AI That Talks to Customers vs AI That Never Does: Camping World and Avid Solutions Compared

Key Takeaways

• Camping World, the NYSE-listed retailer that describes itself as the world’s largest RV retailer, built a customer-facing virtual agent, named Arvee, on IBM watsonx Assistant integrated with the LivePerson conversational cloud platform. Since deployment, customer engagement across its platforms grew 40 percent, agent efficiency improved 33 percent, and average wait times dropped to 33 seconds, a figure that sits at the edge of what call centre industry benchmarking generally treats as acceptable rather than exceptional, useful context rather than a knock against the result.
• Avid Solutions, a research and development firm working with government agencies on self-sustaining agricultural systems, took the opposite approach: AI that customers never see. Using IBM watsonx Orchestrate to automate back-office workstreams, customer onboarding, project management and expense reporting, the company cut project management errors 10 percent and onboarding time 25 percent, with a stated target of 50 percent and 20 percent respectively as the automation expands further.
• The two deployments answer genuinely different questions, and comparing them directly on a single metric would miss the point. Camping World’s AI has to perform live, in front of a customer, in real time, where a bad answer is immediately visible. Avid Solutions’ AI works entirely behind the scenes, where the standard for success is quieter: fewer errors, less time lost, work a customer never has to notice happened at all.
• Both tools sit on the same underlying IBM watsonx platform but do meaningfully different jobs. Watsonx Assistant, paired with LivePerson’s Conversational Cloud, a real integration with roots in a 2017 partnership between IBM and LivePerson, is built for live conversational interaction. Watsonx Orchestrate is IBM’s agentic workflow automation product, explicitly positioned as complementary to, rather than a replacement for, traditional robotic process automation, adding a layer that interprets intent and adapts across multi-step workflows rather than only executing pre-scripted rule-based steps.
• Independent, authoritative benchmarks for both companies’ specific figures are limited. Contact centre wait-time benchmarking is dominated by vendor and industry-blog sources rather than a single independent research body, and no credible independent baseline exists for typical onboarding time or project error rates at small professional and technical services firms, so both companies’ percentage improvements are best read as real, specific, self-reported results rather than performance measured against an audited external standard.

Two AI deployments, built on the same underlying IBM watsonx platform, doing almost opposite jobs. Camping World’s virtual agent talks directly to customers, in real time, across web and phone, where every response is visible the moment it happens. Avid Solutions’ automation never talks to a customer at all, it sorts emails, tracks onboarding steps and flags project errors entirely inside the business, where success looks like nothing going wrong rather than anything dramatic happening. Read together, the two cases are a useful reminder that “AI for customer experience” and “AI for operations” aren’t the same investment, aren’t judged by the same standard, and shouldn’t be evaluated with the same checklist.

Camping World: AI that has to perform live

Camping World, which describes itself as the world’s largest retailer of recreational vehicles, relies heavily on its contact centres to serve three distinct groups of customers: retail buyers, financial services and insurance customers, and its dealership network. A post-pandemic surge in customer volume exposed gaps in agent management and response times, and the retailer had no 24-hour call centre, so questions arriving outside business hours were delayed, dropped, or simply went unanswered. “We are a unique business where we serve three very distinct sets of customers who are into the RV lifestyle. There’s the retail side, financial services like insurance or goods, and the dealership side,” said Saurabh Shah, Chief Digital Officer and Chief Information Officer at Camping World. “We have a decently-sized call center, but I can’t have one agent meeting the needs of each of the three different business units. That creates significant complexities in staffing up our call centers.”

Brenda Wintrow, Senior Vice President of Sales and Customer Experience at Camping World, said the resulting wait times had become a real problem for customers, compounded by the team’s limited visibility into performance and chat volume. Camping World turned to IBM Consulting to modernise its contact centres, deploying a solution powered by IBM watsonx Assistant integrated with the LivePerson conversational cloud platform across its web properties and phone channels. The resulting virtual agent, named Arvee, routes and manages capacity dynamically, connecting customers to a live agent for anything too complex for the assistant to resolve, including after-hours lead generation the team previously had no way to capture. “We were looking to create more free time for our agents to build meaningful and impactful conversations with our clients. That meant removing noisy, quick, simple queries that could be answered faster with automation,” Shah said. Since deployment, customer engagement across Camping World’s platforms has grown 40 percent, agent efficiency has improved 33 percent, and average wait times have dropped to 33 seconds.

That 33-second figure is worth reading against a benchmark rather than taken as self-evidently excellent. Contact centre industry commentary generally treats an average wait time under 20 seconds as strong performance and the 20 to 30 second range as acceptable, with times beyond 30 seconds flagged as needing improvement, a widely used framing though it comes from vendor and industry-blog benchmarking rather than a single independent research body. On that scale, Camping World’s 33 seconds sits right at the edge of acceptable rather than in the standout range, which doesn’t diminish the achievement, going from an unstaffed after-hours gap to a 24-hour, capacity-managed system is a genuine operational improvement, but it’s a more accurate way to read the number than treating “33 seconds” as an unqualified win in isolation.

