Train Your AI
Give AI the organizational knowledge and rules it needs before it goes into a workflow.
AI operations · Bring them together
Put AI into the way your organization works.
Design and operate workflows where people, AI, data, systems and governance work together.
What we mean by AI operations
Not just AIOps. AI in the operations of the whole organization.
“AI operations” is often used to mean AIOps: monitoring IT infrastructure with AI. We mean something broader.
AI operations is how AI becomes part of everyday work across finance, legal, HR, sales, support, procurement, IT and operations: with clear ownership, sensible review, reliable information and measurable results. It is where trained people and trained AI come together.
From pilot to practice
Why AI experiments stall.
Pilots often show promise and then stop. The reasons are rarely about the model.
Read: From experiments to real workflowsNo workflow change
AI is added beside the work instead of redesigning it.
No owner
Nobody is accountable for the outcome once the pilot team moves on.
No information layer
The AI lacks reliable, permissioned access to the right sources.
No evaluation
Quality is judged by anecdote, so trust never builds.
Unclear risk
Review and escalation are undefined, so the safe choice is to stop.
What we do
Everything it takes to run AI in real work.
Each engagement focuses on the workflows that matter most, and the parts of this list they need.
Workflow redesign
Map how the work happens today, decide what AI should do and what people should do, and redesign the process rather than bolting AI onto the old one.
Agentic workflows
Multi-step workflows where AI agents gather information, prepare work and take bounded actions, with autonomy matched to the risk of each step.
Operations automation
Automate repetitive, well-defined steps such as intake, triage, extraction, drafting and routing, so people spend their time on judgment.
Human review + escalation
Decide where approval is required, what reviewers check, how exceptions are escalated, and who is accountable for the outcome.
System integration
Connect AI to the systems where work actually happens, such as CRM, ERP, document management, ticketing and collaboration tools, with the right permissions.
Evaluation + monitoring
Measure quality, errors, turnaround and adoption before and after launch, and keep watching as models, data and volumes change.
AI operating policies
Turn policy into practice: which tools, which data, which decisions, what gets logged, and what happens when something goes wrong.
Continuous improvement
Capture corrections and feedback, review performance regularly, and extend what works to the next workflow.
AI for business operations
Examples by function.
Illustrative workflows where organizations commonly apply AI. The right starting point depends on your volume, risk and systems.
Finance operations
Invoice and expense review, reconciliation support, variance commentary and month-end preparation.
Legal operations
Intake and triage, first-pass contract review, clause comparison and matter summaries.
HR operations
Policy questions, onboarding coordination, job description drafting and case summaries.
Revenue operations
Account research, CRM hygiene, proposal preparation and pipeline notes.
Customer support
Response drafting, case summaries, knowledge base upkeep and escalation routing.
Procurement
Supplier research, requirement drafting, bid comparison and contract data extraction.
IT operations
Ticket triage, knowledge articles, change summaries and first-line support.
Operations
Reporting, scheduling support, procedure upkeep and cross-team coordination.
Autonomy, matched to risk
Decide what AI does alone, and what it does not.
Not every step deserves the same level of automation. We design each workflow with explicit levels.
- 01
Assist
AI drafts or suggests. A person decides and acts.
- 02
Prepare
AI completes the work. A person reviews and approves before it goes anywhere.
- 03
Act with oversight
AI acts within tight limits. People monitor, sample and handle exceptions.
- 04
Act
AI acts on low-risk, well-tested steps. Logged, measured and reversible.
Governance that runs
Policy, translated into system behaviour.
Governance that exists only as a PDF does not protect anyone. In a well-designed workflow, the rules show up as permissions, review steps, logging and escalation.
We build those controls into the operation from the start, reflecting Canadian privacy and accountability expectations where they apply.
Questions
AI operations FAQ
What is AI operations?
At Trained, AI operations means designing and running the workflows where people, AI, data, systems and governance work together. It covers workflow redesign, automation, agents, human review, integration, evaluation and operating rules. It is broader than AIOps, which usually refers to using AI to monitor IT infrastructure.
Is this the same as AIOps?
No. AIOps typically means applying AI to IT operations such as monitoring, alerting and incident management. Trained's AI operations work applies across the organization, including IT operations when that is the workflow that matters most.
What is an agentic workflow?
An agentic workflow is one where an AI agent carries out several steps toward a goal, such as gathering information, using tools and preparing actions, rather than answering a single prompt. Good agentic workflows set clear limits on what the agent can do alone and where a person must approve.
Where should we start with AI in business operations?
Start with a workflow that is frequent, well understood, measurable and low-to-moderate risk, with an owner who wants it improved. Proving value in one workflow, with clear review and measurement, builds the foundation for the next.
Do you build the integrations?
Trained designs the workflow and implements the AI components, and works with your technology team or existing vendors on integration with your systems. The approach depends on your environment and who will operate the workflow long term.
How do you keep people accountable when AI is involved?
Every workflow has a named owner, defined review points, escalation paths and logging appropriate to the risk. AI can prepare and recommend; accountability for consequential decisions stays with people.
Keep exploring
AI Training
Prepare the people who will work alongside, review and improve AI.
AI operations: from experiments to real workflows
How to move from promising pilots to dependable operations.
Which workflow should AI improve first?
Tell us where work is slow, repetitive or inconsistent. We will help you find a starting point that is worth doing and safe to run.