Category Archive: Blog Posts

Revolutionizing Field Service Training: The AI Edge

In an era when the field service sector grapples with widening skills gaps and a looming retirement crisis, the urgency for innovative training and onboarding methods has never been more acute. 

Nearly half (46%) of North American field technicians are over 50, and more than a quarter of the labor force is expected to be 55 or older by 2031, signaling an urgent need to replenish retiring talent and improve the skills of the remaining workforce. Additionally, the US Bureau of Labor Statistics anticipates around 582,100 annual job openings in installation, maintenance, and repair roles, attributed to employment growth and the necessity to replace retirees.

The shortage threatens customer satisfaction and the financial stability of affected organizations. Aquant’s 2024 Field Service Benchmark Report underscores these stats, revealing the staggering cost discrepancies between the lowest and highest-performing employees and the significant financial benefits of narrowing this gap. Traditionally, training new technicians has fallen to more experienced senior technicians. However, we find ourselves in a situation where the remaining senior technicians simply lack the time for such mentorship.

This blog delves into the potential of AI-driven tools and methodologies to address these challenges, revolutionizing field service technicians’ onboarding and ongoing skill development. In doing so, it sets a new benchmark for operational excellence.
 

The Importance of AI in Onboarding and Upskilling

The field service industry stands at a critical juncture where the difference between the top and bottom performers can drastically impact service costs. One way to address these costs is through proper employee onboarding, which can in turn, have a positive impact on overall employee engagement and retention.

• According to BCG, employee onboarding is among the most influential factors regarding employee experience. Companies with effective onboarding processes achieve 2.5x more revenue growth and 1.9x the profit margin compared to organizations with poor onboarding strategies.
• A Gallup report found employees who had a great onboarding experience are 2.6 times more likely to be “extremely satisfied” at work.
• Effective onboarding can shave months off a new hire’s time-to-productivity, according to a SHRM Foundation guide.

Integrating AI in training and onboarding processes offers a path to achieving these outcomes, mainly through 2 main applications:

AI Troubleshooting and Diagnostic Tools: These tools can significantly reduce the time it takes for new technicians to become proficient in diagnosing and resolving issues, effectively fast-tracking their ability to contribute to the company’s productivity and service quality goals.

Business Analytics Tools: Such tools can pinpoint areas where the workforce might be underperforming or where there are opportunities for improvement. Imagine being able to ask, “Which techs are underperforming?” and receiving actionable insights in seconds—this is the power AI brings to the table.
 

Case Studies Highlighting AI’s Impact

Aquant’s research shows that companies that use AI-powered troubleshooting and analytical tools have reduced the time it takes for their employees to reach competency by 50%. This improvement suggests that integrating AI-driven solutions can significantly speed up the learning process, allowing employees to diagnose and resolve complex issues more effectively. These findings indicate that AI improves operational efficiency and skill development in the workplace.

Ricoh’s Transformation

During the COVID-19 pandemic, Ricoh faced daunting challenges, including high churn rates and the necessity for remote onboarding. By partnering with Aquant and embracing its AI technology, Ricoh transitioned to an effective remote onboarding model and significantly improved its service metrics. The results speak volumes: 

• 17% increase in Remote Resolution Rate.
• 22% improvement in First Time Fix Rate.
• 75% improvement in onboarding times for support reps.

Success Story

Ricoh achieved notable improvements across critical KPIs, operational efficiency and productivity, employee satisfaction, and workforce retention. Read the full case study here.

The Semiconductor Company Conundrum

Contrastingly, a leading semiconductor company in North America struggled with high turnover and extended onboarding durations for its engineers and technicians. The lack of a structured onboarding process led to inconsistent experiences and hindered productivity. Recognizing the need for change, the company is now looking to overhaul its approach to onboarding, aiming for enhanced employee retention and reduced time-to-competency.
 

Implementing AI: A Step-by-Step Guide

1. Assess and identify needs. Begin by conducting a thorough assessment of your current training and onboarding processes. Identify gaps and areas where inefficiencies are most pronounced. This step should involve gathering feedback from new hires and experienced technicians and analyzing performance data to pinpoint areas for the most significant improvements.
2. Choose the right AI tools. Select AI tools that best align with your identified needs. For troubleshooting and diagnostics, look for platforms that leverage historical data and machine learning to provide actionable insights. For workforce analytics, opt for solutions that integrate with your existing HR systems and provide real-time performance analysis. Ensure your tools are scalable, user-friendly, and compatible with your current technology infrastructure.
3. Train your team and integrate AI into your workflows. Implementing new technology requires buy-in from all stakeholders. Invest in comprehensive training sessions for your technicians and managers, ensuring they understand how to use AI tools effectively. This training should cover the technical aspects and emphasize the benefits.

