Why AI is surfacing the limits of our current systems—not solving them
Last week, I wrote about transformation readiness — and the uncomfortable truth that we rarely feel as prepared as we think we should be. Transformation doesn’t wait for permission. The signals are quieter than we expect. More scattered. Easier to dismiss as “just a rough patch.”
This week, I want to go deeper.
Because the transformations that reshape organizations most profoundly often don’t announce themselves with urgency. They show up as friction. And the sooner we get better at recognizing those early signs, the less likely we are to find ourselves suddenly behind.
When Effort Stops Translating Into Outcomes
One of the patterns I’ve noticed — both in my own work and with the teams I support — is when the same level of effort stops producing the same results.
It’s not a performance issue. People are working hard. Maybe harder than before. But somewhere between effort and impact, traction just… disappears.
And it doesn’t happen overnight. It shows up in small ways:
- The same campaign structure that worked last quarter doesn’t convert this quarter
- Sales cycles stretch longer without clear explanation
- Projects expand in scope, but progress doesn’t match the activity
- A backlog of good ideas piles up, and no one’s quite sure which one to prioritize
- You’re in more meetings, but making fewer decisions
If you zoom out, the pattern becomes clearer: we’re not falling apart. We’re just not moving forward the way we used to.
That’s when I know transformation isn’t just helpful — it’s necessary. Not because something’s broken, but because something’s misaligned with the season we’re in.
COVID: When Crisis Made Prioritization Simple
There was no time for strategic planning cycles or committee approvals. Organizations had to make immediate decisions:
- How do we work when we can’t be together?
- How do we serve clients without physical access?
- What absolutely must stay operational?
And we moved. Fast. We launched solutions we’d been “evaluating” for years. Not because we were ready, but because there was no alternative.
McKinsey later described this as companies being pushed over the “technology tipping point” — noting that digital adoption accelerated by three to four years in a matter of months.
It wasn’t pretty. But it was clarifying. We stopped debating what mattered and started executing.
The gift hidden inside that chaos? Crisis showed us we could prioritize when we had to. We just usually don’t give ourselves permission to be that decisive until we have no choice.
AI: We’re Prioritizing It — Just Not Strategically
Unlike COVID, AI hasn’t ambushed us. We’ve seen it coming. We’re talking about it constantly. And most organizations are trying to act.
But here’s what I’m observing: we’re not ignoring AI — we’re approaching it backwards.
This isn’t an AI-native workforce. These are capable, experienced professionals doing their best to integrate rapidly evolving technology into systems that weren’t designed for it.
So we reach for what feels concrete:
- Add a tool here
- Automate a task there
- Run a pilot, try a plugin, look for quick wins
- Respond to the vendor pitches and internal pressure to “do something with AI”
But most of these efforts aren’t grounded in strategic architecture. They’re tool-first, not problem-first. We’re implementing before we’ve designed. And without clarity on what we’re actually trying to solve — and why — it’s hard to tell whether we’re transforming or just creating expensive noise.
The numbers bear this out. A recent BCG study found that only 5% of companies report capturing measurable business value from AI investments.
That gap isn’t about lack of effort or investment. It’s about approach.
The organizations making real progress aren’t just adopting AI tools. They’re redesigning how decisions flow, how work gets structured, and how human judgment intersects with automation. That’s what AI-era transformation actually requires. And most teams haven’t gotten there yet — not because they’re behind, but because they’ve never needed this kind of systems-level thinking before.
The Signal That Actually Matters: Subtle Traction Loss
Let’s come back to those early indicators — the ones that matter most and are easiest to dismiss.
In my experience, the clearest signal that something deeper needs to shift isn’t dramatic. It’s when the edges of your business start feeling… off. Not in crisis, but in small, persistent ways that accumulate:
- Teams generate plenty of ideas, but decisions feel stuck in molasses
- Simple processes that used to take days now take weeks
- You spend more time fixing than building
- The mechanisms that used to drive results — your CRM workflows, marketing funnels, lead generation systems — feel just slightly out of tune with how your market actually behaves now
- Scope creep stops feeling like a project management problem and starts feeling cultural
- The algorithms and platforms you rely on seem to shift their rules faster than you can adapt
This is what I mean by subtle traction loss. Not collapse. Not failure. Just the slow recognition that the old mechanisms aren’t moving the business forward at the pace they used to.
And when you start seeing this pattern across multiple parts of the organization? That’s not a team issue. That’s a system issue.
It doesn’t mean something’s broken. It means you’re outgrowing your current design.
What COVID Taught Us (That We Can Use Right Now)
The opportunity in front of us — especially with AI — is to apply what COVID taught us without waiting for another crisis to force our hand.
COVID showed us what prioritization looks like under pressure:
- What must stay operational?
- What must change immediately?
- Where are we losing value fastest?
We can ask those questions now. With intention instead of panic.
- Where are we leaking value because processes haven’t been updated?
- Where have our systems aged out of relevance with how the market actually works today?
- Which workflows are over-functioning because we haven’t questioned them in years?
- Where could AI create real leverage — if we designed the system first, then chose the tool?
These questions create meaningful transformation. And they don’t require drama to answer. They just require honesty about where the work is getting harder than it needs to be.
Recognition Before Readiness
If there’s one thread connecting all of this, it’s this: readiness rarely kicks off transformation. Recognition does.
Recognition of friction. Of stall-out. Of energy being expended without proportional return.
If that’s what you’re noticing right now, you’re not behind. You’re at the beginning of something important.
At Alchemy Leadership Lab, we help executive teams turn that recognition into strategic action. Not by naming more problems, but by designing better systems — human-centered, AI-informed, and aligned with where your organization is actually headed.
If you’re seeing these patterns and ready for a different conversation about what comes next, let’s talk.
Check out last week’s post here.
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References
Harvard Business School Online. (2024). The Role of Artificial Intelligence in Digital Transformation.https://online.hbs.edu/blog/post/ai-digital-transformation
McKinsey & Company. (2020). How COVID‑19 has pushed companies over the technology tipping point—and transformed business forever. https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever
Business Insider. (2025). BCG says only 5% of companies are deriving value from AI.https://www.businessinsider.com/industries-seeing-value-from-ai-bcg-consulting-report-2025-10