Governed context: The key to scaling enterprise AI

Gartner projection: >40% of agentic AI initiatives will be cancelled by end of 2027

EXL case study: neuro-symbolic design increased accuracy on disputed-charge verification from ~40% to >90%

Two-layer architecture: neural (LLM) for interpretation, symbolic (rules/ontologies) for validation before action

Three practical moves: start narrow (one domain, one decision), make context shared (business + tech + risk + compliance), be deliberate about build vs. buy

Concept: enterprise context layer becomes 'system of record for institutional knowledge'

Published by CIO.com; authored by industry perspective (EXL framing)

Gartner projects >40% of agentic AI initiatives will be cancelled by end of 2027, driven by escalating costs and unclear business value

EXL deployment data: neuro-symbolic architecture increased accuracy on disputed-charge verification from ~40% to >90%

The design pattern: LLM interprets unstructured data; rules layer validates against regulations, precedent, and policy before recommending action with evidence

Recommended three-move approach: start narrow with one high-value domain, make context a shared responsibility across business/tech/risk/compliance, be deliberate about build vs. buy for ontology and governance capabilities

Framed as 'system of record for institutional knowledge'—analogous to ERP for transactions and CRM for sales