AI is moving too quickly to build a strategy around where the technology is today. A tool that seemed impressive six months ago may already feel ordinary, while something that seems unreliable today could become a practical business tool before the end of the year.
That was one of the biggest takeaways from our conversation with Laura Chattington this week. We spent two episodes talking about businesses chasing the latest AI tools, struggling to measure their return, and trying to understand how developers and quality professionals fit into this rapidly changing environment.
For this week’s challenge, we’re going to stop chasing the AI ball and look ahead instead.
Stop Following the AI Herd
Rob used a sports analogy that captures what we’re seeing with AI. If you’ve ever watched young kids play soccer, you’ve probably seen what happens when the ball moves across the field. Instead of maintaining positions, nearly every kid runs toward the ball at the same time.
AI can feel a lot like that.
A new model appears, and everyone starts talking about it. Agents become popular, and suddenly everyone needs an agent. Coding tools improve, and every development team is expected to become AI-first. Then something else arrives and the herd changes direction again.
The problem isn’t experimentation. We should be experimenting with these technologies. The problem is spending so much time chasing where AI is today that we never consider where it is going.
A better analogy comes from hockey: skate to where the puck is going, not where it has been.
Look at the Direction, Not Just the Technology
This challenge isn’t about predicting which AI company will release the best model or whether the next model will be 10 percent faster. Those predictions may be interesting, but they aren’t particularly useful for most businesses.
Instead, look at what AI can accomplish today compared with three, six, or twelve months ago. Then look at the direction those capabilities are moving.
Voice is a good example. Conversational AI has improved to the point where talking naturally with an AI system no longer feels particularly futuristic. The experience continues moving closer to the computer interfaces we’ve watched in science fiction for decades.
The important question isn’t which model has the best voice benchmark. The business question is what becomes possible when customers and employees can have useful conversations with software instead of navigating traditional interfaces.
That change could affect customer service, scheduling, sales, training, accessibility, technical support, and dozens of other business processes.
That’s the level at which we want you thinking this week.
AI Is Removing Our Automation Excuses
During our discussion, Michael argued that businesses have been doing too much manual work for too long. We’ve spent decades building software, websites, databases, CRMs, and business systems, yet organizations still have people copying information between systems, manually creating repetitive documents, and performing tasks that follow predictable rules.
Some of those processes couldn’t realistically be automated before. Others could have been, but the cost of building the automation was greater than the inconvenience of continuing to do the work manually.
AI is changing that calculation.
Rob pointed out that developers have probably experienced this themselves. We find a repetitive task and think about automating it, but then decide writing the automation would take longer than simply doing the task. With AI-assisted development, that excuse is becoming much harder to justify.
Something that once required several days of development may now be practical to build in an afternoon. That changes which business problems are worth solving.
Don’t Automate a Problem You Don’t Understand
Faster automation doesn’t eliminate the need to understand the problem first. In fact, it makes understanding the problem more important.
This connects directly to our conversation with Laura. AI gives organizations tremendous leverage, but leverage works in both directions. A good process can become dramatically more efficient, while a poorly designed process can become a faster way to produce bad results.
We’ve seen this repeatedly throughout this season. AI shines a light on weaknesses that were already there. Poor requirements, missing quality gates, inconsistent processes, bad data, and unclear ownership don’t disappear because AI enters the workflow.
AI can amplify those cracks.
Before automating something, make sure you understand what you’re trying to accomplish, why the process exists, and what a successful result should look like. Then use AI to remove the repetitive work while keeping the appropriate human judgment and quality controls in place.
Think About What Becomes Possible
One of the most useful ways to think about AI isn’t asking what it can automate today. Ask what problem is about to become practical to solve.
Maybe you have a process that currently requires too much human interpretation for traditional automation. Perhaps AI can handle 60 percent of it today, but that isn’t reliable enough to justify implementing it.
What happens when it reaches 80 or 90 percent?
Maybe your business receives hundreds of documents that employees manually review. AI might not be reliable enough today to completely automate that workflow, but it may soon be good enough to organize the information, identify exceptions, and send only the difficult cases to a person.
The opportunity isn’t necessarily removing the human. It may be changing where the human spends their time.
Instead of having people perform repetitive work at keyboards, AI can handle more of the routine processing while people focus on customers, exceptions, growth, decision-making, and the work that actually requires their experience.
Your Weekly Challenge
This week, take a few minutes and look back at how AI has progressed over the last three, six, and twelve months. Don’t focus on individual models or technical benchmarks. Look at capabilities and what those capabilities mean for businesses.
Then write down three things you believe AI will make practical within the next three to six months.
For each one, ask yourself a few simple questions. What business problem does this solve? Who currently spends time dealing with that problem? What becomes possible if AI makes that work dramatically faster or easier? What risks or quality controls would still need to remain?
If three ideas feel like too much, start with one.
Identify one problem that AI cannot reliably solve for you today but that you believe will become practical within the next three to six months. Then think about what you could do now to prepare for that capability.
Prepare Before Everyone Else Gets There
The goal isn’t to become an AI fortune teller. Most predictions about technology will be wrong in one way or another, especially when the technology is changing this quickly.
The goal is to develop the habit of looking ahead.
If you can identify where AI capabilities are moving, you can start preparing your processes, data, quality controls, and business strategy before the technology reaches that point. When the capability becomes practical, you aren’t starting from zero while everyone else scrambles to catch up.
That’s where the competitive advantage begins.
Don’t spend all your time chasing the AI ball around the field. Look at where it’s moving, understand the business problems that become solvable when it gets there, and start positioning yourself now.
This week, stop chasing AI and start skating to where the puck is going.
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