2026.12: Myth 2 - AI Is the Technology That Changes Everything.
Happy Friday, from Toronto. Yup, it is still cold and raining.
This is Part 2 of the Five Myths About AI Transformation series. Last week we covered Myth 1 - You Don’t Need an AI Strategy, and Microsoft's Copilot reality check. This week's story is messier, funnier, and more instructive.
Let's break it down.
The Signal:
Klarna's AI Experiment Went Exactly How You'd Expect.
In 2023, Klarna stopped hiring. By 2024, they'd partnered with OpenAI, cut roughly 700 customer service jobs, and replaced them with AI agents. CEO Sebastian Siemiatkowski told the world that "AI can already do all of the jobs that we, as humans, do." They claimed $10 million in savings. Headlines everywhere.
By early 2025, customers had a different opinion. Satisfaction dropped. Complaints piled up. Internal reviews revealed the AI couldn't handle nuance, empathy, or the kind of problem-solving that people with billing disputes actually need. Customers described the responses as generic and repetitive.
Siemiatkowski admitted it: "We focused too much on efficiency and cost. The result was lower quality, and that's not sustainable."
Then it got worse. Klarna couldn't rehire fast enough. Reports surfaced that the company started pulling software engineers and marketers out of their actual jobs and into call centers to fill the gap. The CEO who said AI could do everyone's job was now reassigning his technical staff to answer phones.
The solution that would have worked from the start: AI handles the routine stuff, humans handle the complex stuff, and you design the process before you pick the tool.
That's not a headline. But it's what works.
The Scale:
Myth 2: AI Is the Technology That Changes Everything
Reality: The biggest wins still come from boring, proven technology applied to the right problem.
Klarna didn't have a technology problem. They had a sequencing problem. They applied the most sophisticated tool available to a workflow they hadn't redesigned. That's Substitution on the SAMR scale. Swap 1 thing for another. Same process. Same structure. Just cheaper. Until the quality collapsed.
This pattern is everywhere. MIT's "GenAI Divide" report found that companies were pouring more than half their AI budgets into sales and marketing tools. But the highest ROI came from somewhere nobody was looking: back-office automation. Document processing, compliance, internal workflows. The boring stuff.
Deloitte's 2026 State of AI report put a number on the gap. 74% of organizations hope to grow revenue through AI. Only 20% are actually doing so. The companies in the 20% didn't start with the flashiest use case. They started with the most friction.
Sangeet Paul Choudary made this case in Harvard Business Review last month. He studied Shein vs. traditional fashion houses. Traditional companies use AI to speed up design sketches, but the underlying structure stays sequential. They made the season faster. They didn't rethink the season. Shein reduced the unit of work from the seasonal collection to continuous small-batch experiments. AI doesn't sit on top as a fancy addition. It connects sensing, decision-making, and action into a single learning loop.
That's not AI changing everything. That's a company rethinking how it works, then using technology to make the new structure possible.
Stephen Andriole observed this back in 2017 in MIT Sloan. Most short-term impact comes from conventional operational technology. Not from the hot new thing. Think about Uber. The technology behind Uber wasn't exotic. Phone in your pocket, GPS, mobile payments, a well-designed app. All proven. The innovation was applying it to a business model problem.
Donella Meadows wrote that the most powerful place to intervene in a system is at the level of information flows and delays, not at the level of new components. If information moves slowly through your organization, if decisions stall because data lives in 7 different spreadsheets nobody reconciles, adding AI is like putting a turbocharger on a car with flat tires.
Fix the tires first. Then talk about turbochargers.
The Deep Dive:
This week's deep dive goes all the way in on Myth 2, including a practical sequence for finding the boring wins hiding in your business and the math that shows why they matter more than you think. Read: AI Gets the Headlines. Boring Technology Gets the Results.
Thanks for reading!
Next week: Myth 3. "Profitable Companies Are Best Positioned for AI." Why comfort is the enemy of adaptation, and why the companies riding high are the ones most likely to get left behind.
If you know someone chasing AI when they should be fixing their plumbing, forward this. They probably won't thank you. But their customers will.
Hit reply and tell me: what's the most boring fix that produced the biggest result in your business?
See you next Friday.
Best,
JT
P.S. — Read the full Five Myths Paper on Substack.
