Author Archives: Micaela Mcpadden

  1. Aquant CEO Explains How AI is Transforming Service Workflows Across Industries 

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

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

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

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

    Bridging the Talent Gap with AI

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

    Considering AI a Co-Pilot, Not a Replacement

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

    Democratizing Expertise through AI

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

    AI Cuts Through the Noise

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

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

    AI’s Direct Impact on Service Workflows

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

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

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

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

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

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    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/ 

  3. Aquant Wins “2023 Technology Innovation Leadership Award” From Analyst Firm Frost & Sullivan

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    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,” said 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.

  4. OpenAI Contemplates Launching an AI App Store: What Does This Mean for Developers and Tech Buyers?

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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.

  5. Safer and More Accurate Generative AI Begins with Clean Data Processes

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    Building a fully self-governed AI system with guardrails, without outsourcing is a complex task – but it is possible. It starts with fostering a clean data mentality. 

    Before delving into the process of building a fully self-governed AI system equipped with guardrails, it is essential to establish a clear understanding of what guardrails entail. Essentially, they are a set of measures, guidelines, and constraints implemented to ensure that AI systems operate safely, ethically, and in alignment with desired outcomes. These guardrails serve as checks and balances to mitigate risks, prevent unintended consequences, and uphold ethical standards in AI development and deployment. Some examples of AI guardrails include bias mitigation, privacy protection, and interpretability or explainability.

    It’s common for enterprises building AI models in-house to seek external guidance or outsource certain aspects of AI guardrails to ensure their models are unbiased, compliant, and accurate. Implementing AI guardrails requires expertise in various domains, including ethics, fairness, legal compliance, and data governance. Many organizations or vendors may not have all the necessary expertise in-house, particularly when it comes to specialized areas like bias mitigation or privacy protection.

    At Aquant, we understand the critical role of high-quality data in developing reliable AI models. By establishing strong relationships with data sources and implementing transparent data collection practices, we can foster a collaborative environment that ensures accurate and unbiased AI outputs. This clean data approach is what enables Aquant Service Co-Pilot to operate responsibly, and should be the foundation of enterprise AI deployments, including Generative AI.

    For organizations aiming to demonstrate a commitment to ethical data practices, it’s critical to train your data to only pull from sources that have consented to it. This approach involves implementing rigorous data collection practices and obtaining explicit consent. This process typically includes the following steps: 

    1. Clearly communicate the purpose and scope of data collection, providing transparency about how the data will be used. 
    2. Seek informed consent from individuals or organizations, ensuring they understand the implications and are willing to share their data for specified purposes. 
    3. Establish robust data management protocols to ensure that data is handled securely and in compliance with privacy regulations. 
    4. Regularly review and update consent agreements, allowing individuals to withdraw their consent if desired. 

    While robust data governance practices must be in place to maintain data privacy, and security, it’s just as critical to prioritize ethical considerations like transparency, explainability, and fairness to build trust in AI outputs. The most critical aspect of this is to undergo thorough testing of data integrity. By rigorously addressing data integrity through QA processes, you’re ensuring that the models are built on accurate and consistent data, leading to more reliable predictions and insights for end-users.

    Lastly, collaboration among stakeholders is vital for effectively integrating generative AI tools into workflows. By collaborating, stakeholders can collectively define clear guidelines and develop best practices that address legal, ethical, and safety considerations. This collaborative approach fosters a shared understanding, minimizes risks, and enables the responsible and compliant deployment of generative AI tools within workflows, ensuring the protection of user interests and alignment with regulatory frameworks.

    By emphasizing clean data practices, ethical considerations, and stakeholder collaboration, organizations can develop and deploy AI systems that are consistently reliable, safe, and generate accurate and actionable outputs – without the need to rely on external guardrails.

  6. Aquant’s Service Co-Pilot Delivers Generative AI Purpose-Built for Service

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    Aquant, an enterprise AI software vendor, has launched Service Co-Pilot, the latest generation of its customer experience offering. This new release, enhanced by generative AI, is built for servicers and manufacturers of complex equipment and machines. The offering aims to identify the best solution to any service-related issue (e.g. how to fix an MRI machine or how to identify which customers are at risk), improve customer experiences by resolving cases faster and more efficiently, and minimize costs. 

    Service Co-Pilot uses a ChatGPT plugin to generate recommendations sourced from historical service data and data synthesized from the knowledge of an organization’s subject matter experts. This innovative approach allows the engine to predict the best solutions to even the most complex customer service issues, empowering every stakeholder (e.g., the end customer, contact center agent, field technician, or service leader) to diagnose and resolve problems like an expert. The omnichannel platform can be leveraged, via desktop, mobile app, or chat, at every touchpoint across the customer experience.

