AI consulting case studies by Joel & Nanz Inc.
An illustrative AI implementation case study for a Canadian insurance brokerage network.
A Canadian insurance brokerage network 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 176,000. Return on investment: 2,833%. Payback period: 0.4 mo. The recommended Maple product for an insurance brokerage network is MapleConcierge (an AI front-office and operations assistant for admin-heavy teams), part of the MapleWorkSuite AI platform by Maple AI Consultants (Joel & Nanz Inc.).
Running a insurance brokerage network means juggling high-volume routine work against the deep, judgement-heavy tasks that actually move the business forward. When the routine work wins, service slips and margins erode. Here is how AI automation changed that balance for one Canadian insurance brokerage network.
Quoting, renewals, and service requests were automated across the brokerage network.
High service volume and renewal processing slowed staff.
Our approach was deliberately incremental. We sat with the insurance brokerage network'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 insurance brokerage network, the core pieces were as follows.
Within the first operating cycles, the impact was visible in the numbers the insurance brokerage network already tracked.
“We were skeptical that AI could fit a business like ours. What sold us was that it handled the boring, repetitive work and left the judgement calls to us.”
Service flow definition
Automation
Rollout
Insurance brokerages depend on CRM, document management, and workflow automation for renewals and servicing.
The lasting lesson from this insurance brokerage network 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 6,000 | CAD 176,000 | 2,833% | 0.4 mo |
| Hybrid | CAD 80,000 | CAD 176,000 | 120% | 5.5 mo |
| Custom Build | CAD 220,000 | CAD 176,000 | -20% | 15.0 mo |
For an insurance brokerage network, 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 insurance brokerage network reported annual savings of CAD 176,000, an ROI of 2,833%, a payback period of 0.4 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 an insurance brokerage network handle growth without burning out the team or hiring through every peak.
The first step for any insurance brokerage network 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 MapleConcierge — an AI front-office and operations assistant for admin-heavy teams — part of the MapleWorkSuite AI platform. It is the closest off-the-shelf fit for an insurance brokerage network like the one above, and it deploys far faster than a custom build.
Most insurance brokerage network teams combine MapleConcierge with these complementary tools from the Maple suite:
An AI front-office and operations assistant for admin-heavy teams for a insurance brokerage network.
An AI phone receptionist that answers, books and routes calls 24/7 for a insurance brokerage network.
AI workflow and process automation for a insurance brokerage network.
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