If you charge for resolved customer problems, you have to define 'resolved.' The hardest cases to define are the ones your AI deliberately hands to a person.

An AI agent can answer correctly, carry out the normal steps and recognize that a person needs to take over. Everything worked as designed. The customer still needs help.

Now follow that case. The human may need to see a device, check a document or guide the customer through something that has already failed several times. If that work happens somewhere else, how does the service platform know what finally happened?

This is the part I want to understand when we talk about AI-driven customer service. We can spend a lot of time discussing how many cases the AI completes. There is also a product question in the cases it deliberately hands over.

Follow the case beyond the handoff

As an example, take a customer trying to get their broadband working. The AI has the account information, checks the service status and guides them through the standard troubleshooting. The customer says they have followed the instructions. Still no connection.

A specialist joins. Perhaps the useful next step is to see the installation while asking the customer to move the camera or try something. A photograph could help, but it will not always show what changes when the customer follows an instruction.

At this point, the product decision is very practical. The human needs live visual access. How does the platform provide it, and what will the case contain when the interaction finishes?

The specialist could open a separate meeting tool, and with some manual work, they may solve the problem and add a note afterwards. That can be perfectly adequate for an individual case.

At platform scale, though, I would want to know whether the visual interaction was connected to the case, what the customer confirmed and whether they contacted support again about the same fault. If we cannot answer those questions, the resolution claim has become difficult to measure.

The customer has already supplied the simplest acceptance test: does the broadband work now?

Video has to behave like service work

A CRM may already have a good visual-assistance tool. The agent can start a call, share a screen and help the customer. There is no reason to dismiss that capability.

The larger opportunity is to make that interaction something the service workflow can request and manage. An AI agent or a case rule identifies the need. The appropriate specialist receives the work with the history of the case, including what has already been tried. The customer joins with as little additional effort as possible.

Then the result comes back.

The details decide whether this works. The routing system needs to know what kind of work it is assigning and how it affects the agent's capacity. The case needs the relevant session information. A recording, where appropriate and consented to, needs a clear retention policy and a way to retrieve it.

The outcome also needs more detail than 'video call completed'. Did the customer demonstrate that the equipment worked? Was another visit required? Did the interaction establish that the problem was somewhere else?

A completed call and a completed case can be different things. A service platform needs to understand the difference.

That makes live visual escalation useful across the product, wherever a workflow needs it. The same capability might support technical diagnosis or help someone complete a complicated application, with different rules for each. The platform continues to decide what happens next.

The commercial model needs the result

If a platform charges for successful resolution, the human-assisted cases deserve careful attention. Some will be eligible for a charge under the commercial agreement; others will not. Adding video does not settle that question.

What it can do is make a previously difficult step easier to complete and give the platform a better account of the result.

Think about an application that has stalled because a customer cannot complete a required document check. The AI has gathered the information and identified what is missing. A qualified person still needs to review something with the customer.

If that interaction can happen inside the service workflow, the application may continue during the same contact. The record can show what was checked and what still needs to happen. There is a concrete outcome to evaluate.

For a product team, this opens a useful commercial discussion. Does the capability help enterprises complete more of the cases their automation cannot finish? Does it reduce another contact or an unnecessary dispatch? Is there enough value for them to pay for it?

I would want evidence from those workflows before assigning a revenue number. But the unit we are examining is clear: an eligible customer problem that reached a human, received visual assistance and had a recorded outcome.

The platform can decide how that fits its pricing. It first needs to be able to see the work.

What VideoEngager brings into the workflow

At VideoEngager (VE), we have built live visual interaction into CRM and contact-center environments. The customer can join through a browser without installing another application, and the agent can use video alongside the existing service workspace. Screen sharing and snapshots with annotations support the interaction when they are useful.

This runs in production today. VideoEngager integrates with Salesforce, Genesys, Amazon Connect, Five9, Talkdesk, Zendesk and Verint, and serves enterprises in financial services, insurance, healthcare and government.

VideoEngager is SOC 2 Type II audited. For healthcare, HIPAA-ready deployments are supported with a BAA available. Deployment options also support GDPR requirements.

The product conversation then becomes specific to the environment: how the session is invoked, which context travels with it and how the result is attached to the case. A fully routed service channel has additional integration requirements.

The platform already has the customer relationship and the service process. VE supplies the live visual capability that process can call on when a person needs to see.

Recording also needs to fit the enterprise's requirements. VideoEngager supports recording storage in cloud, private-cloud and on-premises environments. The implementation needs to establish data residency requirements, access controls and retention settings, including how an authorized person retrieves the recording from the service record.

Today, session transcripts and video recordings attach to the case automatically.

That may sound less exciting than a video demonstration. Ask the team responsible for finding an interaction six months later. They will have a different opinion.

Prove it on the cases where sight can help

I would start with one or two defined workflows. Guided diagnosis is a useful candidate. A document review may be another, subject to the organization's approved process.

First establish how those cases perform today. Then compare what happens when live visual assistance becomes part of the workflow. How many customers join successfully? How long does it take to reach useful assistance? Does the case finish, and does the customer come back about the same issue?

The cost per completed case is worth examining too. A longer interaction may be a good investment if it avoids a field visit or another round of support. The data should tell us.

There will also be escalations where video adds very little. A policy restriction remains a policy restriction. A camera will not change a customer's entitlement. We should identify the cases where seeing can change what the human is able to do, and measure those.

For me, that is the product opportunity. The AI recognizes the limit of what it can finish, the platform brings in the appropriate human capability, and the resulting work remains part of the service journey.

The next time someone demos AI customer service, ask to see the case after the human leaves. What changed, where is the evidence, and how do we know the customer's problem is resolved?