    “Service organizations are challenged with hiring and retaining skilled service professionals while managing rising customer expectations and sky-high costs. One in three service calls result in a truck roll, which can cost upwards of $2,500 – this is no longer sustainable,” says Assaf Melochna, President and Co-Founder of Aquant. “Generative AI can reduce these costs and bridge the customer experience gap. To help organizations keep pace with these changes, Service Co-Pilot is enabling users to access critical information without escalating an issue. This helps alleviate the strain on the service workforce so organizations can exceed customer expectations.”

    Service Co-Pilot’s unique approach to AI

    Service Co-Pilot combines Aquant’s proprietary technology with open foundational models. With ChatGPT alone, customer service professionals will have difficulty relying on answers due to the risk of hallucinations and challenges with more advanced troubleshooting scenarios. However, Aquant’s AI technology goes further. First, it mines structured and unstructured service data, including work orders, machine logs, service manuals, and free text notes using a service domain-specific natural language processing model. Then, it improves AI performance by datafying expert knowledge – the process of converting the knowledge stored in the minds of your experts into synthetic data. 

    This approach trains Service Co-Pilot over time, making it a best practice machine that can adapt and adjust based on real-world feedback, rather than relying on hard-coded workflows that may not be optimized for all scenarios. Aquant’s internal data shows that incorporating human expertise is critical: 30% of solutions are not found in historical service data, but are found in the data provided by experts. By tapping into the knowledge of subject matter experts, Service Co-Pilot achieves more personalized and reliable results.

    Service Co-Pilot’s New Features: Search and Self-Service

    Service Co-Pilot’s generative AI search feature allows stakeholders to ask a chatbot for the right answers to any service question, at any time. This feature helps users solve specific customer issues and provides guidance along with useful links to the exact point in manuals where the answer exists. If more than one answer exists, the user will be asked a series of questions generated by AI to triage the issue and narrow down the most viable and cost-effective solutions.

    In addition to the search feature, Service Co-Pilot includes new self-service capabilities. So now, end customers – in addition to technicians and contact center agents – can make intelligent, informed decisions using Self-Service Triage or Intelligent Triage, Service Co-Pilot’s troubleshooting and diagnostic tools. 

    Additionally, the platform includes Service Insights, an analytics dashboard built for service leaders to access clear, detailed, and holistic recommendations to improve workforce performance, customer risk management, and product quality trends.

    “Leading organizations are increasingly shifting toward low-touch, more efficient customer experiences. In service, we call this approach shifting left,” said Shahar Chen, CEO and Co-Founder of Aquant. “Aquant’s Service Co-Pilot helps organizations reduce costs and the time it takes to solve cases. This technology is no longer optional for organizations that want to survive long-term.”

     

  7. The Rise of Third-Party Field Service Teams: How ABB Is Adapting to a Changing Landscape

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    Standards for customer experience are higher than ever. On each episode of Aquant’s Service Intel Podcast, we sit down with leaders raising the bar and creating incredible experiences for their customers. These top names in the industry have all agreed to share what they’ve learned about navigating today’s service landscape so our listeners can not only get inspired, but put their own bar-raising service plans into action.

    As technology evolves and customers demand more efficient and cost-effective solutions, third-party service providers are increasingly important in delivering high-quality field service.

    In a recent podcast interview with Service Intel, ABB’s Vice President of Service, Salvador Accardo, discussed the rise of subcontractor work and third-party field service teams and how his company adapts to this changing landscape.

    According to Salvador, several factors are driving the growth of third-party service providers. First and foremost, there’s an increasingly finite amount of resources among teams, and at the same time, customers are looking for more flexible and customized solutions. They want services tailored to their specific needs rather than a one-size-fits-all approach.

    In addition, many companies are looking to reduce costs and streamline their operations. By outsourcing their field service needs to third-party providers, they can focus on their core business activities while ensuring that their customers receive high-quality service.

    For ABB, this shift towards third-party service providers presents challenges and opportunities. On the one hand, it means that ABB needs to be more flexible and responsive to customer needs. They must be able to work with a wide range of service providers and integrate their systems and processes with their partners.

    On the other hand, it also presents an opportunity for ABB to expand its reach and increase its customer base. By working with third-party service providers, ABB can offer a broader range of services and solutions and tap into new markets and industries.

    So how is ABB adapting to this changing landscape? Salvador explained that the company is focusing on three key areas:

    Partnership: ABB is partnering with a range of third-party service providers to ensure they can offer their customers the best possible service. This includes developing strong relationships with partners and sharing knowledge and best practices. This requires a balance between internal vs external. There needs to be an understanding and clear communication regarding responsibilities and swim lanes between all workers – both internal and external – because oftentimes, internal folks can view external workers as a threat.

    Technology: ABB invests in technology to help them work more effectively with third-party service providers. This includes developing software and tools that can integrate with their partners’ systems and provide real-time data and insights.

