When artificial intelligence first made its mark in the professional sector, many perceived it with caution. Today, as AI continues to be interwoven with every business function across industries, the conversation has fundamentally changed. “There is a real need, or want, for some guidance around how to implement an AI strategy,” says Nathan Reynolds, the co-founder ofHypershift Technologies. “CEOs are no longer wonderingifthey should adopt AI, they’re askinghow.”

There is an excessive prevalence of AI tools continuing to pervade every sector, leading to greater curiosity about its uses. But as the interest rises, so does the sense of trepidation. Reynolds understands that companies have a general notion of not wanting to fall behind when it comes to the use of AI, but they have a challenging time navigating the dynamic AI landscape.

This is where strategic thinking is vital. It’s not enough to implement AI to check a box or chase a trend; companies must incorporate strategy behind their AI integration initiatives. “You have to think about it strategically, not just for the sake of not falling behind,” Reynolds adds. Hypershift’s own journey with AI reflects that ethos. The company has adopted AI internally in ways that boost speed and accuracy within decision-making and operations.

Founded in 2019, Hypershift’s AI integration isn’t led by the perception of AI as a novelty, but as a system of automation that aligns with outcomes. “The most important thing to think about AI is to view it as a path of automation,” Reynolds states. “With AI, what we’re automating is our intended outcome, but we’re letting the machine determine the steps to get there, which is extremely productive.”

However, AI is not without its errors. “AI is both a genius and a clown,” Reynolds says, quoting a phrase he’s grown quite fond of. “It can be both at the same time.” This duality in AI is the reason why implementation must be driven by strategy, intention, and a deeper understanding of why you’re automating something in the first place. “Are we trying to create greater access to goods and services? Are we trying to lower prices? Produce greater volume? Deliver quickly or improve customer experience?” These questions, Reynolds explains, are the core aspects that should guide automation strategy.

One of the more transformative cases Reynolds points to involves redefining access. “Wealth management is a very lucrative business for a bank,” he explains. “But there’s a significant human cost to deliver those services. If they could apply AI, they could offer wealth services at a lower cost to a larger volume of customers. That’s greater access.” This shift from serving a few to serving many demonstrates how AI can be more than just a backend solution, and instead can be an effective tool for democratization.

Despite the hype around Gen-AI models and large language models, Reynolds urges companies to broaden their lens and view it as more than just a chatbot. “A lot of us think of AI as a chatbot, but it’s so much more than that,” he adds. Hypershift has helped retail companies implement AI that automates entire inventory and supply chain processes, an example of the vast potential of AI that Reynolds believes often goes unnoticed. “You can even feed data from your accounting systems and have it understand the seasonality and trends to adjust your cash flow,” he adds, highlighting its versatility that goes beyond its typical uses.

As Hypershift continues to leverage AI strategically within its IT, cloud, and cybersecurity processes, Reynolds advocates for the importance of an intentional use of AI, urging leaders to think beyond the chatbot. “Think about what outcome you’re trying to achieve, look at the repetitive and manual tasks between you and that outcome, and ask yourself if AI can take it off your plate – there’s your strategy,” Reynolds explains. This kind of proactive integration of AI is pivotal for organizations to stay ahead of the curve, helping them build smarter and adaptive systems from the inside out.

As the AI landscape continues to evolve, so do its complexities. But for Hypershift, the way forward is clear: to focus less on noise and more on the why. “That’s where the real transformation begins,” Reynolds adds.