Visual Intelligence for Support and Troubleshooting

Impact on customer satisfaction

Impact of the visual channel on CX

Scope note: the percentages cited come from Metrigy studies (656 companies globally and 503 consumers in North America) and reflect results reported in those samples.

WiFi Instability in Households

Home connectivity has become a minefield for technical support. One figure sums up the magnitude of the problem: 68% of households face recurring WiFi instability. It’s not just “slow internet,” but a set of intermittent failures—outages, signal drops, degradation due to router placement or interference—that are hard to describe accurately over the phone or chat.

In these types of incidents, language falls short. The customer usually reports symptoms (“it cuts out in the bedroom,” “it works fine and then it doesn’t,” “the video call freezes”), but the real diagnosis depends on visual and contextual variables: where the equipment is, which lights are on, how the cables are connected, whether there are

obstacles, or even which exact router model is being used. The distance between what the user thinks they see and what actually happens is a constant source of repeat contacts and frustration.

Step-by-Step Visual WiFi Diagnosis
Brief visual diagnostic flow (WiFi) — what to ask for and in what order
1) Equipment identification (30–60s)
– Ask the customer to show the router/ONT label/model and the general status.
– Checkpoint: if the model isn’t visible or is blurry, repeat with better light/focus (avoids wrong steps).
2) Indicator status (LEDs) (30s)
– Request a slow pan of the front/top to see lights (power/wan/internet/wifi).
– Checkpoint: if there are abnormal LEDs, capture a still image (serves as evidence for escalation).
3) Physical connections (60s)
– Guide them to show the back: power, coax/fiber, Ethernet, ports.
– Checkpoint: confirm the connector “click”/seating and that there are no loose adapters or damaged cables.
4) Location and environment (60–90s)
– Ask for a short shot of the area: height, furniture, walls, proximity to microwaves/metal.
– Checkpoint: if the router is hidden or at floor level, propose a temporary relocation to validate the hypothesis.
5) Targeted test (2–3 min)
– Do a simple test with the customer (for example, move closer to the router and repeat the action that fails).
– Checkpoint: if it improves near the router, the issue points to coverage/interference; if not, to backhaul/service/equipment.
6) Close with evidence
– Summarize what was observed (model + LEDs + connections + location) before reboots/changes.
– Checkpoint: if it’s escalated, hand off with screenshots and findings to avoid “restarting the diagnosis.”

This is where visual intelligence—live video, guided capture, and increasingly, AI-assisted analysis—changes the dynamic. Instead of asking the customer to translate a technical problem into words, support can “see” the environment and the device. The expected result is less trial and error and more accuracy from the first exchange, something especially valuable when the problem is intermittent and the customer is already coming in with little patience.

The paradox is that WiFi is a critical service in the modern home, but its support still depends, in many cases, on channels that don’t capture what’s essential: visual evidence of the problem.

Use of Video in Companies and Its Limitation

Video is no longer a rarity in everyday life, but in the customer-company relationship it remains an underutilized capability. According to a global study by Metrigy (656 companies), 48% of companies use video. The nuance is decisive: almost half of those organizations only use it when an agent allows escalation.

Early vs. late video
Video “as escalation” vs. “from the start” (in casesvisuals)
– Video as escalation (late)
– Pros: less initial operational change; reserved for complex cases.
– Cons: the customer has already invested time answering questions; early evidence is lost; the risk of recontact increases.
– Typical signal: video appears when “everything has already been tried” and the case is loaded with frustration.
– Video from the start (early, with triggers)
– Pros: rapid capture of evidence (LEDs, cables, environment); speeds up diagnosis; reduces trial and error.
– Cons: requires journey design (when to offer it), capture guides, and agent training.
– Typical signal: the visual channel is offered when the problem is hard to describe or there is a high risk of misunderstanding.
Rule of thumb: if the customer needs to “show” to avoid ambiguity, video works better as an entry point (with an option to exit) than as a reward at the end.