    Training and tracking performance: ABB is providing training and support to its employees and third-party service providers to ensure they have the skills and knowledge needed to deliver high-quality service. Regarding tracking performance, ABB conducts monthly health checks and quarterly quality reviews to ensure they’re meeting both company and customer standards. 

    Overall, Salvador emphasized the importance of understanding the competitive environment, given that most contractors are likely also working for competitors, safeguarding intellectual property presented to third parties, and ensuring the quality of work through regular auditing. He noted that while the rise of third-party service providers presents challenges, it also presents an opportunity for companies like ABB to innovate and improve their services.

    Lastly, Salvador stressed not to be afraid of the third-party model. The field service industry is undergoing a significant shift, and third-party service providers are playing an increasingly important role. By partnering with these providers, investing in technology, and providing training and support, companies can adapt to this changing landscape, continue delivering high-quality service to their customers, and maintain a competitive advantage.

    Listen to the full podcast here!

  8. United Service Technologies: Hiring, Retaining, and Managing a Multigenerational Workforce

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    Standards for customer experience are higher than ever. On each episode of Aquant’s Service Intel Podcast, we sit down with leaders that are raising the bar and creating incredible experiences for their customers. These top names in the industry have all agreed to share what they’ve learned about navigating today’s service landscape so our listeners can not only get inspired, but put their own bar-raising service plans into action.

    In a recent episode, we invited Rodger Smelcer, President of United Service Technologies (UST), a leading provider of maintenance and repair services for commercial kitchens, refrigeration systems, and HVAC systems. Rodger shared his insights, based on personal experience, on hiring, retaining, and managing a multigenerational workforce. 

    Challenges associated with managing a multigenerational workforce have only intensified over the years as skill gaps continue to widen. Each generation has different values, experiences, and attitudes that shape their work ethic and expectations. To effectively manage a multigenerational workforce, companies must understand these differences and create a work environment that accommodates everyone’s needs.

    Hiring and Retaining Multigenerational Workforce

    When it comes to hiring a workforce that spans multiple generations, UST looks for individuals who share the company’s core values, including integrity, customer service, and safety. Rodger emphasized the importance of finding people who are a good fit for the company culture and possess strong soft skills, regardless of their age. UST’s recruitment strategy includes using multiple channels to reach potential candidates, including online job boards, social media, and referrals from existing employees. Roger also noted that the company has found success in hiring both employees with experience in the automotive industry as well as military veterans, who often have valuable skills and experience that are transferable to UST’s industry.

    Retaining employees from different age groups can be a challenge, as each generation has different priorities and expectations. Today, younger generations are less interested in pursuing careers in field service compared to 20 years ago an job jopping is far more common than it used to be. According to Roger, UST has found success in offering standard benefits like a comprehensive benefits package, including health insurance, retirement plans, and paid time off. In addition to that, the company views offering ongoing training and development opportunities as a critical benefit, which can appeal to employees who want to advance their careers.

    Roger emphasized the importance of creating a positive work environment that accommodates everyone’s needs. For example, UST has a “resume-building culture” which means their employees are not only building the skills and knowledge they need to develop professionally but UST is helping them document those skills.

    Managing a Multigenerational Workforce

    Managing a multigenerational workforce requires a flexible approach that can adapt to each generation’s needs. Roger noted that UST’s management team receives training on how to effectively communicate with employees from different age groups. This includes understanding each generation’s communication style, work preferences, and career goals.

    UST has a flat organizational structure as opposed to one with numerous layers of management. However, this doesn’t mean that they don’t recognize and promote employees that deserve it. Roger said, “The best way to get promoted is to promote someone beneath you. Lift them up and that will lift you.” He also highlighted the importance of recognizing and celebrating each employee’s contributions to the company. UST holds regular employee recognition meetings, which can help boost morale and create a sense of community among employees.

    Managing a workforce can be a bumpy road, but any company can do it seamlessly with the right strategies in place. UST’s approach to hiring, retaining, and managing employees from different age groups provide valuable insight into how companies can create a positive work environment that accommodates everyone’s needs. By focusing on core values, offering comprehensive benefits, and creating a flexible work environment, UST has attracted and retained top talent from different generations, which has helped the company stay competitive in its market.

    Check out the full podcast here!

  9. How to Shift Left: Your Six-Step Framework

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    Shorten the service lifecycle, improve customer relationships, and reduce service costs. Here’s your six-step framework to shift your customer experience left. 

    The concept of Shifting Left sounds great but how can you, as a service leader, put this concept into reality? Is there a way to solve a greater percentage of field events remotely? Can more remote interactions be resolved through self-service? Is it possible to prevent problems before they even occur? 

    It all starts by partnering with an AI engine that gives you meaningful and accurate insight into your customers and workforce. We broke it down for you in six easy steps. 

