Enterprise AI pilots have a 95% rate of failure. Could it be that people have experiment fatigue and that companies are taking a piecemeal approach to AI innovation?

AI has many productivity benefits, but those benefits may only accrue towards corporations who now have new tools to exploit workers.

AI is coming for many jobs. Bullshit jobs will be the first to go. Those of us with a real job must cultivate a few characteristics to be AI-first in the way we work and stay relevant in the job market.

We often derive satisfaction from difficult activities, even if they aren’t pleasurable. Does this effort paradox hold any learning for us all?

In teams, no one is, or should be, indispensable.

Before companies seize productivity advantages through agentic AI, they’ll have to contend with a messy period of agent sprawl, poor design choices and inefficient workflows. When the dust settles, success won’t just be about the ease of agent building. The companies that succeed will most likely prioritise thoughtful design and problem solving, and find clever ways to balance bottom-up innovation with lightweight governance.

Don’t give into AI FOMO. Board the train at your preferred station.

AI transformation is more about behaviour change than technological change. Job insecurity, misaligned incentives, incompatible work environments and fragmented work systems can be impediments to this new way of working.

AI is rarely perfect or production-ready. Working with AI means embracing a perpetual beta mindset. And perpetual betas always need humans in the loop.

The days of manually curating and organising company knowledge will soon be behind us. Knowledge managers can’t operate as librarians anymore. They must, instead, elevate their AI literacy, implement an AI-first KM stack and deploy themselves as consultants to implement knowledge-enabled workflows.

The Chinnaswamy stampede highlights the daily risks the Indian salaried class faces. While there is no silver bullet solution to these problems, remote work can provide Indian knowledge workers some relief from these stresses.

It takes a special discipline to push back against novelty and embrace routine, boring solutions. That discipline, however, can lead to surprising efficiency.

In the age of AI-generated outputs, there’s still room for the glorious unpredictability of being human.

If AI-first knowledge work benefits from a new way of organising, is an RTO-mindset a limiting factor for succeeding in the AI age?

The AI-first software development revolution will also change how we collaborate. I argue that async-first collaboration is the perfect companion to AI-first development.

It’s OK to say “No” to incoming work if you deliver high-quality outputs, communicate proactively, and meet your deadlines. The bargain is as simple as that.

Too often, we say an instinctive “Yes” to every new idea and every new request for our time. To be productive, I argue that our default response must instead be a strategic “No”.

We’re living in an AI gold rush, where product teams are pushing at the fringes of tech for fear of missing out. But in the process, are we ignoring the core of our products and the majority of users?

Fresh off a snow leopard expedition, I reflect on the accelerating power—and persistent limits—of artificial intelligence.

Are internal comms likely to communicate better than you can? Even if you’re the expert on your topic? This is a nuanced topic - part myth, part possibility!