INBOX INSIGHTS: When AI-First Goes Wrong, Using AI on Your Dark Data (2025-05-28) :: View in browser
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When AI-First Goes Spectacularly Wrong: Part 1
Last week, we talked about why leading with fear doesn’t make you a leader. If you missed that issue, you can find it here. It seems like this trend is coming to light more and more.
Over the weekend, I read about Duolingo’s spectacular communication meltdown. CEO Luis von Ahn didn’t just implement a questionable AI strategy—he committed one of the most catastrophic communication failures I’ve seen in recent memory.
And here’s the kicker: just one week later, he was on LinkedIn doing damage control, walking back almost everything he’d said.
The Announcement That Broke Trust in Record Time
In late April 2025, von Ahn announced Duolingo’s “AI-first” strategy with these key messages:
- Plans to “gradually stop using contractors to do work AI can handle”
- Only add new employees “if a team cannot automate more of their work”
- Evaluate employees’ AI fluency in annual reviews
- AI would soon teach better than humans at greater scale
Every element sent the same message to his workforce: You are a problem to be solved, not an asset to be leveraged.
The response was brutal. Users flooded social media with criticism, with one TikTok commenter capturing the sentiment: “mama may I have real people running the company 💔” The backlash forced Duolingo to execute a complete social media blackout.
The Inevitable Backtrack
One week later, von Ahn was back on LinkedIn with completely different messaging:
“To be clear: I do not see AI as replacing what our employees do (we are in fact continuing to hire at the same speed as before). I see it as a tool to accelerate what we do, at the same or better level of quality.”
This is exactly what he should have led with. The fact that he had to completely reverse course reveals a fundamental misunderstanding of change communication.
Framework 1: The ADKAR Failure
Jeff Hiatt’s ADKAR model from Prosci shows us exactly why Duolingo’s announcement failed. ADKAR stands for Awareness, Desire, Knowledge, Ability, and Reinforcement—the building blocks people need for successful change.
Von Ahn’s original messaging:
- Awareness: âś… Everyone knew about the AI strategy
- Desire: ❌ Created fear, not desire for change
- Knowledge: ❌ No explanation of employee transitions
- Ability: ❌ No concrete training plans mentioned
- Reinforcement: ❌ Reinforced human obsolescence
Score: 1 out of 5.
His corrected LinkedIn post hit all five elements—clear human roles, AI as acceleration, partnership approach, promised workshops, and ongoing support. This should have been his opening strategy.
Framework 2: The Golden Circle Disaster
Simon Sinek’s Golden Circle framework reveals another critical error. Leaders should communicate Why (purpose), then How (process), then What (outcome).
Von Ahn’s Backwards Approach:
- What: “We’re going AI-first and eliminating contractors”
- How: “AI can handle human work”
- Why: “Because AI is the future” (barely articulated)
This triggered immediate threat responses.
What He Should Have Done:
- Why: “Help more people learn languages while making our team’s work more impactful”
- How: “AI handles routine tasks so experts focus on innovation and cultural nuance”
- What: “New tools and expanded AI-enhanced career opportunities”
Starting with Why creates excitement instead of fear.
What Every Leader Should Do Before AI Announcements
Before communicating any AI strategy, run it through both frameworks:
ADKAR Checklist:
- Does my message create awareness without fear?
- Will people desire this change?
- Am I providing clear transition knowledge?
- Are there concrete ability-building plans?
- What reinforcement am I promising? Golden Circle Test:
- Am I starting with Why this benefits people?
- Do I explain How we’ll partner, not replace?
- Are the What outcomes exciting, not threatening?
The Real Lesson
Von Ahn’s backtrack revealed he understood the right approach all along. His tragedy was leading with replacement rhetoric instead of partnership language, creating an entirely avoidable crisis.
The most damaging part? Once you’ve told your workforce they’re replaceable, you can’t just take it back with a LinkedIn post. Trust, once broken, is incredibly difficult to rebuild.
Research shows that 3 in 4 AI initiatives fail to deliver promised ROI, and AI hasn’t delivered the massive productivity gains leaders promise. Yet they keep making bold proclamations to excite investors, then scrambling to reassure employees when reality hits.
The question for your organization: Are you communicating AI as a threat or as an opportunity? Because your people are listening, and they’re making decisions about their future based on what they hear.
This is Part 1 of a 5-part series on AI implementation failures and how to avoid them. Next up: Why the AI hype-backtrack cycle is becoming predictable across companies—and how to avoid it entirely.
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
Sources:
- Hiatt, J. (2006). ADKAR: A Model for Change in Business, Government and Our Community. Prosci Learning Center Publications.
- Sinek, S. (2009). Start With Why: How Great Leaders Inspire Everyone to Take Action. Portfolio.

