Nighthawk Platform — 5-Year Roadmap
The strategy that takes fragmented operational tooling to a unified AI-native platform.
Problem
Most operations-heavy organizations — utilities, industrial manufacturers, regulated infrastructure — run their day-to-day work on a portfolio of disconnected point-tools. Each tool has its own login, its own database, its own update cycle, its own data model. The cost isn’t just inefficiency; it’s that no one feature can compound on another. Adding AI to a stack of disconnected tools means integrating AI ten times, once per tool. The platform problem has to be solved before the intelligence problem can be.
OG&E Grid Operations was in this state at the end of 2025: legacy SCADA viewers, ad-hoc dashboards, spreadsheet KPIs, separate on-call systems, ETL scripts no single person owned. The opportunity was bigger than any single tool replacement — it was a platform decision.
Approach
Design and author a multi-year roadmap that delivers in phases:
- Foundation — consolidate the tools first, under one auth model and one shared data layer, before adding any new capability on top.
- Intelligence — once the foundation is in place, layer AI as a single integration that every downstream feature reuses.
- Agentic — once AI is integrated, evolve from answering questions to taking action, under a human-in-the-loop governance framework.
- Enterprise — once agentic patterns are proven, extend the platform across the organization, not just one department.
The phasing order is non-negotiable. Adding AI to a fragmented platform produces fragmented AI. The platform comes first.
What Phase 1 Delivered (Complete)
- Unified application shell — 8+ legacy tools consolidated into one platform with shared header, navigation, search, and authentication.
- Multi-database architecture — MySQL, Oracle, SQL Server, SAP HANA (migrating to Snowflake), and ClickHouse accessible through a shared data layer with Azure AD SSO across all of them.
- Three-tier RBAC — department · user · superadmin, with audit logging on every authorization decision.
- Container-orchestrated deployment — Docker images orchestrated with Tilt onto Kubernetes.
- Change Control Process v4 — formal governance for production changes, RACI matrix, capital-classification process.
- 15+ applications now running on the platform — Resource Availability Suite, Strategic Undergrounding Analytics, Grid Operator Dashboard, Circuit Operations App, ADMS QA, Wall Map, Reliability hub, and others.
What Phase 2 Adds (In Flight, 2026–2027)
- AI Model Integration on AWS Bedrock with Anthropic Claude models — the platform layer for four production-bound LLM capabilities (covered in detail on the AI Integration case study).
- Predictive analytics — Accurate ETR Status, ADMS model improvements.
- Regulatory compliance reporting — AR-PSC automated submissions.
- Itron AMI data connection — bringing meter-level data into the platform.
- Legacy migration — ~700 iDashboards users migrated onto the Nighthawk BI layer.
What Phase 3 Plans (2028–2030)
- Agentic AI under a four-tier human-in-the-loop governance framework I authored:
- Tier 1: read-only operations (no approval needed)
- Tier 2: low-risk actions (auto-approved with log entry)
- Tier 3: operational actions (supervisor approval)
- Tier 4: critical actions (manager approval + audit review)
- Five named agents: Schedule, Storm Preparation, Compliance, Dispatch Recommendation, Auto-Reporting.
- Each agent operates inside its tier with explicit guardrails and full per-action audit trails.
Why the Pattern Transfers
This roadmap structure isn’t OG&E-specific. The Foundation → Intelligence → Agentic → Enterprise progression is the right phasing for any operations-heavy organization running on a stack of legacy systems. Utilities, healthcare, defense, finance, manufacturing — the constraint is the same: you can’t compound features on top of a fragmented platform, so the platform consolidation has to come first.
The differentiator is owning the strategy, not just the implementation. A platform engineer with a five-year roadmap negotiates very different conversations with leadership than one with a backlog of tickets.
Results to date
- Phase 1 marked complete on schedule.
- Phase 2 deliverables on track for 2026–2027 horizons.
- Phase 3 governance framework authored and reviewed.
- Stakeholder alignment from Director of AI through Grid Operations leadership.
Impact thesis — four pillars: reliability (the SAIDI lever inside the utility), cost (O&M reduction), safety, and regulatory compliance — the same scorecard the AI integration optimizes for, with platform consolidation as the precondition that makes those gains possible.