#35: Finding real value in AI
Is an AI strategy necessary, or is it just hype? How do we really use AI as an enabler across complex enterprises, and what happens when strategy fixates on technology instead of business goals? Ben Torben-Nielsen, PhD, MBA, a seasoned AI expert with over 20 years in the field, dives into these critical questions with insights from his experience at some of Europe’s largest companies. I’m thrilled to have him on The Only Constant for this thought-provoking episode, exploring where AI fits in - and where it doesn’t.
Read more about Ben at the bottom of this newsletter.
As always, I am very happy that we once again managed to make an episode that explores more than it explains 💡
Listen to the episode here:
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What made this episode special to me?:
Ben is evidence that I do not just invite my own echo chamber onto the pod. I met Ben a couple of months back in a LinkedIn discussion on whether companies need AI strategies. Ben works on building and advising companies on AI strategies, and I am slightly annoyed at it, so we clashed lightly on the topic. But Ben was both well reflected and very experienced, so I got in touch with him and luckily we ended up recording this splendid episode together.
That said, I am not sure I have recorded a more amiable yet thought-provoking conversation, where differing views brought depth to the discussion on AI strategy and its role in modern business. We have so much fun during the episode, and I think this episode is simultaneously the episode with the most laughs and jokes as well as the most interruptions because we disagree slightly. How is it - you'd vote for someone you'd have a beer with? I'd want a beer with me and Ben after listening to this episode.
My key post-recording deliberations are:
Is an AI strategy truly necessary, or are we at risk of overemphasizing technology at the expense of actual business goals? How do we ensure AI remains an enabler rather than a distraction?
As AI evolves so quickly, how can businesses avoid a reactive mindset where they continuously chase the latest technology? Should the focus shift to readiness and adaptability rather than specific AI applications?
In a world where technology keeps accelerating, how do we redefine transformation beyond adding functional tools? Can we create genuine transformation by rethinking organizational structures and roles around new capabilities?
Good energy, good content and lots of fun - I am pleased to present the 35th episode of The Only Constant for all to enjoy!
About Ben Torben-Nielsen, PhD, MBA:
With a career spanning over two decades in the AI industry, Ben Torben-Nielsen has dedicated his life to translating cutting-edge research into practical business solutions. His journey began in academia, where he led a small research group and earned a PhD in Artificial Intelligence and Neuroscience. This foundation, coupled with an Executive MBA, has enabled him to drive innovation at the intersection of technology and business.
Throughout his career, as a contributor or as a leader, Ben has developed AI-powered products, secured multiple patents, and authored over 40 peer-reviewed papers to the scientific community. A highlight of his professional journey includes shaping the AI strategy for one of Europe's largest companies. His mission revolves around the ethical application of AI and data, aiming to create solutions that not only optimize performance and decision-making but also generate meaningful employment opportunities. As a passionate advocate for AI's potential, Ben continually strives to foster both business success and human-centric outcomes in this rapidly evolving field.
Independent Think Tanks Professional
1moVery helpful
Professionelle Problemlöserin | Ich helfe KMU, produktiver zu werden | Analytics, Consulting, Change Management
1moAs I, too, find myself in the „no-one needs an AI Strategy“ camp, this is another episode I really look forward to listening to! Even more so as I find myself at odds with quote #2: Everyone wo wants to can read up on how an MRI works, and why the results it shows are as they are. On the other hand, even people who are actively building LLM‘s can not accurately say why and how they deliver the exact output they are, it seems.