Author Archives: Janice Camacho

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

    Leave a Comment

    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 journey that Sidney and Joe embarked on is not just a story about adopting new technologies. It also represents the strategic, data-driven mindset required to succeed in today’s service industry. By following their six-step framework, organizations can navigate the complexities of modern service delivery, achieving efficiency and cost reduction and building lasting relationships with customers.

    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.

     

    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.

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

    Leave a Comment

    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!

     

    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.

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

    Leave a Comment

    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.

    Take the 7 Day Challenge

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

    Leave a Comment

    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.

  5. 2023 in Review: Aquant’s Year of Breakthrough Innovation 

    Leave a Comment

    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 Sucess, 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.

  6. From AI to Z: 22 Generative AI Terms Service Leaders Need to Know in 2024

    Leave a Comment

    Generative AI is one of the most groundbreaking technologies in recent years, fueling innovations in every field from manufacturing to service. It’s also one of the fastest-evolving technologies. Here’s a primer on the AI concepts you need to understand in the context of service and the customers you support. 

    Bookmark this generative AI glossary — and have it ready for your next meeting. We won’t tell anyone. 

    General

    • Bias: Unfair or skewed judgments, decisions, or outcomes in machine learning models or algorithms. Bias can arise from biased training data or the design of the algorithm itself and can result in discriminatory or unfair behavior, particularly in areas like automated decision-making, lending, and hiring.
    • Chatbot: A computer program or AI application that simulates human conversation through text or speech. Chatbots can be used for various purposes, including customer support, information retrieval, and virtual assistants. They use natural language processing techniques to understand and respond to user queries or commands.
    • ExpertSync: Aquant proprietary process by which we datafy the knowledge of your seasoned experts into the diagnostic AI solutions. ExpertSync has proven to elevate the quality of problem-solving in service through the unique combination of Aquant AI drawing from a specifically curated mix of historical data, unstructured technician notes, and validated expert solutions.
    • Generative AI: A subset of artificial intelligence that focuses on creating or generating new content, such as text, images, music, or other media. Generative AI models, like GPT-3 and GANs (Generative Adversarial Networks), have the ability to produce creative and realistic content based on patterns and data they’ve been trained on.
    • Generative Pre-Trained Transformer (GPT): A family of large-scale language models developed by OpenAI. GPT models are pre-trained on vast amounts of text data and can be fine-tuned for various natural language understanding and generation tasks. GPT-3, for example, is known for its ability to generate human-like text and perform a wide range of NLP tasks.
    • Horizontal AI: Artificial intelligence technologies and applications with broad and general functionality across various industries and use cases. These AI systems are not industry-specific and can be applied horizontally to address many problems. For instance, NLP tools used in service, finance, e-commerce, and other sectors are considered horizontal AI.
    • Large Language Model (LLM): An artificial intelligence model trained on massive datasets to understand and generate human language. These models, such as GPT-3 and GPT-4, are known for developing coherent and contextually relevant text, making them useful in various natural language processing tasks.
    • Natural Language Processing (NLP): A field of artificial intelligence focusing on the interaction between computers and human language. NLP technology enables computers to understand, interpret, and generate human language, making it valuable for tasks like language translation, sentiment analysis, and chatbots.
    • Service Language Processing (SLP): Aquant’s unique NLP engine, which is designed to read service language and identify observations/symptoms and solutions described in free text. 
    • Vectors: Numerical representations of words in a high-dimensional vector space. These representations capture semantic relationships between words, allowing algorithms to understand the meaning and context of words based on their proximity in the vector space. Word vectors are fundamental in natural language processing tasks like word similarity, sentiment analysis, and machine translation. They are typically generated using techniques like Word2Vec or GloVe.
    • Vertical AI: Artificial intelligence systems and applications for specific industries or niches. These AI systems are highly specialized and optimized to address particular tasks, challenges, or workflows within a specific vertical market. For example, vertical AI solutions may include healthcare AI for medical diagnosis or manufacturing AI for optimizing production processes.

