INBOX INSIGHTS: How to Measure the ROI of AI, Using Conference Data For Content Gaps (2026-10-14)

INBOX INSIGHTS: How to Measure the ROI of AI, Using Conference Data For Content Gaps (2026-10-14) :: View in browser

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How do you measure the ROI of AI?

Yesterday I had the privilege of teaching the Claude for Business workshop at MAICON in Cleveland. I opened my Claude for Business workshop with the question I get asked more than any other. Not “which model should I use” or “is Claude better than ChatGPT.” It’s this one: how do you measure the ROI of AI?

It’s a fair question, and most organizations can’t answer it. McKinsey’s State of AI in 2026) found that nearly nine in ten respondents say their organization regularly uses AI in at least one business function. Only 37 percent attribute any EBIT impact to it, and just 6 percent qualify as high performers, meaning they can attribute 5 percent or more of their EBIT (earnings before interest and taxes) to AI. That’s a lot of AI and not a lot of return.

Helping companies answer this exact question is a big part of what Trust Insights does. The question isn’t “what’s the ROI of AI?” It’s “what’s the ROI of this one task, and were you measuring it before AI showed up?”

Nobody measures the ROI of their kitchen renovation

Think about how you’d judge a kitchen renovation. You don’t calculate the return on the oven. You notice that dinner takes forty minutes instead of ninety, that you stopped ordering takeout on Wednesdays, and that someone else in the house can finally cook without asking you where everything is.

I used that structure and framed the solution that way for the workshop. You start with your Project (your kitchen). This is your context that you load once and don’t have to do again. Then you have your Skills (your recipes) that you write down for your repeatable process. Next is your Plugin (your cookbook) that you can hand off to someone else to execute against. Each piece on its own isn’t an ROI. But all together, you can start to measure how fast it took to get dinner on the table. That’s how you start to get to the answer that plagues everyone. AI works the same way. The value shows up in a specific piece of work getting faster, less dreaded, and you can only see it if you knew what that work looked like before.

Four steps, and the first two don’t involve AI

For those of you who couldn’t make it to Cleveland, this is the short version of three hours.

Score the task before you touch a tool. We use TRIPS: Time, Repetition, Importance, Pain and Sufficient data, each scored 1 to 5. If the average comes in under 3, leave it alone. Automating low-value work is how AI projects earn a bad reputation.

Write down the requirements. The 5P Framework by Trust Insights™ asks five questions: what decision this supports (Purpose), who reads it and who builds it (People), whether there’s a standard or it lives in someone’s head (Process), where the finished thing needs to live (Platform), and how you’ll know it worked (Performance). Performance is where your ROI lives. Whichever question you can’t answer is the one to fix first.

Let those answers write the prompt. Four of the six lines in a good CASINO prompt (Context, Audience, Scope, Intent, Narrator, Outcome) come straight from your 5P Framework by Trust Insights™ answers. Purpose becomes Intent, People becomes Audience, Process becomes Scope, and Performance becomes Outcome. Bad AI output usually means missing context, not a weak prompt.

Pick one task and give it one month. Don’t go after every task you score. Pick one that you can focus on. Take your baseline measurement (if you skip this, you can’t measure your ROI), run it for a month, then measure it again. That number is your answer to the ROI question. That is the number you can bring to your leadership team.

The pattern I see most often is a company that rolled out a tool and waited for the return to show up. BCG’s AI at Work 2025 research, which surveyed more than 10,600 employees in 11 countries, found that regular use among frontline employees had stalled at 51 percent. Only 36 percent of employees said their training was adequate. That isn’t a technology problem. The technology is almost never the problem. It’s a people and process problem, which is why Platform is fourth in the 5P Framework by Trust Insights™, on purpose.

Coming soon. Most AI reporting I see counts usage: seats, logins, tokens, hours someone says they saved. That tells you people are using the tools. It doesn’t tell you whether the output, or the business, got any better. We’ve been building a scorecard for the ROI of AI, one where every measure has to be something you can actually observe. Starting with a “how confident do you feel?” survey is good, but I want to get deeper and more accurate. More on that soon.

You don’t measure the ROI of AI. You measure the ROI of the work, one task at a time, and you start before you ever open the tool.

How are you measuring it today?

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This week, Katie and I are at the annual Marketing AI Conference, MAICON, in Cleveland. By the time you read this, Katie will have crushed it on stage leading the Claude for Business workshop and Anthropic will have changed Claude’s interface 4 more times. We’ll be on the show floor recording a live edition of In-Ear Insights, the Trust Insights podcast and you’ll be able to catch that on our YouTube channel.

This week, however, I want to share a different take on conferences and what you can do with conference information, even if you never leave your desk.

To make the most of this issue, consider what conference or event is most relevant to your company and your marketing. We’ll use MAICON as the example. With your favorite AI agent of choice, such as Claude Code / Claude or ChatGPT Work, point your agent at the conference agenda and ask it to download the agenda in a structured data format. I recommend an AI-friendly format like YAML.

You’ll also want to have a catalog of your own content, what you’ve created in a significant time period, like a year or two. For this experiment, I’ll use the last 2 years of Trust Insights content. We use WP Engine and WordPress as our content management system, so I can export all our blog content (which includes our newsletter, podcasts, livestreams, etc. – a great reason to put a copy of all your content in one place you control) as a single data file. WordPress uses the MySQL database, so I can export it as a structured data format, SQL.

With these two data sources, we can ask our AI this key question:

“Given our content from the last two years in the SQL export and the MAICON conference agenda in Markdown files by day, split the agenda into individual sessions. Discard non-content sessions like breaks, meals, anything where learning content isn’t being presented. Then compare each session one by one to see if Trust Insights has published content on the topic, scored by similarity using a measure like cosine similarity. Present the MAICON conference agenda, by learning session, in descending order by cosine similarity. Show the session title, speaker, and description along with the score in HTML and CSV export formats. In the HTML, color code each row by cosine similarity in interquartile ranges – light blue for the top range, light green, light yellow, and light red for the lowest cosine similarity range. Ask me up to 20 clarifying questions until you have enough information to succeed at the build.”

What comes out of this is an analysis using a statistical technique called cosine similarity, a way of measuring topical similarity regardless of content length. It’s how many AI tools that do text analysis do comparisons, and it’s fast and free in the Python programming language.

What do you do with that analysis? This is a map of what you’ve covered in your content compared to what your industry cares enough about that they’ll put it in a paid (sometimes quite expensive!) conference agenda, an agenda that they think will be compelling enough to convince people to attend the event. It’s a content gap analysis on the highest profile content in your industry, telling you what you haven’t covered and what you might want to make content about.

In our analysis, we’re generally well-positioned; our lowest similarity scores (in a floating point range from 0 to 1) are in the 0.6 range, so even our least similar content is still fairly similar to the conference agenda. That’s helpful to know, and those sessions that are in the red for us are specific points we could create new content on instead of having to pursue entirely new topics – tuning, not turning.

Do this for yourself, for your company and your industry, and see where the content gaps are for you. And you don’t have to limit this to conference agendas – you could do the same exercise for your blog versus a competitor’s, or your YouTube channel, etc.

If you’ll be at MAICON this week, come say hello!

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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