Five Ways Organizations Can Build Better AI Strategies

Artificial intelligence technologies offer new possibilities and solutions for businesses worldwide, enabling them to make better decisions, boost innovation and serve customers better and faster.

However, for a company to “win” with AI, leaders can’t treat it simply as a technical upgrade — it should be a strategic pillar for the organization, said Dr. Shrihari Sridhar, senior associate dean of Texas A&M University’s Mays Business School.

At Mays, students are being prepared with the skills needed to succeed in an AI-driven world, which includes a strategic, outcome-driven business mindset that can help organizations generate more revenue and lower costs.

“AI adoption, if done correctly, is going to increase capacity at every role it is touching, reduce the amount of human error, and at the same time, create efficiencies across the whole organization and allow you to move at a much more rapid pace,” said Arnold Castro, Mays’ assistant dean for AI. “What a lot of companies have to realize is that in order for AI to be integrated properly, data needs to become the foundation.” 

Castro and Sridhar explain five common pitfalls companies should avoid when implementing AI strategies:

Stop Treating AI Like IT

Many companies fall into “shiny new object syndrome,” Sridhar said, thinking they can expect instant results after implementing AI tools throughout the organization.

“That doesn’t happen, because it will just sit around and nobody will use it. So you shouldn’t treat it like IT,” he said. “Instead, treat it like it’s part of your core existence.”

Take UPS ­ — the company’s job is to get things from point A to point B, and it utilizes AI to optimize its routes. Sridhar said this is an example of how companies should anchor AI in strategy, not tech. “If you treat AI like an add-on, it won’t work,” he said.

Start With The Outcome, Not The Algorithm

Sridhar and Castro say they frequently see companies become enamored with every new version of AI as they rush to implement strategies.

“A lot of companies say, ‘OpenAI is really popular, I want that,’” Castro said. “But what business problem are you trying to solve with that?”

Instead of chasing algorithms, businesses should chase outcomes, they said. For example, if the desired outcome for a hospital is to reduce the number of patient deaths from heart attacks, they should start there, asking what causes heart failure and gathering the appropriate data.

“You look at your outcome, then you build backward,” Sridhar said. “You don’t just chase algorithms.”

Measure What Matters

Too often, companies build dashboards to measure key performance indicators that aren’t relevant to their business goals.

“Companies don’t measure what matters,” Sridhar said. “Instead, they build dashboard after dashboard. You can build hundreds of AI dashboards in a few seconds, but if you keep building those and measuring KPIs that are technical, like latency, that’s not useful.”

Instead, he suggests building dashboards around KPIs that actually align with the company’s strategic goals. “If your AI dashboard itself needs a translator, that’s useless,” Sridhar said.

Think Of AI As A Capability, Not A Project

Projects end within a few months, but capabilities compound. Companies need to keep this in mind when designing their AI strategies, Sridhar said, thinking of it as a tool that everyone in the organization should have.

“Great companies don’t build projects — they build momentum,” he said. “For example, Amazon didn’t launch AI as a one-off initiative — it built capabilities like recommendation engines and dynamic pricing that have scaled over decades. These capabilities evolve, self-improve with data, and unlock entirely new ways of doing business. Thinking this way helps companies reframe AI from a cost to an enduring asset.”

Treat AI And Humans As Collaborators, Not Competition

Sridhar said companies should start by evaluating the work their employees are performing and identify how AI could help them do their jobs better. For example, airlines are increasingly using AI-powered chatbots to handle routine customer service tasks, freeing up human agents to focus on building stronger customer relationships.

Castro adds rather than replacing humans, AI can empower them to do the work of 10 people. This is part of how Mays Business School is rethinking how its students are trained to use AI in their future careers.

Mays’ new AI and Business minor launching this fall will make students “AI-ready,” Castro said, giving them the tools to utilize AI and machine learning in a business setting. Students will learn how to use data to think strategically and solve problems in the workplace, Castro said, allowing them to become “instant leaders.”

“They’re going to be able to move a lot faster in an organization, be able to knock out a lot more work in a more efficient way,” he said.

To learn more about AI at Mays, visit the Mays Business School website.