Transforming Field Service Operations with AI-Driven Insights

Any FSM stack needs people to make split-second decisions, many field service orgs need AI analytics to get the results they’re looking for.

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    One of the key things any FSM stack needs to do is empower people to make split-second decisions that deliver a specific outcome – whether that’s ensuring customer assets achieve peak performance or fueling long-term growth at the highest level.

    Obviously, a lot of this comes down to arming everyone – from field techs and support agents to the sales team and the C-suite – with the insights they need to make every day decisions. 

    However, information can’t drive action on its own. Today’s field service orgs need AI analytics to get the results they’re looking for.

    In this article, we’ll explain what AI analytics are, why they matter, and where they fit into the broader FSM stack.

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    What are AI Analytics?

    AI analytics (aka: augmented analytics or advanced analytics) is a type of data analysis that uses artificial intelligence and machine learning to identify trends, surface patterns, and augment how people explore, analyze, and work with data. 

    Unlike “traditional” analytics or business intelligence (BI), which rely on humans to interpret and operationalize insights, augmented analytics leverages AI for data preparation, insight generation, and, often, insight explanation or guidance. 

    In other words, they don’t just spit out a bunch of metrics, these tools can surface the most relevant insights for each user. More than that, they can help that user understand those insights in context with the situation, and take the right set of actions. 

    Without AI, field service pros must rely on gut instincts, experience, and assumptions to make decisions with very real consequences. So, if a field tech makes the wrong call while troubleshooting a machine, the customer will likely wait longer for a resolution. Worse, that mistake could cause further damage to the asset, resulting in more expensive repairs or costly downtime.

    Or, a business leader might misinterpret demand patterns and fail to order enough parts to accommodate all services within the next quarter. So, there, you’re looking at delays, unhappy customers, client churn, and financial losses. You get the idea.

    Augmented analytics offer some important advantages over traditional reporting tools. First, AI and ML allow organizations to work through massive amounts of data at a speed and scale far beyond human capabilities – or, really, human comprehension. This means AI analytics platforms can surface patterns, trends, and unexpected connections human users would never discover on their own. 

    AI analytics also play a key role in democratizing data science and development. For example, self-service analytics tools like the Microsoft Power Platform let anyone (with some basic data literacy skills) mix and match data points from multiple sources – from any device or location.  Additionally, AI can remove much of the inherent bias human analysts have when interpreting data. Algorithms don’t have the same baggage, blind spots, and frames of reference as human users, and, thus, results are based on their “unbiased” reading of the “facts.”

    That said, AI analytics are only as powerful as the data they’re working with. Without a complete, accurate data ecosystem and a solid data management strategy in place, powerful algorithms can only wreak havoc on your system and your “IRL” business. It’s also important to understand that AI-driven insights don’t replace human understanding. For example, you’ve probably heard of machine learning bias or AI hallucinations.  

    Even when trained on “pristine” data sets, algorithms can form “biases” that have real-world consequences (think — racial or gender bias in facial recognition technology used for hiring, surveillance, or law enforcement). In other instances, AI just “makes up” plausible answers based on learned patterns. 

    The point is, people need to work closely with these tools to ensure they’re working as intended — and not causing any unintended harm. 

    Impact of AI Analytics

    When we talk about analytics – AI-enabled or otherwise –  it’s usually in the context of reporting and forecasting

    We can all imagine how this particular use case might play out. Users – whether we’re talking finance leaders, sales reps, or field techs – pull real-time insights, then leverage AI recommendations to take action, and, ideally, deliver the right outcome ASAP.  

    Obviously, that’s huge for any business.

    But – while smarter reporting tools have the potential to shake up the entire field services industry on their own, it’s important to understand that AI analytics is so much more than this. 

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    This technology touches every aspect of field service management from scheduling and inventory planning to asset management, process automation, and route optimization.  

    Here are a handful of examples to give you an idea of how deep its power runs:

    Real-Time Intelligence. AI analytics tools allow field orgs to transform real-time insights into real-world outcomes – particularly if they’re using a connected, IoT-enabled field service management strategy. 

