In a world where technology promises perfection, it’s no surprise when users expect faultless products. Yet, even with technological advancements, occasional hiccups remain inevitable. But imagine a scenario where a solution is presented to you before you experience the problem. This isn’t futuristic fiction—it’s the power of predictive and proactive AI in support and field services.

Gone are the days when users waited for products to show defects before seeking solutions. The shift from reactive to proactive engagements is not only redefining customer support, but the entire user experience. That is why we have chosen to explore this topic in our latest Research Journey.

However, it’s evident that before we can enjoy the lasting benefits of predictive analytics and proactive AI, some fundamental challenges need to be addressed. Whether enabling better data collection, establishing connectivity, or knowing what to do with the data once you get it, the path ahead is filled with potential. Still, it will require concerted efforts across the enterprise.

As we embark on this Research Journey, we aim to establish best practices, identify opportunities, and create a roadmap for future innovation. By exploring real-world responses and synthesizing them into cohesive insights, we aim to bridge this knowledge gap.

So jump in with us as we:

• Define the Problem: By clearly identifying the business challenge, we can start tackling it.
• Launch Discovery: With polls, surveys, and interviews, here’s where we dig deep into the facts and contributing factors.
• Develop the Theory: With data in hand, our researchers and analysts can develop frameworks.
• Guide the Industry: Correlations to financial results lead to conclusions that will help your organization navigate AI.

If you’ve ever wondered about the future trajectory of support and field services and the role of AI, this journey is for you. Join us, and you won’t just gain a fresh perspective; you’ll be equipped with strategic tools and a unique edge in understanding this transformative phase. Subscribe to this Research Journey today and get industry insights delivered straight to your inbox.

Define the Problem: The Struggle of Harnessing Big Data in Support Services

AI has shaped and will continue to shape predictive and proactive support in field services, support services, and supply chains. However, organizations are struggling to leverage and get total value from AI. Let’s explore why.

First and foremost, there’s a vast gap in strategic alignment. Many businesses, surprisingly, are still unaware of the raw potential of the data they can collect from their products. To give you a bit of perspective, our TSIA benchmarks showed a startlingly low use of proactive technologies across the installed base. This disconnect is not just limited to traditional businesses but extends to cloud-native companies as well. It might come as a shock, but even software companies, which we’d assume have inbuilt mechanisms for data collection, often miss the mark.

So, why is there this massive gap?

• Awareness: Many businesses, surprisingly, are still unaware of the potential the data they can collect from their products carries with it. I often tell companies that none of their customers actually care about their product. They only care about the product to the extent that it can help them deliver better business outcomes. The data collected from the product has the power to reduce the cost to serve, improve efficiency, and inform new offers. Ultimately, the data collected will be more valuable than the product itself.
• No Feedback Loop: According to our TSIA benchmarks, support and field services provide formal feedback to product teams on a dramatically lower level than five years ago. Requests to make products smarter and more supportable often take a back seat to sales-driven product features. In simpler terms, while they may wish to shift from a reactive “catcher’s mitt” approach to a more proactive one, they often lack the required tools to do so.
• Lack of Actionable Data: The wide availability of low-cost sensors allows companies to capture a digital representation of any electro-mechanical device. Software can be designed with hooks to capture performance around the clock. The lack of actionable data is two-fold: customers must enable telemetry, and organizations become overwhelmed with too much non-critical data.

The struggle to use the vast amount of available information to recognize patterns and anticipate needs is precisely where AI should come into play. Yet, with businesses that are struggling at the foundational level of data collection and comprehension, the higher goal of AI implementation remains a distant dream.

The Clock is Ticking: The Imperative of Modernizing Support

The world is rapidly evolving and, as it does, the pressures on businesses increase. It’s not merely about competition anymore but about smart competition. Business models are changing with a new reliance on service revenue. Every support and field service organization is struggling with the scaling of its operations.

The dilemma of proactive and predictive support isn’t just a fancy feature for the future—it’s a time-sensitive issue demanding immediate attention. Here’s why:

