Bigge stands up the Department of AI Engineering

Bigge officially has a Department of AI Engineering.

I am Keenan Chiasson, Director of AI Engineering, and I lead the department. I joined Bigge earlier this year as a Senior Developer working with the WorkPro team on Field Service, and since then my role has expanded into building and leading the team responsible for AI development across the company.

AI Engineering sits inside Cal Hart's Innovation organization. I report to Cal, and our teams work very closely together. There is a distinction between our primary responsibilities, but it is not a wall. In practice, there will be plenty of overlap.

Innovation leads a lot of the enablement work across Bigge: helping people adopt tools like Claude, ChatGPT, Copilot, and other emerging platforms, making sure people have access to them, and helping teams figure out how to use them effectively.

AI Engineering is primarily responsible for building: production AI systems, agents, automations, integrations, models, and internal applications that solve problems specific to Bigge.

But those responsibilities naturally cross over. I will absolutely be involved in enablement, training, and helping teams figure out how to use these tools well. Cal is also deeply involved in advanced automation and in identifying where new technology can change the way a process works. We are not trying to create two separate AI worlds inside Bigge. We are building one capability from two complementary sides.

The AI Engineering team today is James Iorio and me, and we are hiring. The headcount is small. The build list is not.

We currently have nineteen initiatives being tracked and four in active development. The full list is in On the Board, where you can see what is being built, what is being evaluated, and what is waiting in the queue.

What AI Engineering owns

Our charter covers the development and operation of technology that automates work, connects systems, applies AI to business processes, or creates new internal capabilities.

That includes AI agents, workflow automation, predictive models, integrations between systems, document processing, internal applications, and custom software built around the platforms Bigge already uses, including WorkPro, D365, Microsoft 365, Salesforce, FastField, and others.

Some projects start with an AI use case. Others start with a process problem, a system gap, or something that simply takes too much manual effort. Either way, if the end result needs to be designed, built, deployed, integrated, and supported as a real system, AI Engineering will usually be heavily involved.

How AI Engineering and Innovation work together

The easiest way to think about the relationship is in terms of primary focus rather than strict boundaries.

Innovation is generally the first home for company-wide adoption and enablement: Claude, ChatGPT, Copilot, new AI tools, training, licenses, experimentation, and helping departments understand what is possible.

AI Engineering is generally the first home for things that need to be built specifically for Bigge: custom software, agents, automations, integrations, models, and production systems.

1. 01Want help using Claude, ChatGPT, Copilot, or another existing tool more effectively: Innovation will usually lead.
2. 02Need something custom built, automated, integrated, or deployed: AI Engineering will usually lead.
3. 03Working on an advanced automation or a problem that crosses both areas: expect both teams to be involved.
4. 04Not sure where it belongs: send it anyway. Cal's team and mine work together and we will figure out the right path.

Nobody should need to understand our org chart before asking for help, and we do not want people worrying about whether they picked the right team before bringing us a good problem.

How to send us work

There are two ways to get something into the queue.

1. 01Run the Automation Scout skill in Claude. It will walk you through a few questions and turn your answers into a brief our team can review. This issue's Featured SKILL explains how it works.
2. 02Or post the problem directly in the AI General channel on Teams. Plain English is fine. No template required.
3. 03Both routes feed into the same intake process and are reviewed against the rest of the department's work.

You do not need to come to us with a polished proposal or a technical solution. Describe what is happening, who it affects, how often it happens, and roughly how much time it takes today. That is enough for us to start evaluating it.

If you already have an idea for the solution, include it. If you do not, that is fine too.

What happens after you submit something

Every request goes into the same queue and gets evaluated against the work already there.

We look at the size of the problem, the number of people affected, how often the work happens, the systems involved, the effort required to build something useful, and the value we expect the company to get back.

Some items will move directly into development. Some will need discovery first. Some will wait behind higher-impact work. Others may be better handled through enablement, an existing AI tool, or a combination of work from both teams.

The important part is that there is now a defined place for this work to go and people accountable for moving it forward.

What you will see from us

This publication is part of how we plan to keep the rest of Bigge connected to what the department is doing.

Each issue will show what is in development, what shipped, what is coming next, which tools and skills are available today, and where people across the company are already getting value from them.

You will also see the build queue evolve over time. Projects will move, priorities will change, new requests will come in, and some ideas will disappear entirely once we learn more about them.

That is normal. The goal is to make the work visible and make it easy for anyone at Bigge to understand where AI Engineering is spending its time.

Where this is headed

Weston has been clear about the direction Bigge is moving: more people working alongside more agents, more automation, and more AI software.

My team's job is to build the systems behind that direction, while working alongside Cal and the Innovation team to make sure those systems and the tools already available across Bigge actually get adopted and used well.

That work is already underway.

The department is up. The queue is open. If there is something in your part of Bigge that you think belongs on it, send it.