Webinar Transcript
David Swift:
Hello, and welcome to the first webinar in our Video Intelligence Conference series, hosted by SightCall. We’re excited to share some important updates about our products and discuss the trends we’re seeing across the service industry.
Today’s session is titled “How to Capture and Scale Service Expertise Without Adding Overhead.” This is a major challenge facing field service organizations today, and we’re looking forward to sharing insights and practical solutions.
Before we begin, I’d like to introduce our speakers.
First, we have Thomas Cottereau, CEO and Founder of SightCall. My name is David Swift, and I lead Product Marketing at SightCall. We’re also joined by Bogdan Cirlugea, our Head of Product and the driving force behind many of the exciting innovations you’ll see demonstrated today.
We’re especially excited to show you a live product demonstration later in the session.
With that, I’ll hand things over to Thomas.
The Field Service Knowledge Crisis
Thomas Cottereau:
Thank you, David.
I’d like to start by discussing what we call the Field Service Knowledge Crisis.
Based on extensive industry data, I believe that field service organizations today don’t have an AI problem. They have a knowledge problem.
Organizations are losing expertise faster than they can capture and document it, while service complexity continues to grow. Several forces are driving this challenge simultaneously.
First, there’s workforce disruption. As shown in these statistics, 46% of field technicians are over the age of 50, meaning a significant portion of the workforce is approaching retirement.
Second, when technicians encounter problems in the field, 54% still rely on calling or texting a colleague for help. That tells us critical knowledge remains trapped in people’s heads rather than being accessible across the organization.
Third, 32% of field engineers have fewer than three years of experience in their current role, highlighting the ongoing challenge of onboarding and knowledge transfer.
Finally, nearly 50% of technicians report that their training does not adequately prepare them for real-world service situations.
Taken together, these statistics reinforce a simple conclusion: field service doesn’t have an AI problem. It has a knowledge problem.
Let’s take a closer look at what we call the perfect storm facing service organizations today.
First, there’s the workforce challenge. Experienced technicians are retiring, while new employees are entering the workforce with less hands-on experience. As a result, the skills gap continues to widen.
At the same time, valuable tribal knowledge is at risk. As senior technicians leave, they often take decades of expertise with them. That knowledge is difficult to capture, difficult to scale, and often difficult to trust once it’s documented.
Finally, customer expectations and service complexity continue to increase. Products are becoming more sophisticated, technologies are more interconnected, and customers expect faster resolutions than ever before.
The impact is significant. Customer satisfaction declines, repeat service visits increase, and operating costs continue to rise.
So how do we close this gap?
At SightCall, we’ve traditionally focused on what we call the service gap. The gap between an organization’s service capacity and capabilities and the expectations of its customers.
Today, however, that service gap is increasingly becoming a knowledge gap.
Even as organizations expand service teams and introduce AI capabilities, product complexity and the pace of technological change continue to accelerate. The challenge is no longer simply having enough people. It’s ensuring those people, and increasingly AI systems, have access to the right knowledge at the right time.
Let’s consider one simple reality: when experts leave an organization, their knowledge often leaves with them.
Think about the last time one of your most experienced technicians retired. What happened to their expertise? In many organizations, much of it simply disappeared.
This isn’t just a knowledge management issue. It has direct financial consequences.
When expertise is lost, onboarding new technicians becomes more difficult. Time-to-proficiency increases, and organizations struggle to get new employees productive quickly.
This challenge is especially significant when you consider that many newer technicians no longer stay with a single company for decades. While previous generations often spent 20 or 30 years with one employer, today’s average technician tenure is closer to three years.
If it takes two years to fully train a technician, the economics become difficult to justify.
The result is longer onboarding periods, lower first-time fix rates, more escalations, additional dispatches, and ultimately higher service costs. Ironically, these outcomes are the exact opposite of what many organizations hope AI will deliver.
Industry data supports this reality.
According to Aquant’s 2026 research, approximately 20% of service costs are linked to failed service visits. Among lower-performing organizations, that figure can climb as high as 44%. Aquant also estimates that organizations can achieve up to 26% cost reduction through better service intelligence.
TSIA’s 2026 research identified the talent shortage as the number one challenge facing service organizations today.
