22 mins ago
Why AI-Native Operating Models Will Drive Enterprise Value
Many companies have started using AI, but they are still learning how to make it useful across the whole business.
AI can help with writing, coding, analysis, and making decisions.
These abilities are becoming cheaper, so companies want to use them more often.
However, many businesses still use work systems designed mainly for people and slower changes.
AI works best when it is connected to trusted company data, everyday tasks, and clear rules.
One survey found that many companies see AI helping innovation, but far fewer see a clear effect on profits.
The article says companies should connect their data, workflows, governance, and AI tools into one operating model.
Businesses that do this may gain more lasting value than those that simply buy powerful AI models.
AI’s business value depends on connecting intelligence with data, workflows, governance, and operating models.
Cheaper AI capabilities are increasing demand for autonomous execution, personalisation, and governance.
McKinsey’s 2025 survey found 64% of respondents said AI enables innovation, but only 39% reported enterprise-level EBIT impact.
Fragmented workflows, isolated pilots, and siloed data prevent organisations from scaling successful AI use cases.
The article argues that AI-native enterprise architectures will create more sustainable competitive advantage than access to models alone.
- Who
- Business leaders and enterprises adopting artificial intelligence; the article was written by Nagendra Bandaru, CEO of Wipro’s AI-Native Business & Platforms Unit.
- What
- The article outlines how enterprises can build AI-native operating models to turn AI adoption into measurable business value.
- Where
- Across enterprises and business functions; no specific geographic location is identified.
- When
- The article discusses current AI adoption and cites McKinsey’s State of AI: Global Survey 2025.
- Why
- To explain why AI investments often produce limited measurable returns and why data, workflows, governance, and operating models must be integrated.
Key facts
- Author
- Nagendra Bandaru
- Author’s role
- CEO, AI-Native Business & Platforms Unit, Wipro
- Survey cited
- McKinsey’s The State of AI: Global Survey 2025
- Innovation impact
- 64% of survey respondents said AI is enabling innovation
- Enterprise EBIT impact
- 39% reported AI impact at the enterprise EBIT level
- AI-attributable EBIT
- Less than 5% of respondents’ EBIT was attributable to AI
- Core recommendation
- Connect data, workflows, governance, and AI services through a unified operating model
Sources
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