• Rising cost pressure: Being the low-cost provider never goes out of style. The most expensive way to resolve a problem is to send someone on-site with a spare part to fix it. Having the customer call or email a support agent is only marginally lower in cost. Every organization is looking for a cost-effective way to scale its operations.
• Outdated install base: I had a manager who used to say, “When you’re digging yourself into a hole, the first thing to do is stop digging.” Today, the longer we wait to make this change, the more we’ll continue to install products that lack embedded diagnostics, are not under contract, are not connected, and are not providing meaningful data.
• The shift toward outcome services: It’s crucial to underscore that effective customer success and managed services strategies are largely tethered to implementing a stout telemetry system. Predicting and proactively addressing client needs hinges on precise data gathering—essentially forming the bedrock upon which modern service models are built and scaled. A glance at the past decade reveals a persistent disruption of historical business models, chiefly steered by two dominant mega-trends: the commoditization of feature functionality, often characterized by generic speeds and feeds, and the decrease in consumption-based business models, notably technology-as-a-service, which pivot from traditional capital expenditures (CapEx) toward operational expenditures (OpEx) on the customer’s end. Neglecting a data-driven approach and merely selling a product supplemented with break/fix services looms as a strategy on the verge of obsolescence.
• Lean digital customer experience: Harnessing the potency of a lean digital customer experience demands a meticulous approach to telemetry data, not only to steer predictive support but also to catalyze the advent of new product offers and proactive support innovations. The journey begins with the imperative of data collection and its subsequent effective analysis. The next step is ensuring an alignment between task complexity and the capability of digital toolsets to automate processes. A lesson can be drawn from Amazon’s initial simplicity in selling books—a practical application that avoids complexity, providing a tangible pathway for traditional companies that were not born in the cloud to avoid hesitancy rooted in perceived operational complexity. The adage that “our operations are too complex” becomes an untenable excuse in the light of a strategic, phased digital implementation.

The need to address the predictive and proactive support challenge is pressing. Whether it’s to manage costs, modernize hardware, shift service models, or innovate with new offers, the foundation is data. Companies can’t afford to lag in this areaif they aim to be competitive. The era of waiting and watching is over—it’s time to act.

Who’s Affected?

The complications of the changing technology landscape don’t exist in a vacuum. Key players impacted include:

• Service delivery personnel
• Support and field executives
• Product
• Supply chain
• Offer management
• Analytics specialists

The Cost of Inaction

Now, let’s delve deeper into the potential consequences for these stakeholders.

1. Disruption from non-traditional competitors: Navigating through the competitive technological race, traditional manufacturers and service providers find themselves on a precipice where swift adaptation is essential to fend off disruption from non-traditional competitors like Google and Amazon. These tech giants are already delving into realms like equipment performance analysis and collaborating with vendors to integrate sensors on existing apparatus, wielding their superior data-processing capabilities. With the market leadership mantle at stake, the competitors of tomorrow may not be those faced today, but rather, those who can best deliver improved business outcomes as demanded by customers. Hence, a pertinent query surfaces: Will traditional companies grasp the utilization of data and AI at a quicker pace than tech companies that are learning the ropes of traditional vertical markets? As we approach this unprecedented starting line, the competitive race unfolds with a singular victor’s spot available on the podium, underscoring a critical juncture in technological and operational adaptability.
2. Enterprise AI and Neural Networks: Embarking on the journey with enterprise AI and neural networks is not about singular, isolated initiatives but rather a mesh of interconnected projects, reflective of a neural network’s functionality—identifying relationships in data akin to how the brain processes. The foundational elements, like knowledge management and self-service systems, are pivotal to effectively deploying advanced tools like AI chatbots, which not only feed into predictive solutions but also curtail costs and facilitate novel service offerings. Consequently, organizational success lies in progressively potent projects, each enabling and enhancing the next, thereby not only building new business capabilities but also mitigating previous complexities. In essence, similar to a neural network’s mechanism—translating input data to insightful output through a concealed layer of analytics—organizational success mandates managing a network of projects where immediacy in building your network is imperative—if not five years ago, then today.

The overarching message? Businesses not embracing this shift won’t merely lag behind; they risk complete obsolescence.

In the face of rapid technological advancements, industries must prioritize staying updated and integrating emerging solutions into their operations—those who don’t stand to lose ground to tech-savvy entrants that are ready to leverage these new tools. The writing is on the wall: adapt or face extinction.

Mapping the Research Journey: From Data to Solution

While we’ve explored the challenges ahead, the path to understanding and solving these problems necessitates a robust Research Journey. Let’s delve into how we plan to unpack this issue step by step.

Our starting point was to determine the practices essential for our Research Journey. Recognizing the intertwined nature of support and field services, our approach combines ‌insights from both.

To gather data with precision, our task is not to start from scratch but to build upon and augment ‌existing work. This is the reason we’ve integrated quick polls into our methodology. For support services, our methodology comprises a quick poll coupled with a survey. This is because we still have unresolved questions about proactive support technologies. Our research doesn’t just culminate in data accumulation. The real magic is in crafting compelling narratives out of this raw information.

By joining us on this journey, you’ll be equipped with:

• Knowledge: Understand the importance of field and support roles in the larger corporate structure. Learn AI’s place in the field and support services past, present, and future.
• Strategy: Implement the Total Cost to Serve framework in your organization.
• Differentiation: Ensure that your business stands out in delivering exceptional support experiences.