Similarly, Service Council research found that 83% of technicians believe the knowledge required to service products is changing rapidly, and that today’s service roles require significantly more technical expertise than in the past.
All of these signals point to the same conclusion.
Yet there’s one surprising blind spot.
As highlighted in the white paper we released last year, virtually every company is investing in AI. However, very few are investing in the knowledge foundation that AI actually requires.
And that’s important to remember.
AI is only as effective as the knowledge and data that power it.
With that context in mind, let’s take a look at what service leaders are telling us.
What Service Leaders are Telling Us
David:
Thank you, Thomas. Those insights really resonate with what we’re hearing from our customers.
Across the surveys we’ve conducted, one theme consistently stands out. The biggest challenges facing service organizations, both externally and internally, revolve around the workforce. Whether it’s the ongoing talent shortage or the need to better train, engage, and retain existing employees, workforce-related issues are driving many of today’s service challenges.
Not surprisingly, these challenges directly impact what our customers identify as their top priority: operational efficiency.
Service leaders understand that without an efficient, capable workforce, it’s nearly impossible to achieve the KPIs that drive service performance and customer satisfaction.
And as Thomas mentioned, our survey results closely align with broader industry research. Across our customer base, the number one strategic priority over the next five years is clear: AI and automation.
That led us to ask a simple question:
What if you could scale the way your experts and trainers solve complex issues, elevate average performers into top performers, and do it faster than ever before?
We explored that question with several customers.
At Peak Scientific, they shared that their top five failure causes account for nearly half of all service calls. Capturing the expertise and troubleshooting approaches used to resolve those issues would create tremendous value for onboarding new technicians.
They also highlighted another challenge: onboarding technical support specialists. These individuals often provide remote assistance to technicians in the field, making their expertise critical to service success. Faster onboarding and accelerated proficiency would deliver meaningful business value.
We asked Canon a different question:
What if you could reduce the time and cost of technical content creation by 80 percent?
Their response was immediate. Creating technical content is both expensive and time-intensive. Distributing that content across the organization adds another layer of complexity. Canon emphasized that producing and maintaining meaningful knowledge assets remains a significant operational burden.
We also spoke with several global home device manufacturers and asked:
What if recorded service interactions could automatically become scalable organizational know-how?
Their feedback was consistent. They want to scale expertise faster without pulling experienced employees away from their day-to-day responsibilities. Creating multimedia instructions and training content consumes significant time and resources, especially across large product portfolios. They want to automate the process, but many haven’t found a practical way to do so.
Even in the healthcare technology sector, we heard similar concerns.
When we asked one of the world’s leading healthcare device manufacturers:
What if tribal knowledge could be easily documented and instantly retrieved?
Their response echoed what we’ve heard throughout the industry. Creating knowledge assets is difficult and time-consuming. As a result, many known solutions never get documented at all. Valuable expertise remains trapped within individuals instead of becoming an organizational asset.
That brings us to the central challenge:
How do you capture expertise without asking technicians to become documentation writers?
Technicians are often among the most knowledgeable people in an organization. The same is true for the support engineers who assist them. Yet neither group wants to spend their day creating manuals or writing documentation.
At the same time, documenting complex procedures requires significant effort, creating a bottleneck that limits how quickly knowledge can be shared and scaled.
At SightCall, we believe the answer is Video Intelligence.
Video Intelligence enables organizations to capture service expertise and transform tribal knowledge into trusted, reusable assets that can scale across the entire enterprise.
At its core, Video Intelligence combines three key capabilities.
The first is Remote Visual Support.
This allows experts, technicians, and customers to connect through live video interactions. Using tools such as augmented reality annotations, screen sharing, and mobile collaboration, organizations can resolve issues faster while reducing unnecessary site visits.
The second capability is AI-Powered Insights.
Our customers told us that resolving issues remotely wasn’t enough. They wanted to understand what was happening across those interactions.
By analyzing service conversations and interactions, organizations can uncover operational trends, identify recurring issues, detect knowledge gaps, and better understand root causes. Service leaders can ask SightCall’s AI detailed questions about service performance, parts issues, troubleshooting trends, and more, enabling more informed decision-making.
The third capability, and perhaps the most transformative, is Xpert Knowledge™.
