If you’re an agency owner, you’ve probably felt at least some anxiety about artificial intelligence lately. Maybe you’re worried you’re falling behind (as I have argued many of you are), or you’ve tried a tool or two but aren’t sure what to do with it. Maybe you’re concerned that AI will somehow make your services less valuable or your expertise obsolete.
Here’s the actual problem: most of the conversation about AI focuses on tools, tactics, and fear—and that’s exactly the wrong place to start. What you need instead is a way of thinking about AI that won’t break every six months when the next shiny tool comes along.
This isn’t about becoming an AI expert or learning prompt engineering. It’s about applying the same clear thinking you use for every other business decision to this new category of tools.
Start with pain, not possibility
Before you ask what AI can do, ask what hurts.
Where is your agency bleeding time? Where are you burning energy on work that doesn’t require your expertise? Where is inconsistency, overwhelm, or repetitive work preventing you from focusing on what actually matters?
AI is most valuable when applied to real pain, not abstract opportunity. If you’re experimenting because everyone else is talking about it, you’re doing it backwards. If you’re trying it because you’re drowning in a specific problem and need relief, you’re on the right track.
This is how I approach tools: relief precedes growth. Sanity is a legitimate business objective. Tools—including AI—earn their place by solving actual problems, not by being interesting or novel.
Evaluate AI by outcomes, not ideology
There is no permanent list of what AI “should” or “shouldn’t” do. Capabilities evolve constantly. What was impossible six months ago might be reliable today.
The real question isn’t “Can AI do this?” It’s “Is AI good enough to do this today, in this specific context, with acceptable outcomes?”
When evaluating any AI application, consider: accuracy, reliability, risk if it gets something wrong, oversight required, and impact on trust with your team and clients. Don’t let anyone—enthusiasts or critics—tell you there’s one right answer. The answer depends on your specific situation, your tolerance for risk, and the current state of the technology.
As is always said, generative AI right now is the worst it will ever be. It’s only going to improve. But that doesn’t mean you should wait for perfection—it means you should evaluate based on whether it’s good enough right now for your specific use case.
Personal benefit comes before agency-wide change
Most AI advice jumps straight to scaling output or replacing labor. That’s backwards for agency owners.
AI should first make your life better. It should reduce your mental stress, improve your thinking quality, shorten decision cycles, and eliminate low-value work. If AI makes your agency busier before it makes your life better, it’s being misapplied.
This is why owner-led agencies should start with personal productivity before rolling out AI across the team. Use it as a thinking partner for strategy. Use it to draft that difficult client email. Use it to synthesize background research before a prospect conversation.
You don’t scale chaos—you remove it. And the chaos often starts with the owner being overwhelmed. Once you’ve found personal leverage, then you can consider how AI might help your team work more effectively.
AI must improve the time–quality–cost equation
Agencies always operate within constraints: time, quality, and cost. Every project navigates these three dimensions, both internally and for clients.
AI is valuable when it meaningfully improves at least one without breaking the others. For example: saving significant time without hurting outcomes, improving results without increasing internal cost, or holding quality steady while reducing the time or internal cost to produce it.
If an AI tool doesn’t improve this fundamental equation, it’s motion—not progress. This is why you shouldn’t feel pressured to adopt AI just because it exists. Some tools might speed up one thing while creating new bottlenecks elsewhere. Some might reduce cost but introduce quality risks you’re not willing to take.
The question is never “Should we use AI?” The question is “Does this specific application improve our position in a way that matters?”
Roles matter more than tools
There is no “right” AI stack. Tools change constantly—new platforms launch weekly (if not daily!), features evolve, companies get acquired. What’s popular today might be obsolete in a year.
Roles, however, endure. Instead of obsessing over which specific tool to use, think about the roles AI might play: thinking partner, writing assistant, research accelerator, documentation helper, administrative support.
When you focus on tools, adoption stalls—you get paralyzed choosing between options or worry you chose wrong. When you focus on roles, tools become interchangeable. You can experiment, switch when something better comes along, or use multiple tools for the same role.
You don’t need to become an expert in every AI platform. You just need to understand what role you need filled and find something that does it well enough.
Look beyond efficiency: Where can AI add new value?
AI isn’t just about doing the same work faster or cheaper. It can enable things agencies couldn’t realistically do before—or at least couldn’t do without significant additional cost or time.
Better preparation for client conversations because you can synthesize complex information more quickly. More consistent internal communication through better documentation. Faster team learning curves through improved onboarding materials. More thoughtful leadership because you have cognitive bandwidth for the work that requires it.
I’ve seen agency owners use AI to generate more comprehensive competitive analysis than they could previously given budget constraints, and to answer complex questions in minutes that might have consumed hours of research.
This is different from efficiency gains. Efficiency is doing the same thing with less time or cost. This is raising the ceiling on what’s possible—enabling work at a level of quality or consistency that wasn’t previously realistic.
AI adoption Is iterative, not binary
You’re not “good at AI” or “bad at AI.” Adoption happens in stages. You experiment, learn, and adjust. You find something that works, then you find its limits.
It’s okay to test an application and stop using it. To change tools when you find something better. To realize a use case doesn’t work as hoped. To start slow and expand gradually, reevaluating as the technology improves.
This is the same practical approach you take to every other business tool or process. Confidence comes from learning and iterating, not from locking in decisions.
The agency owners getting the most value from AI aren’t the ones who adopted everything immediately. They’re the ones who identified a specific problem, tried something, evaluated honestly whether it helped, and then decided what to do next.
A calm path forward
AI is neither savior nor threat. It’s a tool that earns its place by solving real problems.
The goal isn’t to use more AI—it’s to use it intentionally, in service of the business and life you’re building. It’s the same principle that serves as the foundation for the Build to Own approach I created and advocate for owner-led agencies. Your agency should serve your goals, and AI can help you do that.
AI can reduce the stress that prevents you from doing your best work. It can improve profitability by reducing time or internal costs without sacrificing quality. It can open up options you didn’t have before. And it can enable you to focus on the work that actually gives you satisfaction.
It’s not valuable just because it’s new, cool, or because everyone else is talking about it.
Start by identifying one pain point—just one—where you’re genuinely struggling. Then ask whether AI might provide relief. Experiment with a low-risk application. Evaluate honestly whether it improved things. Adjust based on what you learn.
That’s not a dramatic transformation. It’s not a complete overhaul of how you work. It’s just clear thinking applied to a new category of tools.
And that’s exactly how it should be.
Turn ideas into action
Identify one specific pain point (15 minutes): Don’t try to solve everything at once. Write down the single most frustrating bottleneck or time drain in your work this week. Be specific—not “I’m overwhelmed” but “I spend 3 hours every week editing drafts of blog posts and press releases for clients.”
Test a focused AI application (1 hour): Find one AI tool that might address that specific pain point and try it with a real, low-stakes example from your work. Don’t overthink the tool choice—just pick something reasonable and see what happens. Evaluate honestly: did it actually help, or did it create more work?
Define your evaluation criteria (30 minutes): Before experimenting further, write down what “success” looks like for AI in your agency. How will you know if it’s actually improving your time–quality–cost equation? What would make you confident enough to expand usage? Having clear criteria prevents endless tinkering without purpose.
