ai-tech
Where Do Advanced Global Companies Stand on AI Transformation? The Widening Gap Between 'Deep Transformation' (34%) and 'Surface-Level Adoption' (37%)
According to a Deloitte survey conducted in August–September 2025 and released in January 2026, only 34% of global enterprises are in the deep transformation stage of fundamentally reimagining business models with AI, while 37% remain at surface-level adoption, revealing a stark performance gap. A McKinsey survey released in November 2025 shows that while 88% use AI, enterprise-wide value creation remains limited to a few, with only 39% reporting an impact on EBIT (mostly under 5 percentage points of improvement). JPMorgan Chase has accumulated nearly a decade of experience since 2017—from COiN (saving 360,000 hours annually) to the 2024 LLM Suite (deployed to over 230,000 employees)—now operating 450 use cases, with CEO Jamie Dimon citing up to $2 billion in annual business value. Siemens leads manufacturing AX with industrial AI agents and digital twins, establishing 2026 as a 'starting point' with its Erlangen plant as the initial blueprint, alongside a successful 8-hour autonomous logistics demonstration using humanoid robots in April. Common traits among high-performing companies include workflow redesign, proactive governance, and parallel growth goals, while key focal points for the second half of the year include the disparity in adoption speeds across industries (including healthcare's 'fast to explore, slow to deploy' pattern) and the standardization of ROI measurement frameworks.
According to a Deloitte survey conducted in August–September 2025 and released in January 2026, only 34% of global enterprises are in the deep transformation stage of fundamentally reimagining business models with AI, while 37% remain at surface-level adoption, revealing a stark performance gap. A McKinsey survey released in November 2025 shows that while 88% use AI, enterprise-wide value creation remains limited to a few, with only 39% reporting an impact on EBIT (mostly under 5 percentage points of improvement). JPMorgan Chase has accumulated nearly a decade of experience since 2017—from COiN (saving 360,000 hours annually) to the 2024 LLM Suite (deployed to over 230,000 employees)—now operating 450 use cases, with CEO Jamie Dimon citing up to $2 billion in annual business value. Siemens leads manufacturing AX with industrial AI agents and digital twins, establishing 2026 as a 'starting point' with its Erlangen plant as the initial blueprint, alongside a successful 8-hour autonomous logistics demonstration using humanoid robots in April. Common traits among high-performing companies include workflow redesign, proactive governance, and parallel growth goals, while key focal points for the second half of the year include the disparity in adoption speeds across industries (including healthcare's 'fast to explore, slow to deploy' pattern) and the standardization of ROI measurement frameworks.
From JPMorgan's COiN in 2017 to the recent LLM Suite, AI automation has been built up over nearly a decade, while Siemens has deployed AI agents to factories. Based on data, we examine the reality of AI transformation (AX) in 2026 between companies that remained stuck in pilots and those that redesigned core tasks. According to the 'State of AI in the Enterprise' survey conducted by Deloitte between August and September 2025 and released in January 2026, 34% of companies responded that they are fundamentally reimagining new product development, core processes, or business models using AI. Meanwhile, 30% responded that they are redesigning core processes around AI, and 37% remained at a surface-level adoption stage with little change to existing business processes. The survey was conducted among 3,235 executives at the director level or higher across 24 countries. This indicates that whil…
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