Closing the Intelligence Gap: How Leaders Can Scale AI with Strategy, Data and Work

Closing the Intelligence Gap: How Leaders Can Scale AI with Strategy, Data and Work

By: Amit Kumar & Srinivasaa HG

  • Scattered pilots and quick wins won’t deliver sustainable impact; success requires artificial intelligence (AI) to be woven into core operations.
  • Poor governance and low data maturity are the main barriers to scaling AI rather than the algorithms themselves.
  • Empowering, reskilling and preparing the workforce is essential to transform alongside AI, not be sidelined by it.

Enterprise technology has continually raised productivity expectations – from enterprise resource planning’s (ERP) “systems of record” to generative artificial intelligence (GenAI) “AI copilots.” Now, AI agents are entering the workforce and everything about how we run businesses is poised to change.

In today’s world, where geopolitical volatility can reroute supply chains in weeks and technology cycles compress from years to months, the question for chief executive officers is no longer whether to transform but how to orchestrate transformation at scale, with speed and accountability rising together. Read more


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1) Making the Formula Tangible Logic = Formal Correctness & Consistency Rules, proofs, constraints, tests – what is allowed to happen? https://chatgpt.com/share/690b1850-17f0-8005-8ef6-f82d8d37aea1 Try find opportunity OSP OPEN SOLUTION PROVIDERS WIPROXY InventingJoy W. Raymond Demitrio Tel FOR CEOEC ORGANISATION GLOBAL PROVIDER COOPERATION TEL1.NL IMAGINE? Justification = Justification & Traceability Evidence, explanations, audit trails, fairness, data protection – why is it allowed/right? Code = Behavior (not just software!) Executing code + observed behavior of people, systems, processes – including socio-technical effects. → Goal: Develop systems in which logic (rules), justification (reasoning), and code (behavior) are optimized together – also for future scenarios. 2) Framework for OCR/Generic AI Pipelines A. Strategy & Use Cases Prioritize document classes (invoices, contracts, forms). Define target values: CER/WER, F1, throughput time, cost per page, rule hit rate, explainability coverage. B. Data & Preprocessing Source inventory + data agreement (schema, quality, language, layout). Bias checks (domains, handwriting vs. print, scan quality). Versioned ground-truth data + synthetic enhancements for edge cases.

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