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Why Your Agency Needs a Chief AI Officer (And It's Probably Not Who You Think)

• 12 min read • brain-food

Most agencies waste thousands on AI tools nobody uses. The problem isn't the tools—it's that nobody owns AI strategy. Here's who should.

I was on a call last month with an agency owner who'd spent $12,000 on AI tools in the last six months.

ChatGPT Plus. Jasper. Midjourney. Claude. Eleven Labs. Descript. Notion AI. The list went on.

"So which ones are your team actually using?" I asked.

Long pause.

"Honestly? Maybe ChatGPT. Sometimes. If they remember their login."

Twelve thousand dollars. Seventeen different AI platforms. And his team was still manually writing every social post, every email, every client brief the exact same way they did two years ago.

This is the pattern I see constantly. Not just with agencies—home service companies too. Contractors excited about AI dispatching that never gets turned on. Companies with AI phone systems that nobody trusts enough to actually deploy.

The problem isn't the tools. The tools are incredible.

The problem is nobody owns AI at these companies.

The Real AI Implementation Problem

Here's what usually happens:

The business owner reads an article about AI (probably on LinkedIn). Gets excited. Signs up for three different platforms. Maybe watches a YouTube tutorial. Then dumps it on their team with "Hey, we should be using AI for this." This is often me, BTW 😜

Six weeks later, nothing's changed except the monthly software bills.

I've watched this pattern dozens times now working with clients. And what fascinates me isn't that the technology is too hard—I've seen technophobes in their 60s become AI power users. The capability isn't the issue.

The issue is ownership.

When nobody owns AI strategy, implementation, and ongoing iteration—when it's just another thing everyone's supposed to figure out on top of their actual job—it doesn't stick.

The CEO is three webinars deep into AI-powered marketing funnels. The creative director is wondering if AI makes their designers obsolete. Account managers are questioning if Claude can write better strategy briefs.

And nothing's getting implemented because everyone's already at capacity doing their actual job.

Home service companies have a different version of the same problem. They've been burned by technology promises before. "This CRM will revolutionize your business!" (It didn't.) "This scheduling software will solve all your dispatch problems!" (It created new ones.)

So when AI comes along promising to answer phones, qualify leads, optimize routes, and diagnose HVAC issues—they're skeptical. Rightfully so.

But here's what I'm seeing: The companies that succeed at AI aren't the ones with better tools or bigger budgets. They're the ones where someone specifically owns making AI actually work.

They have a Chief AI Officer. They just might not call them that.

What a Chief AI Officer Actually Does (For Real Businesses, Not Enterprises)

Most articles about CAIOs are written for Fortune 500 companies. They talk about AI governance frameworks and enterprise-wide deployment strategies and C-suite integration.

That's not helpful if you're running a 15-person marketing agency or a home service company with 30 technicians.

So let me tell you what a CAIO actually needs to do in businesses like yours.

For Agencies: Making AI Work With Creative Humans

The CAIO's job at an agency isn't to replace the creative team. It's to multiply them.

Workflow mapping comes first. I spend the first week with agency clients just watching how work actually flows. Not how the org chart says it should flow—how it really moves. From that initial client conversation through strategy, creative, production, delivery, and iteration.

Then we find the bottlenecks. And there are always bottlenecks.

It's usually in one of three places: client onboarding (where you're trying to extract strategy from scattered thoughts), content production (where one blog post takes three days of back-and-forth), or client reporting (where analysts spend 6 hours building a deck that gets glanced at for 6 minutes).

A good CAIO doesn't solve all three at once. They pick one, build an AI solution that actually works, get the team using it, and then move to the next.

I'm currently working with an agency where their creative director was spending 8-10 hours a week just doing revisions on client creative briefs. The first drafts were always too generic. She'd spend hours adding specificity, brand voice, actual strategic thinking.