    Data & Accuracy

    • Data Cleansing: Identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This includes removing duplicate records, handling missing values, and rectifying formatting issues to improve data quality. It is also known as data cleaning or data scrubbing.
    • Data Ingestion: Collecting, importing, and preparing data for machine learning or data analysis applications. It involves acquiring data from various sources, transforming it into a suitable format, and storing it in a data repository for further analysis.
    • Data Integrity: The accuracy, consistency, and reliability of data throughout its lifecycle. It ensures that data is not corrupted, altered, or compromised, maintaining its quality and trustworthiness for analysis and decision-making.
    • Data Validation: A set of checks and procedures to ensure data is accurate, consistent, and compliant with predefined criteria or standards. It involves verifying data quality through various validation techniques, such as cross-validation, to assess model performance or data reliability.
    • Hallucinations: A situation where a model generates incorrect or fabricated information in its output. It occurs when the model produces text not based on factual or accurate data, leading to misleading or erroneous results.
    • Parameters: Internal variables or weights a machine learning model uses to make predictions or decisions based on input data. These parameters are learned from training data and are essential for the model’s ability to generalize and perform well on new, unseen data.
    • Prompt Engineering: Designing and crafting specific instructions or queries (prompts) to elicit desired responses from natural language processing (NLP) models. It involves formulating prompts that guide the model’s output toward the intended information or behavior.
    • Reinforcement Learning: A machine learning paradigm in which an agent learns to make decisions or act in an environment to maximize a cumulative reward. The agent learns through trial and error, adjusting its actions based on feedback from the environment. This approach is commonly used in applications like game playing, robotics, and autonomous systems.

    Ethics

    • Guardrails: A set of predefined rules, constraints, or ethical guidelines that are put in place to guide the development, deployment, and usage of artificial intelligence systems. These guardrails serve as boundaries or limits to ensure that AI technologies are used responsibly and ethically. They help prevent AI systems from engaging in harmful or undesirable behaviors and promote transparency, fairness, and accountability in AI applications. Guardrails may include guidelines for addressing bias and discrimination in AI models, ensuring data privacy and security, and complying with legal and regulatory requirements. They are an essential component of AI governance and help organizations and developers navigate the ethical and societal implications of AI technology.
    • Privacy: Protecting individuals’ personal information and data when AI systems are involved. It ensures that AI applications and data processing activities adhere to privacy laws and regulations. Measures may include data anonymization, consent management, and access controls to safeguard sensitive information.
    • Security: Protecting AI systems and their data from unauthorized access, breaches, and cyber threats. It encompasses measures such as encryption, access controls, secure coding practices, and regular security audits to mitigate risks associated with AI deployment. Security is crucial to prevent AI systems from being exploited or compromised.

    Aquant’s Service Co-Pilot offers a distinct edge over other generative AI tools because of its deep understanding of service and the quality of data it collects. Request a demo to learn more about how these terms and tools fit into your AI journey.

  7. How Every Stakeholder in Your Service Cycle Benefits From Generative AI

    Leave a Comment

    As AI technology becomes more widely available, tech-savvy service leaders are utilizing AI as part of their day-to-day processes. It can automate tasks such as reporting and help with more complex issues such as making proactive recommendations to help avert service disasters. 

    But can AI benefit everyone else in the service cycle? 

    The answer is simple: yes, every stakeholder in your service ecosystem can reap the benefits of Generative AI! 

    And that’s no exaggeration—primarily since comprehensive Generative AI tools for service, such as Service Co-Pilot, aim to provide a well-thought-out user experience for every touchpoint of the cycle. 

    Every stakeholder’s user experience can be simple, efficient, and positive, from executives to technicians to customers. Read on to learn how.

    Service Stakeholders and Their Use Cases

    AI that understands the context of each issue and your industry can be helpful at every stage of the service cycle, no matter the user. Here’s how:

    • VPs and executives: Proactively generate analytics that help make better business decisions. These decisions can guide strategic operations, mitigate escalations, and grow the organization.
    • Day-to-day managers and directors: Get daily updates that provide performance metrics and resource allocation guidance. See data that pinpoints team strengths and weaknesses for personalized coaching and feedback. Assign work based on agent and technician strengths and experience.
    • Field workers and technicians: Instantly access resources to troubleshoot and resolve issues effectively, predict failures, proactively address maintenance issues, and reduce downtime.
    • Call center agents: Diagnose issues correctly with personalized guidance for each machine or client. Get automated prompts to ask questions in multiple ways, resulting in instant responses that can guide a customer to address issues remotely. Provide comprehensive intelligence for field teams before arrival.
    • Customers: Experience personalized recommendations and solutions based on each specific customer and their unique needs. Automate self-service interactions that do not require agent help.

    Understanding Generative AI for Service

    With the number of choices on the market, the pressure’s on service organizations to understand the emerging landscape and pick the right Generative AI solution for their teams and customers. 

    Read our latest eBook, The 2023 Guide to Generative AI for Service, for an in-depth look at how to leverage this new technology for each of your stakeholders and improve service experiences across the board. 

