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IA FORUM MEMBER INSIGHTS: ARTICLE

 

By: Sai Krishnan Mohan, Head of Global Data & Analytics, SULZER; Doug Shannon, Global Intelligent Automation & GenAI Thought Leader & Global Intelligent Automation Manager, GLOBAL PHARMACEUTICAL COMPANY; James Gowen Jr., Ph.D., Former Global Head of Data Engineering Controls - Data Quality Platforms, CITI; Jay Dhar, Director, Emerging Capabilities & AI Enablement, U.S. BANK; Sudipta Dey, Lead Solution Architect, Cloud Infrastructure Modernization, ELSEVIER

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When executive teams discuss technical debt, the conversation often begins with legacy applications, aging infrastructure, brittle integrations or platform lifecycle decisions. These are important concerns, but they represent only part of the picture.

 

Technical debt affects far more than IT cost. It influences how quickly leaders make decisions, how reliably teams interpret performance, how efficiently processes operate and how prepared the organization is for AI-enabled transformation. Its most consequential effect is often decision debt: the growing difficulty of making timely, confident decisions because information, business logic and operational knowledge are fragmented across the enterprise. Over time, technical debt becomes business debt.

 

Modern enterprises increasingly depend on data, algorithms, platforms, process knowledge and decision models. When the architecture supporting these assets becomes fragmented or poorly governed, the organization loses data value, incurs operational risk and reduces its capacity to respond to change.

 

This becomes especially visible as organizations adopt advanced analytics, automation and agentic AI. AI does not create enterprise complexity; it exposes and can amplify it. Initiatives rarely struggle because of the model alone. More often, the enterprise context surrounding the model is incomplete, inconsistent, inaccessible or insufficiently governed. As autonomy increases, architectural weaknesses become operational constraints. AI therefore acts as a diagnostic lens, revealing complexity that restricts intelligence operating across systems, data and business processes. Read More...

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