The imperative to be “AI-first” is percolating in boardroom conversations, internal company memos, and predictions from industry analysts.
While there’s no single definition of the term, being AI-first entails integrating the ever-advancing technology into a company’s core – as the new North Star. It means that AI becomes the prevailing mindset, permeates the company culture, and even points the compass for decisions about operations, workflows, and more.
It’s a vital evolution, and companies want to jump in headfirst. But according to McKinsey Partner Megha Sinha, who counsels Fortune 500 C-suites on AI and product transformation, companies can’t be AI-first without being product-first.
“[A] product operating model is the absolute critical foundation to leapfrog into an AI-first organization. You cannot bypass that and go there,” Sinha declared in a recent webinar with Planview, referencing the importance of a product operating model for achieving bottom-line benefits for businesses.
So what exactly is happening in this phase of the AI era? And why does your operating model matter so much? In this blog post, we’ll explore the four paradigm shifts AI is bringing to enterprises and what organizations that want to accomplish more with AI must do to prepare.
Listen to the full webinar, “Product Operating Model Principles Transforming Fortune 500s,” for board-ready examples for product operating model investments, implementation roadmaps, and success metrics, all with experience-based commentary from Sinha.
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The Four Paradigm Shifts to Prepare For
Based on early proof-of-concepts and scaled implementations with clients, Sinha outlined four major shifts that AI will bring to enterprise organizations. Some leading software companies in Silicon Valley are already experiencing these changes. For Fortune 500 companies across industries that aren’t yet immersed in AI, these changes are rapidly approaching.
1. Product Development Lifecycles Will Transform Completely
The traditional product development lifecycle – where someone writes a product requirements document (PRD), translates it into epics and user stories, then begins building – is becoming obsolete.
“Your product development lifecycle will transform significantly in the world of AI,” Sinha explained. “There will be a complete redesign of the workflow.”
What’s replacing it?
- Spec-driven development and testing. Instead of lengthy PRDs, your prototype becomes your new PRD. The cycle from concept to working code compresses dramatically.
- Compression across all phases. Organizations will see compression in product discovery and viability phases, in build and test phases, and in monitor and operate phases. What takes weeks now will take days. What takes days will take hours.
- A shift in bottlenecks. “In the current world, organizations say that my software developers can’t write code fast enough,” Sinha noted. “Tomorrow, your bottlenecks will shift from code writing to code review.”
In other words, the constraint won’t be getting code written. AI agents will handle that. The constraint will be ensuring the right things are being built and maintaining quality standards.
This shift has massive implications for how organizations staff teams, what skills they need, and where senior talent focuses their time.
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2. New Ways of Working Will Emerge
Sprint cycles are about to get much, much shorter. “Sprint cycles could look as short as a day,” Sinha said.
Here’s how a one-day sprint could work: AI agents and humans working together complete an entire cycle in 24 hours – quick customer research, rapid prototype development, immediate customer feedback, AI-generated code, automated testing. Done.
“All of that is happening…in the span of a day or less, whereas it used to take two weeks to do that,” Sinha said.
But, Sinha advises, you must have the right operating model foundation to accelerate sprint cycles effectively. You need:
- Clear product ownership and decision rights
- Empowered teams that can move quickly
- Outcome-based goals rather than output-focused metrics
- Continuous deployment capabilities
- Tight feedback loops with customers
Without these elements in place, faster development cycles will create chaos rather than value.
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3. Team Structures Will Evolve Dramatically
Sinha made a striking prediction: “Nearly 100% of your humans will have new roles in two years.”
Why? Because AI agents will handle full development cycles that humans manage now. This doesn’t mean eliminating people – it means evolving what people do.
“Your developers will now start to focus more into your specialized R&D roles, will start to focus more on higher-order problems, and will transform into what we call definers, builders, and a small number of additional roles.”
The shift is from executor to definer. From writing every line of code to defining what should be built and why. From tactical implementation to strategic direction.
And team sizes will shrink. Sinha referenced Amazon’s famous “two-pizza team” concept—teams small enough to be fed with two pizzas. In an AI-enabled environment, that becomes a “one-pizza team.”
Smaller teams, more focused, solving higher-value problems while AI handles more of the execution.
But this raises critical questions for every organization:
- How do you reskill your current workforce for these new roles?
- What does career development look like when traditional engineering paths change?
- How do you attract and retain talent in this new model?
- What happens to people whose roles are most affected by AI automation?
Organizations that address these questions proactively will have a significant advantage over those that treat them as non-essential actions.
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4. Velocity Will Accelerate, But Focus Must Shift
The combined effect of these three shifts? Dramatic acceleration in what teams can accomplish.
Sinha outlined the potential impact:
2-3X faster feature development. The time from idea to deployed feature will compress significantly. Organizations already moving quickly will move even faster.
5-10X improved innovation capacity. This is the big one. When teams spend less time on execution mechanics, they can explore more ideas, test more hypotheses, and innovate at a pace that’s hard to imagine now.
More time on value-added work. Instead of spending time on translation between teams, coordination overhead, and manual processes, people focus on customer problems and creative solutions.
But the key thing to remember here is this: Velocity without direction is chaos.
As individual productivity accelerates through AI, the strategic functions become even more important:
- Defining the right problems to solve. What aligns with strategy? What delivers customer value? What creates competitive advantage?
- Coordinating across teams. When teams can build faster, dependencies and integration points become more critical, not less.
- Connecting work to strategy. With more initiatives possible, ensuring alignment to business objectives is paramount.
- Managing flow and removing bottlenecks. As Sinha noted, bottlenecks shift. Organizations need to identify and address them quickly.
In the planning and portfolio management space, this means connected work capabilities like Objectives and Key Results (OKRs) – linking individual initiatives back to strategic objectives – become essential infrastructure, not nice-to-have features.
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What Does This All Mean?
These four paradigm shifts are already happening in leading organizations. Is your company ready to capitalize on these changes? Remember, readiness isn’t just about AI tools or technology investment. It’s about having the operating model foundation that allows you to absorb and productively use these capabilities.
Discover the five pillars of the product operating model and get detailed assessments to uncover exactly where your organization stands today: Download your copy of “Creating an Outcome-Based Enterprise.”
About the Expert: Megha Sinha is a Partner at McKinsey & Company based in New York. She counsels C-suite executives on product, technology, and AI topics and has led over 25 large-scale product operating model transformations across financial services, fintech, retail, telecommunications, and healthcare in the past four years.
