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Key Takeaways Most leaders assume AI’s biggest value shows up in bold, high-visibility initiatives — but that’s not where the strongest returns are emerging.
The companies seeing measurable impact from AI aren’t chasing headlines; they’re applying it in places many competitors still overlook.
AI isn’t just for players in tech-heavy sectors. Businesses across the economic spectrum — even those in traditionally low-tech environments — can make substantial strides in productivity and innovation by embracing AI.
A common perception is that AI adoption requires specialized data scientists or massive upfront infrastructure investments. In reality, the true value of modern AI lies in its ability to solve common, costly operational challenges that affect nearly every organization. AI can analyze vast amounts of data far faster than human teams, delivering predictive insights that optimize supply chains, streamline administrative work and improve decision-making across departments.
The real challenge is knowing where to begin. The AI landscape can be overwhelming, and many executives remain unfamiliar with how to apply it effectively. According to HR Dive reporting, 58% of C-suite leaders have never undergone any AI training. That knowledge gap leaves many organizations at a disadvantage when it comes to leveraging AI to improve performance and outcomes.
The key is identifying specific areas where small efficiency gains can translate into meaningful improvements in profitability and growth. One of the best ways to start is by examining how other organizations have successfully integrated AI into their workflows to accomplish more without sacrificing momentum or market share.
1. Lean into AI to fill gaps in the project lifecycle
Every company manages short-term, medium-term, and long-term projects. Regardless of what you call them, most projects include friction points that could benefit from AI support.
Successful project execution isn’t just about organizing tasks; it’s about ensuring that knowledge and data flow seamlessly as teams evolve and timelines shift. For example, AI-powered project management platforms like Digs act as centralized, tech-enabled repositories for every stage of a construction project, from initial concept to end-user handoff.
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