Enterprise Digital Twin implementation: build around one real decision
An implementation guide for decision context: source ownership, temporal records, retrieval, permissions, MCP boundaries, failure tests and a staged rollout around one workflow.
From the archive
Author: Ajith Vallath Prabhakar
Ajith Vallath Prabhakar is a seasoned AI strategist and technologist with over 20 years of experience. Passionate about the latest AI advancements, Ajith shares insights on cutting-edge research, innovative applications, and industry trends. Follow to stay updated on AI’s transformative power.
Articles
Enterprise Digital Twin: give AI the context a decision needs
A purchasing example shows how current roles, policies, operational state, decision history and constraints change what an AI system should recommend or do.
Enterprise AI governance: make the controls reach the work
Connect AI policy to decision rights, current evidence, action permissions and recovery. A practical operating design and test plan for one enterprise workflow.
Compound AI and multi-agent systems: choose the architecture the work needs
Distinguish compound systems, controlled workflows and multi-agent coordination. A worked timing example and practical acceptance tests for enterprise architecture.
Version drift: when a well-sourced AI answer uses the wrong policy
Keep policy versions, effective dates, permissions and approvals aligned from retrieval to action. A worked example and test plan for enterprise AI version drift.
Decision-centric AI: improve the path from evidence to action
Design AI around an owned decision and a confirmed outcome. Use a practical decision canvas, workflow example and measured expansion gates.
MCP for enterprise AI: a practical guide to integration, authority, and operations
Understand MCP's current architecture, version compatibility, discovery and authorization, then design a controlled enterprise integration with clear action boundaries.
Neuro-Symbolic AI for Multimodal Reasoning: Foundations, Advances, and Emerging Applications
Neuro-symbolic AI connects learned representations with explicit structures such as rules, graphs, or planners, but the term covers multiple designs. This guide compares representative approaches and applications while examining what…
Red-teaming AI systems: a practical playbook for testing real actions
Scope an AI red-team exercise, test prompts and tool permissions, inspect destination outcomes, protect evidence, and verify fixes without mistaking a refusal for a security boundary.
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