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Enterprise knowledge management is a foundational capability for modern Field Service and Technical Support organizations. As service operations scale, internal knowledge bases are expected to reduce resolution times, improve first-time fix rates, and support both human agents and AI-driven systems. Yet many organizations struggle to move beyond early-stage documentation.  Most internal knowledge bases don’t fail loudly. They fade quietly.  A new wiki or knowledge portal launches with momentum. A few troubleshooting articles and internal guides are published. Support teams bookmark them. Field service leaders feel progress. For a while, it works.  Then the cracks appear. Articles feel inconsistent across technical support and field service teams. Information becomes harder to scan during live incidents. Exports shared with customers or partners look unpolished. No one is certain which version of a troubleshooting guide is approved. Usage plateaus early, long before the knowledge base reaches meaningful scale.  This is rarely a tooling problem. It is a knowledge governance problem.  In the early days, ad-hoc documentation feels sufficient. Small support teams move fast. Field technicians rely on experience and informal notes. Standards feel unnecessary because everyone knows where to find information, or who to call when something breaks.  But enterprise Field Service and Technical Support organizations do not operate in early-stage conditions. As operations scale, more subject-matter experts contribute content, more agents and technicians consume knowledge, and more documents are shared externally with customers, partners, and auditors. Expectations rise, even if the knowledge system does not evolve.  What once felt flexible begins to feel fragile.  Most knowledge management systems focus on storage. Can content be written? Can it be searched? Can it be shared? These capabilities are table stakes. They are not what make enterprise knowledge management work at scale for service operations.  Scalable knowledge for Field Service and Technical Support depends on structure, consistency, and trust. Without these, content may exist, but confidence does not. And knowledge without confidence is rarely used during high-pressure service scenarios.  Readability is one of the first things to break at scale. When technical support agents or field technicians open a knowledge article, they are not reading for context. They are scanning for answers while managing live escalations, customer calls, or on-site repairs. They need to identify headings instantly, distinguish step-by-step instructions from explanations, and move quickly under operational pressure.  When font sizes blur hierarchy and sections look indistinguishable, cognitive load increases. Small delays compound. Resolution times stretch. First-time fix rates suffer. Teams fall back on memory, tribal knowledge, or escalation instead of documentation. The cost of poor formatting in service knowledge is operational, not aesthetic.  Templates are often misunderstood as rigid or restrictive. In reality, they are how enterprise service organizations scale quality without policing behavior. Guidelines tell teams how they should document issues. Templates quietly ensure they actually do it.  When templates enforce structure while allowing controlled flexibility at the section level, knowledge authors gain freedom without introducing chaos. Technical support teams gain predictability. Field service technicians gain clarity. Knowledge becomes easier to create, easier to consume, and easier to trust.  Branding and exports represent another silent failure point. Service knowledge rarely stays inside internal systems forever. It is exported as PDFs, shared with customers, included in service reports, or reviewed during audits. When headers break, footers disappear, or formatting changes unexpectedly, the knowledge system loses credibility.  These details may seem minor, but enterprise Field Service and Technical Support leaders notice them. Consistent branding and reliable exports signal maturity. They communicate that service knowledge is governed, not improvised.  Tables play a similarly critical role. Much of Field Service and Technical Support knowledge lives inside tables. Diagnostic steps, parts lists, troubleshooting workflows, and comparison matrices depend on clarity and alignment. When tables render poorly, instructions become ambiguous. In execution-heavy service environments, ambiguity translates directly into risk.  Knowledge governance is often framed as control, but its real purpose is visibility. Service organizations need to understand how troubleshooting guides and operational procedures evolve over time. Who updated them. When changes were made. Which version should be trusted during an escalation or a site visit.  This visibility builds confidence. Support agents and field technicians rely on knowledge when they believe it is accurate, current, and accountable. Without that belief, even the most detailed service documentation is ignored.  The shift enterprises must make is not from one tool to another, but from one mindset to another. Knowledge for Field Service and Technical Support cannot remain an informal byproduct of work. It must become part of the service operating model.  This means moving from ad-hoc documentation to structured authoring. From isolated service documents to governed knowledge systems. From knowledge as static content to knowledge as operational infrastructure.  Organizations that make this shift see tangible results. Technical support teams resolve issues faster. Field service teams reduce repeat visits. Dependency on individual experts declines. AI-driven service systems perform better because the knowledge feeding them is consistent, structured, and reliable.  Knowledge that scales across Field Service and Technical Support is not written once. It is designed, governed, and maintained.  And that is what enterprises actually need.  To truly move from ad-hoc documentation to governed, scalable knowledge that empowers Field Service and Technical Support teams, enterprises need more than basic tools; they need intelligent automation that turns fragmented information into structured, actionable knowledge . Ascendo AI’s Knowledge Agent  does exactly that by ingesting case notes, transcripts, manuals, and legacy knowledge bases and converting them into rich, searchable documentation with diagrams, tables, flowcharts, and persona-driven outputs that align with how your teams work in the real world. With template-based generation and enterprise-ready automation, the Knowledge Agent accelerates onboarding, reduces manual documentation toil, and helps service leaders ensure that their knowledge isn’t just stored but trusted and used everywhere it matters  — from field bulletins to support playbooks.

