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AI Enabled vs AI Native

· 2 min read
Amaresh Tripathy
Amaresh Tripathy
Co-Founder and Managing Partner

Celonis popularized the concept of 'happy path'. Essentially how a process (and the software) is supposed to work. In reality, the processes often deviate significantly.

AI Enabled vs AI Native

Understanding the Difference​

  1. AI-Enabled Systems

    • Traditional systems with AI features added
    • Limited integration
    • Bolt-on AI capabilities

  2. AI-Native Systems

    • Built from ground up with AI
    • Deep integration
    • Natural AI workflows

Key Learnings from Building Enterprise AI Applications​

At AuxoAI, we've gained valuable insights from twelve months of building enterprise AI applications across various functions and industries. Here are our key learnings from developing probabilistic software systems that integrate data analytics, modeling, UX, and deep engineering:

1. Not Everything Needs GenAI​

Zero or very low tolerance of error systems, especially those without human oversight, aren't ideal starting points for AI implementation. Traditional automation and ML models often suffice - there's no need to force-fit novel technology.

2. POCs Often Miss the Mark​

Many Proof of Concepts focus too narrowly on technical validation rather than delivering real business value. The tendency to default to simple document Q&A systems overlooks opportunities to impact critical enterprise metrics.

3. Process Reimagination is Key​

The most significant value from AI comes from reimagining processes entirely. This requires:

  • Future Back thinking aligned with Today Forward execution
  • Breaking down the reimagined process vision into manageable steps
  • Regular value delivery while managing organizational change

4. Beyond Simple GenAI​

Complex AI applications require:

  • Multiple agents working in concert
  • Sophisticated information exchange systems
  • Advanced engineering beyond basic prompt engineering
  • Reliable integration of multiple components

5. Importance of Auditability​

Particularly in RAG (Retrieval-Augmented Generation) implementations, transparency is crucial:

  • Clear audit trails
  • Visible processing steps
  • Direct connections to source documents

6. Document Complexity​

While basic RAG implementations are straightforward, challenges arise with:

  • Rich formatted documents
  • Images
  • Nested tables
  • Complex document structures

7. Cross-Document Intelligence​

High-value use cases often require:

  • Information retrieval from multiple documents
  • Multi-step processing of retrieved information
  • Integration with historical conversation context