Most mid-sized companies that have tried AI have the same story. The pilot looked good. The demo impressed the leadership team. Then the tool met your ERP, your messy data and your people, and use stalled.
The problem is rarely the model. It is that nobody owns the gap between what the tool can do and how your business actually runs. Some companies are now hiring for that gap directly, with people who work inside the business rather than in a separate technology group.
The gap is a staffing problem
A vendor will configure a tool and leave. An internal IT team is usually busy keeping existing systems running. A data scientist may build a good model but never sit on the shop floor, in the finance close or on a sales call.
What is missing is a person who:
- understands the workflow well enough to know where AI helps and where it gets in the way
- can write code, connect systems and fix what breaks
- stays on after launch to watch results and adjust
The title varies. Some call it an embedded engineer, an AI implementation lead or an applied AI engineer. Large technology vendors use the term forward deployed engineer. The label matters less than the job: adapt the system to the business, and keep it working as the business changes.
Why AI needs this more than ordinary software
Traditional software behaves the same way on day 200 as on day 1. AI tools do not. Outputs shift as inputs change. Edge cases show up only when real users push on the system. Data quality problems that nobody noticed for years suddenly matter.
That means launch is the start of the work. If your plan ends at go-live, you have budgeted for the easy part.
What this looks like by function
Operations and manufacturing. An AI tool for scheduling, quality inspection or maintenance prediction depends on clean sensor and ERP data. Someone has to sit with supervisors, learn why the schedule gets overridden on Tuesdays and build that into the system.
Finance. Invoice matching, forecasting and close automation all hit exceptions. A controller needs a person who can tune the rules with the accounting team, not a ticket queue with a vendor.
Sales. Lead scoring and CRM assistants only help if reps trust them. That takes someone who watches how they are used, fixes bad outputs quickly and reports back to sales leadership.
Contract and project work. Many employers do not need this person permanently. A defined rollout, such as 6 to 12 months of integration and tuning, is a good fit for a contract specialist.
How to hire for it
This is a hybrid profile, and hybrids are scarce in both Canada and the US. Do not expect a perfect match. Decide which side you can train.
- Technical first: a strong engineer or data professional who is curious about your industry and comfortable sitting with end users. Easier to find, but test their patience with non-technical colleagues.
- Operations first: a process-minded analyst or systems person who can build and script. Harder to find, but they often earn trust faster inside the business.
When you interview, skip the abstract AI questions. Ask the candidate to describe a system they put into production that behaved badly, how they found out and what they changed. Ask how they handled a user who refused to use it. Look for plain answers with specifics.
Also be honest about the job. This person will spend time on data cleanup, access requests and meetings. Candidates who want to work only on models will leave.
Set the role up to succeed
- Give them access. They need real data, real systems and real users from week 1.
- Give them a business sponsor. A plant manager, controller or sales leader who will act on what they find.
- Define success in business terms. Hours saved, error rates, cycle times, not number of features shipped.
- Plan for upkeep. Decide who owns the system after the first phase, whether that is this person, a successor or a contract extension.
- Pay for the hybrid. Candidates who can both code and work with operations teams are in demand, and a below-market offer will sit unanswered.
Do not skip the basics
If your data is scattered across spreadsheets and your processes live in people's heads, hiring one specialist will not fix that. Some employers are better served by first hiring an operations analyst or a data engineer, and adding the embedded role once the foundation is in place.
What to do next
Write down the one AI project that has stalled or that you are about to start. Name who will own it 90 days after launch. If the answer is nobody, or the vendor, you have found the role to fill. Decide whether it is a permanent hire or a contract engagement, and set the business outcome before you write the job description.
Traverse recruiters place AI and data, operations and contract professionals across Canada and the US, and we can help you define and fill this kind of role.