Your participation is not just an opportunity for growth but a step toward creating a more efficient and customer-centric business world. Subscribe now, be a part of this transformative journey, and ensure your organization is ahead of the curve.

Timeline of This Research Journey

Define the Problem

By clearly identifying the business challenge, we can start tackling it.‍October 2023

Read This Blog (Above): The Struggle of Harnessing Big Data in Support Services

AI has shaped and will continue to shape predictive and proactive support in field services, support services, and supply chains. However, organizations are struggling to leverage and get total value from AI. Let’s explore why.

First and foremost, there’s a vast gap in strategic alignment. Many businesses, surprisingly, are still unaware of the raw potential of the data they can collect from their products. To give you a bit of perspective, our TSIA benchmarks showed a startlingly low use of proactive technologies across the installed base. This disconnect is not just limited to traditional businesses but extends to cloud-native companies as well. It might come as a shock, but even software companies, which we’d assume have inbuilt mechanisms for data collection, often miss the mark.

Launch Discovery

With polls, interviews, and more, here’s where we dig deep into the facts and contributing factors.October 2023

View the Webinar: Proactive Services and Predictive Analytics: The Future of Field Services

In today’s market of field and support services, there are three types of service: reactive, predictive, and proactive. For companies looking to move away from reactive toward predictive and proactive, the first step is data.

In order to make this shift, it’s critical for field services organizations to tie data analytics and use cases to their services. Doing so will help OEMs imagine what else can be done with the data to help drive their customers' business outcomes.

Join TSIA’s Vele Galovski, TSIA’s Distinguished VP of Support and Field Services Research, and Kevin Bowers, TSIA’s Director of Field Services Research, for a highly interactive 45-minute webinar where they’ll be addressing some of the most pressing questions the field services industry is facing today, including:

• What do you have to do with your install base to break the cycle of being purely reactive?
• What are the three types of analytics and the corresponding use cases?
• What frameworks can we use to build a foundation to drive customer outcomes in the future?

Check out the Industry Story: Real-World Applications of AI for Technology Companies in Support Services

This report examines Nokia's use of chatbots with generative AI in support services. It explores the AI capabilities deployed, such as natural language processing and the Nokia Language Model. The report highlights the business benefits achieved, including improved support response times and customer satisfaction ratings.

Read the Quick Poll Insights: Data's Crucial Role in Support

Without data, the application of artificial intelligence (AI) or machine learning is not scalable and repeatable. The TSIA 2023 The Role of Data in Predictive and Proactive Support quick poll revealed that if most companies have at least some data capabilities, there is significant opportunity to improve the use of product and software telemetry and prescriptive analytics.

Watch the Webinar: The State of Support Services 2024

January 10, 2024In 2024, traditional business models continue to be disrupted, a new post-pandemic workplace has emerged, and technologies like Generative AI have presented challenges and opportunities for all tech companies. Support services will be at the forefront of these issues. Is your organization ready?Join Vele Galovski, TSIA’s Distinguished VP of Support and Field Services Research, as he shares his findings, based on TSIA’s benchmark data, highlighting this year’s support services trends and business challenges.He’ll also build upon our current Research Journey—AI for Predictive and Proactive Support—by providing insights from our most recent quick poll, “The Role of Data in Predictive and Proactive Support.” In addition, he’ll be launching the next phase of this Research Journey, focused on driving support efficiency and scalability with AI.Key topics for this 45-minute webinar include:

• An overview of industry trends impacting support organizations
• The top business challenges faced by Support Services organizations
• Discussion of our current Research Journey, AI for Predictive and Proactive Support
• Insights on the role of descriptive, predictive, and proactive data analytics
• Practical AI applications that drive support efficiency and scalability
• The Support Services capabilities needed for 2024 and beyond

If your company provides remote services to help customers maintain best access to your technology, this webinar is for you—we’re looking forward to seeing you there!

Develop the Theory

With data in hand, our researchers and analysts can start developing frameworks.February-April 2024

Watch the Webinar: The State of Field Services 2024

February 28, 2024

Throughout 2023, economic uncertainties, the ubiquitous presence of artificial intelligence (AI), and the day-to-day realities of running a large, critical organization significantly impacted the business challenges faced by TSIA Field Service members heading into 2024. The major themes? Improving operational performance, transforming Field Service organizations, and how AI can be deployed to help address major challenges.

Join Vele Galovski, TSIA’s Distinguished VP of Support and Field Services Research, as he shares his research findings, based on TSIA’s benchmark data, highlighting this year’s Field Services trends and business challenges.

He’ll also build upon our current Research Journey—AI for Predictive and Proactive Support—by providing insights on the results of our most recent quick poll, “Organizational Enablers and Roadblocks of AI in Services.” In addition, he’ll be launching the next phase of this Research Journey, the development of a framework to help the industry leverage AI to recognize patterns and anticipate needs to reduce the cost to serve and inform new offers.

