Over the past four weeks, we've covered what AI is, why you can't always trust it, what the job displacement headlines are actually saying, and what AI agents do that a chatbot doesn't. If you've made it this far, you're probably somewhere between "this is interesting but I still don't know what to do" and "I know I should do something but I don't know what." Both are reasonable places to land.
Most businesses start with the technology. They sign up for a tool, explore what it can do, and try to find problems that fit. This tends to go badly, because AI tools are general-purpose and your business is specific.
Start with the friction instead.
Where does something break down repeatedly in your operation? What tasks sit on someone's desk not because they require that person's judgment, but simply because someone has to do them? Where does information get entered twice, moved manually, or copied from one system to another? Those are the seams in your operation, specifically the ones that make someone sigh quietly every Tuesday morning.
Good starting points tend to share a common shape: they're repetitive, they cost measurable time, and what "done" looks like is clear without needing years of experience to recognize it. Tasks that require relationship context, industry nuance, or judgment built over years are rarely the right place to begin, no matter how appealing the idea sounds.
PwC's research on this is worth sitting with. Technology delivers about 20 percent of the value in any AI initiative. The other 80 percent comes from redesigning the work around it. That's the part most businesses underestimate, and also the part that determines whether an investment compounds or just adds another subscription nobody uses six months later.
Starting with something small and well-defined builds the knowledge to take the next step without throwing money at the wrong problem. The businesses that get lasting value from AI tend to start with one thing that works, learn from it, and expand. The ones that lead with ambition and skip the foundation tend to have interesting stories to tell about what went sideways.
The right starting point often isn't obvious from the inside. What feels like your biggest inefficiency may not be the most tractable one, and what looks like a simple handoff to AI often has more complexity underneath than it appears. Getting that read right at the beginning saves a lot of expensive backtracking later.
I wrote this series to give you enough grounding to ask better questions. If you're ready to figure out where your actual starting point is, that's exactly the kind of conversation I have every day.
You know where to find me.