Providing consistent technical support across multiple languages is a growing challenge for global enterprises. As organizations expand into new regions, language differences, technical terminology, and cultural context can slow response times and increase operational complexity. Multilingual AI is increasingly being considered as a way to support enterprise service teams in addressing these challenges at scale. This article outlines practical best practices for implementing multilingual AI in enterprise technical support environments, with reference to how platforms such as Ascendo AI are designed to support scalable, multilingual service operations. Multilingual AI dashboard displaying real-time support in multiple languages Understanding the Role of Multilingual AI in Enterprise Support Enterprise support teams often serve customers from diverse linguistic backgrounds. Traditional support models rely heavily on multilingual human agents or third-party translation services, which can introduce delays and increase costs. Multilingual AI offers an alternative approach by assisting with real-time language handling while maintaining alignment with existing knowledge systems. When implemented thoughtfully, multilingual AI can help support teams manage interactions across languages while preserving consistency in responses and workflows. Platforms like Ascendo AI are designed to integrate AI capabilities with enterprise knowledge and service data, enabling teams to explore more efficient ways of delivering global support. Key Best Practices for Implementing Multilingual AI 1. Start with Reliable Language Identification Accurate identification of the customer’s preferred language is a foundational requirement for multilingual support. Enterprise AI systems typically use natural language processing techniques to identify language patterns within customer queries, depending on configuration and data quality. Best practices include testing language identification on real support interactions, accounting for regional variations or mixed-language inputs, and offering customers the option to manually select their preferred language when needed. This helps reduce friction early in the support process. 2. Focus on Context-Aware Responses Simple word-for-word translation is often insufficient in technical support scenarios. Effective multilingual AI implementations prioritize context, product terminology, and intent rather than literal translation. Enterprise platforms are most effective when AI responses are grounded in approved documentation, service procedures, and product knowledge. Ascendo AI is designed to connect AI-generated outputs with enterprise knowledge sources, helping teams maintain consistency across languages while preserving technical accuracy. 3. Align AI with Domain-Specific Knowledge Generic AI systems may struggle with specialized technical language. Enterprises benefit from aligning AI behavior with their own support data, including historical tickets, documentation, and operational workflows. Ascendo AI is built to work with structured and unstructured enterprise knowledge, allowing organizations to continuously align AI-assisted support with evolving products, services, and customer environments. 4. Maintain Human Oversight for Complex Scenarios While multilingual AI can assist with many routine interactions, complex or sensitive cases still require human expertise. A hybrid approach—where AI assists with language handling and knowledge retrieval while human agents handle judgment-intensive issues—helps maintain quality and trust. Providing agents with AI-assisted translations, summaries, and context can reduce resolution time without replacing human decision-making approach ensures quality support without overloading human teams. 5. Prioritize Data Privacy and Compliance Multilingual support often involves handling customer data across regions and jurisdictions. Enterprises must consider data protection requirements such as GDPR, CCPA, and other regional regulations. AI platforms used in enterprise environments should be designed with security and compliance considerations in mind, including controlled data access, encryption, and configurable retention policies. These considerations are essential when scaling support operations globally. Measuring Success and Continuous Improvement Multilingual AI is becoming an important consideration for enterprises supporting global customers. When implemented with attention to language accuracy, contextual understanding, human oversight, and data governance, it can support more consistent and scalable technical support operations. Platforms such as Ascendo AI are designed to support these enterprise requirements, enabling organizations to explore how AI-assisted multilingual support can fit into their existing service and operations workflows. Final Thoughts on Multilingual AI in Enterprise Support Multilingual AI is no longer a luxury but a necessity for enterprises aiming to serve a global customer base effectively. By focusing on accurate language detection, context-aware responses, domain-specific training, human oversight, and data privacy, organizations can unlock the full potential of AI-powered multilingual support. Ascendo AI’s platform offers the tools and flexibility enterprises need to build scalable, reliable multilingual support systems. The next step is to evaluate your current support workflows and explore how multilingual AI can improve your customer experience and operational efficiency. Book demo with us: Contact | Ascendo AI | Generative AI Service CRM

Time is valuable and this applies to the world of customer service as well. When a customer contacts customer support, they expect a solution to their problem right away. If customers receive a reply days after their query, they will be dissatisfied with the company. The lesser the customer has to wait, the better it is for the company's reputation. So, it is important for customer support teams to be responsive and effective while facing customers. What is Time To Resolution? Time To Resolution (TTR) is an important metric for measuring how quickly a company responds to its customers' needs. It helps companies understand when customers will stop waiting for service and start looking elsewhere. Time To Resolution (TTR) measures the average time it takes for the customer service team to resolve an open ticket or any other customer issue. It is calculated by dividing the sum of all times spent (days or hours) by the number of cases resolved. Mean Time To Resolution formula (is also referred to as Call to Resolution): Average Time To Resolution = sum of all times to resolution/ total # of cases resolved Why is Time To Resolution important? There's a direct correlation between time to resolution and customer satisfaction, or CSAT. Customers don't take the time to reach out to customer service organizations because they're bored or lonely. They only take the initiative when they have a problem that needs solving. Making them wait to resolve this problem creates friction, and sometimes ill will or even the loss of a customer. Time To Resolution metrics also impact team efficiency and effectiveness. For this reason, companies measure Time To Resolution both as an aggregate and on a per-employee basis to single out underperforming employees. OKRs help teams set objectives that can help teams determine what is expected of them and their performance. Everyone on the customer support team– whether they answer phone calls, respond to tickets, or answer queries– can help to reach service goals when their department utilizes OKRs. Customer satisfaction can be improved when Time To Resolution is optimized. Here are 10 ways to improve Time To Resolution: 1. Find & Analyze Average Time To Resolution In most cases, customer service managers will want to analyze the overall Time To Resolution in their team. This formula is generally applied to batches of cases. For example, you can find out the average Time To Resolution for a particular month by compiling all the data. Problems range from simple to complex. There are also outliers. AI tools automatically categorize the type of problem and provide Time To Resolution for a particular category of the problem which is way more accurate and real-time than a human downloading some of the tickets from CRM and mapping them in spreadsheets. 