CX expert in robotics tells us how to choose which CX workflows to overhaul with AI

Jesica Chavez is the CEO of Robo Success, a boutique agency that helps robotics and technology companies scale while improving their CX operations. Before this, she held such roles as Head of CS and VP of CXS at various robotics organizations.

We sat down with Jesica as she walked us through her workflows and shared her advice for other CX leaders who are adopting AI within their organizations.

Practical, responsive, and results-driven

I’m Jesica Chavez, CEO of Robo Success. For more than 20 years, I have led customer experience initiatives and helped organizations build CX functions that support sustainable growth.

Robo Success is a boutique, growth-focused agency that helps robotics and technology companies achieve stronger outcomes through strategies aligned with their growth goals. Our focus is on startups and scaling businesses building or strengthening their customer experience capabilities.

We understand the unique challenges startups face when establishing their CX departments, and we take a broader view: CX is one essential part of delivering an exceptional overall experience. Our focus is making every interaction practical, responsive, and results-driven — from the first conversation through long-term success.

How AI can be used to streamline Help Center content creation

In the last year, we used AI to streamline Help Center content creation — a critical part of scaling customer support as a company grows.

Scribe is an AI tool that helps us turn repeatable product workflows into clear, step-by-step guides with screenshots automatically. Loom enables us to create short, personalized video explanations for more visual or complex topics — and its AI suite helps with editing and creating metadata. We use established messaging, terminology, and templates to ensure both formats reflect the company’s brand voice and communicate information consistently.

We then use this approved content to inform AI chatbots, giving customers access to consistent, on-brand guidance and support whenever they need it, 24/7. This creates a more seamless self-service experience, reduces friction and repeated support requests, and ensures customers receive the same clear information across channels.

As a result, we reduced the time previously spent producing articles and instructional videos. For example, an article that previously took more than two hours, plus a revision cycle, now takes approximately ten minutes. And by using approved content to inform AI chatbots, Tier 1 chat-support time has reduced by 30%.

We now redirect that capacity toward improving the overall customer experience strategy, identifying customer needs earlier, and helping teams move from reactive to proactive support.

A real-world Help Center content workflow

That AI-powered CX workflow begins when a new product feature is completed and published in Jira. This triggers a Slack notification and automatically creates a Jira task for the support team to prepare customer-facing content.

The support team uses Scribe to create a step-by-step guide with screenshots and Loom to produce a video walkthrough. Claude and Zapier support the workflow, coordinating agents and automating information movement between systems. Before publishing anything, a human reviews the content to confirm it is accurate, useful, and aligned with the brand voice.

Once approved, we publish the written guide to the Help Center, publish the video to YouTube, and feed both resources into the AI chatbot. This enables the chatbot to provide customers with consistent, up-to-date guidance 24/7 as soon as a feature is available.

We also share the same approved content with Marketing so the team can create feature announcements and customer communications. This keeps Support, CX, Product, and Marketing aligned around one accurate, consistent message.

Why humans are responsible for strategy and relationships

In addition to that, we use AI to inform and scale repeatable CX activities, including analyzing recurring support themes, identifying content gaps, and powering chatbots with approved, brand-consistent information. AI also helps us personalize guidance based on common customer needs and surface issues that may require attention.

The decisions that require judgment remain explicitly human. These include setting CX priorities, designing customer journeys, managing sensitive escalations or service recovery, and making strategic recommendations to leadership. AI can identify patterns and accelerate execution, but people provide the empathy, business context, and accountability required to make the right decisions for customers and the company.

Our approach is to use AI to reduce manual effort and improve responsiveness — while keeping humans responsible for strategy, relationships, and high-impact customer moments.

Why automated support sometimes fails — and how to avoid it

When we initially used AI for direct responses to customer support tickets, it resolved or advanced the majority of requests, but the responses sometimes failed to address the customer's needs. Even if AI helped ten customers, one frustrated customer was too many, especially when the interaction required context, empathy, or a more nuanced response.

That might lead one to believe that high-quality customer experience always requires a human. That was my assumption initially. But we have learned that customers do not necessarily need or want a human for every question — they need timely, accurate, and easy-to-understand help.

As a result, we changed our approach. AI now supports the process by drafting responses, summarizing information, and identifying relevant resources, but for more complex or sensitive cases, a human reviews the responses before sending them. And we regularly review the less sensitive responses that our chatbot sends automatically.

It's also important to note that AI is only as reliable as the information behind it, so outdated or incomplete source content can lead to unclear or inaccurate responses. We address this by using only approved content, becoming more disciplined about keeping our knowledge base current, and maintaining clear paths to human support.

How to choose where AI should be applied in CX

Jesica Shares

In robotics especially, every customer journey looks very different…So, my advice is to start with one priority metric and identify a specific area where AI can make a meaningful difference.

AI and automation can strengthen customer experience in many ways, but leaders should first assess what matters most for their business and how changes will affect their key metrics.

In robotics especially, every customer journey looks very different. The biggest mistake I have seen is treating robotics customer experience exactly like SaaS. Robotics often involves physical products, implementation, training, operational change, and ongoing support in real-world environments. Those moments require a different level of context, coordination, and customer education.

So, my advice is to start with one priority metric — such as SLA attainment, CSAT, customer lifetime value (CLV), or support-resolution time — and identify a specific area where AI can make a meaningful difference. For example, if faster response time is the goal, AI can help improve self-service content and automate Tier 1 support. If CSAT is the priority, using AI to identify recurring pain points and improve the customer journey may be more valuable.

The key is to begin with a focused use case, measure its impact, and iterate. AI should support a clear customer and business outcome — not be implemented simply because it is available.

Why AI adoption can be more difficult than CX leaders expect

Uff! I wish I had known how much time successful AI adoption requires for iteration, learning, testing, and evaluating different models.

Launching the pilot was only the beginning; the real work was refining prompts, improving the knowledge base, reviewing outputs, and learning where AI could add value without compromising the customer experience.

And then there's the team side. AI adoption is not only a technology decision — it is a people decision. Teams may feel excited about saving time, but they can also feel uncertain about how their roles will change, whether they can trust the outputs, or whether AI will replace their expertise.

Leaders should involve teams early, explain the purpose of AI, provide training, and make clear that AI is there to support, not replace, their work.

Then, set clear processes and well-defined goals. If the team cannot clearly explain how they handle customer issues, who owns the customer knowledge base, or which outcomes they are trying to improve, AI will likely add confusion rather than value. AI needs structured workflows, accurate information, and a measurable purpose.

Four pieces of advice for CX leaders implementing AI within their workflows

My advice to CX leaders is this:

• Embrace AI and treat it as an amplifier, not a replacement for customer understanding.
• Start with high-volume, repeatable work, such as content creation, knowledge management, and basic support guidance, where AI can improve speed and consistency without compromising trust.
• Keep humans accountable for strategy, complex decisions, sensitive escalations, and service recovery.
• And measure more than efficiency. Monitor accuracy, customer sentiment, resolution quality, and escalation patterns. One poor interaction can outweigh many successful automated ones if it damages customer trust.

Follow along

You can follow Jesica Chavez's work on LinkedIn.

More expert interviews to come on the CX Lead!