The SMB Leader’s Guide to Building Real AI Capability
AI adoption is starting to feel like one of those home improvement projects that begins with “we’ll just paint the kitchen” and somehow ends with three contractors, a new electrical panel, and someone saying, “While we’re in here…”
That is why my conversation with Maryrose Lyons, founder of the AI Institute, was so useful. Maryrose works with companies that are actively trying to train their teams, adopt AI tools, build agents, and improve productivity in the real world. Her message for SMB owners and marketing directors was refreshingly practical: AI adoption works when people learn it through their actual work, with real examples, live guidance, and a clear plan for what happens next.
At Avenue9, we help businesses use Human-First AI Marketing® to amplify their voice, empower their people, and market with the clarity and capability of a much larger brand. This conversation lined up with one of our core beliefs: AI success starts with people, process, and purpose.
Let’s talk about the biggest lessons.
1. AI training has to stay alive
Maryrose made a great point early in the conversation. The AI Institute decided to deliver all of its training live with a human because AI tools change too quickly for static courses to stay useful. A recorded class can go stale faster than the bananas nobody eats in the office kitchen. She shared that her team is constantly updating courses around tools like Claude Code, Copilot, and new AI workflows because the pace of change is so fast.
For SMBs, this matters because many leaders are still asking, “Should I get my team certified?” Maybe. Certificates can help with structure and motivation. The bigger question is whether your team is learning skills they can use this week.
The better training question is:
Can my team apply this to the work we already do?
That one question cuts through a lot of AI noise.
2. Teach AI through use cases, not tool tours
Maryrose described her approach to training corporate teams by focusing on use cases. Instead of teaching “these are the features of Copilot,” her team says, “You’re a finance team in an architecture firm. These are the things you probably do all the time. Let’s show you how to do those things with AI using your own files.”
That is the whole ballgame for SMBs.
Most teams lose momentum because AI training feels abstract. Someone shows them twenty features, five settings, and a magic prompt formula, then sends them back to work. By Friday, they are back in spreadsheets, inboxes, slide decks, and customer calls, wondering where the robot magic went.
Use case training works because it starts where the work already lives.
For a marketing director, that could mean:
- Turning a customer interview into a blog outline
- Repurposing a webinar into LinkedIn posts
- Building a campaign brief from sales call notes
- Creating follow-up emails from common objections
- Summarizing competitive research into positioning angles
For a business owner, that could mean:
- Reviewing proposals faster
- Preparing for sales meetings
- Drafting SOPs
- Analyzing customer feedback
- Creating training materials for the team
AI adoption becomes much easier when the tool stops feeling like a mystery box and starts feeling like a better way to do Tuesday’s work.
3. Every team has dreamers, believers, soldiers, and cynics
One of Maryrose’s most useful observations was her description of corporate learners. She sees dreamers, believers, soldiers, and cynics inside companies. Some people are excited. Some are ready to execute. Some will do exactly what they are shown. Some walk in with folded arms and good questions about hallucinations, ownership, data, and bias.
This is where leadership matters.
A lot of SMB leaders accidentally design AI adoption for the most excited person in the room. That person is already using ChatGPT, experimenting with agents, and naming their custom GPTs like office pets. Great. Give them room to run.
Your adoption plan also has to serve the person who feels threatened, skeptical, or overwhelmed.
Maryrose shared that cynics often change their posture when they see AI help them complete a familiar four-to-six-hour task in 15 to 20 minutes. Once the work is their work, the lightbulb turns on.
That is the leadership lesson: adoption is emotional before it is operational.
People need to see themselves becoming more capable. They need to feel safe asking basic questions. They need to know AI is there to help them do higher-value work, with more confidence and less drag.
4. The companies that trained early are already ahead
Maryrose shared that companies that invested in deep AI training two years ago have already moved ahead. Some have hired AI program leads, heads of AI, and internal AI teams to support adoption, programs, and governance.
This is the part many SMBs should pay attention to.
AI adoption has moved beyond “Who has played with ChatGPT?” The leaders pulling ahead are building internal capability. They are deciding which work should be automated, which work should be augmented, and which work should stay deeply human.
That is also why SMBs have a real opportunity right now.
Large companies have committees, approval cycles, legacy systems, and seventeen people required to rename a shared folder. Small and mid-sized businesses can move faster. You can pick a department, choose five high-value use cases, train the team, and build a simple governance rhythm before the big companies finish scheduling the kickoff meeting.
Speed helps when it has direction.