The field service industry is undergoing significant changes, driven by demographic shifts and the need for greater operational efficiency. This environment demands a transformation in training and onboarding practices, with AI leading the charge.

As referenced above, the experiences of companies like Ricoh and a notable semiconductor firm underscore the importance of integrating AI into training processes. This integration is advantageous and crucial for future-proofing businesses against upcoming challenges.

At Aquant, we are committed to leading this charge. Interested in learning more? Sign up for a free demo.

About the Author

Sidney Lara, Service Principal, Aquant

Sidney Lara serves as the Service Principal at Aquant, specializing in enhancing customer experience through AI software. With a 20-year background in operations and service leadership, he is dedicated to streamlining business processes to maximize customer value. Beginning his career as a field technician, Sidney advanced through technical and leadership positions before serving as Vice President of North America Service at RATIONAL USA. 

AI and Shift Left Will Propel Success in Medical Device Service, Says New Aquant Report

The global medical device market has experienced significant growth in recent years but still faces several challenges, including supply chain distribution, rising service costs, impending retirements, and knowledge gaps. According to Aquant’s 2024 Medical Device Service Benchmark Report, investing in appropriate technologies and tools, such as Generative AI, and implementing strategies like Shift Left can help these companies maintain their edge in the industry.

This year, we analyzed findings from leading medical device companies specializing in patient-facing technologies, as well as medical device companies specializing in lab equipment technologies. Our dataset spanned:

• 50+ organizations.
• More than 7.6 million work orders spanning nearly 1.4 million assets.
• More than 26,000 technicians.
• Over $3.7 billion in service costs.

Key Findings

We reviewed trends across technologies typically found in laboratory and patient-facing settings. We learned:

Patient-Facing Medical Device Companies

• In the case of First Time Fix Rates, the skills gap is more apparent in medical device companies with patient-facing technology, possibly due to the more dynamic nature of the environment. Managing spare parts and tracking parts for work orders and repairs can directly impact First Time Fix Rates. Connectivity is also an issue in hospitals; many knowledge solutions can fail because they lose connection.
• They have more Visits Per Asset Per Year than medical device companies specializing in lab equipment because patient-facing assets are designed to operate at scale (due to many daily users). Thus, the equipment requires more maintenance, and service styles tend to skew toward break-fix reactions.
• The Resolution Time is faster—the expectation is that the product will break due to the level of daily use. The ramifications of downtime are more critical, so companies put more effort into fixing an issue as soon as possible. At medical device companies with patient-facing technologies, a failed first visit could lead to three visits overall to resolve the issue and 13 extra days added to the Resolution Time. Also, bottom-performing companies take five times longer to resolve an issue than top-performing companies.
• In medical device companies with patient-facing technologies, bottom performers at top companies cost 31% more than the highest performers. However, bottom-performing companies’ lowest-performing employees can cost 94% more than the top performers! There is also a cost-saving opportunity if overall workforce performance was boosted to the level of the top 20% of performers—these organizations would see cost reductions as high as 16%.

Lab Equipment Medical Device Companies

• Generally, medical device companies with lab technologies have higher First Time Fix Rates than those with patient-facing technologies. They also have a smaller skills gap. This can be attributed to the more straightforward nature of their equipment, consistent builds, and streamlined service training process.
• They typically have more extended Resolution Time than companies with patient-facing technologies. In the case of medical device companies specializing in lab technology, a failed first visit could lead to three visits overall to resolve the issue, as well as 15 days added to the Resolution Time. Additionally, bottom-performing companies take four times longer to resolve an issue than top-performing companies.
• In top medical device companies with lab technology, bottom performers cost 25% more than the highest. However, the lowest-performing organizations have the most expensive workforce gap, with their bottom performers costing 106% more than the top-performing employees. That’s more than double the cost! However, if everyone had the knowledge and skills to perform like the top 20% of the workforce, service costs would be reduced by as much as 19%.

[Free Resource] The market for medical devices has been rapidly growing in recent years—but is currently facing issues with supply chain distribution, increasing service costs, upcoming retirements, and knowledge gaps. Download the 2024 Medical Device Benchmark Report to see where you stack up.

Customer Experience Gap

Many companies measure their First Time Fix Rates within 7-day or 14-day windows, but this approach can lead to a significant gap and frustrating customer experiences. It’s easy to overestimate First Time Fix Rates and underestimate Resolution Costs when using smaller increments.

We recommend measuring the First Time Fix Rates over 30 days. It’s also essential to upskill your team and ensure every service team member has equal knowledge about the equipment and best practices.

“Leveraging data isn’t just about collecting it; it’s also about making sense of it in a way that matters.

Using data to boost the customer experience and improve things for our employees is critical. After all, retaining people is much easier than hiring new ones. This means we need ways to turn all that data into fundamental insights, helping our customers and team members thrive.”

