“Can we?” vs. “Should we?”: How AI changes every business decision

A few phrases almost always pop up when companies talk about AI: "Streamline," "efficiency," "save time," "productivity," "faster…"

No surprise there. AI tools, like automation before them, are usually sold mostly as ways of doing the same things faster, with fewer people, at a lower cost. It's one of the main benefits of my own company's AI, Verbal, which streamlines healthcare compliance, helping QA teams save time and be more efficient.

And that's obviously a good thing. Who doesn't want to be more productive while saving time, money and energy?

But too often we stop there, missing a bigger question: If AI can eliminate one of the most fundamental constraints in business – capacity – what comes next? When most things become doable (or at least much easier to do), what do you choose to do?

When capacity is no longer the limiting factor, many business decisions hinge on what you truly value. What you're willing to bet on.

It's no longer a question of whether we can, but whether we should. No longer about capacity, but about priority, about conviction. Not "Can we?" but "Should we?"

Before AI: "Can we?"

Capacity is easily the biggest challenge for organizations when it comes to healthcare compliance. Leaders no doubt understand the importance of compliance, but without AI, audits simply haven’t been doable at scale. Staff can spend hours manually reviewing charts and still only cover a fraction of interactions.

The question for most organizations becomes: "Can we even do QA?" or "How much can we realistically do?" Priority hardly factors in. It's either doable or it isn't (and for most organizations, it simply isn't).

This constraint shows up everywhere in business. Marketing teams that recognize the value of personalized outreach but can only manage a handful of prospects manually. Small companies that understand market research matters but lack the bandwidth for comprehensive analysis. Operations teams that know process optimization would help but don't have time to audit current workflows.

Before AI, the conversation usually ended with a shrug: "We know it would be valuable, but we just don't have the capacity."

After AI: "Should we?"

Now that AI can handle much of the heavy lifting, it’s a fundamentally different paradigm. 

Suddenly, something like comprehensive healthcare compliance auditing becomes feasible. Marketing teams can personalize outreach to thousands of prospects. Small companies can conduct market research that rivals what large corporations do with dedicated teams.

The limiting factor shifts from "Can we do this?" to "Is this worth doing?"

This creates an interesting psychological challenge. 

For decades, we've been conditioned to think in terms of resource constraints. We've gotten comfortable saying no because we literally couldn't do more. Now, with AI removing many capacity limitations, we have to develop new muscles around strategic prioritization.

It's like being given unlimited wishes by a genie – suddenly the quality of your decisions matters almost as much as (and in some cases more than) your ability to execute them.

In healthcare compliance, this shift is profound. Instead of asking "How many patient interactions can we realistically audit?" the question becomes "What level of compliance visibility do we actually need?" Ask me, it’s ideally 100%. That’s something that was impossible before. Now just a business decision. Something you either prioritize or don’t.

The trap: efficiency vs expansion

Here's where most organizations get stuck. They approach AI as a replacement tool rather than an expansion tool.

The Efficiency Trap looks like this: "Great, now we can do our current QA process 10x faster and with fewer people." You're still thinking within the same constraints, just optimizing them.

The Expansion Mindset asks: "What becomes possible now that wasn't before?" Instead of auditing 15% of patient interactions faster, what if you could audit 100%? Instead of just reducing costs, what if you could fundamentally improve patient safety and organizational risk management?

For example, AI enables small companies to do competitive intelligence that was previously only feasible for large corporations with dedicated teams. It allows individual contributors to produce analysis that once required entire departments. It makes real-time personalization scalable for companies of any size.

The question isn't whether AI can do the same thing as you could do, just faster, but whether it enables you to do things you could hardly have thought possible before.

The new strategic challenge

This shift from capacity-constrained to choice-constrained business creates new challenges:

Vision becomes the bottleneck. When you can do almost anything, knowing what you want becomes crucial. Organizations that thrive will be those with clear strategic vision and strong decision-making frameworks.

Risk tolerance matters more. Efficiency improvements are relatively safe bets. Expansion opportunities often require more courage and tolerance for uncertainty. The organizations that just optimize existing processes might find themselves outpaced by those willing to reimagine what's possible.

Organizational hierarchy gets disrupted. When individual contributors can accomplish what once required teams, traditional org structures start to feel misaligned. The most successful companies will be those that can adapt their structures to match their new capabilities.

Different levels, different implications. For individual contributors, this might mean taking on higher-level strategic work. For managers, it could mean shifting from resource allocation to vision setting. For executives, it's about identifying expansion opportunities that create sustainable competitive advantages.

Bottom line

AI's real power isn't in doing things faster or cheaper – it's in making previously impossible things possible.

The companies that win won't just be those that get more efficient. They'll be the ones that recognize when capacity constraints disappear and have the vision and courage to expand into new territory.

In healthcare, this might mean shifting from "How do we manage compliance risk?" to "How do we use complete visibility to fundamentally improve patient care?" In marketing, it might mean moving from "How do we optimize our current funnel?" to "What entirely new ways can we connect with customers?"

The tools to do almost anything are increasingly available. The question is: What do you actually want to accomplish?

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Note: This article originally appeared on Mexico Business News

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