AI Native: How organizations can create value & prepare for the transition_Joanne Z. Tan_AIXD.World

Описание: Going AI Native is not a tech upgrade. It is a business model shift.

AI native organizations are not layering AI onto legacy systems. They are architecting products, services, and operations with AI embedded at every level. That distinction is where competitive advantage is being built.

*What “AI native” really means*

Not AI as a feature, but AI as the foundation
Systems where components use AI with and among each other, not bolted-on tools
Data-driven, knowledge-based ecosystems replacing static, rules-based logic
Fewer legacy constraints, more adaptability, intelligence, and scale

👉 To read this as a blog: (coming soon)

Ericsson describes this as the only approach that avoids compromise and unlocks full system intelligence.

*Why the market is paying attention*

47% of AI-native companies** have reached critical scale and market fit vs 13% of AI-enabled peers, according to Iconiq Capital
Nearly *80% of AI-native builders* are investing in agentic workflows, systems that take multi-step actions on behalf of users
Deloitte reports *78% of tech leaders* expect transformational AI agent integration within five years

But reality check from Menlo Ventures: most “agents” today are still structured copilots. The infrastructure race, departmental AI tools, and on-device compute are the real near-term battlegrounds.

*Two paths to becoming AI native*

1. Direct impact: Transform the offering**
Use AI to make the product or service fundamentally better.

Samsung embeds on-device AI for real-time translation and advanced image processing, delivering visible customer value
Microsoft integrated Copilot across Microsoft 365, tying AI directly to subscription growth and valuation

2. Indirect impact: Transform the organization**
Use AI to improve performance, lower cost, and increase trust.

Target applies AI-native inventory and demand forecasting to ensure availability and pricing confidence
JPMorgan Chase uses AI-driven pattern recognition for real-time fraud detection and data protection

*Hard truths before you go AI native*

Start with the problem, not the technology.AI without a business case is expensive theater.
Governance is brand strategy. Trust, bias control, data protection, and compliance are now competitive differentiators.
Data strategy is infrastructure strategy.Siloed, legacy platforms block AI value. Accessible, secure, unified data enables it.

AI native is not about experimentation anymore. It is about redesigning how value is created, delivered, and captured.

*About AIXD and the author Joanne Z. Tan*
As a globally known brand strategist, thought leadership coach, and startup mentor in Silicon Valley, Joanne Z. Tan offers her expertise in customer experience design, human centric AI experience design, to formulate AI strategies for different business models and services. Joanne contributes 15 years of experience derived from architecturing business models, end user experience, and brand experience. She also authored more than 12 articles on AI, published on 10PlusBrand.com.
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