AI consulting case studies by Joel & Nanz Inc.
An illustrative AI implementation case study for a Canadian public transit authority.
A Canadian public transit authority 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 248,000. Return on investment: 3,207%. Payback period: 0.3 mo. The recommended Maple product for a public transit authority is MapleWorkflow (AI workflow and process automation), part of the MapleWorkSuite AI platform by Maple AI Consultants (Joel & Nanz Inc.).
Every public transit authority 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.
Passenger inquiries, service alerts, and internal incident reporting were automated.
High call volumes for schedule and delay information.
Instead of a big-bang rollout, we scoped the public transit authority engagement around quick, measurable wins. We profiled the highest-volume tasks, confirmed the data needed to automate them existed and was clean, and sequenced the build so the team felt relief early rather than waiting months for results.
The solution combined automation of routine intake with AI-assisted handling of the work that follows. The components below were configured specifically for a public transit authority.
The results showed up quickly - and, importantly, in metrics the public transit authority cared about before the project ever started.
“It paid for itself faster than anything else we have invested in. The quieter win is that our staff are less burned out.”
Message design
Deployment
Optimization
Transit authorities use operations systems with alerts, workflows, and reporting to manage service reliability.
What makes this kind of project work in a public transit authority specifically is fit. Generic automation tends to break on the edge cases that define an industry. By tailoring the rules, the language, and the escalation paths to how a public transit authority actually operates, the system handled the common cases cleanly and routed the unusual ones to a person before anything went wrong.
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 7,500 | CAD 248,000 | 3,207% | 0.3 mo |
| Hybrid | CAD 100,000 | CAD 248,000 | 148% | 4.8 mo |
| Custom Build | CAD 280,000 | CAD 248,000 | -11% | 13.6 mo |
For a public transit authority, 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 public transit authority reported annual savings of CAD 248,000, an ROI of 3,207%, a payback period of 0.3 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 public transit authority handle growth without burning out the team or hiring through every peak.
Yes. For a public transit authority we integrate with existing systems using secure connections, keep data within appropriate boundaries, and configure access controls so the automation only touches what it needs. As a Canadian firm we build with Canadian privacy expectations in mind.
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 MapleWorkflow — AI workflow and process automation — part of the MapleWorkSuite AI platform. It is the closest off-the-shelf fit for a public transit authority like the one above, and it deploys far faster than a custom build.
Most public transit authority teams combine MapleWorkflow with these complementary tools from the Maple suite:
AI workflow and process automation for a public transit authority.
An AI front-office and operations assistant for admin-heavy teams for a public transit authority.
AI analytics and operational insights for a public transit authority.
Get started with MapleWorkflow on MapleWorkSuite, or book a free consult and we will scope the right configuration and ROI for your team.
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