What are needed for going AI native - Joanne Z. Tan, thought leader, the originator of AIXD (AI Experience Design), global brand strategist (image of AI agent & systems)

Going AI Native: Impacts and Strategies

The Shift to AI Native Business Models

To succeed, products and infrastructure must evolve to create new value for customers, not just upgrade technology.

AI native products, services, and enterprises are grabbing headlines. To go beyond the headlines, organizations need to envision exactly how AI will create value and prepare for the transition.

What Does It Mean to Be “AI Native”?

“AI native” is a term that can refer to products, services, and even whole organizations designed with AI at their core. Rather than adding AI features to an existing structure, AI native systems embed AI capabilities into every layer.

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In an informative white paper, telecom giant Ericsson notes several approaches to AI, such as:

  1. Replacing an existing component with an AI component
  2. Adding an AI component to an existing system
  3. Adding AI controls to an existing system
  4. Designing a system in which all components use AI in and among each other

Only the last example is truly “AI native.” It avoids compromises required for backward compatibility with less capable legacy systems and leverages a data-driven, knowledge-based ecosystem to augment or replace static, rules-based systems.

In this article, we examine today’s AI native systems and ways to create value through AI native implementations.

AI Native Technologies Today

AI Native in the Market

AI native solutions are finding their place in the market. According to Iconiq Capital’s 2025 State of AI Report, nearly half (47%) of AI-native companies have reached critical scale and proven market fit compared with just 13% of companies building AI-enabled products.

The report notes that agentic workflows and vertical applications are key focus areas. Nearly 80% of AI-native builders are investing in autonomous systems designed to take multi-step actions on behalf of users.

The Potential

Deloitte’s Tech Trends 2026 forecast highlights strong momentum for AI native enterprises. According to the report, 78% of tech leaders anticipate broad or transformational integration of AI agents into architecture workflows over the next five years.

Three major shifts are reshaping the industry:

  • Priorities: Generative and agentic AI are moving from experimentation to core strategy.
  • People: Leaders are increasing hiring, including new roles like AI architects, emphasizing augmentation rather than job loss.
  • Purpose: Technology divisions are shifting from service providers to revenue generators.

A Reality Check

Despite momentum, Menlo Ventures cautions that most AI agents today are still early-stage systems, often structured as simple if-then logic around model calls. Copilots dominate enterprise applications rather than fully autonomous agents.

AI native startups are gaining market share in:

  • Departmental AI tools built for specific job roles
  • Inference platforms powering AI infrastructure

On-device computing is expected to grow due to latency, privacy, and security considerations.

Going AI Native – Direct and Indirect Impacts

Transitioning to an AI-native enterprise requires fundamental re-architecture of the business model.

Writing for CIO.com, Joey Ahnn, CTO of South Korea’s SSG.com, writes “The transition to an AI-native enterprise demands more than just technology adoption; it requires a fundamental re-architecture of the business model.”

Two primary paths exist:

  • Improving the product or service itself
  • Improving organizational efficiency and performance

Creating Direct Impacts – Improved Offerings

AI native integration can create visible customer value, as shown in these two examples:

  • The first is Samsung, which is incorporating on-device AI technology into consumer products, enabling features like real-time translation and advanced image processing without relying on the cloud. The result, he writes, is “clear customer value” through improved performance.
  • The second example cited is Microsoft‘s “monumental AI-native transition,” achieved by embedding the Copilot digital assistant “across the entire Microsoft 365 suite.” According to the author, “The focus on core business value has been key to [Microsoft’s] rising subscription rates and company valuation.”

Creating Indirect Impacts – Improved Organizations

When AI is not directly embedded into products, it can enhance internal systems to improve customer outcomes, cited by Ahnn in these two more examples:

  • Retailer Target incorporated an AI native approach into its inventory control and demand forecasting systems. The result has been increased consumer trust that Target will have products “always available at the right price.”
  • JPMorgan Chase used an AI native strategy to improve efficiency, but – even more importantly – to protect privacy and customer assets. Advanced pattern recognition enabling real-time fraud detection has been a major focus of the effort, according to the author.

Preparing for Going AI Native

Before investing in an AI native strategy, leaders must address key challenges.

Solve a Real Problem

Start with the business problem, not the technology. Adopting a technology strategy without a clear plan risks diverting resources without a corresponding gain. Deloitte reports 71% of organizations are modernizing core infrastructure. But without a clear understanding of the business case and desired outcome, “it could be easy to invest in AI and receive no return.”

Develop AI Governance Standards

Strong governance is essential for brand trust, regulatory compliance, protection of customer and proprietary data, whether the organization creates or simply deploys AI solutions.

Create a Data Strategy

Creating a data strategy begins with protecting the organization’s data, but it doesn’t end there. To be useful, all relevant data must be accessible across the organization rather than distributed in different silos or “buckets.” The effort will eventually require a strategy to replace “legacy platforms patched together for survival,” as Deloitte puts it. Legacy platforms often require modernization to enable effective AI-native systems.

If you would like to learn more about becoming an AI native enterprise, please contact us.

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About AIXD™ and the Author Joanne Z. Tan

Joanne Z. Tan is a globally recognized brand strategist, thought leadership coach, and startup mentor in Silicon Valley. She specializes in customer experience design and human-centric AI experience design to formulate AI strategies for diverse business models and services.

With over 15 years of experience architecting business models, end-user experience, and brand experience, she has authored more than 12 articles on AI published on both 10PlusBrand.com and AIXD.world.

© Joanne Z. Tan, Jan. 2026. All rights reserved.