By Mariana Abdala
Lately, due to an increase in layoffs and downsizing in the Tech sector, there hasn’t been a ton of discussion about scaling technology organizations, and yet there has been a gradual uptick in the number of Product roles opening up. The Product Manager role is no longer unique to tech companies and is increasingly becoming the role that is replacing Business Analyst, Systems Analyst, Project Manager, and Delivery Manager roles. This means that we can expect to see more Product organizations growing, especially as the role evolves into one that functions as a gatekeeper at the crossroads of Business, Engineering, and AI.
Scaling a Product organization and maturing the Product role isn’t just about adding more people. It’s about changing how decisions get made, how information moves, and how context is preserved. What worked when the team was a few people in a room often breaks when the organization spans multiple squads or pods, Product lines, and time zones. Alignment becomes harder, decisions take longer, and the role of Product itself starts to shift, from builders and translators to connectors and enablers. The Product leader who once managed backlog priorities now shapes culture, structure, and governance.
The Shifts That Matter Most
From Ownership to Orchestration
A well functioning Product team is accountable and takes ownership. Each PM owns a problem space and makes rapid decisions close to the work. The value is speed. However, as teams multiply, that same autonomy can create chaos. Without shared decision principles, the organization risks building fast in opposite directions. We see this with our training and coaching clients who want to scale their Product organization by shifting from individual ownership to orchestrated alignment. This is more complex to navigate than it sounds. It takes more than rethinking the operating model.
What do we mean by “orchestrated alignment”. This does not mean centralizing every decision, or reaching consensus with cross-functional teams. Instead, we can interpret it as standardizing and codifying how decisions get made: what the company optimizes for, which metrics guide trade-offs, and how context flows across teams. The maturity of a Product function is measured less by how quickly it acts and more by how consistently it acts in service of strategy.
From Conversations to Systems
In small teams, alignment happens through conversation. People talk daily, sync organically, and sense misalignment early. Once the team grows, that natural communication breaks down. Context gets lost in translation, and people start working from outdated assumptions. At scale, the conversation must be embedded into systems. This is where Product operations (Product Ops) emerges, not as bureaucracy, but as connective tissue. Product Ops enables rhythm and consistency: how data is shared, how planning cycles run, and how learning loops close. The irony is that great Product Ops is invisible. In our experience, when this shift works, what we expect to see is teams spending less time reporting up and more time shipping meaningful outcomes.
The Product Role in the Age of AI
AI has changed what scale even means. Tasks that once required a full team, such as competitive research, user analysis, idea exploration, or documentation, can now be accelerated or automated by AI. But the core challenge remains: judgment. AI can suggest, summarize, and simulate, but it cannot decide which trade-offs to make or which problems are worth solving. This evolution reframes the Product role. As AI augments execution, the Product manager’s comparative advantage shifts toward framing. The question is not how do we build faster, but how do we ensure what we build still matters. AI also pressures teams to get sharper about context management. When every team member can instantly access summaries, prototypes, and ideas, the differentiator becomes interpretation. The PM’s role is to connect pattern to purpose, to turn abundant data into directional insight. Forward-thinking organizations are already experimenting with AI copilots for backlog management, requirement drafting, and user research synthesis. Yet the ones that succeed are those that pair automation with increased human clarity. The Product manager of the future is not a project overseer but a meaning-maker, someone who ensures the narrative behind the work stays coherent even as “machines” accelerate the work itself.
The Takeaway
With just about every enterprise client we’ve served this year, we’ve observed that scaling the Product function is less about headcount and more about mindset. It is the shift from fast to scalable, from reactive to rhythmic, from individual intuition to collective judgment. And as AI accelerates execution, the role of Product becomes even more essential. The more the work speeds up, the more that organizations need people who can pause, interpret, and steer. I believe that Product leaders who embrace that duality of velocity and reflection build a strong team that can scale with purpose and confidence.
