Creator of Generative AI tool “Service Co-Pilot” identified as best in class in the North American service intelligence industry
Aquant, an enterprise AI software vendor providing customer service solutions for manufacturers and servicers of complex equipment, has been awarded the “2023 Technology Innovation Leadership Award” by Frost & Sullivan, a US-based analyst firm. Aquant was recognized as a leader in the North American service intelligence industry. Frost & Sullivan believes that Aquant’s Service Co-Pilot platform could fundamentally restructure how customer service teams function across industries. Organizations such as Hologic, Ricoh, and Canon currently use Aquant to improve machine uptime, enhance customer experience, and reduce service costs.
“This accolade reflects the commitment of our team in harnessing the power of data and AI to tackle the industry’s most significant challenges.” said Aquant CEO and Co-Founder Shahar Chen. “I’m incredibly thankful and proud of our forward-thinking team, whose hard work turns our vision into reality day in and day out. I also extend my heartfelt gratitude to our innovative customers for placing their trust in us to support them in reaching their goals.”
Aquant and its product offerings were scrutinized under a rigorous analytical process, with Frost & Sullivan determining their prowess in the service intelligence sector. Aquant was evaluated against its technology performance and business impact.
Frost & Sullivan specifically recognized Aquant’s Generative AI service tool, Service Co-Pilot, for its ability to democratize service knowledge, improve machine uptime, and enhance the overall customer experience. Beyond product offerings, Aquant’s Shift Left ideology, praised by Frost & Sullivan, represents a pivotal approach in revolutionizing the customer service landscape. The idea is to shift the resolution away from field-based engagements and customer escalations and more towards remote solutions and self-service.
“One of Aquant’s key focus areas is de-escalating the entire resolution process by allowing users to solve more issues themselves, enabling customer service executives to solve more problems remotely.”
— Hiten Shah, Senior Consultant at Frost & Sullivan.
Aquant distinguishes itself through its capacity to gather insights from a customer’s subject matter experts. With numerous frontline employees lacking sufficient product knowledge, Aquant fills the gap by collecting valuable expertise that is often undocumented in manuals and guides. This engine then transforms this specialized knowledge into actionable data, resulting in substantially enhanced answer accuracy.
You can explore Frost & Sullivan’s detailed dossier of Aquant’s service intelligence capabilities here.
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.Andthe 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
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-Pilotsuite 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.
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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.