  8. The Future of AI is Personalization — and Service Organizations Have Everything to Gain

    Leave a Comment

    AI can improve productivity and customer experience through predictive tools and automation. But as leaders plan to increase AI use across every part of the organization, it’s essential to understand the different types of AI and how to choose the right tools to help personalize the service industry. 

    According to Twilio’s State of Personalization 2023 report, 69% of businesses are increasing their investment in personalization. And the service industry is no exception, especially since the end goal is to deliver seamless service experiences to customers. This means interactions that are quick, efficient, accurate, and contain the least amount of touchpoints possible. 

    Using AI developed for service, organizations can make good on that goal and deliver memorable experiences that their customers expect and prefer. Read on to learn what AI-driven personalization is, what’s next for personalization in vertical AI solutions like Aquant’s Service Co-Pilot—and how service organizations like yours can get the most value out of this ever-evolving tool.

    What is AI-driven personalization?

    Simply put, AI-powered personalization involves collecting and analyzing customer data within generative AI models. In the case of service organizations, these data types can range from free text to machine documentation to intel from subject matter experts (and beyond!). When such data is plugged into a generative AI platform and analyzed, the outputs yield insights, patterns, styles, and correlations that provide tailored experiences for current—and future—customers. 

    Generative AI can be split into two categories: horizontal and vertical. Horizontal AI tools, like ChatGPT and Google Bard, boast generalized capabilities. But vertical solutions, like Service Co-Pilot, address industry-specific challenges. 

    Whether you’re looking to meet rising CX standards, fill labor gaps, or upskill workers quickly, vertical solutions for service help diagnose and resolve issues faster and more accurately than ever. They use data to offer accurate self-service options, allocate resources, and minimize downtime through proactive maintenance. And once you add the personalized element, you get the efficiency and experience that today’s customers expect.

    The benefits of personalizing your service delivery

    Meaningful customer experiences are vital to a service organization’s success. Customers want to feel valued and cared for—personalization is integral to providing that. Some benefits of personalizing your service delivery include: 

    • Enhancing CX and improving customer satisfaction: Personalized interactions indicate that the business is attentive to customers’ preferences, history, and habits. McKinsey’s Next in Personalization report revealed that 71% of customers expect personalization and 76% get frustrated when they don’t experience it. 
    • Increased customer loyalty and retention: Customers that feel understood are more likely to purchase again. These positive, repeat interactions foster trust and reduce churn (and could result in referrals!).
    • More upselling and cross-selling opportunities: Service orgs that understand customer purchasing history/behavior can recommend other offerings that align with buyer interests, which increases potential revenue. 
    • Gaining valuable customer insights: Customer data holds valuable insights about preferences, behavior patterns, and trends. These findings can help frame strategic decision-making, product development, and marketing efforts—all of which help service orgs stay ahead of the competition. 

    How AI for service is becoming more personalized

    We can expect service AI tools to embrace personalization in the following ways:

    • Improving data collection by adopting multi-modal fusion and hybrid approaches: Generative AI is about structure and context. You can use generative AI to integrate data from multiple sources within your service org, such as miscellaneous technician notes, product documentation, subject matter experts, and existing service data. This creates a solid foundation that you can use to establish KPIs, develop data models, and understand your customers and their habits. Additionally, service AI combines your in-house data with aggregate knowledge, widening its scope and providing better outputs.
    • Providing context: Besides improving how it collects and analyzes user data, service organizations can use the outputs to gain context into customers’ preferences, behaviors, patterns, and more. Additionally, you can use AI personalization with your workforce. For example, let’s say two technicians have the same amount of experience in the field. Technician A is better at solving mechanical issues while Technician B is great at fixing electrical problems. Personalized generative AI can help you assign major tasks that suit a technician’s core skill set, or you could use it to upskill your technicians. 
    • Integrating user feedback to adapt and learn: True AI and machine learning cannot be hardcoded by experts or from manual ingestion—if that were the case, troubleshooting guides would be enough to solve all service issues. Great generative AI for service incorporates user feedback and improves personalization with usage—allowing it to learn, adapt, and make more accurate predictions based on feedback. In short, the more you use it, the better it becomes! The models can refine their outputs by analyzing user feedback, interactions, and ratings. This allows them to provide more personalized, accurate, and relevant recommendations for any service scenario. 
    • Preserving privacy: As privacy concerns continue growing, generative AI will incorporate privacy-preserving techniques to ensure user data is protected. This includes methods such as federated learning, where models are trained locally on user devices without exposing sensitive data to centralized servers. Privacy-enhancing technologies will allow generative AI to personalize recommendations while respecting user privacy. Ultimately, service problems can still be solved without divulging sensitive details, so continuously training your AI to recognize recurring issues and the best fixes will drive the desired outcomes.

