Why Most Enterprise AI Projects Never Scale | Ft. Vikas Bhalla, EXL

2026-07-26 · Source: AIM Network · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Corporate Strategy & Leadership, Project & Product Management · Depth: Intermediate, extended

Summary

The discussion with Vikas Bhalla, President and Head of AI Operations and Services at EXL, details the significant evolution of enterprise AI since the Q4 2022 ChatGPT moment. EXL, a global data and AI solutions provider, observes enterprises moving from initial experimentation to production at scale, driven by advancements from articulate AI to agentic systems. Bhalla emphasizes that successful enterprise AI implementation requires deep domain context, robust data modernization, and effective execution. He highlights AI's dual impact: enhancing existing workflows, such as improving insurance claims processing, and enabling transformative new capabilities like accelerating drug research. EXL focuses on specific verticals including insurance, healthcare, and banking, leveraging domain expertise to manage regulatory constraints and risks. Bhalla also stresses the critical need for the human workforce to adapt by working on, with, or infusing intelligence into AI solutions.

Key takeaway

For Directors of AI/ML prioritizing scalable enterprise AI initiatives, focus on selecting a few high-impact areas rather than broad experimentation. Your success will depend on integrating deep domain context, ensuring robust data infrastructure, and orchestrating effective execution. Invest in continuous workforce training to ensure your teams can actively work with, on, or infuse intelligence into AI solutions, mitigating the risk of being outpaced by AI-savvy competitors.

Key insights

Enterprise AI success hinges on integrating context, data, and execution, not just technology.

Principles

• Humans skilled in AI will replace those unfamiliar with it.
• Continuous education is vital for AI workforce adaptation.
• Deep domain understanding provides essential AI solution context.

Method

Implement enterprise AI by combining industry context, modernizing structured and unstructured data, and orchestrating diverse technologies for execution.

In practice

• Apply fine-tuned LLMs to optimize insurance claims workflows.
• Utilize AI to significantly accelerate drug research for chronic diseases.
• Train workforce to create, work with, or infuse intelligence into AI.

Topics

• Enterprise AI
• AI Implementation
• Data Modernization
• AI Workforce Transformation
• Domain Expertise
• Drug Research
• Insurance Claims

Best for: Director of AI/ML, AI Product Manager, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by AIM Network.