For the last two years, software companies have been racing to prove they have AI.
It has been everywhere. Product launches, keynotes, landing pages, earnings calls. Having AI was itself a story… But that’s no longer the case.
AI is quickly becoming something customers expect in the software they use. Soon, saying your product has AI will be about as interesting as saying it runs in the cloud.
That creates a more interesting challenge for marketers: Why does your AI matter?
Because every company can, and does, make an AI claim, buyers need a way to understand why one product will produce a different result from another; that will be a benefit. That is where I believe the next phase of competition will happen.
What does AI actually deliver?
B2B marketers have been told for years to lead with outcomes. That advice still holds, but AI has quickly changed the scale of the outcomes we can produce.
Take revenue cycle management.
Providers have always wanted to get paid faster. For years, RCM companies have promised to reduce days in accounts receivable, prevent denials and accelerate reimbursement. Eventually, every company starts to sound the same.
AI gives us an opportunity to make the outcome much more concrete. Imagine being able to show that a provider gets paid seven days sooner.
A week of cash flow returned to a healthcare practice has real economic value. That money can go toward payroll. It can fund an investment the practice has been putting off. It can simply give an owner more breathing room.
“Get paid faster” is a marketing promise. Seven days sooner is a business result.
This is where I think AI marketing needs to go next.
If the technology creates a meaningful economic advantage, show it. Quantify it when you can. Put it into the context of how your customer actually runs their business.
One of the simplest exercises a marketing team can do is remove the word “AI” from the message and read it again.
Is the story still compelling? If it isn’t, you may be relying too heavily on the AI label.
Marketers Need to Understand What’s Behind the AI
There is another complication. Two companies can both say they use AI and be describing very different products.
One system may use a large language model to summarize information. Another may use data from across a business to identify patterns and inform what needs to happen next.
To a buyer, those distinctions aren’t always obvious. That puts marketers in a difficult position, because we need enough technical fluency to explain why the technology produces the result we’re promising.
Marketers need to understand which technical differences matter to customers.
The customer doesn’t need a lesson in machine learning. What information can the system access? What happens as work moves through the business? Does the technology learn from what happens next? Where does human judgment come into play?
Those are practical questions. The answers affect what the product can actually do.
If a system can work across a workflow, explain why that matters. If its architecture gives it access to context a point solution doesn’t have, make that understandable. If human expertise is part of the process, explain where and why.
Good marketing creates clarity around complex issues and AI is giving us plenty of complexity to work with, with more to come.
Help Buyers Ask Better Questions
The market is also still learning how to buy AI. For a while, asking “Does it have AI?” made sense. Today, that question tells you very little.
The more useful questions get underneath the label. What data can the AI access? What happens when the system is wrong? Can it act across a workflow, or does someone still have to move the work from one step to another?
The answers help buyers understand what they’re purchasing and how much of the promised value is likely to show results.
When a technology is still maturing, the market hasn’t fully decided how to evaluate it. Companies have a chance to help establish those expectations.
We’ve seen this before with cloud software. Buyers eventually learned which questions mattered, and questions that once needed lengthy explanations became part of the normal buying process.
AI is going through its own version of that transition.
Companies with meaningful differentiation shouldn’t wait for customers to figure out the evaluation criteria themselves. Help them understand what to look for. Give them language they can use. Make them smarter buyers.
Yes, that may make the sales conversation harder sometimes. It also helps buyers recognize where AI can create value for their business.
So, What Are We Selling Now?
AI isn’t becoming less important. The words “we have AI” have become less valuable. That distinction matters.
The next phase of AI marketing will require companies to show the business impact and explain enough about the technology for customers to understand where that impact comes from.
A strong AI story should leave a buyer with a clear understanding of the impact on their business and why. If all they remember is that your product has AI, you’ve missed the story.
The AI arms race got everyone building, launching and talking.
Now comes the part that will separate the companies that built something meaningful from everyone who joined the race: Proving what all that AI is actually good for.