 

– Paul McDermott, Director of Service and Support, T2 BioSystems

 

How are best-in-class medical device companies getting ahead and staying ahead?

Top-performing medical device organizations have a few things in common. They tend to:

1. Emphasize soft-skills training and in-house technical expertise as technology advances.
2. Build and expand in-house technical teams to offer a more personalized and immediate response to technical challenges.
3. Embrace the connectivity and serviceability trends, especially since modern medical devices are becoming more complex, incorporating advanced technologies such as AI, robotics, and wireless connectivity.
4. Implement the right analytics, the Shift Left strategy, and AI and AR tools that provide actionable insights, drive operational efficiency, improve patient outcomes, and inform strategic decision-making.

Empower your medical device service organization with the right insights, tools, and plans to hit (and exceed!) business goals.

Inspired by the report findings? Sign up for Aquant’s 7 Day Challenge!

Our AI engine will process your data to show your key metrics (including First Time Fix Rate) and ways to increase workforce efficiency and save money.

The Power of AI in Service Management: A Journey Towards Boosting Efficiency and Customer Satisfaction

Businesses are always looking for ways to improve their service management practices to increase efficiency and make customers happier. One way they’re doing this is by integrating Artificial Intelligence (AI) into their service delivery processes, which helps them achieve their goals by transforming how they deliver their services.

Joe Lang, VP of Service at Comfort Systems USA, recently joined Aquant’s Service Principal, Sidney Lara, for a webinar, Close the Skills Gap & Cut Service Costs: Unveiling Aquant’s 2024 Field Service Benchmark Report. 

Joe and Sidney discussed strategies for implementing AI during their session. They highlighted the Shift Left concept to shorten the service lifecycle and significantly reduce service costs. Organizations can improve customer outcomes and build better customer relationships by streamlining service delivery with AI.

Let’s dive into the key takeaways from the webinar. 

The Six-Step Framework for Transformation

1. Collect and understand data: The first step involves gathering comprehensive service data to understand customer and workforce interactions thoroughly. Organizations can set a robust foundation for accurate insights by standardizing and sanitizing their data. By analyzing documented data, Sidney illustrates how orgs can identify opportunities to resolve issues over the phone or through self-service platforms, such as chatbots, significantly enhancing customer satisfaction while reducing the need for in-person visits.
2. Dive deep into service issues: Understanding the nature of service issues is crucial. Joe’s experience at Comfort Systems USA spotlights the necessity of moving beyond symptom-fixing to address the core problems. AI facilitates deeper understanding, which sifts through historical data and expert insights to pinpoint effective solutions.
3. Leverage expert knowledge: During the session, Sidney pointed out that 30% of solutions are derived from highly skilled technicians’ unspoken or implicit knowledge. Incorporating this human element into the AI engine ensures that the most effective resolutions are identified and applied.
4. Operationalize AI for targeted solutions: With insights in hand, the next step is operationalizing AI with predictive analytics. Joe shared how Comfort Systems is taking this step by releasing Aquant’s Service Co-Pilot to empower technicians to solve issues independently before escalating them, enhancing first-time fix rates and overall operational efficiency. Organizations can significantly improve service delivery and cut costs by investing in technology that enhances technicians’ capabilities and reduces reliance on external help.
5. Establish continuous improvement loops: The journey doesn’t end with the implementation. Continuous measurement and feedback are vital to refine and enhance service interactions. Maximizing the benefits of new technology requires empowering your team during onboarding.
6. Shift Left for proactive problem solving: Ultimately, the goal is to solve problems before they occur. Sidney and Joe stressed the potential of self-service and remote diagnostics in reducing the need for in-person visits, enhancing customer satisfaction, and lowering costs.

Why It’s Time to Shift Left Now

The stakes in service management have never been higher. Rising customer expectations demand immediate, effective responses. Sidney’s insights reveal a world where customers prefer self-service options.

Moreover, the talent shortage and skills gap present formidable challenges. Joe emphasizes the significance of data in bridging gaps, while Sidney advocates for utilizing AI, which provides a clear path for service organizations.
 

Aligning every team member’s capabilities to match the top 20% of your workforce could reduce your service costs by up to 22%. And it all starts by utilizing valuable insights, tools, and benchmarks.

The time to embark on this transformation is not tomorrow; it’s today.

Ready to adopt the Shift Left approach? Request a demo today to learn more.

About the Author

Courtney Stafford, Marketing Programs Lead, Aquant

Hi there! I’m the Marketing Programs Lead at Aquant. My passion lies in creating unique in-person and virtual experiences, crafting engaging content, sharing best practices—and, most importantly, empowering service teams like yours to thrive.

From Data Overload to Actionable Insights: The Journey with Aquant’s Service Insights

One of the biggest challenges in effectively using data is getting the right insights to the right people in an efficient manner.