That constraint has operational consequences. If video is enabled late, the customer has already gone through a path of questions, checks, and basic steps that could have been sped up with a simple visual inspection. In addition, support loses an opportunity to capture early signals: a poorly connected cable, a status light that reveals the equipment’s state, or an installation error that can be detected in seconds.

The limitation is also one of experience design. When video depends on the agent’s discretion, the customer does not perceive that the company truly “offers” a visual channel; they experience it as an exception. This connects with another finding: 40% of consumers say companies do not make it easy to communicate by video, and 29% state they would like to do so, but no company they interacted with offers it. It’s not just technological availability: it’s accessibility, clarity, and friction in access.

In technical and field support, video does not compete with voice or text: it complements them. But if it is kept as occasional escalation, its impact remains limited, and the organization stays trapped in diagnoses based on incomplete descriptions.

Consumer Preferences in Visual Interactions

Customer demand is clearer than many companies assume. In research by Metrigy with 503 North American consumers, more than 90% want to interact visually with companies, especially in scenarios where “seeing” reduces ambiguity: problem resolution (troubleshooting), consultative calls, online purchases, and orientation or training sessions.

When asked about specific use cases, the preference becomes even more specific. 71% want to use video or video with screen sharing for troubleshooting new products. And 73% want the same for consultative calls with professionalsas doctors, lawyers, or financial advisors. The takeaway is straightforward: in high-context interactions—where details, trust, and precision matter—the customer perceives the visual channel as an advantage.

Customer Preferences for Video

However, the desire collides with the reality of what’s offered. A significant portion of the market feels that video isn’t “within reach.” That gap between expectation and availability translates into a missed opportunity, especially in industries where remote support can avoid visits, reduce time, and improve the experience.

There’s also a balance point: not every interaction requires video. But the pattern is consistent: when the problem is hard to describe or the risk of misunderstanding is high, the customer wants to show, not explain. In experience terms, the visual channel works as a cognitive shortcut: it reduces user effort and increases the quality of the information the agent (human or AI-assisted) receives.

Benefits of Visual Communication for Customer Satisfaction

When companies do enable visual capabilities, the reported results are compelling. In the data cited by Metrigy, 74% of organizations that offer visual capabilities recorded a 41% improvement in customer satisfaction. In addition, 67%increased resolution speed by 40%, and 43% increased sales by 33%. These are not marginal improvements: they are leaps that impact core CX and business metrics.

Early visibility, better results
Causal chain (why “seeing” moves CSAT and times)
1) Early visual evidence (video / guided capture / screen)
→ 2) Less ambiguity (model, status, connections, environment are validated)
→ 3) Better diagnosis on first contact (fewer inferences)
→ 4) Fewer redundant steps and fewer recontacts
→ 5) Lower total resolution time + greater sense of customer control
→ 6) Improved satisfaction (and, in some cases, impact on sales through better guidance/less friction)
Checkpoint: if the visual channel is offered late, step 1 breaks and the rest of the chain loses strength.

The explanation lies in the nature of diagnosis. With voice or text, support depends on the customer’s ability to describe and the agent’s ability to infer. With video or screen sharing, the interaction becomes more “evidence-based”: symptoms, context, and configuration are observed. In device troubleshooting—for example, an internet router or a home appliance—the difference between seeing the back panel and hearing “I think it’s connected properly” can be the difference between resolving in minutes or opening a case that drags on.

Visual intelligence also enables a more efficient hybrid model: an agent (or an AI avatar) can guide the customer to capture specific views of the device, request different angles, and suggest concrete actions. If it isn’t resolved, escalation to a human happens with the basic work already advanced. In that setup, the specialist comes in with context and evidence, not a blank sheet.

Metrigy even quantifies the cost of not seeing: if the customer only speaks or writes the problem, resolution time can increase by up to 80%. In an environment where speed and accuracy determine satisfaction, recontact, and costs, visual communication stops being a “nice to have” and becomes an operational lever.

“By combining visual capabilities with AI, agents—AI or human—can identify and understand a problem much faster and in greater detail than with voice or text.”