2. Know Your Customer Customer-centricity is a key business strategy that puts customers at the core of one's business in order to provide a positive experience and create long-term relationships. It’s important to understand who your customers are and how they use your product or service. This will allow you to provide better customer service and make sure that you meet their needs. Focusing on customers and their needs will be profitable for the company in the long run. 3. Different Issues, Different TTR You should also know how much time it takes to resolve an issue with your product or service. This will help you understand what your customers expect from you. It's important to keep in mind that some issues take longer than others to resolve. For example, if a customer has a problem with a new feature, it might take several days before he or she gets a response. However, if a customer has an old complaint, it might take only a few minutes to resolve. 4. Set Expectations TTR is measured by the number of days between when a customer contacts your business and when they receive a response. If a customer calls your business and asks for help with a problem, then waits three weeks before receiving a response, you should consider them unsatisfied. You need to set expectations early so that customers feel comfortable calling back. If you're not sure how much time something will take, set expectations with your customers. This helps them understand when they should expect to hear back from you. 5. Be Transparent Customer expectations are constantly changing. You need to keep up with them by being transparent and providing clear communication. If you aren’t able to answer questions or explain things clearly, be honest with them. Then, go back and find answers for them as soon as possible. Acting quickly to remedy the mistake and taking steps so it will not happen again is the message you want the customer to hear. 6. Take Feedback Customers will often ask you questions when they interact with you. They might even complain about something. It’s important to respond quickly and take feedback. This helps you understand how well you’re doing and where you need to improve. 7. Plan Based On Busy Periods Monitor your ticket volume and find out busier and lighter periods of work. Based on that data, you should make sure to be well-staffed during busy periods so that your agents can share the workload and solve issues promptly. Being understaffed during busy periods will increase your Time To Resolution. 8. Create an Action Plan Once you've identified the root cause of the problem, you need to create an action plan. This includes identifying who needs to do what, when, and how. If you're not sure where to start, ask yourself these questions: Who is responsible for resolving this issue? What steps do I need to take to fix it? How long does it usually take me to solve this type of problem? This is the chance for the agent to be proactive. Use goodwill gestures to win over the customer. 9. Automate Processes Using tools such as a chatbot or artificial intelligence-powered search will automate the issue resolution process. If you have a slow first response time, you can solve it by using these tools. They do not require human assistance. They improve efficiency and decrease Time To Resolution. Implement a system to identify the problem category of issues. This way, the cases can be assigned to agents based on their expertise in a particular problem category. 10. Know When You're Done It's easy to get caught up in the details of solving a problem. However, once you've done everything you can think of, it's time to stop. Communicate to the customer that the issue needs more investigation or that you do not have an instant solution. Once you solve the issue, make sure to add the solution to the knowledge base , so that next time the same issue crops up, you can solve it way quicker. How do benchmark Time To Resolution? Methods used for measuring Time To Resolution might be different across industries and the kind of service provided. Here are some things you should consider while calculating TTR: What customers expect The average time it takes for your employees to resolve issues Benchmark against other companies with similar products How Ascendo Supports Time To Resolution Ascendo provides predictive, artificial intelligence-based solutions to customer issues. It allows for a consolidated view of data which enables agents to access the knowledge base, previous interactions, problem categories and relevant solutions in an easy manner. Customers can transfer from self-service to agent seamlessly and don't have to repeat themselves. With the help of Ascendo, customer support leaders can know who the expert is for a particular type of issue and assign cases accordingly. Ascendo's self-service feature provides a human-like AI Bot or smart search for websites, through which customers can find answers to their queries on their own without having to wait for an agent's help. With primary predictions and actionable secondary predictions, Ascendo aims to decrease Time To Resolution and increase productive outcomes. Ascendo's virtual agents are also available on direct channels like Slack and WhatsApp, making the process more interactive, more accessible and faster than ever before. Ascendo's tools are designed for the continuous improvement of customer support operations. Ascendo fundamentally helps companies to turn proactive in the way they offer support services to their customers. Learn more, Innovative B2B Automated Self-Service Knowledge Intelligence in Customer Service

Improve Customer Services With AI In today's scenario, most businesses are turning to artificial intelligence (AI) to provide the best customer service. AI technology can be used to provide a stellar customer experience. Businesses are looking to take advantage of these dynamic technologies to benefit their organization. The use of AI in customer experience will undoubtedly enhance the customer experience of your organization and take it to the next level. AI has enabled many complex processes to be automated. 10 Ways To Improve Customer Services With AI 1. Predictive Analytics AI can analyze large amounts of data in a short amount of time which contributes to helping customers in making more informed decisions. AI can make predictions about the resolution of customers' problems through predictive analytics. The process of making predictions using statistical modeling is known as predictive analytics. When and how to interact with each customer and accordingly suggest customer-related products and services, making the customer experience more relevant. Helps you predict outcomes with analysis, which can also help predict potential risks. All this information can be used to improve customer satisfaction and improve efficiency. 