    Getting the most value from your generative AI tools

    AI is becoming a must-have for businesses looking to remain competitive: nearly 50% of companies say AI is their top priority for tech spending over the next year

    But while many are eager to invest in AI, it is essential to note that service orgs still face a common enemy: measurement. Typically, they cannot accurately measure their service landscapes and get an accurate read of their KPIs. This results in selecting an AI solution that they assume can help them and hoping their data fits into it. 

    There’s a better way to make the right AI choice the first time: reverse engineer the process.

    First, determine the business outcomes that you want to achieve. For example, perhaps you want to improve KPIs like First Time Fix (FTF) Rate or Resolution Time, provide more remote fixes, reduce unnecessary dispatches, or quickly upskill incoming team members. Whatever the case, this step narrows your goals and helps eliminate AI tools that can’t solve a particular problem.

    Next, think about what you need to get the solution running. For instance, consider the types of data you already have versus the kinds of data you will need to secure, as well as any stakeholders that need to be involved. 

    Once you understand your needs and requirements, you can accurately envision where generative AI fits into your service organization. Aquant’s Service Co-Pilot suite uses generative AI to help service orgs solve common and complex service problems through personalization and continuous learning. Service Co-Pilot synthesizes product documentation, expert knowledge, service data, and human intelligence to provide the best solution for every service scenario—thereby shortening service lifecycles, improving CX, and increasing profit margins. Service Co-Pilot’s capabilities extend to all user types: service leaders seeking comprehensive reporting and analytics, diagnostic options for customers using self-service, call center agents providing phone support, and technicians attending to fixes in the field.

    It’s worth improving your delivery strategy at every stage of the service cycle — we’ll prove it to you. 

    Skip the guesswork. Sign up for our 7 Day Challenge, and we’ll analyze your org’s data, calculate your potential savings, and show you where you can be more efficient.

  9. How to Choose the Right Generative AI Solution for Your Service Organization

    Leave a Comment

    An explosion of headlines has recently emerged, suggesting that ChatGPT and its competitors, like Microsoft Bing and Google Bard, will simplify work by taking on low-complexity tasks—ultimately freeing up the workforce to concentrate on more complex demands. 

    But the service industry is not like other businesses. Using generative AI to obtain “good-enough” answers to service questions won’t cut it. Complex machinery—as well as the need to maximize uptime for end users—means that anything less than the absolute best answer is bad for business, bad for the customer, and bad for the bottom line. In extreme cases, anything but the most specific answer can damage multi-million dollar machines or cause human injury. 

    Here’s what differentiates services from other industries and why these differences require service-centric generative AI to be successful. 

     

    What is Generative AI?

    Generative AI is a way to describe algorithms that use existing data or information to create new content, including text, audio, software code, or images. ChatGPT and rival platforms are created by using generative AI. These generative AI models power software applications like chatbots—and are designed to recognize, understand, and produce coherent answers based on user prompts. 

    Generative AI understands human language and is designed to provide a response that mimics a human conversation. This means you can ask one question in several ways, and the algorithm will understand your core intent, regardless of word choice. The responses can be delivered in sentences that answer questions, in bullet points, or responses may include links to additional collateral material (such as user manuals or links to FAQs).

     

    The Benefits of Generative AI in Field Service 

    Generative AI developed explicitly for service can be quickly deployed to fast-track ROI. Here’s what to expect: 

    • Faster responses to any service challenge
    • Quicker upskilling of the workforce
    • Enabling customer self-service
    • Shifting to predictive maintenance
    • Reduced costs

     

    Why the Service Industry Requires Service-Centric AI

    When deciding to adopt AI, ensuring that its data sources align with your service organization’s specific needs is critical. One drawback to using widely available, generative AI solutions, such as ChatGPT, is that the responses they generate are not tailored to your specific business or use case. That’s because they don’t understand service-specific scenarios.

    Here’s what makes service unique from different types of businesses.

    1. Service has a higher bar for getting things right.

    Improving service outcomes is about finding the best solution to a problem, not the most common. In service, equipment issues can pose mission-critical work stoppages or, in the case of medical device equipment, can pose risks to patients.

    Service organizations have zero tolerance for AI hallucinations—the phenomenon when an AI model produces results that are not in line with the question or presents factually incorrect information with confidence. AI hallucinations might be okay for a casual everyday user of ChatGPT, but finding the right solution to the problem is critical in service.