Every service interaction contains valuable expertise. Instead of allowing that expertise to disappear once an issue is resolved, organizations can capture it, structure it, and scale it instantly.
A remote support session can automatically become a step-by-step guide. A technician’s recorded repair procedure can become a reusable knowledge asset. Even videos recorded independently in the field can be transformed into structured expertise that benefits the entire organization.
The result is a living knowledge library built from real-world service experiences.
For new hires, this means immediate access to proven troubleshooting methods and expert workflows. Instead of relying solely on classroom training or static documentation, they can learn from the actual experiences of top-performing technicians and engineers.
That accelerates onboarding, shortens time-to-proficiency, and helps organizations achieve the service efficiency metrics that matter most.
With that, I’ll hand things over to Bogdan so you can see exactly how all of this works in practice.
Video Intelligence Demo
Bogdan Cirlugea:
Great. Thanks, David, and hello everyone.
I’d like to show you how all of this works in practice within the SightCall platform.
What you’re seeing on screen is the SightCall Console, which gives service teams access to Remote Visual Support, Operational Insights, and Xpert Knowledge™, all within a single platform.
Let’s begin with a common service scenario.
A field technician needs assistance replacing the hot end on a 3D printer. To help, an agent initiates a Remote Visual Support session and sends an invitation to the technician.
As soon as the technician joins, the agent can see exactly what the technician sees in the field. Using live video, the agent provides guidance through on-screen annotations, helping the technician complete the repair more quickly and accurately.
The agent can also leverage several built-in tools to accelerate troubleshooting.
Using the QR code scanner, the platform automatically identifies the device. Using optical character recognition, or OCR, the agent can instantly capture relevant information and repair instructions directly from the equipment.
The agent then pauses the video, annotates a still image with specific repair instructions, and shares it with the technician as a visual reference.
With those instructions in hand, the technician is able to complete the repair and replace the hot end successfully.
Once the work is complete, the agent documents the interaction using snapshots and notes captured during the session. At the end of the engagement, a service report is automatically generated, helping reduce administrative effort and ensuring all relevant information is recorded.
However, something stands out.
The agent realizes this is the second time in a single week that they’ve had to assist a newly hired technician with the exact same issue. That observation prompts a conversation with the service manager.
The manager decides to investigate further to determine whether this is an isolated incident or part of a larger trend.
To do that, they navigate to SightCall Insights.
Insights analyzes Remote Visual Support interactions to uncover operational and technical trends across the organization. In addition to dashboards and KPIs, managers can use natural language queries to explore recurring issues, root causes, and potential solutions.
In this case, the manager quickly discovers a pattern.
The organization’s experienced agents know how to solve this problem, but a recent group of new technicians lacks the training and knowledge needed to perform the repair independently.
This is exactly the type of challenge that Xpert Knowledge™ is designed to solve.
Now let’s see what happens when Xpert Knowledge™ is enabled.
The next time a technician completes a hot-end replacement, the interaction is automatically captured and transformed into structured knowledge content.
Within minutes, the system generates a step-by-step tutorial that explains exactly how to perform the repair.
The tutorial includes written instructions, images, diagrams, and supporting media, making it useful both as a training resource and as in-the-moment guidance during service delivery.
Although the tutorial is generated automatically, subject matter experts remain in control. They can review, edit, and validate the content before publishing it to the broader organization.
For example, an expert might update the estimated completion time or refine a specific instruction before approving the final version.
Once validated, the knowledge becomes immediately available.
An agent can share the tutorial directly with a technician, or users can search for it through the Xpert Knowledge™ library within SightCall.
The Xpert Knowledge AI assistant can also surface specific steps from the tutorial in response to questions, helping technicians find exactly the information they need without having to search through lengthy documentation.
What’s particularly powerful is that this knowledge isn’t confined to SightCall.
Through integrations with existing service platforms, organizations can deliver relevant expertise directly within the tools employees already use.
For example, here we’re looking at an integration with Microsoft Dynamics.
When a work order is created, Dynamics automatically queries Xpert Knowledge and links the most relevant tutorial to that work order.
In this case, a work order involving a 3D printer hot-end replacement is automatically associated with the tutorial we just generated.
As a result, technicians can access the guidance directly from their existing field service application before they ever arrive on-site.
