Unlocking Value with Personalized AI: The Rise of AI in Field Service

Yuliya Shcherbachova
Unlocking Value with Personalized AI: The Rise of AI in Field Service

These days, technology and AI are critical in shaping the speed and complexity of product development. They also help us respond to new challenges and drive strategies. In fact, they are no longer a choice—they have become an influential part of many organizations’ roadmaps.

When Aquant was founded, many service companies were beginning their transformational journeys. However, the founders of Aquant had one mission: to help organizations leverage AI to solve the most challenging service-focused problems.  

Over the years of close work with our customers, we learned how crucial the role of aligning AI with strategy is and the delicate nature of trust in AI.  We also learned that every organization and every situation is very unique. Even if it sometimes feels that there are similarities in problems, with the rising complexity and volume of product development, the cycle of change is getting much faster, and meeting customer needs requires a deliberate and precise approach. 


Starting with AI: Choosing the Right Tools for Your Organization

Can we trust our historical data? 

Do we need to improve data quality first? 

Can AI understand my service business? 

We often hear questions like these, especially from companies that have yet to experience AI. When choosing AI, two components help establish trust and confidence: technical and business.


Technical Criteria 

  • Data used in the system—and how much you can trust it: Raw data is often flawed and far from ready for immediate use. Many organizations are still gathering and structuring data. The volume of collected data continues to grow exponentially. According to IDC’s Global DataSphere, data will expand at a 21.2% CAGR over the next five years, reaching over 221,000 exabytes by 2026. However, data quality remains a significant issue for many organizations regarding AI projects. Through 2025, at least 30% of general AI projects will falter post-proof of concept due to inadequate data quality. It can scale with your organization’s data needs, ensuring that you can leverage the full potential of your data for improved service efficiency and customer satisfaction. It’s crucial to consider the impact of data accuracy and completeness on the system’s ability to provide trusted recommendations.


  • Explainability and human-in-the-loop methodology: You may have enough data to start your process, but in today’s fast-paced world, can you trust resources that worked yesterday? To ensure your AI systems work, you must see real-time results, evaluate them, provide feedback, and improve as you go. Additionally, systems are getting smarter, but they won’t replace the critical thinking of experts. Only your top-performing engineers know how to fix this unique problem. But what if this person leaves your organization? This makes unique domain knowledge a table stake, and your system must be a collaborative experience between AI and your experts. The best results are achieved when humans and machines work together. Our system empowers your experts and ensures their unique domain knowledge is crucial to the solution.


  • Continuous improvement: Just like humans, machines must also learn and improve. This is where the correct methodology and automation bring significant value. ChatGPT was built on 2023 data, which introduces some friction in providing accurate answers. For systems to stay current and effective, they must receive feedback and regularly incorporate updates. This involves a structured process for ingesting new data sources, managing feedback, and making necessary changes.


Business Criteria

The business part of the equation can’t be neglected for a straightforward reason: you can have perfect technical solutions, but you still need business instruments and best practices to succeed. 

Here are essential questions that you should ask yourself while choosing your next AI vendor: 

  • Does this tool understand the nuances of my business? 
  • Can I easily align implementation with my business goals? 
  • Are these recommendations based on cost-effectiveness? 
  • Are there any best practices I can use? 
  • What will the adoption process look like? Who will take care of team enablement? 


The truth is, there are many AI tools out there, and it becomes much more challenging to pick the right one that corresponds to your needs and strategy and sets you up for successful digital transformation. It goes beyond the tool itself; it’s a new way to think and drive your strategy. 


Unlocking Personalized AI with Aquant Service Co-Pilot

We deeply empathize with the unique challenges that service businesses face, and our team has worked tirelessly to address them through Aquant’s Service Co-Pilot, which offers personalized AI suited to each business’s specific needs. 

Aquant’s Service Co-Pilot is uniquely built to provide recommendations based on an in-depth, contextual understanding of every problem—derived from asset history, user interaction, and more. 

We understand that every service challenge is one of a kind, and generic answers won’t cut it. So, let’s look under the hood of personalized AI and how we do it. 

  • Ingest service data: Our usual starting point is with your manuals and documentation, as they’re typically your cleanest data sources. We also ingest work order data, video tutorials, and free text technician notes—regardless of how messy your data is. Our AI system is designed to handle data of varying quality, and we have robust processes in place to ensure the accuracy and reliability of the recommendations it provides. In just two weeks, we can launch a Co-Pilot that offers your team Generative AI recommendations based on your knowledge base. 


  • Embed expert knowledge: On average, 30% of correct solutions aren’t found in historical service data. So, we bring in your subject matter experts to provide tribal knowledge not captured in your manuals and tickets. Our approach is uniquely built based on behavioral science and designed to minimize the efforts of subject matter experts. This ensures that the AI system is not solely reliant on historical data but incorporates your team’s expertise and insights.


  • Turn data into trusted and personalized sources: With a two-phased modeling approach and combining service data and expert data, we can close data gaps and provide accurate and precise recommendations for your business, each asset, and each unique service case. 


  • Continuously improve suggested solutions: By providing a feedback loop and a robust improvement methodology, we can continuously enhance results in real time. This commitment to continuous improvement is designed to build better trust with our users and ensure that our AI solution constantly evolves to meet your changing needs. 


No matter your data quality or where you are in your AI journey, we can help!

Aquant already supports many organizations across different industries, so we’re excited to share our benchmarks and best practices to help you get started. 

Sign up for our 7-Day Challenge to better understand your data, critical problems, and potential savings you can make by leveraging Aquant Service Co-Pilot.



About the Author

Yuliya Shcherbachova, Senior Product Marketing Manager, Aquant

As a Product Marketing Leader at Aquant, I’m passionate about using technology and AI to tackle complex problems. I’m on a mission to make the world a better place through innovative, high-impact projects. Over the last decade, I’ve worked with hyper-growth AI companies—helping them grow, increase their revenue, and develop new product strategies.