Key topics for this 45-minute webinar include:

• An overview of industry trends impacting field and support organizations
• The top business challenges faced by Field Services organizations
• A discussion of our current Research Journey, AI for Predictive and Proactive Support
• Insights on the enablers and roadblocks to leverage AI in services
• Practical AI applications that drive support efficiency and scalability
• The Top 10 Recommendations to implement in your field service organization in 2024 and beyond

If your company has equipment at a customer’s location that you provide on-site and remote services to, this webinar is for you—we’re looking forward to seeing you there!

Check out the Industry Story: Real-World Applications of AI for Technology Companies

This is a case study on how Dell leveraged the AI capabilities of machine learning, information retrieval systems, and content summarization to achieve multiple business benefits, including:

- Reduced time to close cases

- Reduced dispatches and work orders

- Fewer parts per dispatch

- Improved customer satisfaction

- Fewer escalations

- Reduced number of major parts for dispatch (expensive parts versus inexpensive parts)

- Increased percentage of remote software fixes as opposed to issues requiring a hardware fix

- Improved overall agent efficiency

- Reduced training time for support agents

Watch the Webinar: Accelerating the Use of AI for Predictive and Proactive Support

In October 2023, TSIA launched our latest Research Journey—AI for Predictive and Proactive Support—with an aim to establish best practices, identify opportunities, and create a roadmap for future innovation in Field Services and Support Services. It should come as no surprise that the increased pressure for efficiency, cost-effectiveness, and superior customer experiences has made this topic of paramount importance to all service organizations.

Wednesday, March 20, join Vele Galovski, TSIA’s Distinguished VP of Support and Field Services Research, as he discusses the current landscape of AI’s capabilities, and provides frameworks to accelerate the use of AI in Support and Field Services. Key topics for this 45-minute webinar include:

- The critical role of data in predictive and proactive support

- Organizational enablers and roadblocks to implementing AI in services

- The competition between product features and predictive and proactive support

- The difference between AI “Base Campers” and “Mountaineers”

Listen to the TECHtonic Podcast Episode: How to Go From Base Camper to Mountaineer

Join us in the latest episode of "TECHtonic" as we delve into the evolving world of technology support models with TSIA's Distinguished Vice President of Support and Field Services Research, Vele Galovski. In this conversation, Thomas and Vele offer a unique blend of insights and perspectives on the current and future state of technology support.

They begin with the dramatic shifts observed in 2023, marked by divergent perspectives in the industry, including the bifurcation of corporate responses to economic conditions.

Then they talk through the crucial role of support in driving growth, emphasizing the importance of viewing support as a strategic differentiator and an engine for customer satisfaction, while addressing the ongoing challenge of scaling service delivery and monetizing support.

The highlight of the discussion is around AI's role in transforming support services. Vele introduces the concept of 'AI Base Campers' and 'AI Mountaineers,' explaining how companies are positioned in terms of AI readiness and adoption. He provides insightful statistics and real-world examples to illustrate how different organizations are leveraging AI to enhance support services, improve efficiency, and scale operations.

This is a must-listen for anyone interested in the intersection of technology, support services, and AI business strategy!

Read the Quick Poll Insights: Targeting Vertical Markets Effectively for Support

As a part of the AI for Predictive and Proactive Support research journey, the Predictive and Proactive Support versus Product Features quick poll highlights prevalent trends and strategies to reaching and serving customers effectively, particularly horizontal versus vertical alignment, revealing insights into customization options, organizational structures, and understanding of customer processes and outcomes. The findings underscore the significance of tailoring solutions to specific industry needs and shed light on the diverse strategies companies employ to effectively serve their customers.

Guide the Industry

Correlations to financial results lead to conclusions. We’re ready to present our findings!
May 2024

Read the Report: Harnessing Predictive and Proactive AI

This paper highlights the importance of harnessing predictive and proactive AI in support and field service organizations to improve customer experience, reduce costs, and increase efficiency. It provides a foundational framework—the AI Capabilities Landscape, which classifies 20 AI capabilities into four categories based on their adoption and potential in the industry. The paper presents real-world examples of Nokia’s use of chatbots with Generative AI and Dell’s implementation of predictive support to illustrate the benefits of AI. It also emphasizes the need for a data strategy, including a progression from reactive to predictive to proactive services and the crucial role of data in AI scalability and repeatability. The paper identifies two types of organizations in terms of their telemetry and AI capabilities: AI base campers and AI mountaineers. It concludes with a discussion of telemetry roadblocks and enablers and provides recommendations for organizations to take the first step with AI.