For decades the industry digitized: paper workflows became shapefiles, CAD files, databases and spreadsheets. Each tool modelled one slice of the network well, and it made sense — one team owned ducts, another owned splicing, each with its own workflow and its own tool. But a single source of truth stayed out of reach. People call the result “chair-swiveling”: to understand one network, you consult a dozen inventories.
The cost shows up at the worst possible time. During an outage, teams scramble to assemble datasets and argue about the data — how old is this, who maintained it, can we trust this field, how should we read it? — precisely when speed matters most. And because as-built backlogs run six months, sometimes four years, the strategic maps leadership relies on are simply out of date. Sales can’t move faster, time-to-repair stays high, and truck rolls pile up — because the only way to be sure what’s in the ground or in the air is to send someone to check.
Merkator's Luc De Heyn frames the journey as four levels — and most operators are stuck on the first.
A GIS that documents what’s been done — chasing an as-built backlog, ignored by planning and design, siloed from daily work. Where most operators are today.
The whole organisation makes decisions from the same accurate, complete, up-to-date data. Design, build and operations live in the same place; the silos disappear. The hardest, most valuable jump.
Trustworthy data lets you layer history and spot trends — outage hotspots, capacity bottlenecks — before they bite.
AI agents act on the twin — preparing work orders, and eventually orchestrating a network that heals itself.
The temptation is to leap straight from a digital map to AI. But bolt automation onto messy data and you simply automate the chaos — losing money and, worse, the organisation’s trust in the technology. The real return is in crossing from Level 1 to Level 2: getting the foundation right first. You can use AI here too — not to run the network, but to fix it: validating as-builts, cleaning legacy data, automating field-survey ingestion.
See where your data stands →Where AI fits — and where it doesn't →
Merkator innovates continuously — testing new tools and building our own — and we partner with AI specialists who build the next-level use-cases. Our job is to make sure the network is modelled correctly first, so that when the AI runs, it runs on the truth.
