You're a field service professional who's been observing what seems like Artificial Intelligence taking over the world slowly but surely. You're thinking: "I need to get AI to run my field operations and I need it yesterday!".
As a service provider in the field operations industry who talks to hundreds of clients and prospects across different industries, I have good news and bad news for you:
The most popular type of AI today is undoubtedly NLP (Natural Language Processing) - examples are ChatGPT, Gemini, Llama, Mistral, and the like.
However we're more interested in the Machine Learning (ML) part of AI, which is capable of looking through existing data and identifying patterns in either a supervised way (humans helping by tagging the data with labels and tags) or completely unsupervised. DL is Deep Learning, which uses multiple layers of Neural Networks to gain insights into data which can be very useful in business cases, although interestingly, it doesn't usually share how it came upon those insights.
I bet you've noticed already: the common thread here is data. AI's ability to gain insights effectively and suggest improvements hinges on the availability of large quantities of high quality data.
I'm not talking about data that you can download from somewhere on the internet. I'm talking about data that is specific to your organization, your department, and your operations. If your organization is not digitized sufficiently and has not been capturing field operations data for several months or years, no AI system can deliver reliable, actionable insights.
The key takeaway? AI should never be the starting point—it’s the next step after creating a solid digital and data foundation.
Digitization transforms manual and fragmented processes such as paper-based records or siloed digital systems into cohesive digital workflows. This shift is not just a precursor to AI in Field Management; it delivers its own value by enabling seamless data collection, better collaboration, and operational efficiency.
When asked, most companies say that they are "digitized" to some extent, but on digging deeper it's apparent that they have barely scratched the surface.
With over 15 years doing digitization, here's what has worked best for us:
Once we reach here, we are ready to implement AI and automation in field service management.
At Fieldmaster.ai we developed a framework called the Digitization Staircase after surveying over a hundred firms of different industries and sizes, for helping our clients achieve digitization and change management faster. Read more about it here.
AI models depend on high-quality data to function effectively. Without accurate, complete, and consistent data, the outputs of even the most sophisticated AI systems can be flawed or misleading.
Enabling end-to-end digitization and ensuring high-quality data is exactly what we do at Fieldmaster.ai. Click here to read more on why our clients choose us.
A leading logistics firm digitized its fleet management system, integrating GPS tracking and automating work order processes. This effort created a unified, real-time dataset. When they later deployed an AI-driven route optimization system, it reduced delivery times by 15% and fuel costs by 10%, proving the value of laying a digital foundation.
A retailer hastily adopted an AI-driven inventory system without addressing data inconsistencies across locations. The result? The AI incorrectly predicted demand, leading to stockouts in high-demand areas and overstock in others. Rectifying these issues required months of manual corrections and significant financial losses.
By prioritizing digitization and data quality, organizations can:
AI is a powerful tool, but its success depends on preparation. Digitizing field operations and ensuring high-quality data are non-negotiable prerequisites for effective AI adoption. By addressing these foundational steps, your organization can unlock the full promise of AI, improving efficiency, decision-making, and customer satisfaction in the process.