Robin Gareiss, CEO and Principal Analyst at Metrigy

Challenges in the Implementation of Visual Capabilities

The adoption of visual intelligence is not automatic, even when the value seems evident. The first challenge is access and channel design: if video is only enabled as an escalation authorized by an agent, the organization limits its own impact. Consumer data reinforces the point. Implementation, therefore, is not just “having video,” but

to integrate it as a natural option at the right moments in the journey.

The second challenge is the user’s preference for humans (which shapes how visual AI is introduced). Although the future points to more interactions with avatars and AR, today the customer still values the understanding and certainty of resolution they associate with a person. This forces the design of hybrid experiences: AI to speed up what’s repeatable and humans for what’s ambiguous or sensitive.

Effective implementation of visual capture
Implementation checklist (so visuals actually get used)
– Journey and triggers
– Define at which moments video/capture is offered (e.g., “hard to describe,” “intermittent,” “installation,” “first use”).
– Access friction
– One click from SMS/WhatsApp/app; short instructions; quick camera/microphone test.
– Capture guides (standard of evidence)
– Which photos/views are considered “sufficient” (model, LEDs, connections, environment) to avoid back-and-forth.
– Operational integration
– Ensure the evidence is associated with the case/ticket and travels with escalation (without restarting diagnosis).
– Training for agents/technicians
– Micro-skills: ask for slow pans, ask for focus, confirm “what is seen,” and summarize findings.
– Data and continuous improvement
– Review which captures are missing in reopened cases and adjust guides/triggers.
– Hybrid model (AI + human)
– Define when AI tries first and when it hands off; ensure handoff with context.

There are also technological and operational challenges noted in field analyses on Visual AI: upfront investment, data quality to train models, and infrastructure to integrate visual tools into existing workflows. In support and field service, the effectiveness of visual AI depends on the system “seeing well” (useful captures, clear guides) and on the back end being able to turn that evidence into consistent next steps.

Finally, there is change management. Incorporating video, AR, or visual assistance alters routines: agents who must learn to guide captures, technicians who receive remote instructions, and organizations that must standardize what is considered sufficient evidence to close a case. Without internal adoption, the capability exists but isn’t used; and without a simple customer experience, the visual channel remains a promise.

Preference for Human Agents over AI

AI’s progress has not eliminated one reality: the customer, in general, still wants to talk to a person. According to Metrigy, 84% of consumers prefer interacting with a human agent rather than an AI agent, regardless of the format (voice, text, or video). Even if they are assured the issue will be resolved, 80% still prefer humans.

The reasons are revealing.Those who choose humans do so mainly because of the ability to understand the problem (43%) and the confidence that it will be resolved correctly (23%). By contrast, those who prefer AI cite as their main motivation the speed of resolution (41%) and the consistency of responses (19%). There is a key contrast: speed—the big promise of AI—is, at the same time, the least-mentioned reason for choosing humans.

Human vs AI Preferences
How to read the “human vs AI” preference (without falling into black/white)
– Pro-human reasons (what the customer is optimizing)
– Case understanding (43%): “that they understand me” when the problem is confusing or intermittent.
– Certainty of closure (23%): “that it gets properly resolved,” not just “that it moves forward.”
– Pro-AI reasons (what the customer is optimizing)
– Speed (41%): resolve quickly, especially in repeatable tasks.
– Consistency (19%): uniform answers and clear steps.
Implication: hybrid design tends to work best when AI handles the repeatable (with visual evidence) and the human steps in when there is ambiguity, risk, or a need for explanation.

This preference map suggests that the debate is not “AI versus humans,” but how to combine. The same research offers a pragmatic way out: although they prefer humans, 82% is willing to give AI agents a chance if the AI solves the problem or if it offers the option to transfer to a person. In other words, the customer tolerates automation when they perceive control and a clear path to human assistance.

Visual intelligence can be the bridge. If the AI (or an avatar) can see the device and guide concrete steps, the likelihood of a quick resolution increases; and if it fails, the human steps in with the context already gathered. The ultimate goal is not to impose a channel, but to meet the customer’s core expectation: resolve quickly and well, with the right interface for the type of problem.

