Introduction: Why AI Transformation Is Like Fitness
AI today is like a gym membership—everyone gets one, then posts about it.
Setting up AI labs, purchasing the most advanced models, announcing partnerships with tech giants… These actions sound futuristic. But as Alexander Sukharevsky, head of McKinsey Quantum Black, put it bluntly: Getting the membership is just the beginning. The real challenge is whether you’re willing to “sweat” on the treadmill every day.
Getting a membership ≠ Working out. Technology procurement ≠ Value unlock.
AI transformation is never just a technology problem—it’s a profound organizational reshaping. It’s not a multiple-choice question; it’s a “complete recipe” that must be answered correctly in every part.
Sukharevsky has a more vivid metaphor for Agents: Think of an Agent as your “junior colleague”—extremely hardworking, super-fast learning, 24/7 availability. But remember, you’re the boss, and you’re responsible when things go wrong. This isn’t shifting blame—it’s clarifying boundaries: Agents do the work; humans judge, correct, and take responsibility.
I. The “Six-Element Framework” for Value Unlock: A Systematic Project
Sukharevsky’s research yields a brutal conclusion: The success rate of AI transformation is extremely low because it’s a systematic project. All six elements must be in place:
| Element | Core Meaning | Common Pitfall |
|---|---|---|
| Business Domain Reinvention | Focus on ≤2 core domains, completely reinvent business processes | Trying to cover everything, layering AI onto existing processes |
| Data Products | Build reusable data assets | Waiting for perfect data or cobbling together temporary datasets |
| Architecture Reorganization | Let data flow freely, break down department walls | Treating it as purely technical, ignoring political dimensions |
| Governance & Operations | Establish new rules for Agents, traditional governance fails | Managing Agents the same way as people |
| Economic Model | Calculate the full cost: technology + talent + change management | Counting only technology investment, ignoring hidden costs |
| Integration & Implementation | Make all six elements work together | Optimizing isolated points, lacking global perspective |
Sukharevsky has a line worth putting on every CEO’s office wall: If you set the wrong goal, it will execute at extreme speed in the wrong direction.
In the Agent era, this risk is amplified tenfold. Agents execute faster, make decisions more automatically, and have broader impact—once they go wrong, the damage is exponential.
II. From Theory to Practice: When “Six Elements” Meet “Compliance Deep Water”
In 2026, China’s Cyberspace Administration, National Development and Reform Commission, and Ministry of Industry and Information Technology jointly issued the “Implementation Opinion on Standardized Application and Innovative Development of Intelligent Agents,” elevating “safe, reliable, and trustworthy” to unprecedented strategic importance.
What does the Implementation Opinion require?
- Safety baseline: Data security compliance, sensitive data masking
- Reliable operation: Agent behavior traceable and auditable
- Trustworthy governance: Clear human-agent collaboration responsibility boundaries
Without these, it’s like a self-driving car without brakes—no matter how fast it goes, you wouldn’t dare ride it.
Governance isn’t a shackle—it’s an accelerator.
III. “Four Dilemmas” of Enterprise AI Transformation
Dilemma 1: Coordination Becomes Silos
Three Agents work separately, ignoring each other, creating new “AI silos.” Cross-domain business fragmentation means AI decisions only achieve local optimization.
This is politics, not technology.
Dilemma 2: Governance Is a Black Box
AI decision processes are like black boxes—when something goes wrong, who’s responsible? Traditional governance models fail in the Agent era.
Dilemma 3: Degradation Upon Launch
Agents are smartest on launch day. Three months later? Business rules changed, models didn’t. Without feedback loops, Agents can’t evolve.
Dilemma 4: Unclear Costs
Every new scenario is developed from scratch, reinventing wheels. Without a platform foundation, marginal costs don’t come down, ROI can’t be calculated.
IV. Breaking Through: Building an Enterprise AI “Intelligent Cockpit”
Zhengzhou Shuneng Software Company’s solution provides a key to solving this puzzle.
Core positioning: Not a toolbox for CTOs, but a cockpit for CEOs.
- Steering Wheel: LEGO-style orchestration—AI shifts from “lone wolf” to “coordinated army”
- Brakes & Black Box: RBAC permission guardrails + full-link traceability
- Dashboard: Feedback loop—AI capability “gets smarter the more you use it”
V. CEO Action List: From Cognition to Implementation
Five questions every CEO should ask:
- Have you personally used AI tools? Do you know what they can and can’t do?
- How many core domains are you focused on? The answer should be no more than two.
- Have you built a full cost model? Technology + talent + change management costs all calculated?
- Is governance framework in place from day one? Do Agents have brakes and black boxes?
- Do you have a platform foundation? Or is every scenario developed from zero?
Success rate below 3%—what’s missing isn’t technology, it’s systems. Six elements in place, focus on one or two domains, ROI can reach 1:3. Steering wheel, brakes, black box, dashboard—the cockpit four-piece set can break through.
Based on McKinsey Quantum Black leader Alexander Sukharevsky interview and Zhengzhou Shuneng solution.