The integration itself is real rather than a marketing pairing invented for the case study. LivePerson, a publicly listed conversational AI and customer engagement company, has partnered with IBM on Watson-powered customer care products since at least 2017, an arrangement that has evolved into today’s watsonx Assistant integration with LivePerson’s Conversational Cloud platform, still documented in LivePerson’s own developer resources. At least one third-party industry analysis describes LivePerson’s revenue as having weakened in its most recent reported quarter, positioning it as a mid-tier player behind larger conversational AI vendors, a characterisation worth knowing as context, though it comes from a single secondary source rather than an audited industry ranking and doesn’t affect whether the specific integration Camping World deployed is a genuine, supported product.

Avid Solutions: AI that never talks to a customer

Avid Solutions, a research and development firm working with government agencies to build self-sustaining agricultural systems both on Earth and in space, found that the day-to-day logistics of running its consultancy, customer onboarding, project management and expense reporting, were time-consuming and error-prone, pulling focus away from its core scientific work. “Customer onboarding, project management and expense reporting are critical tasks for our business, but they are also time-consuming and error-prone,” said Dr. Malcolm Adams, Chief Executive Officer of Avid Solutions. Adams and his team adopted IBM watsonx Orchestrate to automate the repetitive steps within these workstreams for customer service representatives, project managers and accountants. One early use case has watsonx monitor incoming emails for a specific subject line and attachment, then automatically route the document to the right repository in the company’s cloud-based filing system, removing a manual sorting step entirely.

“By automating tedious workstreams with Orchestrate, we are now able to free up our employees to focus on more strategic work and their own development, while also improving the overall efficiency of our operations,” Adams said. The automation has cut errors in Avid Solutions’ project management processes by 10 percent and reduced the time it takes to onboard new customers by 25 percent. “We have also seen a number of qualitative benefits from Orchestrate. Our employees are more satisfied with their jobs because they are no longer bogged down by repetitive tasks. We have also seen an improvement in customer satisfaction because we are able to respond to customer inquiries more quickly and efficiently,” Adams said. The company is now targeting a 50 percent reduction in onboarding time and a 20 percent reduction in project management errors as it expands the automation further.

Avid Solutions itself is worth being precise about: what can and can’t be independently confirmed beyond IBM’s own case study and the company’s public materials matters here. Its work on precision agriculture and controlled-environment food production tied to space-habitat research is consistent with what the company describes about itself publicly, but this publication could not independently verify which government agencies or contracts Avid Solutions works with, details the case study itself does not specify, and readers evaluating this case study on its own terms should treat those specifics as company and IBM-sourced rather than independently audited.

The tool behind each deployment is built for a different job

Both companies used products under the same IBM watsonx umbrella, but the two products are built for genuinely different work. Watsonx Assistant, the engine behind Camping World’s Arvee, is built for live, real-time conversational interaction, understanding a customer’s question as it arrives and either answering it or routing it to a human. Watsonx Orchestrate, the tool behind Avid Solutions’ automation, is IBM’s agentic workflow automation product, and IBM itself explicitly positions it as complementary to, rather than a wholesale replacement for, traditional robotic process automation. The distinction is a genuinely useful one for a UK C-suite evaluating either kind of tool: conventional RPA executes pre-scripted, rule-based steps exactly as defined, while an agentic layer like watsonx Orchestrate is designed to interpret intent and adapt across multi-step workflows, useful for exactly the kind of judgment-light but variable task, sorting an email by its subject line and attachment, that Avid Solutions automated first.

Two different jobs, two different standards for success

It’s worth resisting the temptation to declare one of these deployments more impressive than the other, because they’re not really comparable on the same axis. Camping World’s AI is judged by customers directly and immediately, every interaction is a live test, and the cost of getting it wrong is a frustrated customer in the moment. Avid Solutions’ AI is judged by whether errors and delays quietly stop happening inside the business, a standard that produces no dramatic customer-facing moment at all when it works. Neither company’s percentage figures come with an independently audited external benchmark to compare against, contact centre wait-time standards are themselves drawn from vendor and industry commentary rather than a single authoritative body, and no credible independent baseline exists for onboarding time or project error rates at small technical services firms, so both sets of results are best read as genuine, specific, self-reported outcomes rather than measured against an external yardstick. What the two cases together show a UK board is that “we’re investing in AI” isn’t one decision, it’s at least two distinct ones, whether the AI needs to perform in front of a customer or quietly get better at not making mistakes behind one, and the two require different products, different success measures, and different tolerance for visible failure while the system is still learning.