Mustafa Suleyman, Chief of Artificial Intelligence at Microsoft, has warned that a majority of white-collar roles relying on computers could be fully automated within the next 12 to 18 months.
Speaking to the Financial Times, Suleyman revealed that Microsoft is developing a “professional-grade AGI”, capable of performing almost all tasks currently done by professionals such as lawyers, accountants, project managers, and marketers. He emphasized that the AI revolution will go beyond incremental productivity gains, potentially causing significant structural shifts in knowledge-based industries.
“White-collar work, where you’re sitting at a computer, whether as a lawyer, accountant, project manager, or marketer, most of those tasks will be fully automated by AI within the next 12 to 18 months,” Suleyman said.
Microsoft’s Enterprise AI Strategy
Suleyman explained that Microsoft aims to capture a larger share of the enterprise market by automating repetitive and routine tasks such as document drafting, data analysis, and project coordination, which currently require skilled human employees.
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He also highlighted the company’s plan to increase AI model production to reduce dependence on OpenAI following their revised agreement. According to Suleyman, creating a new AI model could become as simple as producing a podcast or writing a blog, enabling individuals and institutions to design AI tailored to their specific needs.
AI-Driven Job Restructuring
Other tech giants are also restructuring to accommodate AI. Oracle plans to cut 20,000–30,000 jobs to expand AI data-centre capacity, while Amazon recently announced layoffs of 16,000 employees as part of its AI-driven transformation.
AI’s Economic Potential in India
A PwC India report projects that AI could add nearly $550 billion to India’s economy by 2035, impacting sectors such as agriculture, education, energy, healthcare, and manufacturing.
India has also launched the India AI Mission in 2024 with $1.2 billion in funding, aimed at democratizing access to computing resources, datasets, and talent development, paving the way for widespread AI adoption.