Operating Models & AI Transformations: A Career Pivot
Note: This article was first published in Medium.
For most of my career, unbeknownst to me, a common thread had been developing over the years. That common thread, as I realized later in life, was bringing order to chaos (or at least taming it) and amplifying growth potential. For the first part of my career, I bounced around a couple of different roles, then caught the product management wave and rode that for years. I led product development teams comprising PMs, designers, and engineers before transitioning to digital transformation work. It took a layoff, some idle time, and reflection to see the path my career had taken so far. Only when I zoomed out did I see the pattern — and the natural arc that was beginning to take shape.
As I previously wrote in a Medium post, I pivoted my career after my last layoff. Not 180 degrees or even 90… more like a 30-degree pivot. It wasn’t a big enough pivot to be a complete career change, but it was big enough to feel like a new role grounded in familiar principles. I landed on operating model transformation and product leadership coaching as my newfound professional North Star. My career was still progressing in the same general direction, but now with a slightly different angle.
Why do I focus on Operating Model Transformation?
I’m passionate about purpose. I’m a proponent of the “Start w/ Why” mentality. I need to understand how the big picture connects to the everyday. An effective and intentional operating model brings all of this into both focus and harmonious alignment. Or at the very least, an intentional operating model forces the conversation. I say intentional because every company has an operating model, even if it was not deliberately designed. It’s like culture — it’s there whether you nurture it or not.
Why the product operating model in particular, though? It’s actually not the model itself that I care about; it’s what it drives for the org. My background in product obviously comes into play and informs an inherent bias. However, it’s the focus on the company’s vision, the outcomes that drive that vision, and strategic alignment through prioritization across the organization that is at the heart of the model and what appeals to me.
To put this another way, the process associated with the operating model isn’t the goal. Operational frameworks are not intended to be fences. To truly grasp the purpose of a well-designed operating model, you need to understand both the essence and spirit of the law as well as the law itself. In the case of the product operating model, the purpose is to deliver value faster and more effectively for the org.
How? Designed correctly, an operating model should include a decision-making framework that aligns the entire organization, allowing every team to focus on solving the right problem at the right time in the most effective way. Let me unpack this further.
Solving the right problem: this involves aligning with the company’s vision, strategy, and goals in a way that prioritizes the problems that will have the most significant impact on driving the company forward.
Solving at the right time: this involves prioritizing to maximize both optimal sequencing and timing, taking into account both internal and external factors. At the right time also implies efficiency and nimbleness in delivering value.
Solving in the right way: it’s not enough to solve the right problem at the right time. The organization also requires you to solve it in a manner that aligns with the company’s core competencies, is appropriately differentiated from the competition, and is feasible/scalable from both a technological and business capability perspective.
Ultimately, this is why I focus on operating model transformation: it empowers teams and unlocks potential to deliver on outcomes that matter. I always like to say that it’s not the operating model itself that is the goal; it’s the outcomes you are trying to drive that matter. I find nothing more rewarding than seeing a team or company realize its potential, deliver on its commitments, and be able to trace its contributions to tangible results. Team members are motivated, engagement is high, and results are positive.
And those same conditions — clear ownership of outcomes, customer-obsessed, strategic alignment, and ruthless prioritization — are exactly what your organization needs to create real value through an AI transformation, which brings me to the next chapter: how a tight product operating model creates an ideal path for AI.
How Does Operating Model Transformation Intersect with AI?
AI is an indiscriminate amplifier. It will equally amplify both the good and the bad in your organization. If you have an operating model that reinforces functional silos, handoffs, and unclear accountability for outcomes, AI will only accelerate dysfunction and increase the volume of noise already present. It’s like driving a race car down residential streets with speed bumps, stop signs at every block, and no clear direction — accelerating and stopping and accelerating again in aimless, brain-jarring fashion.
Now, picture the opposite: an intentional operating model with clear ownership and alignment to outcomes, accompanied by an organizational structure that matches. In that world, your operating model establishes a nice, smooth straightaway. AI, then, doesn’t create more noise or brain-jarring starts and stops; it accelerates value and enhances performance. You remove the barriers that prevent AI from creating velocity towards your outcomes.
What AI needs from your operating model first (the prerequisites):
Ownership & interaction interfaces: Clear ownership, boundaries, and interaction models so AI has a clear “acceleration path” not limited by an ineffective operating model or functional silos.
Outcomes & prioritization: A taxonomy aligned to enterprise outcomes, with a strategy and initiatives that connect all the dots to empower teams to point AI in the right direction.
Focus on Customers: Above all else, even with more capability, ability, and velocity, all things ultimately need to point at solving the right problems for your customers to make a meaningful impact for your company.
This is why I start by getting teams radically aligned and operating with intention, reducing noise and increasing focus. Once that’s in place, AI can become the force multiplier the hype promises it to be. Target high-friction workflows first, learn quickly within the new cadence, and scale what actually drives outcomes.
Here’s an outline of what basic milestones could look like for your org for both a shift to the product operating model and an AI Transformation:
Create an outcome architecture: Map strategy → outcomes → products → metrics → accountable owners so “who owns what” is unambiguous.
Layer on a product taxonomy (incl. enabling capabilities): Name customer-facing products and the platforms that power them; debate boundaries, customers, and success measures.
Establish team topology & decision rights: Stand up cross-functional teams per product; codify who decides, who advises, and how work flows across interfaces.
Define cadences for discovery & delivery: Lightweight, repeatable rhythms (hypotheses, bets, demos, reviews) tied to outcomes — not just ceremonies for the sake of process.
Platform foundation (DevEx + AI services): Invest in CI/CD, self-service tooling, and a horizontal AI layer (model access, evaluation, prompt/feature stores, security/cost controls).
Pilot AI tools for your employees inside the operating model: Provide access to AI tools and empower them to use and experiment with them in their everyday roles as they see fit, as the first step in the AI transformation.
Evaluate data readiness & access: In parallel with the previous step, analyze the state of your data systems, designate data accessibility and readiness, define data owners, contracts, and definitions.
Define a Measurement Strategy: Track a small set of key metrics that measure both AI augmentation and automation, appropriately aligned to strategy and outcomes.
Use-case portfolio (value-led): Prioritize 3–5 high-friction, high-volume workflows where AI can remove real constraints; score by impact × feasibility × risk.
Scale & enable people and AI agents alike: Standardize patterns (guardrails, libraries, data access), upskill leaders/teams, and expand to adjacent use cases in waves.
Obviously, this is a very oversimplified list to give you a rough idea of the milestones, not a step-by-step playbook for undertaking this sort of transformation. And to be clear, the data readiness piece of this is huge and worthy of it’s own playbook. I wrote a little bit about data needs for intelligent transformation in a previous article. Nevertheless, I recommend ensuring that your operating model is sound before attempting to adopt and scale AI across your organization.
Conclusion
At a personal level, AI will shape this next phase of my career whether I ask it to or not. The good news: it pairs naturally with a product background. My work helps leaders establish a robust product operating model — and then empowers companies to leverage AI within that model.
What worked in digital transformation still works for an intelligent transformation. In fact, it’s a prerequisite: set direction, reduce handoffs, tighten feedback loops. The difference is we have more horsepower to harness. If the operating model is tuned, AI doesn’t add noise — it’s a performance enhancer. Moving forward, my focus is on helping teams get the system right, ensuring acceleration is safe, repeatable, and aligned with outcomes that matter. Let’s see where that takes my career next…!
