Bookmark: Enterprises are hitting a 'speed limit' in deploying Gen AI - here's why
Enterprises are hitting a ‘speed limit’ in deploying Gen AI - here’s why
Deloitte’s report reveals that enterprises face challenges in deploying generative artificial intelligence (Gen AI) due to regulatory uncertainties and risk management concerns. Over two-thirds of executives noted that less than one-third of Gen AI projects would scale within six months. Regulatory compliance remains the main hurdle, with 38% of respondents identifying it as a barrier—a rise from 28% the previous year. Despite rapid technological advancements, organizational changes lag behind. While companies see potential, deploying Gen AI at scale is laborious, requiring a multiyear commitment to achieve returns on investment (ROI). Applications in IT, operations, and marketing have shown promising ROI, with cybersecurity leading gains. However, functions like sales and finance frequently underperform. The report emphasizes that many C-suite members express an overly optimistic outlook, delaying necessary organizational changes. Like preceding technological waves, Gen AI’s full potential will unfold gradually, necessitating a shift from mere cheerleading to genuine leadership to harness its value for enterprise competitiveness
Featured writing
Why customer tools are organized wrong
This article reveals a fundamental flaw in how customer support tools are designed—organizing by interaction type instead of by customer—and explains why this fragmentation wastes time and obscures the full picture you need to help users effectively.
Infrastructure shapes thought
The tools you build determine what kinds of thinking become possible. On infrastructure, friction, and building deliberately for thought rather than just throughput.
Server-Side Dashboard Architecture: Why Moving Data Fetching Off the Browser Changes Everything
How choosing server-side rendering solved security, CORS, and credential management problems I didn't know I had.
Books
The Work of Being (in progress)
A book on AI, judgment, and staying human at work.
The Practice of Work (in progress)
Practical essays on how work actually gets done.
Recent writing
We always panic about new tools (and we're always wrong)
Every time a new tool emerges for making or manipulating symbols, we panic. The pattern is so consistent it's almost embarrassing. Here's what happened each time.
Dev reflection - February 03, 2026
I've been thinking about constraints today. Not the kind that block you—the kind that clarify. There's a difference, and most people miss it.
When execution becomes cheap, ideas become expensive
This article reveals a fundamental shift in how organizations operate: as AI makes execution nearly instantaneous, the bottleneck has moved from implementation to decision-making. Understanding this transition is critical for anyone leading teams or making strategic choices in an AI-enabled world.
Notes and related thinking
AI didn't deskill us, we were already deskilled
This article challenges the narrative that AI is deskilling workers, instead highlighting how many jobs were already mechanical. It offers a thought-provoking perspective on how AI could be an opportunity to reclaim and enhance human skills.
Redefining Leadership: Embracing Human Judgment Amid AI Disruption
This article offers a critical perspective on how AI is reshaping the job market and challenges leaders to focus on uniquely human skills like judgment and responsibility, providing valuable insights for anyone interested in the future of work and leadership.
AI Slop: The Hidden Cost of Poor Integration
This article challenges the notion that job crafting is the key to successful AI integration, offering a fresh perspective on the importance of a clear strategy to prevent chaos and enhance organizational efficiency.