Why some teams adopt AI faster than others
AI adoption speed varies considerably across teams, even within the same organization with access to the same tools. The variation is not random. Understanding what predicts fast adoption helps teams that are behind identify what to change, rather than attributing the gap to factors they cannot control.
Why most AI training programs fail
Organizations spend significant resources on AI training: workshops, online courses, certification programs, lunch-and-learns. Most of it does not produce lasting change in how people work. Understanding why AI training fails is more useful than adding more training.
Why AI projects need sponsors, not just champions
Most AI projects have champions. The engineer who believes in the technology, the team lead who pushed for the pilot, the individual contributor who made it work. What they often lack is a sponsor: someone with organizational authority who has committed the project's success to their own outcomes. That gap is why so many AI pilots succeed and so few scale.
Why AI adoption fails in the middle
AI adoption has a characteristic failure pattern that does not look like failure at first. The launch goes well, early adopters are enthusiastic, usage metrics look promising. Then something stalls. Understanding what happens in the middle is more useful than studying either the launch or the endpoint.
How to build AI accountability into your team
AI adoption without accountability creates a specific failure mode: the tool gets used, the outcomes drift, and nobody knows why. Building accountability into how a team uses AI does not require bureaucracy. It requires clarity about what AI is supposed to do and honest tracking of whether it is doing it.
The AI reporting problem
Executives want to know how AI investments are performing. Most organizations cannot tell them. The metrics being tracked measure activity, not value, and the reporting structures that work for traditional software do not transfer to AI. Here is what better AI reporting looks like.
How AI changes the onboarding problem
Onboarding new employees and new users is expensive, slow, and often poor quality. AI does not eliminate this problem but it changes its shape in ways that matter. The teams designing onboarding with AI in mind are arriving at different approaches than the ones following traditional playbooks.
The case for slowing down your AI roadmap
The pressure to move fast on AI is real and the costs of moving too fast are underappreciated. The organizations that build durable AI capability tend to spend more time than their peers on evaluation, integration, and the organizational work that determines whether AI actually changes how things get done.
Getting AI adoption right when your team is skeptical
Skeptical teams are not a problem to be overcome. They are a quality signal. The organizations that build lasting AI adoption start by taking skepticism seriously rather than trying to sell past it. Here is what that looks like in practice.
How to structure an AI center of excellence
An AI center of excellence can accelerate adoption and build durable capability, or it can become a bottleneck that slows everything down. The difference is almost entirely structural. Here is what the effective ones do differently.
Why AI habits are harder to build than AI tools
Deploying an AI tool is a technical problem. Getting people to use it consistently is a behavioral one. Most organizations solve the first problem and then wonder why adoption numbers are disappointing. The second problem requires different thinking.
The AI strategy question most companies avoid
Most organizations building AI strategy answer the questions about what to build and how to implement it. The question that gets avoided is the harder one: what will you stop doing because AI changes the economics? Avoiding it produces AI strategies that add cost rather than change the business.