AI consulting case studies by Joel & Nanz Inc.
An illustrative AI implementation case study for a Canadian research university admin.
A Canadian research university admin adopted AI automation to handle high-volume routine work (intake, scheduling, follow-up and reporting) while keeping staff focused on judgement-based tasks. Reported annual savings: CAD 118,000. Return on investment: 2,360%. Payback period: 0.5 mo. The recommended Maple product for a research university admin is MapleInsights (AI analytics and operational insights), part of the MapleWorkSuite AI platform by Maple AI Consultants (Joel & Nanz Inc.).
Every research university admin reaches a point where adding people is the only obvious way to handle more volume - and the most expensive. This case study documents an alternative: a targeted AI implementation that lifted capacity without proportionally lifting payroll.
The university admin automated HR inquiries, procurement alerts, and grant compliance reminders.
Manual policy inquiries and compliance tracking took significant staff time.
Our approach was deliberately incremental. We sat with the research university admin's team, traced a request from first contact to resolution, and isolated the handoffs that caused the most friction. Only then did we scope where AI could remove work safely, keeping a human in the loop for anything involving judgement.
The implementation centred on a small number of high-leverage automations rather than a sprawling platform. For the research university admin, the core pieces were as follows.
Within the first operating cycles, the impact was visible in the numbers the research university admin already tracked.
“It paid for itself faster than anything else we have invested in. The quieter win is that our staff are less burned out.”
Requirement gathering
Bot & workflow setup
Training
University admin teams use SIS platforms with workflow automation and internal support tooling.
The lasting lesson from this research university admin engagement is that adoption beats sophistication. A modest automation that staff actually use every day outperforms an ambitious one they route around. Designing for the real workflow - and for the people in it - is what turned the technology into a result.
SMBs can choose a Maple SaaS deployment, a hybrid integration with existing tools, or a fully custom build. The ranges below reflect realistic first-year figures for each path.
| Approach | First-Year Cost | Annual Savings | ROI | Payback |
|---|---|---|---|---|
| Maple SaaS | CAD 5,000 | CAD 118,000 | 2,360% | 0.5 mo |
| Hybrid | CAD 60,000 | CAD 118,000 | 97% | 6.1 mo |
| Custom Build | CAD 180,000 | CAD 118,000 | -34% | 18.3 mo |
For a research university admin, AI is most effective at absorbing high-volume, repetitive work - intake and enquiries, scheduling and follow-up, data entry between systems, and routine reporting. That frees skilled staff to focus on the judgement-based work that actually differentiates the business, while customers get faster, more consistent responses.
In this engagement the research university admin reported annual savings of CAD 118,000, an ROI of 2,360%, a payback period of 0.5 mo. These figures are illustrative of the kind of outcome a comparable operation can target; actual results depend on volume, current processes, and how much routine work can be safely automated.
A focused project typically runs around six to eight weeks: discovery and workflow mapping first, then a staged build and secure integration, a live pilot alongside the team, and a final rollout with training and dashboards. Starting with one high-impact workflow keeps the timeline short and the results measurable.
No. The goal is to remove repetitive load, not people. We keep a human in the loop for anything involving judgement, and route unusual cases to staff before the automation acts. In practice the technology lets a research university admin handle growth without burning out the team or hiring through every peak.
The first step for any research university admin is a short discovery conversation to map your current workflow and find the highest-impact place to automate. From there we scope a realistic first project with a clear ROI estimate before any build begins.
Explore more in our full case study library, or read about our AI services for Canadian SMBs and the benefits of AI for small business.
The capabilities in this case study are delivered through MapleInsights — AI analytics and operational insights — part of the MapleWorkSuite AI platform. It is the closest off-the-shelf fit for a research university admin like the one above, and it deploys far faster than a custom build.
Most research university admin teams combine MapleInsights with these complementary tools from the Maple suite:
AI analytics and operational insights for a research university admin.
AI workflow and process automation for a research university admin.
An AI front-office and operations assistant for admin-heavy teams for a research university admin.
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