INBOX INSIGHTS: Why Generic AI Training Fails, Citizen Analyst Part 1 (2026-02-11)

INBOX INSIGHTS: Why Generic AI Training Fails, Citizen Analyst Part 1 (2026-02-11) :: View in browser

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INBOX INSIGHTS: Why Generic AI Training Fails, Citizen Analyst Part 1 (2026-02-11)

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Why Generic AI Training Fails

I’ve spent a lot of time lately talking to CEOs who are frustrated. They’ve done the “right” things. They’ve signed the enterprise contracts for the latest LLMs. They’ve held the mandatory all-hands “Introduction to AI” workshops. They’ve checked the boxes.

And yet, when they look at their actual operations, nothing has changed. Their teams are still drowning in the same manual tasks, and that expensive platform they bought is mostly being used to write slightly better-worded emails. Emphasis on “slightly”.

If this sounds familiar, I want to let you off the hook for a moment: the problem isn’t your team’s laziness. The problem is that most corporate AI training is designed for a generic employee who doesn’t exist.

You Can’t Coach What You Haven’t Measured

Think of it like this: you wouldn’t hire a personal trainer and tell them to give the exact same workout to a marathon runner and someone recovering from a knee injury. It’s not just ineffective; it’s actually harmful to your progress.

In the world of AI, most companies are trying to “train” their way out of a “People” and “Process” problem without ever stopping to diagnose where their people actually sit. We see organizations where the Marketing team is ready to orchestrate complex, multi-modal campaigns, while the Legal team is still trying to figure out if they’re even allowed to touch the tool.

When you give them the same generic training, the advanced users get bored and the beginners get overwhelmed. Everyone tunes out.

The Need for a Strategic Mirror

This is why we’ve moved away from “training” and toward “tailored coaching.” But before coaching can happen, you need a mirror. You need a version of a proficiency rubric that actually reflects your unique operational reality.

A custom rubric allows you to identify behavioral proof points that matter to your business. We don’t care if someone says they are “proficient in AI.” We want to know:

Does your Engineer have a “Human-in-the-Loop” checklist for verifying code?
Does your Marketer know how to perform a “Red Team” analysis on a campaign brief?
Does your Product Manager use AI to identify resource bottlenecks, or just to summarize meeting notes?

When you build a rubric tailored to your specific disciplines, you stop guessing. You start seeing the “heat map” of where your organization is strong and where you are exposed to significant risk.

Moving From Fluency to Orchestration

The goal of this isn’t just to make people “faster.” If your team saves five hours a week but just fills that time with more internal meetings, you haven’t gained anything. Real success is measured by whether those efficiency gains actually translate into business value—not just doing the wrong things faster.

Success isn’t about tool adoption; it’s about Strategic Orchestration.

It’s about moving your team members from being “Assisted” (using AI as a simple notetaker) to being “Transformers” (reimagining how their entire job could work). That transition doesn’t happen in a 60-minute webinar. It happens through discipline-specific coaching that is grounded in the tasks they actually do every day.

The Bottom Line

AI isn’t a “set it and forget it” technology. It is a change management challenge. If you want to see a real return on your AI investment, stop looking for the “perfect” tool and start looking at how you are developing your people.

Build your rubric. Measure your baselines. Then, and only then, give your team the tailored coaching they need to actually succeed.

If you’re looking for a place to start with your own team’s audit, we’ve developed an AI Proficiency Matrix and Behavioral Survey that helps leaders identify these gaps.Contact us to find more information about how we help organizations build these tailored roadmaps.

Are you suffering through generic AI training and want to do something about it? Reply to this email or join the conversation in our Free Slack community, Analytics for Marketers!

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss managing AI agent teams with Project Management 101. You will learn how to translate scope, timeline, and budget into the world of autonomous AI agents. You will discover how the 5P framework helps you craft prompts that keep agents focused and cost‑effective. You will see how to balance human oversight with agent autonomy to prevent token overrun and project drift. You will gain practical steps for building a lean team of virtual specialists without over‑engineering. Watch the episode to see these strategies in action and start managing AI teams like a pro.

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we did a tour of Claude Cowork’s new plugins. Catch the episode replay here!

This week on So What? we’ll be showing how to prompt agentic AI! Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, we’re going to revisit something from the archives that Katie wanted to dust off. Back in 2015 when I first started working with IBM (back then, it was IBM Analytics, now it’s Data and AI), the IBM Watson Analytics team popularized the term “citizen analyst” – regular folks who were data analysts in their spare time.

There was a campaign about the citizen analyst, promoting what was at the time known as IBM Watson Analytics, a web-based analysis tool similar in spirit to tools like Tableau. The vision of the campaign was that with tools like Watson Analytics, regular citizens could perform useful data analysis tasks and garner insights about causes they cared about. The tools would enable them to ask questions of their data and be data-driven.

For example, suppose you cared about pet adoption. Suppose there was lots of data available from local, state, and non-profit entities in spreadsheets scattered around the web, and you wanted to understand the big picture. Instead of trying to cobble together a pile of spreadsheets into a master spreadsheet, you’d load your data into Watson Analytics and have it do that, then give you a master data set you could create visualizations and analysis from. Maybe, as a volunteer, you’d find that a certain policy changed how fast pets were adopted, or how a pet’s age or breed influenced adoption rates.

The campaign was generally well-executed, though IBM sunset Watson Analytics a few years later in 2019; the tool struggled to find a place in the IBM family alongside Cognos and the broader Watson portfolio. (IBM would later launch watsonx in 2023 as its next-generation AI platform.)

But the concept itself was sound – empowering regular people who might want to do data analysis but lacked the skills for things like advanced data analysis in languages like Python or R. A web interface where you could load your data and ask questions of it with simplified tools was supposed to open up a golden age of analysis.

The funny thing is, generative AI is that age. IBM was ahead of the curve on the concept, but the technology took a decade to get to where it needs to be where regular folks can load up a spreadsheet into a generative AI tool like Gemini or ChatGPT or Claude and ask questions of their data. Instead of pre-baked tools, AI models now write their own analysis software on an ad hoc basis, generating Python scripts behind the scenes to do much of the data processing. (or explicitly, if you direct them to do so)

In the next couple of issues, we’ll deconstruct the citizen analyst and think through what it looks like today, plus give you some practical examples to try. But the citizen analyst’s time, in the age of AI, has finally come to bear.

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 new 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.

👉 Take this course now to become an AI leader

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Imagine a world where your marketing strategies are supercharged by the most cutting-edge technology available – Generative AI. Generative AI has the potential to save you incredible amounts of time and money, and you have the opportunity to be at the forefront. Get up to speed on using generative AI in your business in a thoughtful way with our workshop offering, Generative AI for Marketers.

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