Enterprise AI's Next Competitive Edge Is Domain Expertise, Not Model Size

The next advantage in enterprise AI will not come from larger models but from AI systems that understand specific industries deeply enough to reason and solve problems that actually matter, according to the founder and CEO of Articul8 AI, an enterprise generative AI company working with more than 50 customers globally.

Arun K Subramaniyan, speaking to Deccan Herald, challenged the widespread assumption that model capability is the primary barrier holding enterprises back from AI in production. The harder problems, he argues, are multi-model orchestration and silent failure — the way an AI system can sound equally confident when it is wrong as when it is right.

Key Takeaways

• 90% accuracy in a pilot is not 90% accuracy in production: In engineering, manufacturing, energy, or financial environments, a wrong answer can carry operational, financial, safety, or regulatory consequences. Systems that work impressively in pilots can fail in ways that are not immediately visible.
• Different models for different tasks: Frontier models excel at general reasoning, but smaller or domain-specific models can outperform much larger general-purpose models when deep industry knowledge is required — and they typically cost less to run.
• Autonomy is not the enterprise objective; outcomes are: AI can already execute increasingly complex workflows on its own, but the right design question is how much autonomy a system should have for a particular decision. Some actions can be automated; others need validation; and some decisions should always escalate to a human expert — especially in high-stakes industrial environments.
• India is a growing enterprise AI hub: Articul8 has expanded its India presence by more than 2x in the past 12 months, with 2x to 3x additional growth planned this year, combining technical development with local market engagement and customers including Aditya Birla.

Read the full article on Deccan Herald