2. Personalized Customer Service Quick replies with real-time responses will take your organization's customer experience to the next level. Artificial intelligence (AI) chatbots play a vital role in resolving the issues of customer service representatives handling customer calls. AI Chatbot is designed to be at the forefront of the user. One can also remove the additional burden on human agents by resolving customer queries with the highest accuracy and human-like behavior. 3. 24×7 Customer Support Every customer expects round-the-clock service at their convenience and businesses that understand the responsiveness of their customers will enhance their potential customer experience by being always available to the customer. Customers want their queries resolved 24×7 without waiting for a long time. The AI ​​system enables us to provide continuous customer service and resolve issues with ease. It enhances customer satisfaction and contributes to enhancing the brand image. 4. Prevent Employee Workload AI has the potential to improve the employee experience by automating routine tasks. Automating repetitive processes can save your employees a lot of time and effort. Doing so will give them more time and opportunities to focus more on essential and complex tasks. Due to this, not only the development of the agents will happen, but as a result of the ideas and activities, the organization will also grow. 5. Monitor key online channels AI can effectively and quickly analyze data from any channel and provide predictions and insights to enhance the customer experience of existing customers. AI is increasingly helping to make accurate predictions about online customer preferences and marketing strategies. By analyzing data from online channels and predicting which leads will act, it can automatically target them with customized marketing messages. Presently, the customer base is increasing a lot for these channels and with the help of AI, you can improve customer services by giving this facility to your customers. 6. Collect Customer Feedback Feedback exercises enable you to gather insights into the positive points of your organization as well as identify gaps that need to be filled. It can be very challenging to handle and analyze all the feedback data manually or through traditional systems. With the help of AI, customers can give feedback through various support channels but they will all be analyzed for actionable insights and sentiment. Apart from acquiring new customers, it is equally important to nurture your existing customer base and strive to keep them happy. 7. Data-Driven Customer Insights When the organization can easily access historical and feedback interactions of customers systematically, the data derived from this can build a definitive 'view' of the customer. And this 'view' can help you get an idea of ​​what customers' experiences, interactions, behavior, etc. will be helpful for both customer support and business growth. Moreover, by sharing this 'view' of the customer across different departments of the company with this data, you will be able to target them more effectively with the right message at the right time. 8. Seamless coordination Customers look for the best customer service and want to get solutions to their problems in a quick manner with smooth coordination. AI chat bots are great for answering frequently asked customer questions. The best part is that they never lose their motivation. Chat bots can provide a precise solution to a specific problem. Thus, using Artificial Intelligence to improve the customer experience can be very useful for your company. 9. Automate Support Experience With this technology, some companies have made their customer system so strong that most people do not even realize that the companies they are doing business with are working to improve the customer experience. Using AI, automating repetitive processes can save your employees a lot of time and effort, from filling out forms to statistics. This will give them more time and opportunities to focus on essential and complex tasks. Leverage AI to enhance your customer experience, so you can drive growth for both your employees and your business. 10. Understand the location-based needs Last but not least, when a customer has some issue or query it's definitely directly related to the product or the services provided by the organization. But with these, there are other factors too which make a customer happy and let them feel a good experience with your customer service. Same as AI technology helps to understand the sentiments of the customers , it also has the power to provide solutions that are location-based, or which will fit a customer in the place where they are sitting at that moment. Conclusion Definitely, Artificial Intelligence can help you create memorable customer experiences and AI has the potential to improve the way you provide customer service at every stage of the buying process. It is up to you to discover new ways and means to take advantage of these dynamic technologies to benefit your organization. Not only do you benefit from the organization but its learning management system allows your service agents to be trained and updated with the latest skills and training prevalent in the customer service industry. By easily understanding your target audience, their interests, their preferences, etc., you will be able to strengthen your support team. Learn more, Innovative B2B Automated Self-Service Technologies Using AI to Drive Service Improvements

Many of your customers, who are already in a stressful or complex situation and need help, may become agitated during an interaction. How do you bubble up the exact issues from their interaction to make meaningful changes? It can be scary, but when you understand where it’s coming from and why it’s happening, it can be easier to handle. For example- often, the customer is frustrated or facing an issue about the situation that led them to the interaction, rather than the interaction itself. Let's look at the stats! 35% of customers have become angry when talking to customer service. (American Express) 27% of Americans report that ineffective service is their number one customer service frustration. (Statista) 12% of Americans rate their number one service frustration as “lack of speed.” (Statista) 78% of customers have given up on a transaction because of a negative customer experience. (American Express) Product teams are always looking for ways to get the emerging trends and the top of mind issues affecting customer satisfaction. Ascendo's " Voice of the Customer " module has an enhanced workflow to allow the user to not only see the trending issues and look at possible opportunities but to develop the solution. Multiple AI models work in tandem to identify the issues, identify opportunities and solutions and provide human feedback to further adjust the issues that need to be addressed. These can be approved with a click of a button and they are automatically sent to AI Bot so they can quickly present the top issues and solutions. More issues are resolved before reaching the agents, higher customer satisfaction by the end customers. Following are what Customer and Product teams can do with Voice of the Customer: Identify areas of product that needs improvement. Identify Customers who have the most issues, lowest sentiment, highest effort and low satisfaction. Manage next steps to make Customer Experience top-notch for every customer. Reward experts and identify which agents need training. Bring out intelligence in knowledge - improvements, duplicates, expiration. Take your top issues and manage self service through self service channels. Voice of the customer provides insight into the support process, agent training, knowledge intelligence or product improvements. Everyone wins! Ascendo autonomous trending issue finder and workflow brings smiles to all!

