Most AI projects fail because they automate a broken process. Datum starts with how your organization actually runs (decisions, handoffs, and accountability), then builds AI systems that fit it.
Governance-grade systems for field-driven Alberta businesses.
Most field-driven businesses run on a few people who know where everything is. That works until someone is on holiday, a crew changes, or the business grows past what one whiteboard can carry.
Job status lives on a wall in the shop. If you’re not standing in front of it, you don’t know what’s current.
Crews send hours, photos and notes to whoever is in their phone. Someone re-types it later, if they remember.
Field tickets, inspections and daily logs come in late, incomplete or not at all. It surfaces at invoicing, or at an audit.
Decisions stall between the field and the office because the handoff was never defined. Everyone assumes someone else has it.
A chatbot or automation is added to a process nobody has mapped. It doesn’t fix the confusion. It speeds it up.
The last tool was supposed to solve this. It became one more place to check, because the way the work flows never changed.
These aren’t technology problems first. They’re problems of decisions, handoffs and accountability. That’s where Datum starts.
How the work gets fixedEvery engagement starts from how the work actually moves today. Scope, timing and fees are confirmed in writing before anything begins.
A structured look at how your organization actually runs, before anything gets built.
You get a clear picture and a ranked list of what to fix first, whether or not you build anything next.
Starting at $____
Redesign one workflow and build the AI system for it.
A working system in the hands of the people who use it, with the structure to keep it reliable.
Starting at $____
Ongoing support once the first system is running.
Senior operations and AI governance support, without a full-time hire.
Starting at $____ / month
Not sure where to start? Begin with the Diagnostic. Book a Diagnostic Consult
The order matters. Structure gets fixed before anything is automated, and oversight is in place before the system goes live.
Map how work, information and decisions actually move. Talk to the people doing it, in the field and in the office. Find where it breaks.
Fix the structure first. Define owners, handoffs and decision rights, then decide where AI belongs and where it doesn’t.
Build the system for one workflow. Test it with the crews and office staff who will use it, and adjust before it rolls out.
Set who reviews outputs, how data is handled, and what happens when the system gets it wrong. Then keep improving it.
If the diagnostic shows the fix is structural (a clearer handoff, a defined owner, a simpler form), that’s the recommendation. Not every problem needs AI.
Field crews reported job status and updates by text message. In the office, those updates were transferred onto a physical whiteboard, and that whiteboard was the company’s real-time view of operations.
If you weren’t in the room, you didn’t know what was current. Updates were re-entered by hand, reports could be missed, and it wasn’t always clear who was responsible for acting on them.
Start with how information actually moved from crew to office to management, and who was expected to act on it. Then design the system around that, not the other way around.
An AI-powered internal operations platform that replaces the whiteboard. Crews keep texting in their reports; the platform takes them in and tracks status in real time, so the office and management see the same current picture.
An AI system inside your operations shapes decisions. Someone needs to own that. Governance isn’t paperwork added at the end; it’s what keeps the system trustworthy after launch.
What goes into the system, where it’s stored, who can see it, and what never leaves your control. Client, employee and site information is handled with Canadian privacy obligations in mind.
AI gets things wrong. Outputs that matter are checked by a person before anyone acts on them, and there’s a clear way to catch and correct errors.
The system can sort, summarize and flag. People decide. Every decision the system touches has a named owner, and the AI’s role is written down.
I’m not a software vendor. My background is governance, operations and organizational development, and I implement AI the way I’d approach any change to how an organization runs: understand it first.
For more than 15 years I’ve served on boards in Alberta, in roles including board chair and board secretary, and I’ve chaired governance committees. I founded Boardwise, a governance advisory practice. That work is about decision rights, accountability and building structure that holds up under pressure.
I’ve also led operations and growth at a field-service software company, where I saw how crews, dispatch and the office actually work together, and how often software gets layered on top of a process nobody has defined. I’ve worked in the oilfield myself, so I know the difference between how work is described in the office and how it gets done on site.
Datum brings those together. Diagnose how the organization actually runs, fix the structure, then build AI systems that fit it, with the guardrails in place.
A focused conversation about how work moves through your business today, where it breaks, and whether a diagnostic makes sense.
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