Sometimes you are not the right person for the job, and need to advise the client to hire another professional to attain the goals they desire.
I learned that lesson back in high school when I took on hand-lettering 45 campaign posters and realized, by poster number five, that my candidate client needed a printing company, not a teenager with a Marks-A-Lot.
Today, I’m seeing small business owners make a similar mistake – but with much higher stakes. They’re treating AI assistants like free labor instead of recognizing them as powerful tools that require skill and oversight to use properly.
The Seductive Promise of Automation
This week, I listened to a fascinating conversation on the Leader Generation podcast between host Tessa Burg and the ModOp innovation team – Fabio Fiss, Aaron Grando, and Javier López. They were discussing AI agents, protocols like MCP and A2A, and the future of automated workflows.
Here is that episode:
These aren’t theoretical futures – they’re building these systems for Fortune 500 clients right now. CRM replacements, automated audits, content workflows that connect product databases directly to creative output.
But here’s what caught my attention: Every single example they shared included human oversight, validation processes, and careful attention to data structure.
Fabio put it perfectly: “You’re gonna be the conductor and you’re gonna have instruments on your orchestra, and you’re gonna be able to orchestrate the music and the songs.”
Beautiful metaphor. But being a conductor requires actually understanding the instruments you’re conducting.
The Responsibility Gap That’s Costing Businesses
Javier López made a crucial point that every business owner needs to hear: “When an AI is your coworker, you are the one responsible for what he does.”
Think about that for a moment. When you hand over tasks to AI assistants – whether it’s rewriting website code, generating customer communications, or automating financial processes – you own the outcomes.
Yet I’m seeing business owners treat AI like a magic black box. Input request, receive output, implement without verification. That’s not automation – that’s abdication of responsibility.
Aaron Grando emphasized something critical during the podcast: AI workflows work best when they “allow for that human in the loop approval or rejection of a concept or an idea before it takes like a further action.”
That human-in-the-loop step isn’t a bottleneck – it’s professional insurance.
What the Experts Are Actually Building
The ModOp team shared real examples of AI implementations that work:
Automated Audits: Instead of manually checking if websites are responsive, have social media connections, or include specific code integrations, they build agents that perform these audits systematically.
CRM Evolution: In the senior living industry, they’re helping clients replace legacy CRMs with AI-powered platforms that integrate with OpenAI APIs for enhanced automation.
Content Workflows: Connecting product databases to content creation tools, allowing AI to pull specific product attributes (like high-protein meals) and generate targeted content.
Notice what these examples have in common? They’re specific, structured, and built with clear parameters. They’re not asking AI to “fix this form” or “make this better.” They’re giving AI defined tasks with measurable outcomes.
The Skills Gap Isn’t Technical – It’s Strategic
During the conversation, Fabio mentioned how senior developers are evolving from code writers to mentors and architects. However, the same evolution is necessary for business owners utilizing AI.
The gap isn’t in knowing how to prompt AI – it’s in knowing when to trust AI, how to include all variables, connections, and the full story of the use or situation, how to verify outputs, and what level of risk is acceptable for different types of tasks
Tessa Burg made a crucial observation: “AI features, AI access points… data that you can leverage to increase the quality of your AI is already inside your business.”
The operative word is “leverage,” which implies skill, strategy, and control.
The Real Cost of “Free” AI Labor
Here’s what small business owners need to understand: AI mistakes compound quickly. A broken website form doesn’t just cost you the repair time – it costs you every lead that bounced because the form didn’t work. A poorly generated customer email doesn’t just waste the sending – it damages your brand relationship.
The ModOp team emphasized the importance of data structure and quality. Fabio was particularly clear: “If you don’t treat your data on your website or application in a way that’s well structured… You won’t surface in their search.”
Poor AI implementation isn’t just ineffective – it can make you invisible to the very systems you’re trying to leverage.
The Questions Every Business Owner Should Ask
Before handing tasks over to AI assistants or agents, consider:
Do you understand the process well enough to spot errors? If you can’t manually verify the output, you shouldn’t be automating the input.
What happens if this goes wrong? Factor the cost of mistakes into your “free” labor calculations.
Are you solving the right problem? Sometimes the issue isn’t efficiency – it’s that you need better systems, cleaner data, or more skilled help.
Who can fix this if it breaks? Having a rollback plan isn’t paranoia; it’s professional responsibility. AND ALWAYS have a back up that is tucked away, just in case.
Is this the best use of AI for your business? Aaron Grando noted that “specialized agents that are good at a very particular task” outperform generalized ones. Focus AI on specific, measurable tasks rather than broad, subjective ones.
Building an AI Strategy That Actually Works
The podcast conversation revealed something important: successful AI implementation isn’t about replacing human judgment – it’s about augmenting it systematically.
Start with your data foundation. Clean, structured data that AI can actually use effectively. You need to understand it before you can ask for help turning it into additional stories.
Define specific use cases. Rather than asking AI to “help with marketing,” identify precise tasks like “generate product descriptions for items tagged as ‘seasonal'” or “audit landing pages for missing schema markup.”
Build validation processes. Every AI output should have a human checkpoint before it goes live.
Test in safe environments. Never implement AI changes directly on live systems.
Plan for failure. Know who can fix problems before they occur.
The Future Belongs to Better Conductors
The technology is advancing faster than our ability to use it responsibly. The businesses that succeed with AI won’t be those that implement it fastest – they’ll be those that implement it most thoughtfully.
As the ModOp team demonstrated, AI is already transforming business operations. However, transformation requires more than enthusiasm; it requires expertise, a well-defined process, and accountability.
The question isn’t whether AI will change how you work. It’s whether you’ll develop the skills to direct that change strategically.
Ultimately, the conductor determines whether the orchestra creates beautiful music or merely makes noise.
Ready to build an AI strategy that enhances rather than replaces human expertise? I’ve been helping small businesses navigate technology changes for over 20 years. Let’s discuss building AI workflows that actually work for your business goals. Start with this no-obligation “hello” form.