The field service management (FSM) industry revolves around incidents that are handled by end employees. The incident life cycle begins with the initial submission and ends with a resolution to the problem. Incident tracking or ticketing systems have been around for years. Moving these systems to the cloud has been the biggest tech-based evolution in the industry so far. The Current State Of Service And Why It Needs To Change Today, the customer service industry is complex. Support technicians need lots of time to resolve issues, use more parts than they need, and are bombarded with having to support multiple products and versions. At the same time, hardware and software product companies are shifting toward service-driven revenue growth. Unfortunately, all aspects of service and FSM / ITSM systems are workflow-driven, time-based or monitoring-based and reactive. Automation of these systems alone will not fulfill the needs of the enterprise. It requires a new class of solutions that taps into the systems of record to create a system of intelligence through which decisions can be made. There are four primary reasons why faster resolution of service incidents should be made more effective: 1. The scaling issue: Incident tickets do not come from humans alone; there are also product-generated alerts. The assignment of a remote service agent can take a while. Agents need to be quick in diagnosing the problem as well as the resolution to the problem. Even with human-generated tickets, products getting so complex, with shorter product life cycles, that remote staff members need help in diagnosing problems and providing quick resolutions. It costs an average of $250-$1,000 per ticket. For hardware companies, costs go further up when having to send a technician for repeat fixes. SaaS companies resolving 86% of customers say they will pay more for better service. By 2020, customer service will drive sales more than product or price. 2. Complexity: According to Field Service’s Future Trends report , 81% of companies believe smart connected products will be implemented in the next five to 10 years. With the advent of robotics and innovations in manufacturing and materials, digital equipment out in the field is complex. Problems are diagnosed remotely. When an issue can’t be diagnosed, a dispatched technician goes in to diagnose the problem and then makes multiple visits to install the part in place. Instead of worrying if a technician has expert knowledge in regard to a particular problem, automation can help identify a problem, make sure the parts are in place and provide detailed instructions for fixing the issue so a field technician can focus on the customer relationship and procuring new opportunities instead of the diagnosis. 63% of agents are solving difficult problems in high-performing organizations versus 43% at under-performing companies. 3. Staff skills are changing : Service staff that supports legacy systems also need to be trained to support new systems. Baby boomers are retiring at a rapid rate , and as they ride off into retirement, companies are losing tribal knowledge. Sixty-five percent of service training is outdated . I’ve seen firsthand how millennial recruits no longer have the touch-and-feel experience or understanding of the equipment, and they don’t consider field service as a long-term career. So, new recruits need to be trained constantly. These new recruits come in with college degrees that companies want to utilize. Businesses also want to put the soft skills of new recruits to good use to increase revenue in customer-centric environments. Before a dispatch happens, a problem is identified, the part that’s required is procured, and cheat sheets on how to resolve the issue are readily sent to the field technician’s device. Having an automatic problem and solution resolution with cheat sheets on how to replace a part allows staff to focus on the customer rather than figuring out how to solve the problem. 4. The business model is evolving to a “pay as you go” model: As companies explore this model, the only front end to the customer is field service. With the new model, companies are forced to have an efficient way for incidents to be automated. As the business model evolves, there is also a servicing model evolution with democratized field service. As of this article, there are around 9,000 data science engineer jobs versus 34,000 field service engineer jobs posted on LinkedIn worldwide. If your company is considering automating aspects of its field service operations, here are a few tips to consider to ensure it’s a successful endeavor: Understand the evolution of data within your organization. Make sure you have the relevant data for the business problem you are trying to solve. Get agreement on the usage of automation from all stakeholders. The last thing you want is to implement something that has organizational pushback in regard to adoption. One of the best ways to increase adoption is via a value calculator. Changes in the field service business environment warrant new technology to help take it to the next level. In the next article, we will discuss the long-term benefits and value calculation/return on investment (ROI) of solutions that address the automated service world. Learn more, 10 Ways To Improve Time To Resolution Innovative B2B Automated Self-Service Technologies

Field service has always been about precision—getting the right person to the right place at the right time. But let’s face it: in the past, achieving that precision was more guesswork than science. Field service can feel chaotic—juggling schedules, last-minute changes, and customer expectations. Fast forward to today, and the game has changed completely, thanks to AI. AI Agents are the key to cutting through that complexity.   From Complexity to Clarity: How AI Agents Are Simplifying Field Service By integrating real-time insights and predictive tools, they ensure technicians are dispatched efficiently and customers stay informed every step of the way. What used to take hours of planning and endless spreadsheets can now be handled dynamically, efficiently, and intelligently by AI-powered solutions—like Ascendo AI’s agentic AI agents and teammates.  These AI Agents aren’t just tools; they’re true teammates—working alongside human field teams to solve problems faster, make better decisions, and deliver more personalized customer experiences. As we delve into this topic, insights  from industry experts like  Michael Kleweken from  Optimize My Day ,  Timo Schramm from  ServiceNow ,  and Johann Diaz from  Service Revolution Academy  illuminate the path forward.  Embracing AI for Enhanced Efficiency   M ichael Kleweken emphasizes the operational advantages AI brings to field service management. He states, “AI has vast potential to improve operational efficiency.” This sentiment resonates deeply with our mission at Ascendo AI, where we believe that AI agents can significantly streamline support processes. By automating routine tasks and optimizing scheduling through predictive analytics, businesses can allocate resources more effectively and respond to customer needs with unprecedented speed.   The shift from traditional reactive support to a proactive model is crucial. As Timo Schramm notes, “Tracking AI’s long-term value requires considering how savings and benefits interact across departments.” This holistic approach allows organizations to see the broader impact of AI integration—not just in terms of immediate cost savings but also in enhanced customer satisfaction and loyalty.  Operational efficiency is non-negotiable in field service, but the manual methods of the past left little room for agility. Enter AI Agents, equipped with predictive analytics and dynamic scheduling. They don’t just “schedule”; they think ahead, optimizing routes in real-time, assigning technicians based on proximity and skillset, and even factoring in evolving conditions like traffic or weather.  