue gain” landed in the sixth spot of top priorities.

Now, as we step into the next wave of AI, enterprises must progress beyond looking for ways to address these quick-win use cases and broaden their thinking to look at goals — like revenue growth and improved customer experience — that will have a bigger impact. What are their ultimate business goals? How can they orchestrate their people, technology, security and governance to work in harmony to achieve these goals?

Only once they can answer that should they consider how AI, including agentic AI, can be leveraged to drive real business value.

AI is paving the path for enterprises to make significant progress on business goals they’ve only started to dream of. Will they seize it?