An AI system that worked at launch can drift within months. Vendors retire models, source data changes shape and staff find new ways to use the tool. We run the systems you already have so they keep giving correct answers, stay inside your access rules and have someone accountable for them.
Most AI projects are planned around the build. The pilot gets a sponsor, a budget and a launch date. Then the project team moves on and the system becomes one more thing that somebody in finance or IT keeps an eye on when they have time.
The trouble shows up slowly. An invoice extraction agent starts misreading a supplier's new template. A reporting assistant gives a confident answer from last quarter's numbers because a data connection failed quietly. A model the prompts were tuned against is retired on the vendor's published schedule and the replacement behaves a little differently. Nobody notices until a month-end figure is wrong or a client receives an odd email.
Each of these is an operating problem, and each needs a routine: someone checking output against known answers, testing changes before they reach production, and reviewing who and what the system can reach. Our post on operating AI agents in production walks through where the ongoing effort goes.
The deciding factor is what depends on the AI system.
Invoice capture, Xero or MYOB reconciliation, document classification or reporting agents that people now rely on every week.
The person or supplier who built the system has moved on and nobody is sure how it works or what it connects to.
Microsoft Copilot or an AI assistant that reads from SharePoint, email or a CRM, where access needs reviewing as sites and teams change.
Assistants or email agents whose answers reach clients, where a wrong answer has a cost to your reputation or under consumer law.
A board, auditor, contract or regulator expects evidence that AI use is monitored, documented and reviewed.
One specialty we also cover: AI and analytics built on plant historians and production systems, where data feeds change with the plant.
For the industrial side, our post on what Ampla support should cover applies the same thinking to production management systems.
The scope is written down per system. These are the areas most arrangements include.
A small evaluation set of real examples with known correct answers, rerun on a schedule and after every change. Error rates, exceptions sent to people and failed runs are tracked so drift shows up in a report before anyone complains.
When a vendor retires a model or changes an API, we test the replacement against the evaluation set before switching. Prompt and configuration changes are versioned, so any change can be traced and rolled back.
Periodic checks of which systems, mailboxes, folders and records each AI tool can reach, and whether that still matches its job. For Microsoft 365 this follows on from our Copilot readiness work.
A written runbook for each system covering how to pause it, who to tell, and how to fall back to the manual process. When something goes wrong we fix it, record the cause and update the runbook.
A monthly report covering volumes, error and exception rates, API or licence spend, changes made and open risks. It is written for the business owner of each system as well as IT.
An AI register, change log and review history kept up to date, which is the evidence an auditor, board or ISO/IEC 42001 programme will ask for.
Running Microsoft Copilot after rollout is one of the most common requests. For the standard behind the governance records, see our ISO 42001 guide.
The cycle is deliberately plain. Most failures we look for are quiet ones: a connection that stopped returning data, a template that changed, a model update that shifted how dates or amounts are read. Rerunning the same known examples every month is the cheapest way to catch them. Our post on choosing an AI model explains why a model swap is never a like-for-like change.
Access reviews matter more as AI tools gain the ability to act. An agent that drafts emails is a different risk from one that posts journals in Xero. Our guidance on data access and human override sets out the controls we check.
Taking on a system starts with understanding it. We do not commit to running something we have not reviewed.
If nothing is in production yet, a managed service is premature. Start with AI strategy to decide what is worth building, or process automation to build the first workflow.
If the system has deeper problems, such as integrations that were never finished or a design that cannot be tested, it needs project work first. That might be system integration or a rebuild through custom AI development. Our post on why AI projects fail covers the warning signs.
If you have an internal team that wants to run the systems and needs extra hands for a period, team augmentation is usually a better shape than handing over operations.
Privacy obligations do not stop at launch. If an AI system handles personal information, the Australian Privacy Principles apply to what goes in and what comes out, and changes to the Privacy Act add transparency requirements for automated decisions that significantly affect individuals. Our posts on Privacy Act compliance and AI and automated decision rules for 2026 explain what that means in practice.
Boards are asking the same questions from a different angle: what AI is in use, who approved it and how it is checked. A maintained register and change log answer those questions directly. Our guide to building an AI governance framework shows how the records fit together.
On cost, see our pricing page for how engagements are structured. A managed arrangement is quoted after the onboarding review, once the systems and the cover they need are known.
Solve8 is based at 9/86 City Rd, Beenleigh QLD 4207. Managed work is done remotely through your admin consoles and monitoring tools, so location rarely matters. See our Brisbane AI consulting page for local services.
Our principal consultant brings 18 years of enterprise delivery experience from projects for BHP, Rio Tinto and Senex Energy, delivered with previous consulting employers. These companies are not Solve8 clients. You work directly with senior people, not a sales team handing you off to juniors.
Book a 30-minute scoping call. Bring a list of the AI tools in use and what depends on them, and we will tell you what an onboarding review would look at.