Think of them as the dispatchers of the future—but with superpowers. These agents don’t just react; they predict. They anticipate equipment failures, schedule maintenance proactively, and ensure resources are allocated where they’re needed most. For companies like Siemens, these capabilities have slashed downtime by up to 20% while increasing first-time fix rates.  At Ascendo AI, we believe your AI teammates should take this even further—constantly learning from past jobs, adapting to new patterns, and keeping your operations not just efficient but ahead of the curve.  AI Agents: The Secret to Exceptional Customer Experience   Efficiency is just the start. Customer trust is the ultimate goal, and this is where AI truly shines. Imagine an AI Agent that doesn’t just automate repetitive tasks like booking appointments but also provides hyper-personalized interactions. By analyzing customer history, usage data, and service patterns, AI teammates can predict when a customer might need support—even before they know it themselves.  And it’s not just about prediction; it’s about communication. AI-powered chatbots, another form of AI Agent, handle routine inquiries instantly, reducing wait times and empowering customers with 24/7 self-service options. At the same time, these bots keep customers informed in real time—updating them on technician locations, arrival times, or schedule changes without requiring a single phone call.  This kind of transparency builds trust. It makes customers feel heard, valued, and prioritized—turning a “service visit” into an experience that strengthens loyalty.  The Integration Advantage   One of the challenges we often hear is, “How do I even start integrating AI?” That’s where adaptable, agentic AI platforms like Ascendo AI make the difference. We design our AI teammates to plug seamlessly into your existing workflows, leveraging flexible APIs and low-code integrations.  Even better? These AI teammates grow with you. As your data expands, they adapt. As new challenges arise, they learn. The result? A system that evolves alongside your business, ensuring your service strategies stay relevant and effective.    Looking Ahead: AI is Your Competitive Edge   The future of field service is exciting, and the innovations on the horizon—like audio-driven AI bots for voice-controlled interactions—are set to make AI teammates even more intuitive.  But let’s not lose sight of the big picture: adopting AI isn’t just about technology; it’s about mindset. It’s about recognizing that these AI Agents are here to augment, not replace, human expertise. They take care of the complexity, so your teams can focus on what they do best—delivering exceptional service and building lasting relationships.  At Ascendo AI, we’re proud to be at the forefront of this evolution, helping companies transform their field service strategies with AI teammates that are intelligent, reliable, and empowering. If you’re ready to explore how AI can elevate your operations, we’d love to collaborate and show you what’s possible.  Because the future of field service isn’t just coming—it’s here. And it’s smarter than ever.    Let’s continue the conversation: How could AI Agents transform your support and field service strategies? Drop your thoughts below!   Learn More: Embracing AI in Technical Support: Navigating Challenges and Opportunities Unlocking the Power of Proprietary Data: AI Agents as Game-Changers in Customer Support

Automated self-service approaches are a set of techniques that allow people to solve problems on their own without having to ask for help. They're often used in situations where asking for help would be too difficult or embarrassing. It's increasingly hard to find retail shops, restaurants, and public spaces that don't utilize self-service technologies in some form. Everybody wants things done quickly and without concern for the process. Likewise, to enhance customer service and cut expenses, organizations are increasingly introducing automated self-service technologies (SST). Besides, if you are still not convinced that self-service is the best way to be served, many companies are adopting the combination of human-machine. That way, they satisfy both needs: speed and personalized service. Let's take a look at how self-service technologies are being employed in different sectors. 1. Travel SST in the tourism and hospitality industry allows for a better customer experience. Airports Common Use Self Service kiosk at an airport in India The airport can be a hectic and crowded environment, with long queues and waiting periods. SST enables airports to replace flight check-in, baggage check-in, and airport parking with automated machines, thereby improving the overall experience of air travel. From the customer's perspective, SST shortens the time it takes to complete formalities and provides flexibility. Hotels At hotels, self-service kiosks give customers a convenient check-in option, especially at late hours. Guests can use contactless technology for various purposes such as checking in and out, updating preferences, room service, and payment. Tourist Places Floor plan provided by an American museum to guide its visitors In the age of COVID, it is more important than ever to employ self-service stations in tourist spots as they keep the public safe by limiting the spread of viruses and other germs. SST also assists tourists in ticketing, navigation, and providing information on exhibits. Parking Kiosks They are compact devices that help motorists obtain parking tickets for parking their vehicles in a parking lot for a certain period. Sometimes, people don’t park their cars in an organized manner and end up occupying so much space. By enforcing smart parking systems such as CCTVs and parking kiosks, the parking lot can be used to its optimum level. Motorists also have a variety of payment options. With SST, the old parking systems' difficulties are gone for customers. 2. Banking Traditional banking structure is rigid and not customer-centric. Previously, opening an account took many days and numerous paperwork. People who travel abroad often could not control their finances easily. Even today, banks are accessible only during a certain time. Customers demanded flexibility in banking. The advent of self-service technology thus drastically improved customer experience. Now, customers can manage their finances at all times without having to visit a branch by using self-service channels like ATMs, mobile banking, banking apps, and internet banking. Automated self-service options have made processes- monitoring account balances and statements, transferring funds, paying bills, and making purchases- quicker and hassle-free. People don't have to worry about long waiting periods, thanks to multiple payment methods and 24/7 access to services. 