AI is often hailed as the hero of app modernisation, but observability is critical

2026-05-27 · Source: Tech Monitor · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cybersecurity & Data Privacy · Depth: Intermediate, short

Summary

An executive roundtable hosted by GlobalData and Hexaware in early May gathered senior IT leaders to discuss AI's role in application modernization and quality engineering. Participants acknowledged AI's rapid evolution from predictive to GenAI and agentic AI, with the US AI market projected to grow from $182bn in 2025 to $888bn in 2029. Key challenges identified include calculating ROI, addressing security, and managing the AI skills gap, with a strong consensus that experimentation is crucial. While developer use of AI for code generation and optimization shows robust ROI, the discussion highlighted AI's broader application in quality engineering, system exploration, and automated testing. A critical focus was placed on observability for responsible AI, particularly for agentic AI, to monitor agent actions, data access, and communications, ensuring trust and scalable adoption.

Key takeaway

For Directors of AI/ML evaluating AI adoption strategies, delaying experimentation is a critical mistake that risks falling behind. Your teams should prioritize immediate, responsible AI deployments. Focus on developer-centric applications for rapid ROI, and implement robust observability for agentic AI. This will build trust and ensure scalable, secure operations, addressing stakeholder demands and mitigating the AI skills gap.

Key insights

Experimentation with evolving AI, especially agentic AI, is crucial for establishing foundational knowledge and strategy.

Principles

• AI adoption requires continuous experimentation.
• Observability is foundational for responsible AI.
• AI extends beyond coding to full app modernization.

In practice

• Use LLMs for code generation and optimization.
• Automate testing with AI for quality assurance.
• Implement observability for agentic AI monitoring.

Topics

• AI Modernization
• Agentic AI
• Observability
• Responsible AI
• ROI Measurement
• Quality Engineering

Best for: CTO, Executive, AI Architect, Director of AI/ML, VP of Engineering/Data, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech Monitor.