If you’ve been in leadership long enough, you’ve probably worked with the McKinsey 7-S framework—a model that helped leaders align strategy, structure, systems, and style. For decades, it was the go-to diagnostic for organizational effectiveness.
But let’s face it. The way we work today looks nothing like the 1980s. AI is rewriting roles, ecosystems matter as much as internal structures, and the best talent often doesn’t sit inside your office—they’re distributed across geographies or even working as partners and freelancers.
This is why McKinsey has introduced a new model called the “Organize to Value” system—a 12-element framework designed to help leaders rewire their organizations for today’s realities.
As someone who has spent years working with talent and culture, I find this model both refreshingly practical and deeply relevant. Let’s unpack what it means, why it matters, and how you, as a leader, can actually use it to drive impact.
And here’s the twist: AI isn’t just one of the 12 elements. It’s the accelerant that makes all 12 come alive.
Why “Organize to Value”?
At its heart, the framework is about one simple but powerful shift: organizations don’t exist to be efficient machines—they exist to create value.
Think about it:
Amazon doesn’t just have efficient logistics. Its structure, partnerships, and culture all orbit around one thing: customer value.
Tesla isn’t just an automaker—it’s an ecosystem of innovation designed around the value of sustainable mobility.
India’s IT services companies, closer home, don’t just sell IT services; they create value by being trusted partners for digital transformation.
This is what the new model forces us to ask: Are we truly organized around value creation, or just around legacy structures?
And in today’s context, Are we using AI as an enabler to amplify value creation?
The 12 Elements of the Organize to Value System — with AI as an Accelerator
1. Purpose
Definition: Why do we exist?
Why it matters: In uncertain times, a clear purpose is a compass for both employees and stakeholders.
📌 Example: Microsoft’s pivot under Satya Nadella unlocked a cultural renaissance by aligning everyone to a refreshed purpose.
👉 Action: Audit your purpose statement. Is it inspiring, actionable, and relevant today?
💡 AI takeaway: AI-powered sentiment analysis and narrative tools can help leaders understand whether employees and customers truly connect with the organization’s purpose, ensuring alignment in real time.
2. Value Agenda
Definition: How the organization creates value.
Why it matters: Without clarity on value drivers, resources scatter.
📌 Example: Netflix’s clarity around personalized entertainment drives its technology investments.
👉 Action: Define 3–5 value creation levers for the next 3 years.
💡 AI takeaway: AI can map customer preferences, predict emerging trends, and surface hidden value drivers, ensuring sharper prioritization of resources.
3. Structure
Definition: How accountable units and teams are designed.
Why it matters: Structure must serve strategy.
📌 Example: Spotify’s “squads and tribes” keep teams agile and innovation-driven.
👉 Action: Ask if your structure aligns with customer journeys, not silos.
💡 AI takeaway: AI-enabled organizational network analysis can reveal collaboration bottlenecks and suggest structure redesigns for faster, cross-functional decision-making.
4. Ecosystem
Definition: How we partner beyond boundaries.
Why it matters: No company wins alone anymore.
📌 Example: Apple’s developer ecosystem powers the iPhone’s dominance.
👉 Action: Identify 2–3 ecosystem partners who can multiply your impact.
💡 AI takeaway: AI can evaluate ecosystem data—suppliers, partners, alliances—to model value creation scenarios and recommend the most strategic partnerships.
5. Leadership
Definition: How leaders decide and act.
Why it matters: Leadership today is about enabling, not controlling.
📌 Example: Nadella’s “learn-it-all” culture redefined Microsoft.
👉 Action: Decentralize decision-making where possible.
💡 AI takeaway: AI-driven dashboards can give leaders real-time insights, improving decision-making quality and speed while freeing leaders to focus on empathy and vision.
6. Governance
Definition: How resources and priorities are managed.
Why it matters: Governance ensures execution discipline.
📌 Example: Unilever balances global and local priorities through governance councils.
👉 Action: Simplify governance rhythms (weekly, monthly, quarterly).
