The Deeper Game: Using AI to Shift from Symptoms to Systems

Have you ever noticed how getting more efficient sometimes makes us feel... emptier? As a strategy consultant who works with organizations on their economic mobility initiatives, I've noticed a recurring pattern. We implement AI tools and save two hours a day on administrative tasks, then immediately fill those hours with more administrative tasks. But there’s a bigger opportunity in the space that efficiency creates.

The Real Question Isn't "How Fast?" But "What Now?"

When organizations integrate AI, there are two distinct approaches:

Option A treats AI like a souped-up calculator; it does what we've always done, just faster.

Option B treats AI as something entirely different: an ‘alien’ intelligence that can surface solutions we'd never consider on our own.

Most organizations start with Option A because it feels safer, and that's fine. But even with the "safer" approach, you end up with freed capacity. The question becomes what do you do with those extra hours?

Companies seeing returns from their AI use are going beyond automating existing processes to fundamentally reimagine how work gets done. This means thinking differently about the free time and capacity that using AI creates.

The Deeper Game: Moving From Symptoms to Systems

The best use of AI-freed time can’t be doing more of the same work. It's about thinking more broadly about the problems we’re solving at their root cause. When AI handles routine tasks, suddenly there's time to ask different questions. Time to talk to clients about their experiences across multiple touchpoints. Time to analyze patterns across demographics, time periods, and intervention types. Time to understand not just what's happening, but why it keeps happening.

How often do we treat the presenting issue instead of the underlying system that created it? A client can't find stable housing: Do we focus on finding them an apartment, or do we dig into why affordable housing is scarce in the first place? Both matter, but one creates lasting change.

What This Looks Like in Practice

As an example, a workforce development nonprofit that automated its intake process had some free hours. Instead of using those hours to process more intakes, they deployed staff to conduct deeper interviews with program alumni.

Through this open-ended curiosity, they learned that their six-month job training programs were successful at placement, but participants were leaving those jobs within a year; not because of skills gaps, but because of systemic workplace issues their training hadn't addressed. Armed with this insight, they could update their program to include workplace navigation and advocacy skills. This deeper work is about delivering sustainable returns and long-term impact for both the organization and the community.

The Courage to Go Deeper

Going deeper requires courage. It means resisting the urge to immediately fill freed capacity with more tasks. It means sitting with uncertainty while you dig deeper into complex problems. It means potentially discovering that some of our current approaches aren't as effective as we thought.

The payoff, though, is the possibility of actually solving problems for good.

Navigating this shift from symptoms to systems is challenging. It often requires an external perspective to help identify blind spots and design new strategic approaches.

So here's my question for you: When you gain capacity, whether through AI, better processes, or any other improvement; what would it look like to use that space not for doing more, but for understanding better?

If you're an organization looking to move beyond surface-level solutions and turn your efficiency gains into systemic impact, let's talk.