5 Insights on the Future of Enterprise AI from Todd James at 84.51°

The next phase of the AI revolution is moving toward agentic AI—artificial intelligence systems and models that can act autonomously to achieve goals without the need for constant human guidance.

At the same time, some enterprise leaders say AI hasn’t lived up to the hype. GenAI is beginning to slide into the trough of disillusionment (when technology fails to meet expectations), according to Gartner.

There are also concerns about hallucination and explainability, and there’s more to be done to make models more reliable and predictable.

The cost of building and using AI is another significant hurdle. In a survey by Gartner, more than 90% of CIOS said that managing cost limits their ability to get value from AI. For instance, data preparation and inferencing costs are often greatly underestimated, explained Hung LeHong, a distinguished VP analyst at Gartner.

Also, software vendors are raising their prices by up to 30% because AI is increasingly embedded into their product pipelines. “It’s not just the cost of AI, it’s the cost of applications they’re already running in their business,” said LeHong.

Not to mention the increased environmental impact of AI, with data centers pulling more power and more water from communities than ever before.

Todd James, chief data and technology officer at 84.51°, recently discussed the shift in perceptions of AI as an abstract technology to an impactful driver of business outcomes at the Gartner IT Symposium. He also shared insights on the evolution of AI adoption, drawing from his experience leading enterprise data and AI initiatives at Kroger. Below are his 5 insights for the future of enterprise AI:

1. AI as a strategic business enabler

AI is no longer a standalone capability; it is becoming central to business strategy, driving tangible outcomes and competitive advantage. As James said, "AI is at the service of business outcomes." This shift requires moving conversations away from the technology itself and toward its impact on the business. As an example, he explained how department leaders sharing their business strategy, highlighting the role and impact of AI within that strategy and the resulting business outcomes, is an effective way to demonstrate how AI directly contributes to a company's strategic goals and overall success.

2. Build AI factories, not workshops

Developing reusable AI capabilities is crucial for fostering continuous innovation. James noted that requiring data scientists to manage AI projects post-development is unsustainable. "Whatever the number of data scientists you have, it is not enough," he said. The solution lies in creating scalable, reusable AI capabilities that reduce development time and costs while automating support and ensuring consistency across projects. This approach allows data scientists to focus on new innovations and involves transitioning from one-off projects to a model that is "performant, responsible, and cost-effective." By adopting an "AI factory" mindset instead of an "AI workshop" approach, he said, companies can build an algorithmic infrastructure that supports scalable solutions and drives business value.

3. Generative AI for personalized customer experiences

Generative AI is revolutionizing customer interactions by enabling companies to efficiently deliver highly customized messages to large audiences. Generative AI, James noted, enables organizations to create marketing messages and content tailored to individual preferences more efficiently, driving greater customer engagement. The technology is “a force multiplier” he said, allowing us “to provide a better, more personalized experience for our customers.”

4. AI-driven optimization

AI’s impact extends beyond customer-facing applications to operational efficiencies that improve the customer experience. As an example, James explained that 84.51° developed an AI-powered dynamic routing and optimization solution for Kroger that evaluates more than 200,000 decision points per second to optimize order routes, reducing the distance fulfillment associates travel across a store by 10% at 2,800 stores. This translates to an ability to fulfill more orders faster and happier customers.

5. Larger AI footprint + democratized science deployment

The future of enterprise AI necessitates stronger collaboration with providers. James described a “new normal” where commoditized and democratized AI capabilities will soon be found across industries. For instance, James explained that by democratizing data science and AI deployment, it will enable business and technology teams to take a greater role in deploying AI solutions. This shift will allow the in-house data science teams in 84.51° to concentrate on higher-value projects. Implementing processes and technologies that enable business and IT teams to deliver AI solutions in a distributed, performant and risk-managed way will foster innovation and scalability, he noted.

The Future

As we move forward, James urges aligning AI integration, scaling effectively, and empowering both business and technology teams to deploy AI responsibly and efficiently to enable new possibilities and position companies for success.