This helps them prepare properly, ensure they have the correct parts, and follow the recommended procedure, reducing the likelihood that they’ll need additional support during the repair.
Field technicians can also access knowledge through the SightCall Expert mobile app.
The app provides access to the organization’s complete knowledge library, including tutorials and repair procedures that technicians can reference in the field.
Content can also be saved for offline use, ensuring technicians have access to critical knowledge even in environments with limited connectivity.
Beyond consuming knowledge, technicians can contribute to it as well.
Using the mobile app, they can record repair procedures, upload existing videos, and share expertise directly with the organization. Those videos can then be transformed into structured knowledge assets automatically.
This creates a continuous cycle where expertise is captured, validated, shared, and reused across the organization.
With that, you’ve seen how the SightCall platform connects Remote Visual Support, Insights, and Xpert Knowledge into a single workflow that helps organizations capture expertise, improve service performance, and scale knowledge across their teams.
I’ll hand it back to David to discuss the business impact and benefits organizations are seeing from these capabilities.
Impact and Benefits of Video Intelligence
David:
Thank you, Bogdan. That was an excellent demonstration.
As you can see, this is much more than a knowledge management tool. It’s about getting the right expertise into the hands of the people delivering service, exactly when they need it.
You also saw how seamlessly Xpert Knowledge fits into the broader service ecosystem. Whether you’re using Microsoft Dynamics, Salesforce, ServiceNow, Zendesk, or other platforms, the goal is to make expertise available within the workflows your teams already use.
Let’s talk about the business impact.
The first benefit is content creation at scale.
One of the consistent themes we heard from customers is that creating and maintaining technical content is expensive, time-consuming, and difficult to keep current.
Xpert Knowledge™ changes that.
Instead of relying on dedicated documentation teams, lengthy content creation cycles, or costly video production projects, organizations can automatically transform real-world service interactions into structured knowledge assets.
And importantly, these aren’t staged demonstrations or scripted training exercises.
They’re actual service experiences.
A remote support session can become a tutorial. A classroom training session can become reusable learning content. A technician can record a procedure in the field and instantly turn it into a knowledge asset for the rest of the organization.
This creates an entirely new model for knowledge creation.
Rather than producing a handful of formal training assets each year, organizations can continuously generate hundreds of highly relevant, real-world learning resources at a fraction of the cost and effort.
The second benefit is knowledge preservation and knowledge sharing.
Every service organization depends on expertise that often exists only in the minds of experienced technicians and support engineers.
Historically, that knowledge has been difficult to capture and nearly impossible to scale.
With Xpert Knowledge™, proven troubleshooting methods and best practices can be preserved directly from real service interactions and made available across the organization.
This reduces repetitive support requests, minimizes recurring escalations, and gives teams access to proven solutions that have already been validated in the field.
And candidly, if this means fewer escalations to Remote Visual Support over time, that’s a good thing.
Our goal isn’t to maximize support sessions. Our goal is to maximize service efficiency.
There will always be complex situations where visual support plays a critical role. Products continue to evolve, technology continues to advance, and new challenges will always emerge in the field.
But if organizations can solve common issues more independently because knowledge is readily available, that’s a win for everyone.
The third benefit is faster onboarding and workforce development.
As Thomas discussed earlier, knowledge loss, retirements, and technician turnover create significant challenges for service organizations.
Bringing a new technician to full proficiency can require years of investment. When that expertise leaves the organization, the cost is substantial.
Xpert Knowledge™ helps reduce that risk by creating a persistent repository of organizational expertise.
And perhaps more importantly, it aligns with how today’s workforce learns.
Most technicians are visual learners. They learn by observing, practicing, and applying knowledge in real-world situations.
Static manuals and lengthy documentation often fail to provide the context technicians need when they’re standing in front of a piece of equipment.
With Xpert Knowledge™, technicians gain access to visual, contextual guidance created from actual field experience.
They can learn before arriving on-site. They can access knowledge while performing work. And they can continue developing their skills through on-demand learning whenever they need it.
Imagine a new technician who has already reviewed tutorials generated from classroom training, ride-alongs, and expert field procedures. They arrive on-site with greater confidence, better preparation, and access to the collective expertise of the entire organization.