5. Use case workshops turn excitement into a roadmap
One of my favorite takeaways from Maryrose was what happens after training. Her team runs use case workshops where employees bring all their “wild and crazy ideas,” then they sort them through different lenses. The output is a roadmap: ideas the team can handle internally, ideas that need support, and ideas worth building into agents.
That is exactly the kind of thinking SMBs need.
AI adoption can quickly become a junk drawer full of experiments. Someone creates a custom GPT. Someone else builds an automation. A third person quietly uses AI to write proposals. Nobody knows what exists, who owns it, or whether the output is any good.
A use case workshop brings order to the chaos.
Here is a simple version you can run with your team:
- List every repetitive, annoying, high-volume, or research-heavy task.
- Score each task by time saved, business impact, risk, and ease of implementation.
- Pick three quick wins and one strategic project.
- Assign an owner, quality standard, and review cadence.
- Document the process so the capability stays with the company.
That last point matters. AI work needs ownership.
6. Agents need governance before they become office gremlins
Maryrose raised a big question for companies building agents: how do you govern a company when everyone is building them, and what happens when the person who built the agent leaves?
This will become a very real SMB problem.
Right now, agents feel exciting because they can handle research, onboarding, reporting, summaries, customer support, sales prep, and internal workflows. Maryrose shared that her own company planned to build one agent in each major business pillar by the end of the year and had already built several iterations across the business halfway through the year.
That is powerful. It also needs structure.
At minimum, every AI agent should have:
- A clear purpose
- A named owner
- Approved data sources
- Instructions for when to escalate to a human
- A quality review process
- Documentation
- A plan for updates
Think of agents like junior employees with excellent speed and no common sense. Give them clear instructions, review their work, and keep them away from the metaphorical forklift until they’ve earned it.
7. The future employee becomes CEO of their own role
Maryrose’s most memorable line was this: people need to realize they are the CEO of their own role. In her view, the future worker will come in each morning, check what their agents or tools did overnight, then decide where to fine-tune, improve, and focus strategically.
That is a huge mindset shift.
The best employees will manage their own AI leverage. They will know what to delegate to tools, what to check carefully, and where their judgment creates the most value.
For marketing directors, this means your team needs more than prompt templates. They need strategic thinking. They need audience clarity. They need brand voice. They need a clear sense of what “good” looks like.
AI can generate output quickly. Your team still needs taste, judgment, empathy, and context.
That is where Human-First AI Marketing® comes in.
8. Better prompts start before you open the tool
Maryrose gave a practical example of how people should approach a task differently. Instead of opening PowerPoint and jumping straight into the work, she recommends pausing to ask: What am I being asked to do? What information would help me do the best possible job? What research, data, or context should I gather first? Then, use that information to create a much stronger prompt.
That 15-minute pause can dramatically improve the result.
This is what we call context engineering at Avenue9. AI needs the right ingredients: your audience, your offer, your point of view, your customer language, your proof, and your desired outcome.
A weak prompt says, “Write me a marketing email.”
A strong prompt says, “Use this customer interview, this offer, this buyer persona, these objections, this brand voice, and this call to action to draft an email that helps a skeptical operations leader understand how we solve their problem.”
Same tool. Very different result.
9. The real win is better work
A lot of AI conversations focus on time savings. Maryrose made the better point: when AI is used well, people produce better work. The work improves because they gather stronger context, think through the assignment, and use AI to help create a higher-quality output.
That is the adoption sweet spot for SMBs.
Saving time is great. Better work compounds.
Better sales follow-up creates more conversations. Better content creates more trust. Better onboarding creates happier customers. Better internal documentation creates less chaos. Better research creates smarter decisions.
AI adoption should make your team feel more capable, more confident, and more valuable.
The Avenue9 takeaway
AI adoption is a leadership project with technology inside it.
The practical path looks like this:
- Train people live and close to the work.
- Start with real use cases.
- Support every learner type.
- Turn ideas into a roadmap.
- Build governance before agents multiply.
- Teach people to manage AI as part of their role.
- Use context to create better work.
At Avenue9, we help SMBs and midmarket teams build Human-First AI Marketing® systems that amplify authentic voices, improve marketing execution, and turn conversations into scalable content. We use our A9^Factor Framework™ to connect audience clarity, brand voice, tool alignment, and workflow integration so your AI adoption creates momentum instead of mess.
Ready to build an AI marketing system that fits your people, your voice, and your growth goals? Let’s map your next best use cases.
