Some organizations are already proving what’s possible. Companies such as West Shore Home and TruckHouse have moved beyond pilots to embed AI into core business processes. Using an iPad, West Shore Home’s customers can see what their bathroom will look like after a remodeling project is complete. TruckHouse, a manufacturer of off-road-capable adventure vehicles, built its own AI assistant for employees. “It knows everything about the company and has access to all of our internal documents,” says CEO Matt Linder.
Their success underscores an important truth: AI can drive real value when it is applied with focus and discipline. But those examples are still the exception, not the rule. For many organizations, moving from experimentation to impact has been far more difficult than expected.
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Challenges Ahead
One challenge is data readiness. AI systems are only as good as the data they are trained on, and many enterprises are discovering that their data is fragmented, poorly governed or locked inside legacy systems. Cleaning, integrating and managing that data is unglamorous work, but it is foundational — and often underestimated. Another hurdle is infrastructure. AI workloads are resource-intensive, requiring scalable storage and reliable networking. Deciding where those workloads should run, how they should be secured and how costs should be managed adds complexity, particularly in hybrid environments.
While challenges remain, the time for managing learning curves is just about up. Businesses that don’t drive value from AI in 2026 will lose out to the companies that do.
