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How Generative AI Drives Intelligent Operations in Enterprises? — A Leap from Cost Optimization to Value Creation
<span>February 27, 2025 ⏰ Thursday 🗓 Lunar Calendar: The 30th Day of the First Month</span>
This article is analyzed and interpreted by AI, the full report can be found at the end of the article Accenture’s “Generative AI Reshaping Operations: Driving Growth, Advancing Transformation” 2.8MB | 36 pages
Hello everyone! Today we are discussing a topic thatfuture enterprises must pay attention to — how generative AI becomes the “super engine” of intelligent operations? Accenture’s latest report reveals the underlying logic and practical paths of this transformation, and we will break it down for you in simple terms 👇
🚀 Why is Generative AI the Key to Operational Transformation?
- 92% of executives are aware of the urgency: Generative AI can quickly achieve large-scale reshaping, but 71% of enterprises are still stuck in the stage of “dare not use, do not know how to use.”
- Data of enterprises in the reshaping readiness phase: Average revenue growth2.5 times, efficiency improvement2.4 times, the success rate of high-value AI use cases is3.3 times that of ordinary enterprises.
- Where is the watershed?: The ability to build the “talent + data + process” iron triangle determines whether enterprises remain in the “basic tool person” stage or become an “AI-driven growth machine.”
💡 The “Four Stages of Evolution” of Intelligent Operations
The degree of enterprise intelligence is divided into four stages (Figure 2), andenterprises in the reshaping readiness phase have entered the highest mode:
- Basic Stage: Using Excel for accounting, processes rely on manual stamping (8%)
- Automation Stage: Low-code tools achieve simple process automation (56%)
- Insight-Driven Stage: Data begins to guide decision-making (20%)
- Reshaping Readiness Stage: Full-process super-automation, AI thinks like an “employee brain” (16%)
Key Differences: Enterprises in the reshaping readiness phase can use AI to handle core businesses such as R&D, customer service, and supply chain, while other enterprises are still making breakthroughs in isolated points.
🛠️ The Three Essential Infrastructures for Intelligent Operations
The report points out that leading enterprises win by simultaneously advancing three core elements:
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Talent Strategy:
- Not eliminating employees, but transforming them from “executors” to “AI trainers.”
- Case: National Australia Bank uses AI to handle legal documents, saving employees 3 days to focus on complex tasks.
Data Assets:
- Data quality > quantity: 71% of enterprises in the basic stage face AI “answering the wrong question” due to data chaos.
- Successful case: An industrial giant achieved70 million dollars in additional profit through a unified data platform.
Collaborative Culture:
- Technical teams and business departments must be “tied together”: 87% of enterprises in the reshaping readiness phase adopt cross-functional task forces, while this ratio is less than 10% in basic stage enterprises.
👥 How Can CEOs Become the “First Promoter” of AI Transformation?
- Top-down setting the tone: Clearly state that “AI is not just a matter for the technical department, but a survival battle involving everyone.”
- Breaking down departmental walls: A certain car manufacturer established a “transformation office,” directly led by the CEO, breaking the collaboration barriers between R&D, production, and sales.
- Let results speak: Set clear KPIs (such as “AI use cases increase by 30% annually”), rather than vague concepts.
📊 Data Governance: The “Food Storage Defense Battle” of Generative AI
- Data should be like “tap water”: Accessible anytime, clean and uncontaminated.
- Metadata Management: Tagging data to let AI understand “what you are asking” instantly.
- Safety first: Protecting data like guarding a bank vault, NAB even put an “insurance lock” on AI models.
Conclusion and Outlook
Generative AI is not just “the icing on the cake,” but theentrance ticket for enterprises’ survival battle. In the next 3-5 years, competition in intelligent operations will focus on three points:
- Who can connect the “talent-data-process” closed loop earlier;
- Whose AI use cases can deeply bind business scenarios (such as using AI to design new drugs, predict supply chain crises);
- Who dares to make “no regrets investment” — investing in both short-term effective coding robots and long-term strategic digital cores.
Finally, a message for everyone: In the era of AI, “those who cannot use tools” will ultimately become “tool people.” Meanwhile, enterprises that actively embrace change are redefining “intelligence” with generative AI.
(If you want to know specific industry cases or implementation methods, please leave a message in the comments 👇)
Disclaimer: This article is based on publicly available reports and does not involve sensitive information. Some case details have been anonymized, and opinions are for reference only.
Original Report
Accenture’s “Generative AI Reshaping Operations: Driving Growth, Advancing Transformation” [https://s.eiix.top/Pxqzvf] Scan to obtain (2.8MB | 36 pages)
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