My position on AI in marketing is boring and I am not going to dress it up. It is a useful tool and a bad substitute for strategy. It compresses work that was already defined. It does not decide what should be done, and it is not accountable when the decision is wrong.
Three years of enthusiasm have produced a fairly clear picture of where it earns its cost and where it quietly does not. Here is the split as I see it in 2026.
The pattern is consistent. AI performs well when the task is bounded, the raw material already exists, and a human checks the output before it reaches a customer.
The costs rarely show up as a line item, which is exactly why they persist.
If output has to be checked carefully, and it does whenever it goes in front of a customer, then generation was never the expensive part. I have seen teams produce four times the content and spend more total hours than before, because reviewing something plausible but subtly wrong is slower than writing it yourself.
Numbers, prices, specifications, regulations, claims about your own service. These are the cases where the output looks most authoritative and where being wrong is most expensive. A wrong price on a landing page is not a content problem, it is a legal and commercial one.
If you and your competitors use the same tools with similar prompts, you converge. The output is a competent average of what already exists. Average is a bad position to occupy when the buyer is comparing three options and looking for a reason to choose.
This is the expensive one. Asking a model for a marketing plan produces something that reads like a plan: channels, phases, KPIs. It has no knowledge of your margins, your capacity, your sales team, or what your last three campaigns taught you. Acting on it feels like progress and often just spreads the budget thinner.
A short test I apply before automating anything.
For a small business, the arrangement I would recommend is narrow and unglamorous. Use AI on internal work first, where errors are cheap and feedback is fast: summarizing calls, tidying spreadsheets, drafting internal briefs, sorting inquiries. Move it toward customer facing work slowly, one task at a time, with a named person accountable for every output that ships.
Feed it your own material. Your real service descriptions, your actual objection handling, transcripts of calls that went well. Generic output comes from generic input, and most disappointment with these tools is a prompt that contained nothing specific about the business.
Keep a record of what you tried and whether it actually helped. Most teams cannot answer that question because nobody measured before and after.
What still has to be human: deciding who you are selling to and who you are not, deciding what you charge and why, knowing which of your customers were profitable, and reading a poor result and correctly identifying whether the problem was the offer, the audience, or the execution. Those are judgment calls made with information that mostly is not written down anywhere a model can reach.
The businesses getting the most from AI are not the ones using it most. They are the ones who already knew what they were doing and used it to do more of it.
Pick the single most repetitive task in your marketing week and try AI on that one only. Time it before and after, including the review. If it does not save real hours, stop and try a different task rather than assuming you are prompting wrong.
If you want to work out where automation fits in your specific setup and where it will cost you, book a free consultation at /contact/.