Your Feedback
Copilot Training Survey Report
Copilot Training Survey Report
Before the Microsoft 365 Copilot training, we surveyed Western Tractor team members to understand their experience with AI tools, familiarity with Copilot, and training needs. 19 participants responded. The results show a strong appetite for practical, role-relevant training: most people use Outlook, Teams, Excel, and Word daily and want to get better at data analysis, meeting notes, and automating repetitive tasks. The report below summarizes the data and what it means for the training program.
Artificial intelligence is often talked about as something futuristic or abstract, but in southern Alberta it is already showing up in very practical ways. Rather than replacing people or transforming farms overnight, AI is mostly being used to support everyday decisions—when to irrigate, which fields need attention first, which animals may need care, or how to plan labour and logistics. In this region, where agriculture operates under tight margins, water constraints, and variable weather, even small improvements in timing and coordination can have meaningful economic and operational impact.
A useful way to think about AI is not as a single technology, but as a set of tools that help turn information into action. Across agriculture and agri-food industries, AI typically works in three steps: sensing what is happening, predicting what might happen next, and supporting better decisions. Sensors in fields and barns, satellite and drone imagery, equipment data, and digital records all feed into this process. The AI itself does not “run the farm,” but it helps surface early signals—such as moisture stress, equipment issues, or changes in animal behaviour—so that people can respond sooner and with more confidence.
In crop production, this shift is especially visible in how fields are monitored. Satellite imagery, drones, and camera-equipped equipment are increasingly used to identify variability within fields that would be difficult to see from the ground alone. The value is not automation for its own sake, but earlier awareness. Detecting stress, weeds, or disease earlier gives producers more options and often lowers costs. In a semi-arid region like southern Alberta, where irrigation decisions are critical and drought risk is real, tools that improve “time-to-signal” can directly support water efficiency and risk management.
Livestock operations are seeing similar patterns. Wearable sensors, cameras, and predictive tools are being used to flag animals that may need attention, shifting labour from constant checking toward targeted response. These systems do not eliminate the need for experience or judgment, but they can reduce missed issues, support animal welfare, and help plan labour during peak periods such as calving. The challenge is not the technology itself, but making sure alerts fit the realities of connectivity, staffing, and day-to-day workflows on local operations.
Beyond the farm gate, AI is increasingly shaping processing, logistics, and supply chains—areas where southern Alberta has a strong concentration of activity. Forecasting demand, planning production runs, managing cold storage, and coordinating transportation are all decision-heavy tasks that benefit from better data and predictive tools. In these settings, AI is less about prediction accuracy in theory and more about operational reliability: reducing waste, avoiding bottlenecks, and responding faster when conditions change.
Importantly, the spread of AI is changing work more than it is eliminating it. As agriculture and agri-food become more data-driven, tasks related to monitoring, documentation, quality control, and decision support are growing in importance. This creates demand for practical skills—understanding data, interpreting outputs, and knowing when to trust or question a system—rather than deep technical expertise. For southern Alberta, this shift connects technology adoption with workforce development, training pathways, and regional resilience.
Overall, the direction of AI in agriculture and industry is incremental rather than disruptive. The most successful uses tend to be “boring wins”: leak detection, early warnings, better scheduling, clearer records. These changes may not grab headlines, but they compound over time. In a region where scale, efficiency, and environmental constraints matter, AI is becoming less about novelty and more about quietly strengthening how decisions are made across the entire food system.
About two-thirds of respondents had already used AI at work—either regularly or occasionally—and several others said they had not yet but were interested. This suggests the group is ready to learn and that the training can build on existing curiosity and some prior experience, while still covering fundamentals for those new to AI.
A majority of respondents said they were not at all familiar with Copilot, with the rest split between somewhat and very familiar. This supports starting with clear explanations of what Copilot is and where it appears in Microsoft 365, then moving into hands-on use so everyone can build confidence.
Responses were spread across Basic, Intermediate, and Advanced, with the largest group choosing Intermediate. Offering a single session that covers basics (what Copilot is, how to start) with optional intermediate tips and use cases helps match this mix of needs. Advanced topics (customization, integrations, automation) can be flagged as follow-up learning for those who want to go further.
Outlook, Teams, Excel, and Word were the most frequently used apps. Focusing Copilot demos and activities on these four ensures the training is relevant to how people actually work. To Do, Planner, and PowerPoint were also mentioned, so including short examples in those apps can help additional roles see value.
Data analysis in Excel was the most requested area, followed by automating repetitive tasks and meeting notes and action items in Teams. Many respondents also asked for “all of the above” or a mix of topics. The current training plan’s emphasis on Excel, Teams, and automation aligns well with this feedback and can be highlighted as a direct response to what people asked for.
Open-ended answers highlighted: saving time on email and paperwork, being more organized, improving communication and reports, automating repetitive work (e.g. work orders, quotes, data entry), and spending more time with customers and employees. Many also mentioned deeper understanding of data, better planning, and “easy buttons” for daily tasks. The training’s focus on prompts for emails, documents, meetings, and Excel supports these goals.
Survey responses consistently said AI will enhance jobs rather than replace them—supporting people with repetitive or time-consuming tasks so they can focus on hands-on work, customer relationships, and judgment. Roles like equipment repair, in-person service, and human connection were cited as things AI cannot replicate.
Over half of respondents said they were not at all familiar with Copilot. The training is designed to start with the basics: what Copilot is, where to find it in Microsoft 365, and how to write a simple prompt. No prior experience is required.
We’ll focus on the apps you use most: Outlook, Teams, Excel, and Word. Demos and activities will include email drafting, meeting notes and action items, data analysis in Excel, and document creation—matching the training areas you asked for most.
Survey answers mentioned work orders, quotes, customer follow-up, reporting, and administrative tasks as time consumers. The session uses Western Tractor–style scenarios (e.g. customer communications, sales data, service meeting summaries) so you can see how Copilot applies to your actual workflow.
The survey results were used to shape the Copilot training: session length, mix of basics vs. intermediate content, choice of apps (Outlook, Teams, Excel, Word), and the balance of demos vs. hands-on activities. Continuing to collect feedback after the session will help refine future training and support.