The Revolution of Visual AI in Technical and Field Support

The promise of visual AI is not just “making video calls.” It is turning the interaction into a guided diagnostic process, where seeing the environment and the device reduces ambiguity. In troubleshooting, this translates into fewer long explanations, fewer misunderstandings, and a shorter route to the solution. Adoption and outcome data suggest that, when enabled well, the impact reaches hard metrics: satisfaction, resolution speed, and even sales.

Optimized hybrid technical support
Recommended hybrid model (visual AI + human) for technical/field support
1) Visual intake (customer)
– The customer starts with guided video/capture (ideally from a simple link).
2) Triage with visual AI

(when applicable)
– The AI requests specific views (model/LEDs/connections/screen), detects obvious signals, and proposes level 1 steps.
– Checkpoint: if evidence is missing (blurry image, wrong angle), the AI asks for a retake before concluding.
3) Resolution or escalation with context
– If resolved: it is closed with a summary + attached evidence.
– If not resolved: it is handed off to a human with the context package (captures, steps already tried, findings).
4) Human in “level 2” mode
– The agent comes in without repeating basic questions and focuses on advanced hypotheses/decisions.
5) Learning
– Unresolved cases feed improvements to capture guides, triggers, and the knowledge base.

The opportunity lies in closing the gap between what the consumer wants (visual interaction) and what many companies offer (limited or hard-to-use video). The challenge is to design a hybrid model that respects the preference for humans, but leverages AI to speed up what’s repeatable. In that balance, a smooth transfer to a human agent is not a “fallback”: it’s part of the product.

The Future of Technical Support with Visual AI

The horizon points to more interactions with AI avatars and augmented reality, trained not only with words, but with visual elements in 3D. As service issues become more complex, the ability to “see to understand” is shaping up to be a requirement, not a differentiator. Competition will shift toward who best integrates interface, visual evidence, and reasoning to resolve quickly and accurately.

Transformation of the Customer Experience

In telecom, where home connectivity is critical and WiFi fails frequently, visual support fits naturally: it makes it possible to inspect routers, connections, and signals without relying on imprecise descriptions. With an accessible visual channel, the customer feels that the company “gets into the problem” with them, instead of reciting scripts.

Efficiency in Problem Resolution

The available numbers point to substantial improvements when visual capabilities are used: higher satisfaction and faster resolution speed. In a sector pressured by cost per interaction and handling times, reducing time to resolution—and preventing it from stretching by up to 80% due to lack of visual evidence—is a direct operational advantage.

Integration of AI and Augmented Reality

Visual support is not limited to an agent looking at a camera. The evolution includes AI that guides captures (“show the back of the router”), interprets what it sees, and suggests actions, as well as AR to point out components or steps. In telecom, this can turn self-service

assisted in a more reliable experience, always with the option to escalate to a human.

Challenges and Opportunities in Implementation

The main risk is implementing video as an exception. If the customer can’t find it or it isn’t easy to use, the investment doesn’t translate into adoption. The opportunity lies in designing journeys where the visual channel appears at the moments of greatest friction (sign-ups, troubleshooting, configurations), and in sustaining a hybrid model that responds to the preference for human agents.

The Future of Visual Support in the Sector

With consumers who want to see and companies that still don’t make it easy, telecom has room to differentiate itself. The direction seems clear: more visual intelligence, more guided automation, and smarter escalation to humans. In a market where the support experience defines loyalty, visual AI is shaping up as one of the next major competitive levers.

Visual Intelligence for Support and Troubleshooting is the most direct way to close the gap between what the customer describes and what is really happening with their WiFi, speeding up diagnostics and increasing satisfaction. At Suricata Cx we work precisely so that this “see to understand” is integrated naturally into telecom and ISP omnichannel support, combining automation with human escalation when the case requires it.

In practice, this means designing flows where visual evidence is captured early (before the case “drags on” due to repeated questions), and where the handoff to a human agent happens with context and traceability of the conversation, not as a restart of the diagnosis.