We built a conversation-to-brief system. Now she has a 20-minute recorded conversation with the account manager. AI extracts the actual strategic insights, synthesizes the brand voice from past work, and generates a brief that's 80% there. She spends an hour refining instead of eight hours rebuilding.

That's what a CAIO does. They don't automate creativity. They remove the bottleneck that was keeping the creative director from doing more actual creative direction.

For Home Service Companies: AI That Actually Serves Customers

Home service companies have a different challenge. Their AI can't just be good—it has to be trustworthy.

Because when a homeowner calls at 9pm with no heat and three kids, they don't want to talk to a robot that sounds helpful. They want someone who can actually help.

So a CAIO in a home service company has to understand customer-facing implications. That AI phone system? It better know when to get a human involved. That automated dispatch? It better account for the fact that your best technician doesn't trust the GPS and always drives his own route.

I'm working with an company right now where the owner wanted to automate everything. "AI can handle the phones, the scheduling, the follow-ups, all of it."

First call we deployed the AI phone system, we got a customer complaint. The AI was perfectly polite, perfectly efficient, perfectly useless. It could book the appointment but couldn't answer "Is this going to be expensive?" in a way that felt human.

We rebuilt it. Now the AI handles the initial call, captures the details, asks the qualifying questions, and then texts the customer: "I've got all your information and Sarah will call you back within 15 minutes to discuss pricing and timing."

Customer gets fast response and human reassurance. Sarah gets a perfectly qualified lead with all the context. The AI did its job—multiply Sarah's capacity, not replace her judgment.

That's the work. Understanding where AI helps and where it hurts.

The Real Question: Who Should Actually Be Your CAIO?

Here's where most articles about Chief AI Officers completely miss the mark for small and mid-sized businesses.

They assume you're going to hire someone. Probably someone with "AI" already in their LinkedIn headline. Maybe someone from a tech company who can talk about large language models and neural networks and transformer architectures.

That's probably the wrong person.

Let me show you the three actual options that work for agencies and service companies.

Option 1: Fractional/Consulting CAIO (What Most Need Right Now)

This is what I do with Digital Ignitor clients. And it's what makes sense for most agencies and service companies with 10-50 employees.

You're not ready for a full-time AI leader. You don't need someone in the office every day maintaining AI systems.

What you need is someone who:

I typically work with clients for 6-12 weeks intensively, then move to monthly optimization calls. By month three, they usually have 2-3 AI systems running that are saving 10-15 hours per week. By month six, AI is just part of how they work.

The fractional approach works because you get strategic thinking without paying for someone to sit in Slack all day. You get implementation expertise without building an internal AI team. And you get someone who's doing this across multiple companies, so when I solve a problem for one client, the next three clients benefit from that learning.

I'm currently building similar voice-capture systems for three different agencies because the first one worked so well. Each implementation is different—different industry, different clients, different team structure—but the core learning transfers.

That's the value of fractional: you're not figuring this out alone.

Option 2: Promote Someone Internal (The Underrated Option)

Here's something I've seen work surprisingly well: promoting someone internal who doesn't have "AI" in their background but has something more valuable—they deeply understand your business and give a shit about solving problems.

I worked with a plumbing company last year where the CAIO ended up being their senior dispatcher. Not the tech-savvy millennial everyone assumed. The 52-year-old woman who'd been coordinating trucks for 15 years.

Why her? Because she knew every edge case. Every exception. Every reason the "logical" routing system would fail.

When we built the AI dispatch optimization, she was the one who said, "That route won't work because Paul doesn't have the right truck for that job" or "We can't send anyone to that neighborhood after 4pm without adding 30 minutes to the drive."

The AI got smarter because she understood the real world it was operating in.

For agencies, I've seen account directors, operations managers, and even senior creatives become excellent CAIOs. The common thread isn't technical background. It's:

If you have someone like that, invest in their AI education. Send them to workshops. Give them access to training. Partner them with a fractional consultant for the first few implementations.

A good internal CAIO who understands your business beats a highly technical AI expert who doesn't, every single time.