3. Healthcare Be it at a hospital or a doctor’s office, SST can improve in-person visits. Patients expect their healthcare experience to be fast and flexible, as other industries are already offering this type of service to customers. Digital technology has given birth to all-in-one platforms that automate the complete medical check-in procedure. Collecting and integrating patients' data is a huge benefit. This means that patients' data will be available to doctors, hospitals, insurance companies, and other medical institutions. In addition to providing a seamless experience for the customer, this process also greatly enhances patient and preventative care. 4. Retail SST in retail spaces has become a part of our daily lives rather than a new feature. Supermarkets Self-checkout machine at a supermarket Shoppers can now scan and check out their purchases without assistance, thanks to self-checkouts. The number one customer pain point, as per Capgemini's Smart Stores report , is having to wait in lengthy lines when it comes to billing. However, through SST, customers enjoy a finer in-store experience and a swift checkout process. Fast Food Chains Customers placing orders on their own at McDonald's Customers don't like to wait around when they visit fast food restaurants in particular. With the help of SST, customers can place orders and pay their bills in a single transaction. Getting the order correct is a metric for customer satisfaction. Self-ordering increases order accuracy. It directly sends the order to the kitchen, expediting the overall process. Vending Machines Automated devices known as vending machines distribute goods like refreshments, snacks, lottery tickets, and other items. By doing away with the requirement for a physical checkout, these stores also have the advantage of being open around the clock and giving customers the quick, easy shopping experience they've grown accustomed to. Self-Service Gas Stations Where customers serve themselves, paying before they pump gas into their vehicles. Self-service gas stations are usually cheaper than full-service ones, and customers can save time by not having to wait for an attendant. This is however a contentious technology as fear of fires and explosions has led some places to ban self-service gas stations. 5. Government Government-run institutions are becoming more customer-friendly by employing self-service technologies. DMV DMVs are changing the perception of who they are by providing a good customer experience with the use of SST. To reduce the queues and therefore wait times and also to save money, kiosks are set up in DMV offices and off-site locations. These kiosks provide various services including license renewal, duplicate printing, as well as registration renewal and printing for cars, trailers, and boats. Some DMVs are collaborating with the judicial system so that court-ordered fines can be paid via self-service kiosks too. They not only give customers a better experience by removing the dreaded wait, but they are also providing them with the ability to do things other than those unique to the DMV. Post Office A digital kiosk at a post office Customers want same-day delivery, real-time tracking of their packages, and the ability to redirect deliveries that are already in flight. To keep up with these demands, post offices must adopt digital technology. Self-service enables post offices to be more agile and responsive, while also allowing for the establishment of new services at a cheaper cost. Post offices with greater accessibility have greater revenue potential and can satisfy their customers while focusing their staff on the most valuable or complicated customer transactions. Customer Automated Self-Service: What Is It & How To Do It Right? Customer automated self-service is the process in which a business gives its customers the ability to find solutions on their own without having to wait for a support representative or another team member. Resolving common customer issues should be automated. This way, your support agents will have time to tackle complex problems. Build your self-service tools based on your customers' issues to gain maximum benefits. In the rare scenarios, automated self-service goes wrong, do let customers know that you've made a mistake and apologize for it. This type of communication works because it shows that you care about making things right with the customer. Track the performance of your SST through various metrics such as Customer Satisfaction Score (CSAT), Customer Effort Score (CES), ticket resolution rate, and the number of unresolved tickets. The data will provide insights for further optimization. Build A Best-In-Class Customer Self-Service Experience Ascendo helps companies offer proactive support with the power of artificial intelligence. Based on customers' queries, Ascendo's AI bot provides intelligent predictions and guides toward resolution easily. Ascendo is available directly on channels like Slack and WhatsApp to assist support teams in handling customer issues, making the process more interactive, more accessible, and faster. All interactions with customers are recorded and used as a knowledge base to understand the trends of customer concerns over time. When an agent handoff occurs, the agent is not only aware of the previous solutions the customer has tried, but also has access to more predictions due to extensive data access. Ascendo empowers support agents with AI Search, a feature that consolidates knowledge from multiple data sources to provide solutions in one platform. Customer support teams can easily add this smart search to their work environment and use it to solve issues efficiently. If you are interested in improving your support operations, contact us.

The field service industry is at a pivotal moment. As technology reshapes how we approach maintenance, troubleshooting, and customer interaction, the question isn't if organizations should embrace these changes but how they can do so effectively. Johann Diaz’s recent insights in “ Upskilling the Field Service Workforce: Preparing for a Less Hands-On Future ”  spotlight the importance of preparing today’s technicians for tomorrow's AI-driven demands. At Ascendo AI, we see this transformation as a chance to redefine what exceptional support looks like—with AI teammates leading the charge.  Revolutionizing Field Service: The Role of AI in Empowering Technicians AI: From Tools to Teammates   For decades, field service technicians relied on intuition and experience, often navigating reactive repair cycles with traditional tools. But the game has changed. As Diaz puts it, technicians must “swap toolbelts for tech platforms, intuition for insights, and reactive repairs for proactive, AI-driven service delivery.” This evolution aligns perfectly with the role of Ascendo AI’s intelligent AI Agents .  Our AI Teammates aren’t just tools—they're collaborators. These digital colleagues empower technicians to:  Access Instant Insights : AI agents analyze vast datasets in real-time, enabling predictive maintenance and issue anticipation before problems escalate.  Streamline Operations : By automating repetitive tasks like logging service calls or generating reports, AI frees technicians to focus on strategic, high-value activities.  Enhance Customer Interactions : With AI agents providing contextual information and empathetic responses, technicians can deliver faster, more personalized solutions.  Upskilling: A Strategic Imperative   As Neil McGeoch from ServiceNow  points out, “90% of organizations will face IT skills shortages by 2025.” This statistic underscores the urgency to upskill field teams—not just to use new tools but to embrace new ways of working. At Ascendo AI, we champion this mindset shift. Upskilling isn't about replacing human expertise; it’s about augmenting it. AI agents become co-pilots, guiding technicians through complex processes while enabling them to develop higher-value skills like data analysis and customer empathy.  From Reactive to Proactive Support   Vodafone’s success story, highlighted in Diaz’s article, exemplifies how AI-powered systems transform technicians into proactive problem-solvers. By leveraging IoT data and predictive analytics, Vodafone trained its teams to anticipate customer needs and address them before issues arose. This proactive approach not only reduced downtime but also elevated customer satisfaction—a testament to the power of AI-enabled upskilling.  At Ascendo AI, we take this a step further. Our AI teammates don’t just assist; they learn and adapt, becoming smarter with every interaction. This ensures that technicians are always equipped with the most relevant, actionable insights, driving operational efficiency and delighting customers.  