Why AI’s Strategic Value Depends on Organizational Speed

For many organizations, the primary path to adopting AI is maximizing the share of their employees who are proficient with off-the-shelf AI tools—Claude, Gemini, and the like. While that’s a reasonable approach and has the potential to generate significant productivity gains, it’s more likely to help these organizations keep pace with others rather than create strategic advantages. After all, everyone’s going to eventually use those same tools.

However, to achieve the most powerful and strategically important applications of AI, organizations will likely have to leverage their unique data and make it actionable. And for many, that will be even harder than getting employees to use ChatGPT more often.

After wrestling with data challenges in my recent experimentation, I realized I needed to close some of my knowledge gaps in that space, which led me to read Fundamentals of Data Engineering last week. This passage from the book stood out: “The central question you should always ask yourself, and your stakeholders, is this: if you have streaming data, what are you going to do with it? What action should you take? [...] Real-time data without action is an unrelenting distraction.” 

The passage captures a fundamental dynamic: unless you’re turning over decision-making to AI, the rate-limiting factor in turning insights into action will be an organization’s human processes.

Imagine you’ve built an amazing AI tool that collects feedback from customers, stakeholders, and employees in an always-on fashion, produces great analysis, and even spots the weak signals and early warning signs that might be hard for a human analyst to see. It sounds valuable, but if no one looks at the reports the tool produces, it doesn’t matter. 

If someone reviews the data, but there’s only one “Customer Feedback Review” meeting per quarter, then it doesn’t matter that the system creates real-time insights.

And if senior leaders’ typical response to data that challenges their existing viewpoints is to cast it aside or say, “let’s wait for more data,” there may not be any progress at all. It’d be like driving a Lamborghini with the parking brake always engaged. 

To fully leverage AI for strategic advantage, organizational processes must match the capabilities of its data infrastructure. First, organizations will need decision-making routines that can metabolize greater amounts of data and new forms of analysis. For example, AI can surface weak signals or combine qualitative and quantitative data in new ways, but if that challenges how a leadership team has traditionally reviewed data or judged what’s sufficient to make decisions, its decision-making might not keep up.  

Second, AI democratizes analytical capability in ways that should reshape where decisions get made. Historically, one rationale for centralizing decisions has been that certain leaders possess superior analytical skills—spreadsheet wizardry, statistical fluency, hard-won pattern recognition. But when AI helps close that gap, enabling frontline managers to access sophisticated analysis instantly, the old justifications for centralization weaken. 

Ultimately, realizing AI’s strategic potential means rethinking the larger questions of decision rights and organizational culture—including the uncomfortable question of where AI tools themselves should be empowered to make decisions without human approval. 

The technology is evolving. Organizations need to evolve to handle it.