De siste årene har jeg jobbet med lederteam som tilnærmer seg AI på svært ulike måter. Noen ser på det som en mystisk “black box”, noen ønsker seg flere nye demoer, mens noen få tar en strukturert og mer nøktern tilnærming og skaper faktisk verdi. Hvis jeg skal gi styremedlemmer ett råd, er det dette: AI er ikke lenger en teknologidiskusjon, det er en ledelsesdiskusjon.  Og den hører hjemme i styrerommet. Her er mine praktiske anbefalinger basert på mine erfaringer. 1. Hold d

In the last few years, I’ve worked with leadership teams who approach AI in very different ways. Some treat it like a mysterious black box, some chase every shiny demo, and a few take a structured, sober approach and actually create value. If I were to give board members one piece of advice, it’s this: AI is no longer a technology discussion, it’s a leadership and responsibility discussion.  And it belongs squarely in the boardroom. Below are practical recommendations based o

Ser du på virksomheter som faktisk har klart å skalere KI utover piloter og eksperimenter, finner du som regel én fellesnevner: de har investert i ML Ops . Det er ikke den mest glamorøse delen av KI. Det får sjelden oppmerksomhet i styrerom eller aviser. Men det er ofte forskjellen på «vi eksperimenterer med KI» og «vi leverer verdi med KI hver eneste dag». Her er min forklaring på hva ML Ops er, og hvorfor det er så viktig. KI som fungerer i teorien er ikke det samme som KI

If you look at the organizations that have managed to scale AI beyond pilots and experiments, there’s usually one common factor behind the scenes: they’ve invested in ML Ops . It’s not the flashiest part of AI. It doesn’t make headlines. But it’s often the difference between “we’re experimenting with AI” and “we’re delivering value with AI every day.” Here’s a pragmatic explanation of what ML Ops is and why it matters. AI that works in a notebook is not the same as AI that wo

Her kommer noen av mine refleksjoner basert på faktiske prosjekter, ekte team og reelle resultater: Når du jobber lenge nok med virksomheter som prøver å ta i bruk kunstig intelligens, begynner noen mønstre å vise seg. Noen team får raskt fart, leverer verdi tidlig og skalerer innsatsen (og resultatene). Andre står fast i piloter, for høye forventninger eller manglende forankring internt. Min erfaring er at det sjelden handler om teknologi. Oftest handler det om mennesker, pr

Reflections from real projects, real teams, and real outcomes. If you spend enough time inside organizations trying to implement AI, patterns start to appear. Some teams get fast traction, deliver value early, and scale their impact. Others get stuck in endless pilots, inflated expectations, or a lack of strategic alignment. From my experience, the difference rarely comes down to technology. Most of the time, it’s about people, processes, and priorities. Below are the key con

Every company today feels the pressure to “do something with AI.” The hype is everywhere, boardrooms, conferences, even dinner conversations. But behind the buzz, many organizations face a quiet struggle: how to justify the investment and prove its value. The truth is, digitalization and AI should never be seen as costs. When done right, they are powerful investments that generate measurable returns in efficiency, quality, and competitiveness. The difference lies in how you a

A new phase in the AI journey Artificial intelligence has already transformed how organizations operate, from automating workflows to personalizing customer experiences. But we’re now entering a new phase: one where AI stops being a passive assistant and starts becoming an active participant  in business processes. This phase is powered by AI agents , autonomous or semi-autonomous digital entities that can understand context, make decisions, and take action across systems. Wh

Automation is only the beginning For most organizations, the first phase of artificial intelligence adoption has been about efficiency. Automating manual tasks. Streamlining operations. Reducing costs. That’s an important start, but it’s not where the real potential of AI lies. If we only use AI to do what we already do, but slightly faster, we’re missing the point. The real opportunity is to create entirely new value , like new products, new services, new revenue streams, an