Overcoming Challenges with Collaboration   Resistance to change is natural. Many technicians wonder, “Why do I need to learn AI when I’ve been fixing these systems for decades?”  Diaz’s solution—storytelling—resonates with us. By demonstrating how AI secures careers and enhances capabilities, organizations can inspire even the most skeptical employees.  We believe collaboration is key. At Ascendo AI, our AI agents work alongside technicians, mentoring them in real-time, much like a seasoned colleague would. This symbiosis reduces resistance and accelerates adoption, ensuring a smoother transition to an AI-augmented workforce.  Building a Future-Ready Workforce   As we move toward 2030, the field service landscape will continue to evolve. Robotics, AI copilots, and drones will take on routine tasks, while human technicians will focus on strategic, creative problem-solving. But as Diaz aptly states, “The future is less hands-on, but it’s not less human.”  At Ascendo AI, we couldn’t agree more. Our mission is to ensure that AI teammates enhance—not replace—the human touch. By investing in continuous learning and adopting intelligent AI systems, organizations can stay competitive, exceed customer expectations, and empower their workforce to thrive in this new era.  The future of field service is here. Is your team ready to embrace it? Explore how Ascendo AI ’s Agentic AI platform can transform your support strategies, streamline operations, and empower your workforce. Let’s reimagine what’s possible—together.  Learn More: Bridging the AI Knowledge Gap: Transforming Support Strategies with AI Teammates Navigating the AI Frontier: Transforming Technical Support Through Intelligent Automation

Chatbot vs AI Bot Businesses today aim to offer better customer experiences while lowering service costs, and they increasingly realize that automated self-services like chatbot software may help them achieve these ends. By enabling users to find answers on their own and directing them to efficient solutions, chatbot software tackles the high volume of repetitive questions and reduces backlog. As a result of this, support staff can devote their time to resolving complex problems rather than answering simple questions. Since its inception in 1950, chatbot technology has evolved massively. At present, unfortunately, Chatbots and AI bots are often considered to be the same thing, leading to confusion and disappointment. Despite both being automated self-services, there are some glaring differences because of which technology is better than the other. What is a Chatbot? Computer programs or devices that can "chat" with you are called chatbots. Text messaging via a chat interface or a messaging app is the most typical way to communicate with chatbots. They converse by following predetermined guidelines (if the customer says "A," answer with "B"). Sometimes the interactions are structured as a decision-tree workflow where users can choose the appropriate responses based on their use case. These bots resemble automated phone menus in which the user must select a number of options before finding the desired information. This technology is perfect for resolving common customer complaints and FAQs. What is an AI Bot? Essentially, an AI bot is a higher-level idea of chatbots. AI bots are frequently embedded in web pages or other digital applications to respond to consumer enquiries without the need for support agents, thereby offering economical, hassle-free customer service. In order to provide a more personalized customer experience, an AI bot combines artificial intelligence (AI) technology with other technologies. Let us understand the key technologies used in AI bots. NLP or "natural language processing" enables a computer to comprehend what we say using our natural language. That is, using slang, acronyms, and other speech patterns that are typical of us. For instance, an AI bot may understand you even if you don't use precise grammar. Machine learning or ML implies that the computer can gain knowledge from interacting with humans. Therefore, it enables the AI bot to learn from its interactions and produce future conversations that will be more insightful and effective. Chatbot vs AI Bot According to Zendesk user data, customer service teams dealing with 20,000 support requests per month can save more than 240 hours in a month by using chatbot software. The key differences between Chatbot vs AI bot as listed below will delineate which is the better software to opt for. Operating Channel(s) In a constantly changing digital landscape, with more social and messaging apps, providing customer service across platforms and being where your customers interact with your brand is critical. Unlike chatbots, AI bots only need to be built once. After that, they can be deployed to multiple channels, with information from each channel streaming to a central analytics hub. Sadly, chatbots require rules, process definition and training to use them in different channels. Flexibility An AI bot with complete access to a database and API has the contextual flexibility necessary to engage in fluid conversations with users. For instance, if a user changes their mind in the middle of a chat, a conversational AI interface will automatically begin catering to the new needs of the user. But, a chatbot is constrained by the script and rules it has been given previously, and it is unable to produce any output that has not been manually added to its flow. Understanding An AI bot uses various technologies to parse and interpret inputs rather than depending on a pre-written script. Chatbots, in comparison, may appear to understand words and sentences while actually only adhering to a strict set of rules. While chatbots focus only on keywords when dealing with customer queries, AI bots can comprehend the context of queries and the intent of users. Maintenance An AI bot draws data from a variety of places, including websites, text corpora, databases, and APIs. It also immediately incorporates any revisions or updates made to these sources. Chatbots, in contrast, need ongoing, expensive manual maintenance to keep their conversational flow productive and efficient. Customer Experience AI bot is distinguished from traditional chatbot by its ability to have natural, human-like conversations. As a result of this, an AI bot is a great choice as a self-service option for customer support. Furthermore, as opposed to chatbot, AI bot can understand complex problems and provide more precise responses. Its user-friendly nature will reduce customer frustration and lead to better customer experiences. Transform Customer Support with AI Bot If you've read up to this point, it must be clear that AI bot is a human-like, cost-efficient, and automated self-service technology that can deliver seamless customer experience across many channels. AI bot's features overcome the shortcomings of a traditional chatbot. Ascendo offers an intelligent, conversational AI bot that can cater to your support needs, irrespective of the industry. You can integrate AIbot in conversational support channels like Slack, Teams and WhatsApp. Ascendo AIBot searches upon knowledge from multiple data sources to provide solutions to customers quickly. AI bot's interactions with customers are saved and analyzed to understand the patterns of customer concerns over time. In the rare case where the AI bot is unable to solve the customer issue, there is a smooth handover to a live agent who can view the customer's question and the solutions provided by the AI bot. This way, customers don't have to repeat themselves when passed on to an agent. To contact Ascendo, click here.