In most industries, decision-makers do not directly deal with data. Briefs are handed out, meetings are attended, and emails are sent—and these might happen before critical information reaches the people who need to act on it.

Aquant designed Service Co-Pilot to provide service leaders unrestricted data access and actionable insights. Over the past three years, Aquant developed Service Insights with the help of service leaders to assist decision-makers in comprehending their business, from technician performance and asset levels to customer insights and business-wide metrics. 

Our recent work relies on something other than LLMs to better understand the data. Instead, it makes insights more accessible to users.

Presenting data analysis results is challenging because it requires telling a story that motivates business leaders to take action. It’s easier to do when the results relate to a single analysis and are straightforward. However, depending on the complexity of the analysis, it can take several hours or even days to deliver actionable insights.

We have been working on our Service Insights platform to improve service leaders’ abilities to tell a coherent story based on unknown results. This task is challenging, but we have been exploring, developing, and refining our process to generalize it better. We have started using large language models (LLMs) as part of this journey.
 

How it Started: Involving LLMs to Accelerate Product Development

Like many other data scientists, I got excited about Generative AI breakthroughs and started thinking about how it could impact existing models and product development.

The beginning of the project, alongside the emergence of ChatGPT, LLama, and Bard, was filled with uncertainty and excitement. It wasn’t entirely clear how this would impact our day-to-day, but one thing was for sure: it would be significant.

I still have screenshots of my coworker’s reactions from Zoom meetings where I showed results from the Proof of Concept. Since then, the team at Aquant has met and overcome the many seen—and unforeseen—challenges that came with adapting to new technology: token limits, load times, and the ambiguity of human language, to name a few. We have crossed the hurdle between an LLM product that could create value to a product that performs consistently.  

From Insights to Value: Putting AI into Action 

Although it is still early, we are witnessing great results in how our customers interact with their data. Our platform not only helps users access their data—it provides relevant insights and background information to extract meaningful narratives. We go beyond simple answers, forming narratives that provide a tailor-made, centralized view for any user question. 

I am very proud of our ability to synthesize data in real-time to create a cohesive understanding of complex data sets—and we are continuously working to improve and refine this ability.
 

What You Can do With Aquant’s Co-Pilot for Service Insights

For example, let’s say a user wanted to determine which customer cost the most to service/maintain over the last quarter. 

This question can be answered through a dashboard or SQL query, but the output will lack details. With Co-Pilot for Service Insights, we can get even more granular answers, including:

• The name of the relevant customer and their total cost.
• Plots that show how the customer compares to other high-cost accounts. 
• Additional contextual information relating to customer costs, such as the average value over that time. 
• Information regarding their risk profile—including any recent changes we may need to be aware of—if the customer is identified as a high-cost entity.
• Specific problematic assets, if applicable. 

Most importantly, getting answers without translating data or researching context is easy. Service Co-Pilot is intuitive, speaks the service language, and corresponds to your daily business questions. This provides users with the information they need. It also aids in understanding the broader context of the results, allowing them to address any issues identified.
 

Click for full size. With Aquant’s Co-Pilot for Service Insights, you can get essential details at a glance. In this scenario, Co-Pilot for Service Insights can determine which technicians are underperforming compared to their peers and which products they should be trained on.

Taking Co-Pilot’s Insights to the Next Level

Co-Pilot for Service Insights has partial coverage of everything offered by the original product, Service Co-Pilot. Narrowing the scope allowed us to focus on the base methodology and logical backend. 

With recent developments, we can offer consistent results to various questions. Up ahead, we are expanding Service Co-Pilot’s toolkit to allow increased customization, as well as more relevant and actionable replies. We are also constantly tweaking and optimizing to reduce wait times and improve accuracy.
 

Enabling Other Teams With Service Data Resources

Leveraging AI systems and generating actionable insights are the tip of the iceberg! AI can unlock various data types and resources for other service teams—including product manuals, video tutorials, and more.  

At Aquant, we work closely with leading service companies to understand their main challenges in a fast-changing environment and how we can bridge these gaps by bringing technology and our service expertise.
 

Learn More

If you are as passionate about AI technology as I am, you should check out Aquant in action. 

I’m excited to uncover more details and truth in future blogs, but don’t hesitate to contact the team with any questions!
 

About the Author

Tommer Vardi, Data Scientist & Team Lead, Aquant 

I am a Data Team Lead at Aquant, dedicated to leveraging Large Language Models (LLMs) to change how we interact with data. The team and I are driven by a passion for making data accessible and insightful. With nearly three years at the forefront of data science innovation at Aquant, I explore how LLMs can transform complex information into actionable knowledge.