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the critical considerations when deciding whether to hire an external AI expert or develop internal AI capabilities.
You’ll learn why it is essential to first define your organization’s specific AI needs and goals before seeking any AI expertise. You’ll discover the diverse skill sets that comprise true AI expertise, beyond just technology, and how to effectively vet potential candidates. You’ll understand how AI can magnify existing organizational challenges and why foundational strategy must precede any AI solution. You’ll gain insight into how to strategically approach AI implementation to avoid costly mistakes and ensure long-term success for your organization. Watch now to learn how to make the right choice for your organization’s AI future.
Watch/listen to this episode of In-Ear Insights here »
Last time on So What? The Marketing Analytics and Insights Livestream, we explored AI-powered lead scoring automation. Catch the episode replay here!
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Here’s some of our content from recent days that you might have missed. If you read something and enjoy it, please share it with a friend or colleague!
- So What? How To Get Started With Generative AI Lead Scoring
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- Almost Timely News: 🗞️ Bringing the LinkedIn Algorithm Guide to Life With AI (2025-05-25)

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In this week’s Data Diaries, let’s discuss some of the dark data you have laying around.
Somewhere on your computer or your server is a folder that’s stuffed full of data. You’ve got reports, analyses, white papers, webinar transcripts – piles and piles of stuff.
Maybe it’s stuff that you swore you’d get around to reading, like that pile of books on your nightstand. One of these days, you say to yourself, I’ll set aside some time to read that stuff.
But we both know that if it hasn’t happened yet, it’s probably not going to happen any time soon.
Yet you know there’s value in there. You know there’s something useful to be learned.
So this week, let’s talk about two tactics for working with it.
Tactic 1: Put data in motion
Data at rest is decor. Data in motion is value. When we don’t use our data, when it sits on a shelf, it’s just decor.
Take a paper like our Unofficial LinkedIn Algorithm Guide for Marketers, or the Marketing AI Institute’s 2025 Annual Report. Maybe you really want to read them, but they’re super dense.
So take each one and put it in a separate notebook in the free NotebookLM software from Google. Then give it a short prompt like this in the Audio Overview (you have a limit of a few paragraph):
Hosts focus on topics relevant to Trust Insights, a management consulting firm focused on midmarket and enterprise companies needing help with analytics, data science, and AI, especially AI implementation, strategy, and change management. Focus the discussion on what Trust Insights should take away from the report and what they should consider adding to their strategies for marketing the firm to prospective customers.
Obviously, modify it for your own purposes.
When you’re done, you get an audio overview, a synthetic podcast summary of the report. Now you can take it with you, literally putting it in motion. You can listen to it anywhere you listen to other podcasts – at the gym, in the kitchen, on the road.
Tactic 2: Put data to work
Suppose you’re so busy that you don’t even have time to listen to podcasts. How could you make use of this dark data you’ve got laying around?
Feed it to generative AI. Suppose you downloaded our Unofficial LinkedIn Algorithm Guide for Marketers. You know that it’s valuable, that there’s useful tips, but you just can’t make the time.
Here’s what you do. Drop the PDF into any existing generative AI workflow. I talked about how to do this at length in my personal newsletter, but here’s a very simple example. Take your social media calendar for LinkedIn. Take the Unofficial LinkedIn Algorithm Guide for Marketers. Ask the generative AI tool of your choice to audit your calendar based on the guide and do some QA. Here’s an example starter prompt.
Audit our LinkedIn content calendar using the Unofficial LinkedIn Algorithm Guide for Marketers as a reference and best practices. Explain what in our LinkedIn content calendar works well with the Guide. Explain what does not work based on the Guide. Explain what is missing from our content calendar based on the Guide. Explain what is unnecessary in our content calendar based on the Guide.
Modify this prompt to be as robust and as detailed as your marketing needs it to be.
Data in motion is data in action, and that means unlocking the value of your data. Take anything you’ve been meaning to get to, and try one of these two straightforward tactics. You’ll start unlocking the value of your data.

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- Seo / Meta Ads Manager at Advanced Hair Restoration LLC
- Vp, Ai Strategy & Innovation at Wpromote

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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.