Most teams are approaching AI from the wrong direction, either chasing the tech with no clear problem or spinning up endless pilots that never earn their keep. In this episode, Amir Bormand sits down with Steve Wunker, Managing Director at New Markets Advisors and co author of AI and the Octopus Organization, to break down what actually works in enterprise AI.

You will hear why the real challenge is organizational, not technical, how IT and business have to co own the outcome, and what it takes to keep AI systems valuable over time. If you are trying to move beyond experimentation and into real impact, this conversation gives you a practical blueprint.

Key takeaways

• Pick a handful of high impact problems, not hundreds of small pilots, focus is what creates measurable ROI

• Treat AI as a workflow and change program, not a tool you bolt onto an existing process

• IT has to evolve from order taker to strategic partner, including stronger AI ops and ongoing evaluation

• Start with the destination, redefine the value proposition first, then redesign the operating model around it

• Ongoing ownership matters, AI is not a one and done delivery, it needs stewardship to stay useful

Timestamped highlights

00:39 What New Markets Advisors actually does, innovation with a capital I, plus AI in value props and operations

01:54 The two common mistakes, pushing AI everywhere and launching hundreds of disconnected pilots

04:19 Why IT cannot just take orders anymore, plus why AI ops is not the same as DevOps

07:56 Why the octopus is the perfect model for an AI age organization, distributed intelligence and rapid coordination

11:08 The HelloFresh example, redesign the destination first, then let everything cascade from that

17:37 The line you will remember, AI is an ongoing commitment, not a project you ship and forget

20:50 A cautionary pattern from the dotcom era, avoid swinging from timid pilots to extreme headcount mandates

A line worth keeping

You cannot date your AI system, you need to get married to it.

Pro tips for leaders building real AI outcomes

• Define success metrics before you build, then measure pre and post, otherwise you are guessing

• Redesign the process, do not just swap one step for a model, aim for fewer steps, not faster steps

• Assign long term ownership, budget for maintenance, evaluation, and model oversight from day one

Call to action

If this episode helped you rethink how to drive AI results, follow the show and subscribe so you do not miss the next conversation. Share it with a leader who is stuck in pilot mode and wants a path to production.