Option 3: The Founder as CAIO (The Reality for Many Small Shops)

If you're running a 3-10 person operation, you're probably the CAIO whether you wanted the job or not.

I'm currently working with a boutique branding agency—just the founder, two designers, and a strategist. The founder didn't want to be the AI person. She wanted to focus on client relationships and creative direction.

But we realized: she's already making every strategic decision about tools, processes, and workflow. Making those decisions about AI isn't a new role. It's the same role with a new tool category.

So instead of trying to avoid it, we built a framework:

Three hours a month of focused AI thinking. That's it.

She's not building custom systems. She's not writing code. She's making strategic decisions about what problems to solve and what tools to try, and then working with her team to implement.

If this is you, you don't need to become an AI expert. You need to become strategically curious about where AI helps your business. The rest you can outsource, hire fractionally, or learn incrementally.

What Good AI Leadership Actually Looks Like

I'm still figuring out some of this myself, but here's what I've noticed across clients who succeed with AI:

They start with one real problem, not ten theoretical opportunities. The agency that's succeeding with AI didn't try to revolutionize everything. They picked client onboarding—the thing causing the most pain—and built one solution that worked. Then moved to the next problem.

They measure in time saved and frustration reduced, not in "AI adoption." One client measures success by how many times their team says "thank god we automated that." Another tracks how often team members voluntarily use the AI system without being reminded. Those metrics matter more than "number of AI tools deployed."

They treat AI systems like team members that need training. That HVAC phone system? It broke during the first week. Then we fixed it. Then it broke in a different way. Then we fixed that. Now it works. But it took iteration, not just implementation.

They know when NOT to use AI. The plumbing company CAIO told me last month, "We tried AI for emergency dispatch and it was terrible. Customers in crisis need a human immediately, not a polite robot. So we use AI for scheduling maintenance calls, not emergencies." That's good AI leadership—knowing where it hurts as much as where it helps.

The Part I'm Still Figuring Out

Honestly, I'm still wrestling with something: How do you build AI systems that serve people without making them dependent?

The treehouse I built with my kids taught me this. AI didn't build the treehouse—but it gave me capacity to build it WITH my kids instead of planning alone while they waited.

That balance—technology serving relationships instead of replacing them—that's what matters most.

I see it with agencies too. The creative director with 8 hours back isn't working 8 fewer hours. She's doing more actual creative direction. More mentoring. More client strategy. The good work that machines can't do.

But I've also seen teams become passive. "Just let AI handle it" becomes the default instead of the exception. That's when AI stops serving and starts replacing.

I don't have this figured out yet. But I know it matters.

A good CAIO thinks about this. Not just "what can AI do" but "what should AI do" and more importantly "what shouldn't AI do."

If You're Wondering Whether You Need a CAIO

You probably do.

Not because AI is magic. Because AI is like any other capability—if nobody owns it, it doesn't happen.

You wouldn't try to "do marketing" without someone owning marketing strategy. You wouldn't "do operations" without someone responsible for operational excellence.

AI is the same. Someone needs to own it.

That could be you (if you're the founder). Could be someone on your team who's curious and capable. Could be someone like me working with you fractionally.

But someone needs to ask these questions:

I'm building these systems with clients every week. Some need full implementation—they want me in their business for 6-8 weeks building solutions with their team. Some just need the roadmap and some coaching to build it themselves.

All of them need someone who's actually doing this work in real businesses, not theorizing about enterprise AI strategies.

If you're stuck in "we should do AI" without knowing what that actually means for your agency or service company, I'd be happy to talk.

Not with hype. Not with promises that AI will transform everything overnight. With the messy, practical work of figuring out where AI actually fits in your business and building solutions your team will use next week.

Let's figure out what AI leadership actually looks like for your business.

Building AI solutions for agencies and home service companies. Documenting what works, what fails, and what I'm still figuring out. If this helped you think differently about AI in your business, join my weekly lab updates where I share what I'm building in real-time.

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