{"id":11207,"date":"2026-07-21T08:03:22","date_gmt":"2026-07-21T14:03:22","guid":{"rendered":"https:\/\/attentionmedia.io\/?p=11207"},"modified":"2026-07-21T08:03:22","modified_gmt":"2026-07-21T14:03:22","slug":"stop-buying-ai-and-start-buying-capabilities","status":"publish","type":"post","link":"https:\/\/attentionmedia.io\/?p=11207","title":{"rendered":"Stop buying AI and start buying capabilities"},"content":{"rendered":"
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I sit through a lot of vendor pitches and conference keynotes. After enough of them, the slides blur. AI-powered routing. AI-powered insights. AI-powered content. By the third talk, you can\u2019t tell whether you\u2019re being sold the same product four times or four products once. The term is broad enough to cover anything, which is the problem. It tells you nothing about what you\u2019re buying.<\/p>\n

I don\u2019t think we\u2019ll be saying AI much within a couple of years, at least not the way we do now. Jay Pattisall and Mike Proulx at Forrester made the point recently<\/a>: It\u2019s the same arc as electric. Everything was an electric [whatever] until it wasn\u2019t. We put food in the fridge, not the electric fridge.<\/p>\n

There\u2019s something useful in being specific about why you\u2019re retiring the term because it\u2019s not going to fade away elegantly on its own. Electricity left our vocabulary because we stopped paying attention to it. It just works. You plug something into an electric outlet, and you get the same current whether you want to or not. If it doesn\u2019t work, you know something\u2019s broken.<\/p>\n

Many AI systems will remain probabilistic. Instead of a lightbulb failing to turn on, AI will fail silently, and overconfident incorrect answers look just like overconfident correct answers unless you\u2019ve done rigorous testing and governance. AI will start to be phased out of low-stakes ambient uses and will cling to the ones where there\u2019s real money on the line for being wrong: diagnosis, credit scoring, and legal liability.<\/p>\n

Four capabilities, not one category<\/h2>\n

Dropping the term isn\u2019t simply about avoiding the overuse of a buzzword. That matters significantly less than the conversations that don\u2019t happen when we instead use an umbrella term that conflates overlapping but nuanced activities and actions. Underneath \u201cAI\u201d sit four different things a marketing, CX, or service system can actually do. Name which one you\u2019re buying, and your decisions, ownership lines, and processes get sharper. Eventually, so does the customer\u2019s experience.<\/p>\n

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Stop worrying about what some platform\u2019s AI means. Break it down into four piles: generation, augmentation, insights, and orchestration.<\/p>\n

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1. Generation\u00a0<\/h3>\n

When the machine produces the artifact, which is the product, and humans need not be the author. Emails in the millions, each personalized for one individual. Synthetic test data that wouldn\u2019t be possible to collect from the real world. Designs crowdsourced across billions of possibilities that no human would ever draw by hand.<\/p>\n

2. Augmentation<\/h3>\n

The second set of hands for a human who still controls the workflow. It\u2019s the real-time assistant that composes a service response. It\u2019s the tool that helps your analyst prototype working models faster. Augmentation contains the largest volume of the four categories. It quietly devours most of what people claim as AI creativity. Augmentation is less a technology than it is a posture. The work still ultimately gets done by a person. They can just do more.<\/p>\n

3. Insights\u00a0<\/h3>\n

This capability feeds a decision. Sometimes at the front of a process (predictive: propensity scoring, churn risk), sometimes at the back (analysis of work already done). The output is understanding, and someone still has to act on it. When that someone is another system, you\u2019ve handed off to the fourth bucket.<\/p>\n

4. Orchestration\u00a0<\/h3>\n

This capability coordinates activities across systems, tools, and agents. It exists on a spectrum of supervision: all steps individually approved by a human at one extreme, fully autonomous at the other. Autonomy is simply the far end of the dial, not something to purchase on its own.<\/p>\n

Most real deployments are two or three of these capabilities at once, and that\u2019s fine. Next-best-action, for instance, is an insights model that feeds an orchestration engine on purpose. The point of naming them isn\u2019t tidiness. A vendor selling you AI gets to dodge which one they actually do. A vendor selling you \u201can insights model feeding your next-best-action engine\u201d can\u2019t.<\/p>\n

You\u2019ll notice this says nothing about AI search, generative engine optimization (GEO), or AEO, on purpose, despite those being pretty hot topics for many marketers. Those are customer behaviors: how people find you through an answer engine. The AI umbrella I\u2019m talking about covers the tools your own teams operate, not the ones your customers do: different problem, different article.<\/p>\n

Where the four capabilities overlap<\/h2>\n

The two capabilities most likely to be conflated or mixed up are augmentation and generation because both produce something. I use one question to sort them, and it works on anything:<\/p>\n

Remove the AI. Could a skilled person still make this, just slower, smaller, or rougher?<\/p>\n

Yes means augmentation. \u201cWrite this in my voice.\u201d \u201cDraft the contract.\u201d A skilled human does both unaided. The AI amplified an author. No means generation by itself: the thing exists only because a machine worked at a scale or dimension that no human authoring loop can reach. A million individually tuned emails. Training data no one ever gathered. No person was going to hand-author those.<\/p>\n

The line is authorship. A skilled human could have written the first set. No one was ever going to write the second. Augmentation is about who\u2019s in the seat. Generation is about what got produced. They stack constantly. The everyday \u201cthe AI made me a thing\u201d is usually generation working for a human author, and the test still tells you which conversation you\u2019re in.<\/p>\n

How to decompose AI into more meaningful functions<\/h2>\n

It doesn\u2019t take a highly technical person to do this well. Take AI-powered subject lines, for instance. Pull it apart in three ways:<\/p>\n