    1. Collect data – Gather service data and understand how data flows through the customer journey. Understand how many self-service interactions, remote/call center interactions, and field interactions your team services on a regular basis. This will help set a baseline for identifying the opportunity areas in your business. 
      • Tip: Normalize data sanitation. Ensure that “clean” data can be easily accessed and analyzed to inform more accurate decision-making. You can do this by setting standards and best practices around your data entry process. 
    2. Understand the service issue – The critical component to shifting left is to get deeper into understanding the types of problems that are being faced in all facets of the service lifecycle.  An example of this would be to understand what percent of service issues reported are related to a pump leaking issue. 
      • Tip: This is where Service Intelligence can help you. Service Intelligence can gather and clean your structured service data (historical data like machine logs, field work orders, customer support tickets, parts, and technician notes) and unstructured data (tribal knowledge from your top-performing workers) and distill data into symptoms and solutions. 
    3. Extract critical insights from your subject matter experts – Unlike AI applications in other areas of the business, service is unique in that just because something occurs the most frequently in the data, that doesn’t mean it’s the best result.  More often than not, the best way to solve a problem is stored in the minds of your best experts. In fact, according to Aquant’s internal data, about 30% of the solutions leveraged by Aquant customers are not identified in historical service data, they are obtained from the data provided by experts. This emphasizes how critical incorporating the human element into your AI engine is. 
      • Tip: Don’t rely on historical data alone. Use the knowledge of your best employees to help identity which issues and solutions can be resolved remotely or via self-service.
    4. Conduct analysis to identify the areas of opportunity – Every business is different, and identifying the area of highest impact whether it’s in the field, remote, or self-service is important to figure out where to focus first.
      • Tip: It’s common for organizations to look at metrics in silos, but looking at all of your metrics side-by-side can give you the best view into your workforce’s performance.
    5. Attack the opportunity by operationalizing AI in your business – Leverage data-driven insights from the analysis to use different tools like AI triage and troubleshooting applications and predictive and performance analytics to begin affecting change in the organization.
      • Tip: Enable teams and optimize user performance by training users and sharing best practices on ways to take full advantage of the tool.  Empowering your team to get the maximum benefit from the tool is the most important part of onboarding any new technology.
    6. Create a clear feedback loop to continuously improve – Ensure that you are able to measure the service interactions moving forward to clearly determine whether a positive impact is being made in the metric you are focusing on (ie. first-time-fix, resolution cost, etc)
      • Tip: Create a detailed plan using this newly uncovered data to start making strategic decisions. Once you have critical information about every aspect of your service organization, you can make data-based decisions that bridge the skills gap, improve customer experiences, and drive growth across your organization.

    Time’s up. Why You Need to Shift Left Now 

    The ability to accurately solve service issues quickly while using fewer resources is critical if organizations are to stay competitive. Only those who are able to navigate a rapidly-evolving service landscape, which includes changes to customer demands, workforce shortages, and economic factors, will survive. 

    Customer expectations are rising significantly. Not only do customers demand a more immediate response, they expect the opportunity to self-serve and fix simple issues themselves without having to escalate an issue. According to the Salesforce State of Service report, 48% of customers have switched brands for better customer service, and 94% say good customer service makes them more likely to make another purchase. Organizations that understand this and take the initiative to respond accordingly will lead the market. 

    The talent shortage and widening skills gap is further exacerbating service issues. More-tenured technicians are retiring faster than their replacements can enter the workforce—so the challenge is to upskill less-experienced workers quickly. On average, bottom performers cost organizations 67% more than top performers. In addition, the variance between top- and bottom-ranking companies has increased. The bottom line? Companies need to pay attention to the skills gap more than ever. If everyone had the knowledge and skills to perform like the top 20% of the workforce, service costs would be reduced by 21%. Teams that look at the bigger picture and approach service cases more strategically can address these issues.

    The engagement crisis is just as bad. Data from the Service Council’s Voice of the Field Service Engineer survey reveals that 65% of Gen Z, 67% of Gen Y, and 54% of Gen X are either not sure they’re going to be or won’t be field engineers for the duration of their career, 40% of which are leaving the role within the next three years. Simply put, most teams won’t have enough resources to resolve a case via dispatch, therefore phone resolutions or self-service may be an organization’s only option. 

    Lastly, economic uncertainty and rising costs are affecting every part of service. Successful teams know that these external factors are out of their control but they’re adjusting their operations and investing in the right technology to combat these issues. The very best organizations are taking it one step further by addressing the pressures to prioritize ESG initiatives. They understand that customers care about corporate social responsibility. Not only are they shifting left to avoid a costly dispatch, but they’re shifting left to reduce carbon emissions resulting from a truck roll. 

    Are you ready to shift left? Request a demo today