Manual coordination was buried inside inboxes, spreadsheets, and weekly status rituals — invisible to dashboards, expensive to leadership.
They had spent enough on advice. They needed work to move.
The company was not skeptical of AI. Leadership knew it would reshape field operations, customer workflows, vendor processes, and back-office coordination. The problem was not belief. It was translation.
Traditional consulting could diagnose the obvious waste and assemble a polished recommendation. The utility needed something harder — a system that entered the business, learned the real operating model, identified practical automation candidates, and left behind deployable capability. Aria was built in that gap.
One register of candidates. One conversation at a time — forty-seven of them.
Embed. Map. Deploy.
Aria entered the operating context. No surveys, no kickoff binder.
Read-only OAuth into twelve systems of record — Field Service Management, Storm Dispatch, Vendor Portal, Crew Scheduling, Outage Management, Asset Registry, and the back-office tools beneath them. Aria observed every event in production while the team kept doing their job.
The mandate was simple: understand where work actually stalled, without asking anyone to maintain another dashboard.
Stakeholder interviews and observed signal converged into one operating model.
Aria ran forty-seven structured interviews across operations, field services, vendor management, and customer ops — each one adapting in real time, surfacing the tacit knowledge that lived only in people’s heads.
Cross-referenced against observed Slack threads, Jira transitions, and dispatch traces, the interviews produced a continuously updated graph of one hundred and forty-two workflow loops. Eleven landed in the red zone — the loops actually losing money week over week.
The output was a register, not a deck. Each candidate was actionable on day one.
Fifteen automation candidates, each tagged with a workflow owner, an estimated cycle-time saving, an effort score, and a deployment path using agents already proven in the Corvana catalogue.
The first three candidates moved into shadow mode the same week the register was delivered. By month three, two had been promoted to live traffic and were reporting confirmed savings against the Cost-of-Chaos baseline.
- Connected to 12 systems of recordFSM · OMS · Vendor Portal · 9 more
- Ran 47 stakeholder interviews6 functions · 4 tenure bands
- Mapped 142 workflow loops, ranked 11 red-zone
- Drafting the Automation Register
Unlike a strategy deck, the engagement is designed not just to diagnose the waste, but to leave behind durable capacity that runs the workflow week after week.
Read by the operating committee on the Monday of week three.
Fifteen places where agents could change the work.
Not a strategy deck. Not a binder. A live, queryable register that ranked every workflow loop by projected annualized impact, workflow owner, effort score, and deployment path. Three candidates entered shadow mode the same week the register was delivered.
By month three, two had been promoted to live traffic and were reporting confirmed savings against the Cost-of-Chaos baseline.
Cost-of-Chaos baseline
Five categories of operational waste reconciled against headcount and hourly cost. The dollar number behind every friction loop.
Process Narratives
Plain-language descriptions of how every workflow actually operated, side-by-side with the SOP. The gap is where the candidates came from.
Knowledge Risk Map
Workflows that ran exclusively in two or three people’s heads. The succession crisis the leadership team had not yet seen coming.
The breakthrough was not proving AI could help. It was proving where to deploy it first.
Run the same play in your operating environment.
Pick one P&L, one function, or one portfolio company. Aria embeds, maps, and hands the operator a ranked register of automation candidates within six weeks. You see the work before you see an invoice.