💡 AI takeaway: AI can automate compliance checks, monitor KPIs continuously, and flag risks early, making governance both lighter and more effective.
7. Processes
Definition: How workflows are designed.
Why it matters: Clear processes enable consistent value creation.
📌 Example: Toyota’s lean model embeds continuous improvement.
👉 Action: Redesign one critical process for speed and customer impact.
💡 AI takeaway: AI can automate routine workflows, optimize handoffs, and predict bottlenecks, freeing people for higher-value work.
8. Technology
Definition: How digital, data, and AI enable value.
Why it matters: Tech is no longer support—it’s the engine of growth.
📌 Example: Domino’s reinvention as a “tech company selling pizza.”
👉 Action: Don’t just digitize—ask how tech creates new value.
💡 AI takeaway: AI augments decision-making, personalization, and prediction—turning technology from cost center into value center.
9. Behaviors
Definition: How culture is lived daily.
Why it matters: Culture is the invisible driver of performance.
📌 Example: Netflix’s “freedom with accountability” model.
👉 Action: Define 3 non-negotiable leadership behaviors.
💡 AI takeaway: AI can detect cultural trends through language analysis of employee feedback, highlighting gaps between “stated culture” and “lived culture.”
10. Rewards
Definition: How people are recognized.
Why it matters: You get what you reward.
📌 Example: Salesforce rewards leaders for customer success, not just revenue.
👉 Action: Ensure rewards reinforce desired behaviors.
💡 AI takeaway: AI can personalize recognition programs, analyze performance drivers, and ensure fairness by eliminating hidden biases in rewards.
11. Footprint
Definition: Where talent is located and deployed.
Why it matters: The right skills in the right places.
📌 Example: Infosys built Tier 2 delivery centers to access untapped talent.
👉 Action: Revisit your workforce footprint for cost, talent, and customer needs.
💡 AI takeaway: AI can model workforce supply-demand, recommend optimal talent hubs, and simulate different footprint strategies.
12. Talent
Definition: How we attract and develop talent.
Why it matters: People bring the strategy to life.
📌 Example: Google’s continuous learning culture future-proofs its talent base.
👉 Action: Invest in capability building relentlessly.
💡 AI takeaway: AI can personalize learning journeys, identify emerging skill gaps, and match talent with the right opportunities to accelerate growth.
The Big Shift from the 7-S Model
The old 7-S framework (shared values, strategy, structure, systems, style, skills, staff) was revolutionary in its time. But it was inward-looking.
The Organize to Value system is:
More external-facing (ecosystem, partnerships).
More future-ready (technology, AI, digital).
More human-centric (behaviors, talent, purpose).
AI makes this shift real. It bridges data with decisions, processes with people, and vision with velocity.
A Blueprint for Leaders: Using AI to Accelerate Organize to Value
Here’s how C-level leaders can put this into practice:
Start with Value, Not Tools: Don’t ask “what AI should we adopt?”—ask “how can AI amplify our value agenda?”
Run a Value-to-AI Map: For each of the 12 elements, identify 1–2 ways AI could accelerate outcomes.
Pilot, Don’t Over-engineer: Start with small AI-enabled pilots in processes or governance, then scale.
Empower People, Not Replace Them: Use AI to augment human strengths—judgment, empathy, creativity. Create Feedback Loops: Let AI continuously learn from organizational data and refine how value is created.
Final Thoughts
The Organize to Value system is the most relevant framework for modern organizations. And when combined with AI as an accelerant, it becomes a powerful blueprint for building high-impact, future-ready enterprises.
As leaders, our responsibility isn’t just to adopt AI—it’s to organize for value so AI becomes a multiplier, not a distraction.
It is no longer enough to ask:
👉 “Are we organized to create value?”
Ask instead, “How can AI help us create, accelerate, and sustain that value?”
External Reads:
A new operating model for a new world – McKinsey
How to get your operating model transformation back on track – McKinsey
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