Last week, Deloitte released a survey report covering 24 countries and 3,235 executives. 66% of companies achieved productivity gains with AI, but only 34% are truly restructuring their business models. This gap shows that most companies are still stuck at the “speed-up” stage and have not touched the fundamentals.
50%
2025 Employee AI Usage Rate Growth
66%
Enterprises improve efficiency through AI
34%
Enterprises begin to restructure core business processes
20%
Establish a Mature AI Governance Framework
1. Deloitte Report: Productivity Improvement ≠ Business Restructuring—There Is a Chasm Between the Two
Deloitte AI Institute's '2026 State of Enterprise AI Report' spans 24 countries, and all respondents are senior executives at director level or above. The adoption of agentic AI is significantly outpacing governance development; currently only 20% of enterprises have established mature autonomous AI governance frameworks. 58% of enterprises have partially used physical AI (factory robots, inspection drones), and the proportion is expected to rise to 80% within two years.
42% of companies believe their AI strategy is highly ready, but their readiness in infrastructure, data, and talent is much lower. Many companies’ judgment about AI runs ahead of their actual execution capability.
Deloitte AI Institute, 2026
2. Microsoft: Stop Treating AI as a Tool—It Should Enter Operational Logic
Microsoft Americas COO Tracy Galloway published an internal observation article on April 1, documenting which companies are moving faster and more steadily in scaling AI deployments. The conclusion was unexpectedly simple: it is not because of technological leadership, but a different strategic perspective.
Slow companies treat AI as a set of tools, evaluating ROI task by task. Fast companies write AI directly into their operating framework, tied to business goals—not “what efficiency did AI improve,” but “in which part is AI responsible for which business result.”
Heavily regulated industries such as healthcare and finance, by contrast, see more solid AI implementation. Because compliance and data responsibility were built in from the start, there is no burden of deploying first and fixing later. The fastest-scaling companies are freeing employees from repetitive work—not replacing people, but pushing them toward positions that require judgment.
3. ERP Evolution Milestone: Natural Language Scheduling Is Turning from Concept into Reality
The forecasted market size of cloud ERP in 2026 is $73 billion. The main logic of traditional ERP is “entry—query—reporting.” Now some new deployments already support orchestrating cross-system operations through natural language—for example, saying to the system, “Sort out last week’s unsettled purchase orders and generate a payment plan,” and the system runs it on its own.
ERP is moving from a “system of record” to a “decision support system.” Large manufacturing enterprises such as BASF have already made early attempts. However, the reality of implementation is not so smooth; the three most frequently reported pain points by enterprises are:
- Data silos—the cost of integrating legacy systems accumulated over many years is very high
- Legacy system compatibility — some old architectures cannot support the real-time data requirements of AI modules
- Insufficient AI literacy—it takes time for employees to transform from "operators" to "AI prompt officers"
4. Anthropic enterprise breakout: annualized revenue from 9 billion to 30 billion, substitution effect begins to emerge
Anthropic's annualized revenue was still $9 billion at the end of 2025, and by April 2026 it had reached $30 billion. More than 1,000 enterprise customers spend over $1 million annually.
$30 billion
Annualized revenue (USD)
1000+
Enterprise customers with annual spending exceeding one million US dollars
In the legal field, AI agents such as Claude Cowork have begun automating contract review and legal document processing, and the stock prices of some legal tech SaaS companies have fallen sharply; the market calls this "SaaSpocalypse." In marketing, AI platforms can already connect to the MCP protocol to directly execute media buying, and traditional manual workflows are beginning to be replaced.
This is not a future prediction that "AI might replace jobs," but rather the present tense already visible in quarterly financial reports. What companies should consider is not "whether to use AI," but "which workflows will undergo fundamental changes in the next 18 months."
Putting these four pieces of information together, the clues are relatively clear: enterprise AI is no longer staying at the experimental stage; scaling, governance, and business process restructuring are becoming things that must be done.
The gap lies in the fact that most companies' strategic awareness is already in place, but on the execution side—data, infrastructure, talent—there is still a considerable distance to go. This window period probably won't last too long.
Data sources: Deloitte AI Institute's '2026 State of Enterprise AI Report', Microsoft Business Insights (2026-04-01), zodioo.com ERP trend analysis, humAI.blog April monthly summary
