Redesigning customer support before choosing what to build.

Motorola Solutions supports complex, mission-critical products used by organizations that expect fast, reliable answers.

Customers needed to search for information, manage repairs, track orders, understand subscriptions, troubleshoot products, and resolve issues across a fragmented collection of digital experiences and support systems.

Behind the scenes, agents and teams worked across search, knowledge management, chat, case systems, repair platforms, ordering, invoicing, subscriptions, and other tools.

The challenge was not simply to improve a help center or add an AI chatbot.

It was to rethink how customer support should operate across the complete journey.

Motorola Solutions wanted to understand where generative AI could create meaningful value across customer support.

VBT began by working with stakeholders across departments to map the experience from product purchase and provisioning through ongoing support, troubleshooting, and repair.

We examined:

• How customers attempted to resolve issues
• Where support agents searched for information
• How cases moved between teams and systems
• Where knowledge became fragmented or difficult to access
• Which interactions required human expertise
• Where customers encountered unnecessary effort or delay
• Which existing platforms supported the future experience and which created constraints

This created a shared view of the complete support operation rather than a collection of isolated technology problems.

Using the principles behind VBT’s Transform Work approach, the engagement examined how people, systems, information, and AI could work together across the future support experience.

Every major component was evaluated against three possible directions:

This prevented AI from becoming a disconnected feature layered onto an already fragmented experience.

VBT envisioned a unified support experience where customers could begin with a need rather than first determining which system or channel contained the answer.

Through one purpose-built experience, a customer could potentially:

• Find trusted product and support information
• Review open support cases
• Track repairs and orders
• Understand software and subscription status
• Receive guided troubleshooting
• Complete common support actions
• Escalate smoothly to a person when expert judgment was required

Behind that experience, the solution would connect the relevant systems, customer context, knowledge, business rules, and human-support processes.

The concept demonstrated how AI could simplify the customer experience without hiding the controls, systems, and people required to support it.

The engagement produced more than a list of AI use cases.

VBT delivered:

• A complete map of the customer-support journey
• An assessment of the existing support ecosystem
• Identified operational bottlenecks and knowledge gaps
• Recommendations for which systems to enhance, replace, or augment with AI
• A proposed future architecture
• Experience concepts and wireframes
• A prioritized roadmap based on business impact and feasibility
• A practical sequence for what Motorola Solutions could build, in what order, and why

The result gave leadership a clearer basis for investment decisions before committing to a large implementation program.

The engagement crossed all three areas, but Transform Work was the organizing idea: begin with the complete work and customer journey, then determine which experiences and systems must change.

Motorola Solutions left the engagement with a shared vision of the future support experience and a prioritized blueprint for moving toward it.

Leadership could evaluate opportunities against a coherent operating model rather than funding disconnected experiments or individual technology requests.

The value of the work was not measured by the number of prototypes created. It was measured by the quality of the decisions the organization was now equipped to make.

It begins by understanding the complete experience, identifying where work and information break down, and defining how people, AI, and systems should operate together.

Only then can a company make informed decisions about what to enhance, what to replace, where AI belongs, and what should be built first.