Large, continuously changing knowledge bases
Procedures, standards, asset documentation, and operating experience come from many sources and require careful version control.
Industry solution 02
Energy & Utilities
Keep AI traceable and controlled in high-accountability operations
Energy and utility work is knowledge-intensive and highly accountable. We treat sources, permissions, review, and evaluation as core architecture, not post-launch additions.
- Retain sources for procedural and expert answers
- Control access by role and data scope
- Keep people accountable for critical decisions and actions
Challenges
01 02 Strict access and safety requirements
Roles have different data access and action authority, requiring fine-grained controls and auditability.
03 Use cases vary widely in value and risk
Office assistance, knowledge retrieval, and operational support require different data, evaluation, and accountability.
Use Cases
01 Procedural and enterprise knowledge agents
Answer policy, asset, business, and service questions from authorized knowledge while displaying supporting sources.
02 Operations and inspection assistance
Help organize records, locate relevant standards, and prepare analysis without replacing critical operating decisions.
03 AI roadmap and governance
Assess departmental needs and data conditions to define pilots, risk tiers, and a responsible rollout sequence.
Approach
- 01
Tier tasks by business risk
Separate knowledge retrieval, content assistance, decision support, and operational action into appropriate risk levels.
- 02
Design knowledge and access
Define sources, versions, role permissions, sensitive information, and accountable owners.
- 03
Evaluate representative tasks
Use realistic but controlled tests to assess answers, citations, refusals, and exception handling.
- 04
Roll out under control
Begin with limited users and use cases, then expand based on evaluation and feedback.
Case Studies
Enterprise Knowledge Agent
The agent brought authorized procedural, asset, and business knowledge together for internal access with citations, role controls, and user feedback.
Challenge
- Procedures, asset documentation, and business knowledge came from many places, increasing search and version-checking effort.
- Data access and answer accountability differed by role and required clear controls.
Solution
- Organized usable knowledge by type, version, role, and sensitivity.
- Built a cited answer experience with refusal, feedback, logging, and human escalation mechanisms.
Outcome
- Employees could retrieve authorized knowledge from one entry point and inspect supporting information.
- Knowledge access, feedback, and governance requirements became part of one operating model.
AI Use-Case Assessment and Phased Roadmap
Stakeholder interviews, maturity assessment, and opportunity prioritization clarified the AI tasks suited for early validation, their risk boundaries, and the delivery sequence.
Challenge
- Multiple departments proposed AI needs with very different value objectives, data readiness, and risk.
- The organization needed a shared decision basis to avoid duplicated investment and guide pilots and rollout.
Solution
- Assessed the current state across strategy, processes, data, technology, talent, and governance.
- Tiered candidate use cases by business impact, delivery conditions, and risk to create a phased roadmap.
Outcome
- Created a cross-functional portfolio and priority order that stakeholders could evaluate together.
- Clarified pilot objectives, validation methods, dependencies, and future decision checkpoints.