That’s how organizations reduce reliance on extended shadowing programs, shorten onboarding timelines, and accelerate time-to-proficiency.
Ultimately, this is about helping every technician perform closer to the level of your top experts, enabling the operational efficiency, service quality, and business outcomes that service leaders are striving to achieve.
With that, I’ll hand things over to Thomas to discuss where all of this is headed and the future vision for service organizations.
The Future of Service Knowledge
Thomas:
Thank you, David and Bogdan, for that excellent presentation.
I’d like to close by talking about the future and the major shifts we believe will shape service organizations over the coming years.
The first trend we’re seeing is a fundamental change in how organizations think about knowledge.
The most innovative service organizations are moving away from what I would call static knowledge.
For decades, we’ve approached knowledge management the same way.
Someone documents a process, publishes it, and moves on. The problem is that knowledge begins aging the moment it’s created. It quickly becomes outdated, it’s often difficult to find, and its quality depends heavily on the individual who originally documented it.
As a result, organizations end up with large knowledge repositories that are expensive to maintain and often underutilized.
This isn’t just our perspective.
In Gartner’s 2025 report, Solution Path for Knowledge Management, they highlighted several findings that align closely with what we’re seeing in the market.
First, Gartner notes that formal documentation and curated knowledge bases remain important, but they represent only a fraction of the know-how that exists within an organization. Most expertise still resides in people’s heads.
Second, employees rarely have the time or responsibility to continuously document and maintain knowledge, leaving knowledge management programs chronically under-resourced.
And perhaps most importantly, Gartner concludes that AI and machine learning do not eliminate the need for strong knowledge management practices. In fact, they make those foundations even more important.
Their recommendation is clear: organizations should move toward capturing knowledge in the moment and enabling continuous improvement through collaboration and feedback.
That’s exactly what we mean when we talk about living knowledge.
Knowledge is no longer something that should be created once and forgotten. It needs to evolve alongside products, processes, and customer expectations.
Instead of relying on a small centralized team to manage knowledge, organizations need a model where expertise is continuously captured across the business, directly from the people doing the work.
That knowledge can then be enriched, validated, and improved over time.
AI plays an important role in this process, but it’s important to remember that AI is the tool, not the source of truth.
The knowledge itself comes from your technicians, your engineers, and your experts.
That’s why human validation remains critical. As Bogdan demonstrated, AI can help transform service interactions into structured knowledge, but experts remain in control of validating and refining that content before it is shared more broadly.
When organizations adopt this approach, they gain access to one of their most valuable strategic assets: the collective expertise of their workforce.
And according to many industry studies, that expertise represents more than 80% of the knowledge that exists within an organization.
The second major trend is equally important.
Service is becoming increasingly visual.
In field service, text alone is rarely enough.
Technicians work with physical assets in physical environments. Diagnosing, repairing, and maintaining equipment requires context that often can’t be captured through words alone.
That’s why we’re seeing a rapid shift toward video, images, and visual interactions throughout the service lifecycle.
The next generation of intelligent systems won’t simply process text and conversations. They’ll understand what they’re seeing.
This creates tremendous opportunities for efficiency, because systems can combine visual understanding with operational knowledge to provide far more relevant assistance.
When you combine these two trends, living knowledge and visual intelligence, you begin to see where service is headed.
We believe the industry is moving from search to guidance.
For years, the workflow looked like this: you encountered a problem, searched a knowledge base, and hoped you found the right answer.
More recently, AI introduced a new model. Instead of searching, you ask a question in natural language and receive a contextual response.
That’s a meaningful improvement, but for many service organizations, it’s still not enough.
Technicians need more than information.
They need expertise.
They need context.
And increasingly, they need systems that understand both the asset they’re working on and the situation they’re facing.
The future isn’t simply AI that can answer questions.
The future is AI that can see, understand, and guide technicians in the moment of service.
That’s where we believe the industry is headed, and it’s the vision we’re building toward at SightCall.
Questions & Answers
David:
Thank you, Thomas.
I think both Bogdan and I are fully aligned with that vision. It’s exciting to see the progress we’re making at SightCall, the validation we’re receiving from customers, and the broader recognition we’re seeing from the market and industry analysts.