The Human Element in AI: Blending Expertise with Machine Learning for Superior Service

As machine learning (ML) practices advance, users can complete more tasks with fewer resources and less time. 

For instance, we can use structured and unstructured service data to get a holistic view of service and make better decisions. We can save time by analyzing contextual information and generating easy-to-read summaries. We can also derive value from piles of books and manuals in seconds.

Overall, it’s simple enough to embed ChatGPT into daily workflows. But to get the best results from your input, there are several best practices that you should be mindful of. 
 

Fine-Tuning Your AI Approach

AI tools deliver the best results with thoughtful prompting.

The accuracy of an AI system’s answer directly correlates with the quality of the prompt. The best way to glean the correct answer is by asking the right questions and providing as many helpful details as possible.

For example, imagine you are looking up today’s weather forecast to determine what to wear. To generate the most accurate answer, you have to add a location. You can also add the time you anticipate being in the area. Additionally, you can ask to have the results delivered in Celcius or Fahrenheit.

The weather forecast is a straightforward example, but the same principle applies to complex questions, problems with multiple answers, and elaborate decision trees. You can quickly feel overwhelmed if you don’t have knowledge or prior experience, so it’s essential to map out your questions and processes to determine the best prompt. 
 

Human knowledge is still front and center.

There is a lot of skepticism about AI replacing specific jobs. However, while AI systems shape our workflow and experiences, they don’t take unique knowledge from us. 

If you crave your aunt’s world-famous apple pie, you might find many recipes online—you can even ask ChatGPT to create a shopping list for you. However, it’s unlikely that anyone else can duplicate your aunt’s exact recipe because she has developed skills from years of baking. For example, your aunt can tell, by touch, what the ideal pie dough consistency feels like; she can even determine doneness from the color of the pie crust once baked. 

As a result, your aunt’s fantastic apple pies result from her baking experience and knowledge. These aspects of baking won’t be explicitly found in a cookbook—so if she doesn’t share her expertise, it gets lost. 

Ultimately, you can follow the recipe for your aunt’s apple pie down to the final instruction, but you will probably have to make a few pies until you get a feel for the process. This aspect is the “human in the loop” component in AI. Similarly, combining human knowledge and resources can achieve the best results in service AI models. Aquant research has shown that 30% of service solutions are not found in historical service data. Instead, the best answers are provided by veteran service experts.
 

Trust is essential.

It’s in human nature to want control over the decision-making process. Even when we look up answers in public documents, we still rely on tried-and-tested best practices and advice from senior colleagues. We care about IP and internal data and trying to protect our organizations and users from irresponsible use of private information. Ask yourself if your solution is trustworthy, and be prepared to fact-check your outputs. 
 

Close the Skills Gap with Service Co-Pilot for Knowledge

At Aquant, we’ve dedicated time and resources to building Service Co-Pilot, a platform that corresponds to today’s service needs and beyond. Service Co-Pilot for Knowledge approaches the skills gap issue by: 

1. Ingesting all available data. Organizations typically need help with many data-related challenges, including sorting through poor-quality data, corralling isolated data sources, or managing diverse data types. Plus, if a company is represented globally, it also requires multi-language support. Service Co-Pilot supports all of the aforementioned items—and in its eyes, there is no such thing as insufficient data! Bring your data—wherever you are in your process—and we will help you get started. We can also provide feedback and recommendations on managing your data strategy across your service business to improve outcomes.  
2. Reviewing and optimizing outputs to get results you can trust. Service Co-Pilot speaks the service language. We combined the best Natural Language Processing (NLP) and Generative AI practices with a human-in-the-loop component to achieve trustworthy results. By involving subject matter experts, we transfer your tribal knowledge to improve results and protect your unique IP. Service Co-Pilot also ingests user feedback, so the tool gets even more accurate through continuous use!
3. Accelerating your learning and reducing service costs. Keep upskilling your employees through data and AI tools to help them perform at the highest level. According to Aquant’s 2024 Field Service Benchmark Report, an organization’s bottom-performing workers can 80% more than their top-performing counterparts. However, if organizations empowered all employees to perform like the top 20% of the workforce, service costs could be reduced by as much as 22%.

Start Your Journey Today

We live in a world where technology amplifies our capacity to make informed decisions and preserve invaluable knowledge. Aquant’s Service Co-Pilot stands at the forefront of this revolution, offering a robust platform that understands and respects the nuances of human intelligence and organizational data. 

By bridging the gap between data complexity and decision-making simplicity, we pave the way for a future where every individual has the tools to excel, and every organization has the means to thrive in the ever-evolving landscape of information and technology.

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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.

Aquant CEO Explains How AI is Transforming Service Workflows Across Industries 

In a recent episode of the “AI in Business” podcast, Shahar Chen, CEO and co-founder of Aquant, joined Matthew DeMello, senior editor at Emerge Technology Research, to share his perspectives on the rapidly transforming landscape of AI in manufacturing and its broader implications across various sectors. 

