INBOX INSIGHTS, June 4, 2025: When AI-First Goes Wrong Part 2, Changing Data Languages - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: When AI-First Goes Wrong Part 2, Changing Data Languages (2025-06-04)

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When AI-First Goes Spectacularly Wrong – Part 2

If you missed Part 1 on Duolingo’s communication disaster, you can find it here. This week: why this pattern keeps repeating across organizations.

Here’s what struck me about Duolingo’s backtrack: this wasn’t a communication failure. It was an organizational behavior failure.

Klarna did the same thing. Shopify too. The pattern is so predictable now: bold AI-first announcement → employee backlash → damage control → “we never meant replacement.”

But why do smart organizations keep making the same mistake? Trust Insights’ 5P framework reveals the organizational dysfunction behind this cycle.

The 5P Analysis: Where Organizations Break Down

Purpose: Misaligned Incentives. The real problem isn’t unclear purpose—it’s competing purposes within the same organization.

CEO incentives: Impress investors, show innovation, hit cost targets

HR incentives: Retain talent, maintain morale, avoid turnover

Operations incentives: Maintain quality, meet deadlines, preserve expertise

When von Ahn announced “AI-first,” he was optimizing for investor perception while ignoring the organizational reality that his business runs on human expertise. This isn’t a messaging problem—it’s a fundamental misalignment in how leadership measures success.

People: The Stakeholder Blindness Pattern. Organizations consistently underestimate how many stakeholders are affected by AI announcements. It’s not just employees—it’s:

Contractors who suddenly feel disposable
Customers who value human service
Partners who rely on your expertise
Future hires who question job security

The behavioral pattern here is classic: leaders focus on the stakeholders in the room (board, investors) while forgetting about stakeholders who aren’t represented in decision-making.

Process: Announcement-Driven Strategy This is where organizational behavior gets really interesting. Companies are announcing AI strategies before they have developed AI strategies.

Why? Because in many organizations, the announcement IS the strategy. Leadership feels pressure to appear innovative, so they commit publicly to force internal action. It’s a backwards approach that creates the backtrack cycle.

The organizational psychology here is telling: when you announce first and figure out implementation later, you’re essentially using external pressure to drive internal change. It rarely works.

Platform: Authority vs. Influence Confusion. Leaders think their authority means their messages will be received as intended. But organizational influence doesn’t work that way.

When von Ahn used LinkedIn to announce workforce changes, he was exercising authority (I can announce our strategy) without considering influence (how people will interpret and react).

The behavioral insight: authority tells you what you CAN say, but influence determines what people WILL hear.

Performance: The Measurement Disconnect. Organizations measure what’s easy to count (cost savings, automation rates) rather than what actually matters for AI success (trust levels, collaboration effectiveness, innovation capacity).

We know that most AI initiatives fail to deliver ROI, but companies keep measuring the wrong indicators. This isn’t an analytics problem—it’s a behavioral one. Organizations gravitate toward metrics that make them feel successful rather than metrics that predict actual success.

If you want a REAL ROI calculation worksheet, you can find one in our new AI-Readiness Marketing Strategy Kit. Download it for free.

The Organizational Psychology Behind the Pattern

Why does this cycle keep repeating? Three key behavioral factors:

Innovation Theater: Organizations feel pressure to appear cutting-edge without doing the hard work of actual innovation. Bold AI announcements serve this psychological need. Stakeholder Myopia: Decision-makers focus on the stakeholders who validate their choices (investors, tech media) while ignoring stakeholders who might challenge them (employees, customers). Sunk Cost Momentum: Once a public announcement is made, organizations feel pressure to defend it rather than adapt to it, leading to doubled-down messaging that makes the backlash worse.

What This Reveals About Organizational Readiness

The companies that avoid this cycle aren’t necessarily smarter—they have different organizational behaviors:

They pilot internally before announcing externally. They include affected stakeholders in strategy development. They measure trust and capability alongside efficiency.

Most importantly, they understand that AI transformation is an organizational change challenge first, and a technology challenge second.

The Real Lesson for Leaders

The hype-backtrack cycle reveals something crucial about organizational readiness for AI: if your organization can’t have honest internal conversations about AI implementation, you’re not ready for external announcements.

Von Ahn’s backtrack suggests that Duolingo hadn’t actually aligned internally on their AI approach before going public. The corrected messaging—AI as acceleration, continued hiring, human-AI partnership—should have been their internal consensus before any external communication.

The pattern keeps repeating because organizations are using public announcements to force internal alignment instead of achieving internal alignment before public communication.

Breaking the Cycle Requires Organizational Change

This isn’t about better messaging. It’s about better organizational behavior. Companies that successfully implement AI do the hard work of internal alignment first.

They get clear on competing incentives. They include all stakeholders in strategy development. They pilot and learn before announcing. They measure what actually predicts success.

The question isn’t how to communicate AI strategy better. It’s whether your organization is ready for AI transformation in the first place.

Next week in Part 3: What organizations actually lose when they eliminate human expertise (and why it creates long-term competitive vulnerabilities).

Oh! And if you want to really assess your AI readiness, download our kit of frameworks.

What are your thoughts on AI-first strategies?

Reply to this email to tell me, or come join the conversation in our free Slack Group, 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 the new AI-Ready Marketing Strategy Kit. You’ll understand how to assess your organization’s preparedness for artificial intelligence. You’ll learn to measure the return on your AI initiatives, uncovering both efficiency and growth opportunities. You’ll gain clarity on improving data quality and optimizing your AI processes for success. You’ll build a clear roadmap for integrating AI and fostering innovation across your business. Tune in to transform your approach to AI!

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In this week’s Data Diaries, let’s talk about a specific format problem with generative AI and data.

The most common form of generative AI is the language model, the underlying model that powers tools like ChatGPT. Language models are, unsurprisingly, very good at handling language.

Yet when we work with data, especially quantitative data, we’re working with it in a very different, non-language way. Consider the average spreadsheet. It’s made of rows and columns, with cells that hold data. None of those things are language objects, and a spreadsheet, while being data, is not language.

Which means if we hand it to generative AI, there’s a good chance it’s not going to use it well or properly in a reliable manner. Yes, sometimes when you load a spreadsheet (especially a simple one) into tools like ChatGPT, you get good results.

And sometimes you don’t.

So how do we alter this? One of the most straightforward, slightly technical ways to do this is to transform the spreadsheet into language.

Language models speak many, many languages, both human and computer languages – which means we can reformat a spreadsheet into a computer language. For example, one of the most popular languages for data is JSON, Javascript Object Notation. If we use common tools like csvjson, a Python data processing tool, we can convert a spreadsheet into JSON – and that means generative AI will have a much easier time reading it.

Here’s a simple toy example. Imagine you have a spreadsheet with a single column called animal, and two values, dog and cat. A JSON translation would turn that into basically a list, with JSON’s special markup. Now, instead of a spreadsheet, you have a list – and that’s much easier for AI to handle. If you added a second colum, the translation tools would effectively turn that into another list, below the first list.

That’s much easier for language models to read and understand.

If you’re running into challenges with quantitative data, consider transforming it to a language that AI speaks more fluently. Chances are you’ll get a much better result.

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