    Good answers are not the same as the best answers. Service leaders are constantly striving for the best solution to challenges as a way to innovate and drive customer satisfaction and loyalty.

    2. The best solutions to problems often don’t exist in your service data.

    The best solution to an issue is often locked in the heads of your service experts; tapping into that knowledge in a standardized way is vital. Internal 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.

    You need the right technology to unlock this data and make it actionable. “Off-the-shelf” AI will scan existing data or the internet for common answers. But service-centric AI taps into expert knowledge, so resolution information is accurate based on every scenario. 

    3. Before identifying the right solution, you must ask the right questions and determine the problem.

    “A scientist is not just a clever person who knows all the answers, but one who knows the right questions to ask.” – Enrico Fermi

    If service were simply about providing the correct answer to a question, standard technologies would be sufficient in delivering an excellent service experience. Machines are more complex today, and repairing or maintaining them requires more sophisticated problem-solving. That starts with knowing which questions to ask and when. Here’s why:

    Complex machines can present many different symptoms for a single root issue. Putting a patch on a secondary issue (the symptom) may only solve the problem temporarily. Diagnosing and fixing the correct issue on the first visit requires that field engineers ask the right questions before they can reach the correct answer. 

    Standard AI cannot guide a technician through the right step-by-step process needed to resolve the issues. 

    4. Service requires multi-layer coordination between customers, support agents, field technicians, assets, and parts. 

    These factors make prioritizing and providing the best service for each situation challenging. Some repair or maintenance jobs may require specially-trained technicians, while others may need special-order parts. Some issues, like medical devices or other mission-critical equipment, may need immediate attention, while other jobs can move lower down the list.  

    Coordinating people, parts, and customer needs, especially for service organizations that deal with a large volume of customer interactions, requires sophisticated technology designed for these types of service situations. Generative AI built for service organizations can break down data silos, standardize holistic metrics, and build risk and performance models that drive actionable and valuable decisions.

    5. Service requires both consistency and sustainability. 

    As machines become more complex and new machines are rolled out, existing data becomes less relevant for maintenance and repair. Today’s organizations can struggle with upskilling employees of any level when there is too much data (i.e. IoT) or too little data (like with new product launches). But how can a service org strike the right balance?

    Your tenured technicians know how to service existing machines, given their years of experience and ample opportunities to learn through trial and error. New hires don’t have time to learn how to service new or old machines effectively. Therefore, it’s all about datafying expert knowledge and preparing workers of all skill levels to make the best service decisions for any scenario — even without specific data. 

    6. Service happens anytime, anywhere, and all at once. 

    With the proliferation of service channels (such as self-service, remote, or field events), service events can occur in various experiences. 

    Service is now a connected journey of multiple individuals of varying backgrounds and skill sets. This requires meeting the customer and all your stakeholders where they are—not the other way around.

    With this in mind, the ideal service AI must be scalable, easy to use, and built for various users, like field technicians, executives, customer service representatives, and customers. To accommodate the breadth of user types, it’s best to utilize service AI designed for all scenarios, no matter the environment. 

     

    The future of service is here. Are you prepared to meet it?

    Given the specific needs of organizations, generalized AI is not good enough for service. Static service manuals are difficult to leverage when searching for answers to particular problems and are hard to keep up to date. Plus, much of today’s off-the-shelf AI is simply incorrect. A wrong service answer is unacceptable—it can have costly effects or worse.

    Service organizations deserve solutions that can be trained to understand their business and the service industry. Understandably, service leaders are skeptical about leveraging AI. But the right tools will enhance the entire service experience, from workers to customers and beyond.

     

    How Aquant Service Co-Pilot is revolutionizing field service operations

    Aquant’s Service Co-Pilot offers a distinct edge over other generative AI tools because of its deep understanding of service and the quality of data it collects. Service Co-Pilot is built into all our products and tools. It helps anyone—including customer service agents, technicians in the field, service leaders, and customers—get answers to their questions quickly and in a conversational format they can understand. 

    And regarding data quality, Aquant’s platform processes information in a way that derives meaningful insights tailored to your company. It goes beyond pulling from open data sources that can be found across the internet and instead collects companies’ historical data, structured and unstructured, and elevates this with subject matter expertise (specialized knowledge from the service company’s top-performing employees). By combining data with human intelligence, Service Co-Pilot can work seamlessly within your natural service workflow, guiding the process, much like having a skilled technician. 

    Learn more about Aquant Service Co-Pilot.