4 key themes to find value in AI investments

After a year where most organizations have engaged in exploring generative AI (GenAI), implemented solutions on a smaller scale and developed a deeper comprehension of its potential, we foresee the upcoming year to be focused on how to scale for the most value and impact. As organizations move beyond out-of-the-box capabilities and seek to implement artificial intelligence (AI) that is rightsized for their needs, we are just scratching the surface of how GenAI can improve processes and industries.

In my conversations with clients, chief innovation officers and more, it is clear that people are beginning to think about the larger impact AI is making, and there is an increased pressure on leaders to showcase ROI on AI investments.

As organizations look to scale their use of AI at the start of this year, there are four key themes to keep in mind to find value in these investments. Several of my @EY teammates highlighted these at The AI Summit New York at the end of last year.

No longer an experiment, AI is a business imperative

With all the discussions around the use of AI, C-suites don’t need to be convinced that it needs to be an area of focus. But that first step of determining how to use it is still a challenge for many organizations. EY Americas Data and Analytics Leader Traci Gusher hosted a session in New York with leaders from the most prominent AI companies, to discuss the many different use cases.

Leaders discussed that a major key to having a successful AI strategy is establishing a comprehensive understanding of the exact problems you want the technology to solve for. After clearly defining how an organization wants to use AI, or GenAI, it then needs to consider its own data readiness, anticipated challenges and existing tech capabilities when strategizing AI architecture. Internal readiness can also impact how quickly AI programs can be integrated into the business.

In most cases, plug-and-play systems are not automatically going to create what an organization needs, which is why strong ecosystems and relationships are important. Those partners are key to strategizing on the best way for an organization to achieve its AI goals and scale the technology.

Reimagining business with the use of AI

In the past 14 months, we have seen hundreds of use cases that while interesting were often not cost effective and did not truly move the needle for enterprises. We’ve seen plenty of use cases to improve supply chains or increase short-term productivity, but we need to be more inventive to think about expanding AI’s uses.

The key to looking forward to the future of AI is thinking outside the traditional use cases that have already been established. The question we’re posing internally is, how do we challenge and disrupt the way we work holistically? We’ve created an EY AI challengers program. The program allows us to continue building on our current AI capabilities, while also incubating new business ideas that can harness the skills and knowledge of the 400,000 professionals across our organization.

Every business function should be looked at. We believe there will be a radically different approach to how value is delivered in the next three to five years, and most jobs will be reshaped. Those who act now to reimagine their business model with AI strategically integrated will be one step ahead.

Creating confidence

One of the major concerns that has surfaced around the use of AI, and specifically GenAI, is how to use the technology responsibly. In our recent AI Anxiety in Business survey, we found that 65% of employees are concerned whether they, personally, are using AI responsibly. These employees are looking for guidance and reassurance that organizations have safeguards in place. They’re looking for more government regulation, with 78% of workers saying the government needs to play a bigger role in regulating AI. With that in mind, every organization needs to have a strong AI governance and risk management program built into their AI structure.

Some of the most pressing issues, especially when it comes to GenAI, include accuracy, managing hallucinations, confidentiality, bias, data-poisoning and copyright. We suggest having a risk management program run by a combination of business leaders and risk management professionals trained in how AI works. These people should be tasked with embedding responsible practices in AI into the organization’s technological ecosystem. By putting humans at the center of AI processes it drives trust and lowers risk.

Build responsibly and be rewarded

Building a responsible AI framework can also lead to value creation. EY Consumer Data & Analytics Leader John Dubois spoke with our client, @Fossil, about how their custom-built GenAI tool has been key to addressing the many common pitfalls with the technology. By working with EY teams to create a localized, private GenAI tool, Fossil can safeguard its IP, control for bias and use only internal information to develop answers. The tool’s ultimate purpose is to enhance the designers’ productivity, not to replace them, making clear its long-term societal impact.

The technology is helping push the limits of creativity of employees instead of creating new products all on its own. By taking control of the AI structure in the beginning, Fossil can mitigate risk and responsibly use it to drive new, innovative product designs.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.