How my Summer Break Inspired an AI-First Model
Ask most people about their August break, and they might describe a fortnight spent on a sun-soaked beach overseas or navigating the quiet hum of a countryside Airbnb in the UK or Europe. For me, the break unfolded in the sunny wilds of Snowdonia, a place where nature’s grandeur invited my mind to wander beyond the routine. Crucially, it offered something rarer still: space to step back from the incessant churn of “project work,” away from the daily grind of the professional services sector—a sector that, ironically, is itself in the midst of an epochal transformation.
Yet, even as I traced the contours of mountain lakes and listened to the hush of streams, my thoughts circled back, time and again, to a singular question: How do we—as professionals, as thinkers—keep pace with the accelerating capabilities of artificial intelligence? The answer, I realised, lies not simply in chasing the latest trend or latching onto buzzwords, but in cultivating a mindset attuned to learning, unlearning, and relearning.
This brings to mind the notion of “cognitive diversity”. When we expose ourselves to divergent viewpoints, we amplify our own capacity for insight. This principle guided my choice of listening and reading material. I read business books from the 1980s and 1990s, as well as current journals and blogs. By going beyond the headlines and hype, I found myself drawn to podcasts that examine the future from multiple perspectives. As I returned to work and drove to a client meeting, listening to the BBC’s “Radical” with Amol Rajan, particularly the episode featuring Dario Amodei on AI, proved a catalyst. As Amodei outlined and exposed, there’s something deeply provocative about the paradoxes at the heart of AI’s evolution—profound potential and profound risk inextricably intertwined. Using it ethically and safely, and securing the benefit. His interview is beneficial when thinking about safe and secure, ethically led AI usage.
On returning to work, I encountered a kind of serendipity. Shane Parrish’s “The Knowledge Project,” a podcast devoted to the art of clear thinking, had just released an episode with Benedict Evans. Evans, a tech analyst renowned for his contrarian takes, offered a view almost wholly at odds with Amodei’s. Where the former saw radical transformation, the latter interrogated hype versus substance, challenging listeners to ask: What matters, really, in technology? It chimed with the books, blogs, and journals I had read whilst away: simplify and focus on what matters.
Here is the crux—practical insights emerge not from consensus, but from the creative friction of opposing ideas. In professional life, as in AI, we progress by embracing contradiction rather than fleeing from it.
As August drew to a close, I found myself down a rabbit hole of how-to videos and dense journal articles, absorbing ideas at a pace matching the technological shifts themselves. The upshot? A decision to adopt an “artificial intelligence-first” model: to place AI not as a bolt-on to legacy process, but as a core driver of client service, efficiency, and business transformation.
Of course, none of this would have been possible without that precious commodity: headspace. The gift of time away – mental distance – allowed me to ask not just, “What can AI do for our Bennett Briegal?” but “What could really move the needle for our clients and ourselves?” This, I believe, is the spirit that should underpin any engagement with new technology—not breathless enthusiasm, but thoughtful curiosity, a willingness to listen to conflicting voices, and the humility to rethink what we think we know.
In the end, the most significant innovation may be in learning, repeatedly, how to innovate—and in finding the clarity, amid the noise, to recognise what truly matters.
A great privilege is the time afforded to think, and using AI creates space in time to think more effectively and analyse large datasets to apply that data more effectively, which is both exciting and rewarding.
The lesson for lawyers: read some AI blogs, listen to Podcasts on usage, and challenge your own thinking to develop an implementation plan by having watched the practical “how to use AI” videos freely available from Microsoft, Google, and on YouTube. These allow you to act on your research and thinking with confidence.