Here is a recap of the main themes and takeaways from the podcast.
 

The Manufacturing Industry is Finally Embracing AI (A Long Time Coming)

Traditionally viewed as slow adopters of AI, manufacturing sectors are now at the forefront of AI integration. This shift is driven by the need to stay competitive—especially in an environment where product differentiation is minimal and margins are tight, notes Shahar. AI emerged as a critical tool in enhancing post-sale support, service, and customer satisfaction.
 

Bridging the Talent Gap with AI

A significant challenge in today’s dynamic service workforce is the retirement of seasoned professionals and the transient nature of the incoming–and oftentimes younger—workforce. This reality has led to a significant gap in expertise and experience. AI technologies step in as a “co-pilot,” supplementing human skills and ensuring that the depth of knowledge remains within the organization.
 

Considering AI a Co-Pilot, Not a Replacement

A crucial aspect of AI adoption is recognizing its role as an enhancer of human capabilities–not a replacement. This perspective helps shift from fear to acceptance, paving the way for the integration of AI and effective utilization. In service, AI acts as a co-pilot, guiding and assisting users throughout challenges—but leaving the control in human hands.
 

Democratizing Expertise through AI

Generative AI tools bring about the democratization of expertise, making organizational knowledge more accessible. This advancement means that the absence of an expert does not hinder problem-solving. AI can provide the necessary guidance and information.
 

AI Cuts Through the Noise

Many service organizations will claim their data is “garbage” or useless. However, Shahar points out that it’s not garbage at all—it’s actually noise.

“Every company has noise in their data,” explains Shahar. “The key to overcoming the noise is by pulling the insights of experts with decades of experience into a co-pilot. This approach significantly shortens the learning curve for new technicians, leading to highly accurate results. For instance, a technician who joined just six months ago can tackle complex problems, such as fixing an MRI at a hospital, in just 10 minutes using a co-pilot. This level of proficiency traditionally would take 30 years to develop–that’s a remarkable advancement.”
 

AI’s Direct Impact on Service Workflows

AI is helping service organizations make sense of their data—while profoundly transforming field service workflows—in several key ways: 

1. AI is a real-time co-pilot, guiding even novice technicians through intricate problem-solving, boosting service quality and operational efficiency. These AI applications streamline processes and ensure consistent and high-quality outcomes in field services. 
2. AI facilitates intelligent troubleshooting, swiftly diagnosing and resolving breakdowns—and minimizing operational disruptions. 
3. Using predictive maintenance, AI forecasts equipment failures before they happen, revolutionizing preventive strategies and drastically cutting downtime and costs. 

Once seen as slow in AI adoption, the manufacturing sector is now recognizing its critical role in this evolving AI ecosystem. As Shahar eloquently put it, AI represents a paradigm shift in industrial operations. At Aquant, we’re leading this transformation, leveraging AI to redefine customer service and field workflows, positioning it as an essential growth partner in various industries. 

Listen to Shahar’s full conversation with AI in Business here or speak with us directly by signing up for a demo.

Close the Skills Gap and Cut Service Costs: Insights from Aquant’s 2024 Field Service Benchmark Report

The race is on for field service organizations to bridge the skills gap, meet customer demands, tame complex machinery, and ride the tech wave — all at the same time. 

Hot off the press, Aquant’s 2024 Field Service Benchmark Report uncovers how a widening skills gap and increasing costs impact today’s service orgs. It also dives deep into how top-performing companies use data, AI, and the Shift Left Method to adapt to the changing times and build service teams equipped for the present and future.
 

We gathered and analyzed actual, anonymized data from:

• 145 service organizations, including service divisions within OEMs and third-party service organizations across manufacturing, medical devices, commercial printing, industrial machinery, food service equipment, and more
• More than 24 million work orders spanning 6.6+ million assets
• Over 582,000 technicians
• Nearly $7.71 billion in service costs 
• An average of 3 years of service data per company

This year, we uncovered:

1. The skills gap is the root of many challenges, including skyrocketing service costs. On average, bottom performers cost 34% more than the highest performers at top-ranking service organizations. But at bottom-performing orgs, low-performing employees can cost 80% more than their top-performing counterparts. Additionally, bottom-performing companies take four times longer to resolve an issue and have three times more Visits Per Asset than their top-performing counterparts. The good news: if every employee had the knowledge and skills to perform like the top 20% of the workforce, service costs would be reduced by as much as 22%.
2. Top-performing organizations are pulling ahead of their lower-performing peers mainly because they have a smaller workforce skills gap. They have higher First Time Fix Rates among their workers. Top-performing organizations also have lower service costs, with only a slight difference between top and bottom performers. Additionally, they typically have more time between visits, which signals fewer visits needed for repairs or addressing maintenance issues. 
3. Companies need to pay attention to their Customer Experience Gaps. Companies who measure First Time Fix Rates in 7-day or 14-day windows are setting the stage for a significant gap, leading to frustrating customer experiences. When measured in short windows, it becomes easy to overestimate First Time Fix Rates and underestimate Resolution Costs. We recommend measuring First Time Fix Rates in 30 days, prioritizing upskilling, and making every service team member equally knowledgeable about equipment and best practices.
4. Best-in-class organizations are getting ahead and staying competitive through data, tools, and the Shift Left method. Their best practices include:

