Category Archive: Blog Posts
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.
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.
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:
- 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.
- 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!
- 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
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:
- 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.
- AI facilitates intelligent troubleshooting, swiftly diagnosing and resolving breakdowns—and minimizing operational disruptions.
- 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.
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:
- 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%.
- 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.
- 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.
- 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.
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.
We were thrilled to receive a few industry awards this year:
- 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.
- 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.
- 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.
- 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.
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/
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.
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.
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OpenAI, the company behind ChatGPT, is considering plans to launch an AI App Store, paving the way for a new era of accessibility and innovation in the AI software industry, according to the tech media outlet, The Information.
This initiative has the potential to democratize access to cutting-edge AI applications, empowering developers and users alike with a diverse range of AI-powered software solutions. By providing a platform for developers to monetize their creations, an AI App Store would catalyze a vibrant ecosystem of AI-driven applications, revolutionizing how businesses and individuals leverage the power of AI.
According to the sources, the proposed marketplace will offer a wide array of fine-tuned AI software, spanning various domains and catering to diverse needs. Users will have access to an extensive library of cutting-edge AI tools, enabling them to drive innovation in their respective fields or their specific use cases.
How will an AI marketplace impact AI developers and buyers?
At Aquant, we look forward to the opportunity to contribute and believe an AI marketplace holds immense promise for AI developers and their prospects/customers. Creating new applications by training a GPT API on industry-specific data is how Aquant is offering service-focused AI to its customers. And it’s how other enterprises could do the same. An app store for AI would enable vendors to easily reach a global audience and generate revenue by offering their specialized AI software to millions of potential users. This initiative empowers developers to push the boundaries of AI innovation to address specific industry challenges or cater to niche markets.
The introduction of a public AI marketplace not only provides a secure environment for buyers but also instills a sense of trust in the reliability and efficacy of the software they adopt. The accessibility and democratization of AI software offered through the App Store will profoundly impact diverse industries. Whether in service, healthcare, finance, manufacturing, or entertainment, businesses will have the convenience of browsing through a curated selection of vetted AI applications. This will enable them to streamline operations, make informed decisions, and elevate the overall buyer journey.
While the launch of the OpenAI App Store is said to be in the planning phase and has not been confirmed by the company yet, the industry eagerly awaits this groundbreaking development. The vision of an inclusive marketplace for AI software has the potential to revolutionize the AI landscape, democratizing access to advanced AI applications across industries.