How AI is transforming businesses: A reality check

Boards and exectuive teams cannot stop talking about AI. How is it already infleuncing their business? What is the impact? What is the potential? But as a professional, how do you apply #AI into your daily work? My newest vlog about this. If you want to dive a bit deeper, visit my column here: https://lnkd.in/eS-r6VUV Let's keep the conversation going! What resonates?

The companies winning with AI aren’t using fancy algorithms. They know exactly where AI fits in their business. 👉 Here’s how companies are making it work: https://lnkd.in/gGmzE5kH

Unsafe AI doesn’t just confuse people. The way we talk about it does too. Over the past few months I’ve been following and contributing to the conversation around AI here on LinkedIn. What’s clear is this: much of what passes for “AI insight” isn’t wrong in intent, but it’s distorted in framing. I’ve pulled together the most common misunderstandings about AI I’ve seen, and how I’ve come to understand them more clearly. (Thread below, feel free to discuss)

How do you create an AI strategy that delivers value? We summed up key elements you can't miss out on in this fresh artcle: https://lnkd.in/dhQdBjW4

AI is no longer a differentiator, but an expectation in agency reviews. This Ad Age article dives into how media agencies are getting ahead of client demands to deliver tangible AI applications and real outcomes. Featuring expert insights from our Global Chief Growth Strategy Officer, Katy Varner, the article highlights the shift towards data-driven personalization and the importance of guiding clients towards practical AI use cases. Discover how to navigate the evolving AI landscape by checking out the full article: https://bit.ly/3V1nZB3 #AI #AgencyReviews

This week in The Pragmatic Engineer, I'm sharing the real metrics that 18 companies use to measure AI impact. Thanks to all of these companies for letting me share their approaches, which gives us all a deeper look into AI adoption and impact in the real world (not just the headlines). Read the full article here: https://lnkd.in/dX2ivkgw

Laura Tacho put in an enormous amount of work to bring this piece to life covering the AI Impact metrics used across a wide variety of companies, from Tech to Finance, and it's featured in the Pragmatic Engineer. Her piece covers: -The real metrics companies like Google, GitHub, and Microsoft use to measure AI impact -How they use the metrics to figure out what’s working and what’s not -How you can measure AI impact -AI-impact metrics definitions and measurement guide

How do companies measure whether or not AI Tools are worth it, and the impact in their teams? Look at the latest writing from Laura Tacho, with notes from companies like Google, Dropbox, Microsoft, Monzo, Atlassian, Adyen, Booking.com. Grab a cup of coffee/tea, enjoy the reading.

Grateful to Laura Tacho and The Pragmatic Engineer for pulling together such a sharp overview of how tech companies are starting to measure the impact of AI. 🙏 It’s a must-read: Practical, grounded, and exactly the kind of clarity our industry needs. One thought I’d add: metrics often reflect where the work is happening today. Most are still anchored at the code layer: PRs merged, test cycles, deployment frequency. And rightly so: that’s where AI is clearly making things faster. But history shows engineering always climbs abstraction layers: binary → assembly → languages → frameworks → cloud → AI-augmented intent . With each step, we gain leverage and accumulate entropy. Abstractions are power multipliers, but they also hide complexity and fragility. So perhaps in addition to asking “how much faster did AI help us ship?”, we should also be asking: At what level of abstraction are we now working? How much entropy has the system absorbed at that level? Are we converting abstraction into resilience, or into debt? Laura’s call to track impact over time is spot on. And I'd extend it: the real signal may lie in longitudinal abstraction and entropy metrics - revealing whether our trajectory is sustainable. Because the long-term test of AI in engineering won’t just be about the code generated this week - it will be whether, years from now, those abstractions leave us with systems we can still trust, evolve, and build upon. How on earth do we find the signals when what we are measuring is changing so fast? Perhaps the answer is to anchor on what endures: reliability, resilience, trust, cognitive load, and adaptability. But are these the right anchors for the age of AI-augmented engineering? #SoftwareEngineering #DeveloperProductivity #AIinEngineering #DevEx #EngineeringLeadership, DX, The Pragmatic Engineer