• Regularly reviewing their organization’s performance. 
• Using quantitative and qualitative data from multiple sources to understand their service landscape.
• Normalizing data sanitation.
• Starting with their existing data and refining outputs to get better results.
• Adopting and implementing the Shift Left approach, which moves service resolutions closer to the end customer so they don’t need to contact the company until necessary. 
• Assembling a workforce with diverse soft and hard skills to create a better customer experience—and committing to upskilling teams. 

How does your org stack up against 2024’s service benchmarks?

Participate in Aquant’s 7 Day Challenge to find out — at no cost. 

Our analysts will process and analyze your data via Aquant’s robust AI engine. We’ll show you the results of your org’s key metrics (including First Time Fix Rate), how you can be more efficient, and where to save money. 

Use your data to uncover the most significant opportunities for performance improvement and see how Shifting Left can help your org stay ahead of the competition.

2023 in Review: Aquant’s Year of Breakthrough Innovation 

As we approach the end of 2023, it’s a great time to think about what we learned and accomplished this year. This year has been remarkable for many organizations, including Aquant, marked by challenges, breakthrough innovations, and transformative changes. We’ve remained dedicated to staying connected with our customers, sharing our expertise to help them navigate the complexities of AI adoption in service businesses.
 

2023’s Key Lessons

Through collaborative work with our customers and partners, we learned:

• The future is closer than we think. Generative AI is rapidly evolving, shaping our daily experiences. It’s redefining how we consume information, learn, make decisions, and drive our businesses. AI is not just a competitive edge, but an essential and inevitable path.
• Consumer expectations are evolving. Service businesses worldwide are focused on lowering First Time Fix Rates and First Contact Resolution Rates. In addition, in a world where customers want to solve problems quickly without escalating to a live agent, self-service has become non-negotiable. 
• Organizations still need to work on bridging their talent gaps. According to Aquant’s 2024 Field Service Benchmark Report, bottom-performing workers can cost their orgs 80% more than their top-performing counterparts. However, if all employees were empowered to perform like the top 20% of the workforce, service costs could be reduced by as much as 22%.
• Workers are retiring faster than organizations can replace them. The Great Resignation is impacting service organizations, further underscoring the importance of upskilling teams. Organizations need to transfer knowledge quickly and efficiently to avoid losing it entirely. 

A Year of Innovation at Aquant

With those lessons in mind, our team worked tirelessly to deliver product capabilities that adapt to industry changes, empower customers to harness the power of AI, and enhance customer experiences. Here’s a glimpse of what we’ve brought to the table:

• Introducing Aquant’s Service Co-Pilot: We launched Service Co-Pilot, a revolutionary service intelligence platform combining the best of human expertise with artificial intelligence to transform service operations. This platform allows for global connectivity, advancement, and scalability of service knowledge. 
• Service knowledge on-the-go with mobile and self-service experiences: In today’s fast-paced world, immediate problem-solving is necessary. We’ve invested in mobile accessibility and self-service options, enabling organizations to provide seamless experiences for their customers and service dealers using information from manuals and other resources.
• Better service decisions with Triage and Insights: We continued investments into Triage and Insights to help users seamlessly pinpoint symptoms, access actionable insights, and streamline the triage process.

2024 Predictions: Navigating AI Transformation Across Enterprises

As we look to 2024, the next challenge lies in navigating this new landscape and operationalizing AI within an enterprise context. Vendors offering guidance will play a critical role in helping businesses harness the full potential of AI while aligning with strategic goals and industry standards.

“In 2024, the AI landscape will see a discernible shift. If 2023 was the year of broad generative AI usage [such as ChatGPT], 2024 will be the year of hyper-personalized and industry-specific solutions.” 