We have time for a few questions from the audience.
The first one is for you, Bogdan:
“It’s great to see these knowledge tutorials surfaced directly in work orders. Can that same knowledge be made available to other AI systems? Many of us already have AI copilots as part of our broader AI strategy.”
Bogdan:
That’s an excellent question, and interoperability has been a major focus for us.
The Dynamics integration you saw during the demo uses our APIs, but we’ve also invested heavily in AI-native integration capabilities through MCP, or Model Context Protocol.
MCP allows external AI agents to discover available tools, understand how to interact with the knowledge base, and retrieve relevant expertise automatically.
We’ve already built integrations with platforms such as Cognigy, and we’ve successfully connected Xpert Knowledge™ to AI assistants powered by OpenAI and Anthropic Claude.
So the answer is absolutely yes. Our goal is to make organizational knowledge accessible wherever your users and AI systems need it, and you’ll continue to see us expand those integrations over time.
David:
Great. Here’s another question, and I think this one is for Thomas.
“Does this require technicians to change the way they work?”
Thomas:
That’s one of the most important questions we hear.
When we developed Xpert Knowledge™, we worked very closely with customers through advisory boards and extensive feedback sessions. From the beginning, we wanted to build a solution for service organizations, not simply a technology solution looking for a problem.
In fact, our earliest versions required users to manually bookmark moments, add notes, and perform additional tasks to help generate tutorials.
Our customers quickly pushed back.
They told us, “Our experts are here to solve problems, not create documentation.”
The same was true for field technicians. Their responsibility is servicing equipment and supporting customers, not authoring knowledge articles.
That feedback fundamentally shaped the product.
Today, knowledge capture happens automatically in the background. Remote support agents continue working exactly as they always have. Field technicians continue performing their jobs as they always have.
As service interactions take place, AI analyzes those interactions, identifies patterns, and builds what we call a knowledge graph, connecting expertise across equipment types, skills, procedures, and recurring issues.
The result is that knowledge creation becomes a byproduct of work rather than an additional task.
Everyone can contribute knowledge without changing the way they work or even realizing they’re doing it.
David:
That’s a great segue into our next question.
“What types of service interactions generate the most value for Xpert Knowledge™?”
Thomas:
We’re seeing several patterns emerge across customer deployments.
The first, and perhaps most obvious, is Remote Visual Support.
When you combine video and audio, you’re capturing not only what’s being said, but also what’s being done. That creates a much richer source of expertise than audio alone.
The second area is training and onboarding.
Many customers are now recording classroom training sessions. The reality is that even after a great training session, people forget much of what they’ve learned within days. Capturing those sessions creates a reusable resource that can be shared across the organization and revisited whenever needed.
We’re also seeing strong adoption around ride-alongs and shadowing programs.
New technicians can record experienced technicians performing procedures in the field, preserving that expertise and automatically transforming it into structured knowledge that can benefit the entire workforce.
The common thread across all of these use cases is simple: whenever you can capture both visual and verbal context, you’re creating high-value knowledge assets that can be reused at scale.
David:
One final question.
“We already have a visual support solution. Can we upload recordings from that platform into Xpert Knowledge?”
Thomas:
Absolutely.
One of our design principles was openness.
Organizations can upload recordings from other visual support platforms, collaboration tools like Microsoft Teams, or even videos recorded directly on a mobile device.
Once uploaded, those videos can be automatically transformed into tutorials and structured knowledge assets, just like the examples you saw today.
The platform is also open on the distribution side.
Through APIs, integrations, and MCP support, organizations can surface that knowledge within the systems they already use, ensuring expertise is available in the right place, at the right moment, and in the context of the service work being performed.
David:
Fantastic.
We’re a few minutes over time, so we’ll wrap things up there.
Thank you all for joining us for the first session of the Video Intelligence Conference series. We hope you’ve enjoyed today’s discussion and demonstration.
You’ll receive an on-demand recording following the event, and we encourage you to join us for the upcoming sessions in the series.
If you have additional questions, please don’t hesitate to reach out. You can contact us through the SightCall website or connect directly with any of the presenters.
Thank you again for your time, and we look forward to seeing you at the next session.
Thomas: Thank you, everyone.
Bogdan: Thanks, everyone.