    Once you’ve established end-to-end visibility across all customers, assets, field ops, and everything else, AI analytics unlock a range of benefits.  

    For starters, top solutions can dynamically analyze service data to surface insights about recurring asset issues, training needs, frequently-used parts, and customer preferences to get ahead bad experiences before they happen.  

    This, in turn, allows providers to move away from reactive, break-fix models and toward a proactive, customer-centric service strategy. It also helps them achieve cost-savings goals, improve org-wide productivity, hit energy consumption targets, and more.

    In a recent white paper, IBM explains that a facilities management company might combine data from HVAC units with hyper-local Weather Company forecasts to prevent waste. In this scenario, IoT sensors can be used to isolate usage to a specific part of a building or even an individual asset. 

    In this scenario, sensors might also be used to monitor water use to improve conservation efforts during a drought. Or – to track asset performance in real-time so that techs can take action before a breakdown occurs.

    Internally, AI analytics support high-level decision-making. They can be used to ID previously unknown relationships or new opportunities. Business leaders can model different scenarios that might assist with inventory planning, resource allocation, or budgeting decisions. 

    For example, we built a Power BI for Field Service Simulator to help our field service clients estimate the impact of improving different parts of the business – scheduling, resource utilization, whatever. Essentially, field leaders can measure the expected outcome of potential improvements against critical goals – say, increasing revenue or improving first-time-fix-rates – and use those insights to make decisions.    

    Customer Experience. According to Senior Consultant Heather Racine, “real-time analytics provide deep insights into existing customer needs, as well as changing expectations and behaviors. That data also gives orgs the ability to build automations that make customer experiences more relevant and reliable.” 

    Algorithms can serve up personalized recommendations for how to approach troubleshooting or repair, as well as how to best engage with each customer. Understanding things like which channels customers prefer, and in what context, or what language resonates with different groups enables providers to not only improve the service experience, but the entire customer relationship. 

    Salespeople and service agents might use insights about communication preferences to inform how they approach each interaction. AI analytics tools can then layer in real-time contextual insights from, say, browsing behavior or chat logs, allowing users to tailor their approach to what’s happening with that customer at that exact moment. 

    For example, if a customer engages a chatbot to get help with a problem, the language they use to report the problem can give agents a sense of whether they’re on the brink of disaster or dealing with something less urgent. Agents can then match the customer’s “energy” with the appropriate response and start escalating the issue while the conversation is still happening.

    Technician Enablement. The ability to access, analyze, and share real-time data from any location or device is a game-changer for technicians. But, the benefits of AI analytics extend way beyond granting techs easy access to real-time information.

    Velosio’s Dave Sigler says, “AI technology has made it easier to report field updates in near-real-time, helping field service companies become more agile and efficient.” 

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    Techs can analyze real-time asset data against known best practices, service histories, and past successes. Embedded AI also helps techs identify root causes and potential fixes. This means, service techs spend less time troubleshooting and more time solving complex problems, thus improving overall service efficiency, quality, and customer satisfaction. 

    Final Thoughts   

    Powerful as they are, AI analytics aren’t a magic bullet. 

    The most promising benefits AI analytics can offer come from several years of focused, incremental improvements (much like how you’ll need to master traditional FSM before you’re ready for connected field service). 

    See, ML algorithms are designed to “get smarter” over time. The idea is, as they collect more data, they learn more about what works or doesn’t work for your business and its customers. Those findings can then be used to improve AI-generated recommendations, predictive forecasts, or even automated workflows. 

    If you’ve only recently digitized operations, you’ll need to give the AI some extra time to gather data and discover patterns. 

    Initially, you’re striving for more modest gains such as eliminating redundancies or streamlining inefficient processes. But, as your data strategy matures, AI analytics can be used in more advanced applications like intelligent inventory management or automated route optimization.

    Velosio’s Field Service team can help you build a powerful AI analytics strategy from the ground up. Contact us today to learn how we help field providers transform service ops with big data, AI, and a rock-solid tech stack. 

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