– Edwin Pahk, SVP of Pre-Sales & Customer Success, Aquant

Successfully incorporating AI into business processes requires careful navigation. Edwin recommends: 

• Embracing change and Shifting Left: It’s all about transformation, and the Shift Left approach proactively resolves service issues and leverages data for meaningful business changes. At Aquant, we help organizations implement this strategy, bringing services closer to the end customer, reducing escalations, improving First Time Fix Rates, and promoting self-service experiences.
• Defining KPIs that align with your business outcomes: Connect your service data and utilize innovative tools to identify opportunities, blind spots, and risks to make better business decisions. Keeping track of critical metrics and comparing them with industry benchmarks might impact how you pivot your new strategy. Customers who measure First Time Fix Rates understand which areas they can improve and how to reduce service costs. 
• Leveraging the collective insights of your best experts, a.k.a. tribal knowledge: According to Aquant research, 30% of service solutions are not found in historical service data. Instead, the knowledge of veteran service experts contains the best answers. Bridge the gap between top and lower performers by providing an easy way to access information and learn. Infuse datasets with subject matter expertise, adopting a “human in the loop” approach to refine models and ensure accurate results. By ingesting organizational knowledge, you reduce the risk of losing critical insights if someone leaves the company.

Awards

We were thrilled to receive a few industry awards this year:

1. 2023’s Best Places to Work from Built In: The annual awards program includes companies of all sizes, from startups to those in the enterprise, and honors remote-first employers and companies in large tech markets across the U.S.
2. 2023 Technology Innovation Leadership Award from Analyst Firm Frost & Sullivan: Aquant was recognized as a North American service intelligence industry leader. Frost & Sullivan believes Aquant’s Service Co-Pilot platform could fundamentally restructure how customer service teams function across industries.
3. CB Insights’ Most Promising Vendor Ranking, Agent Support Tools: This recognition underscores Aquant’s commitment to delivering top-tier solutions to customers and its exceptional standing against its peers. CB Insights’ Challenger designation is an accolade reserved for companies positioned as the most promising within their respective market.
4. Service Council’s Award for Best Overall Solution 2023: The Service Council recognizes Aquant’s ability to deliver comprehensive and impactful services, setting a benchmark in the industry for efficiency and customer satisfaction. This accolade further cements Aquant’s status as a leader in service management solutions.

Thank you for being a part of the Aquant community!

We want to express our heartfelt gratitude to all our employees, customers, partners, and the broader community for their unwavering support throughout the past year. We are truly grateful for the opportunity to share our expertise and knowledge with you.

As we move forward, we are excited to continue investing in innovation and providing ongoing support to our employees, customers, and community. 
 

New Year, New Goals

Want a head start on meeting—and exceeding—your 2024 KPIs? Participate in Aquant’s 7 Day Challenge to discover how your KPIs stack up to your peers—at no cost. Our analysts will process and analyze your data via Aquant’s robust AI engine. We’ll show you the results of your org’s key metrics (including First Time Fix Rate), how you can be more efficient, and where you can save more money.

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.

Aquant Earns Most Promising Vendor Ranking in CB Insights’ Analysis of Agent Support Tools

Aquant, a generative AI vendor purpose-built for service, has been recognized as a “Challenger” by CB Insights, a renowned market intelligence firm, in its ESP (Execution, Strength, and Positioning) matrix for Agent Support Tools. This recognition underscores Aquant’s commitment to delivering top-tier solutions to customers, as well as its exceptional standing against its peers. 

CB Insights’ Challenger designation is an accolade reserved for companies that are positioned as the most promising within their respective market. The Agent Support Tools market provides solutions to common challenges faced by contact centers, such as high employee attrition, low customer satisfaction, and poor agent engagement, and aims to solve these challenges, improve customer experience, and drive revenue growth through the use of AI.

Organizations such as Hologic, Ricoh, and Canon currently use Aquant’s offering, Service Co-Pilot, to equip not only agents but everyone within the service lifecycle with the knowledge they need to resolve complex service problems effectively. This, in turn, leads to reduced truck rolls, increased remote resolution, improved machine uptime, enhanced customer experience, and reduced service costs.

“Aquant’s AI Co-pilot is informed by a unique blend of service performance data and expert knowledge, which empowers service teams to deliver better service outcomes,” said Shahar Chen, Aquant’s CEO and Co-Founder. “Our offering, Service Co-Pilot, harnesses an organization’s intellectual property in a way that enables every stakeholder within the service journey to get fast answers that improve their decision-making and problem-solving in every scenario.”

The ESP matrix is a comprehensive assessment that leverages data and analyst insights to identify and rank leading companies. Companies undergo a meticulous selection process with several stages of analysis, where they are chosen for inclusion in the matrix based on their overall quality and strengths related to their market presence and execution. Each company is assessed using the same criteria to produce an unbiased and visual representation of the market.

Furthermore, Aquant has garnered recognition in two Expert Collections, magnifying its presence in key technology spaces: Artificial Intelligence and Sales & Customer Service. These Expert Collections are curated by analysts and serve as a definitive guide to the companies at the forefront of these critical technology domains.

For more information about Aquant and its offerings, please visit: https://www.aquant.ai/