Artificial intelligence, particularly through the advancements of Large Language Models (LLMs), has a significant impact on our society. This insight story examines how LLMs transform the customer journey using the example of robotic automation systems.
Before diving into the customer journey let’s first define two key challenges, we can tackle with LLMs.
What problems do LLMs address?
When it comes to selling robotic automation systems, finding, and presenting the right information is a major hurdle. Sales teams must efficiently identify relevant sources, navigate through similar datasets, and quickly provide specific details to potential buyers. This can be overwhelming, particularly for those new to industrial robotics, making the sales process daunting due to the flood of similar options available. The complexity of finding the right information often necessitates seeking external assistance. Specialized tools like customer support portals are developed to make finding and retrieving information more manageable. These tools need to be scalable and justified by the demand to be cost-effective.
Generating meaningful information, on the other hand, presents its own set of challenges. Challenges in the sales process are tailoring content in depth to meet the needs of the target audience, producing redundant information to align with the consumer’s language, and managing the complexity introduced by the diversity of sources. This requires a careful balance of information to ensure it is both relevant and concise, where unnecessary duplication can complicate management and inflate costs. Specialized applications have been developed to address these issues, ranging from semi- to fully automated systems capable of interpreting complex inputs, such as CAD files, to generate standardized documentation templates. However, integrating these tools into the workforce introduces additional training and associated costs.
Trends shaping industrial robotics sales
A major trend in industrial robotics is the increasing accessibility for smaller businesses. This trend is driven by lower hardware costs and efficiency gains from AI applications. This democratization of technology enables a broader range of businesses to leverage robotics, opening new markets for sales teams.
LLM applications throughout the robotics sales journey
The following list provides concise insights into where LLMs impact the industrial products’ customer journey. Each step will provide one example where LLMs innovate your customer relationship.
Awareness & consideration phase
The customer journey begins with awareness, where potential clients recognize a need that industrial robotics could fulfill. During the consideration phase, they explore and evaluate their options.
For potential clients, finding detailed and relevant information about robotic solutions is essential. Previously, expert consultants would handle inquiries about automation solutions, customization, and ROI calculations. However, the high costs associated with expert consultations are not viable for clients interested in purchasing just one small collaborative robot (cobot). This has led to a demand for comprehensive educational materials, such as detailed guides and manuals, to help clients understand their options. The challenge lies in ensuring this information is meaningful and accessible to the potential buyer’s needs.
Implementing AI-driven solutions like specialized chatbots can guide potential clients to the information most relevant to their needs, simplifying the initial exploration phase.
Decision-making phase
After reviewing their options, clients move to the decision phase, choosing the robotic solution that best suits their requirements. This involves a deep dive into product features, benefits, pricing, and overall value, including consideration of integration costs for cobots.
Generative scenarios enable customer decisions at a low cost. For SMBs, an investment decision must have a tangible outcome. Simulations can provide proof. Simulations, however, are costly and require exact knowledge of the expected outcome, two things that are likely missing in a cobot use case for an SMB. Artificial scene generation visualizes tangible scenarios that are meaningful to the customer’s purchase decision at a much lower cost than a simulation. Additionally, a self-service AI scene generator consistently drives the idea of customer empowerment.
Purchase phase
The purchase phase focuses on facilitating a smooth transaction and addressing any lingering concerns to prevent last-minute hesitations. AI enhancements can lead to more competitive pricing, possibly the main concern for a potential cobot customer.
Enhancing the efficiency and effectiveness of engineers, the back office, customer support, controlling, and sales helps to compete with lower wage markets. An AI chatbot and AI services, such as a CompanyGPT help employees find relevant information faster and make it easier and less costly to oblige to company processes. AI-empowered employees can focus on value-adding activities, giving your company a competitive edge.
Retention and advocacy phase
Maintaining a strong relationship post-purchase can transform clients into brand advocates. This involves ongoing support, customer service, and engaging clients with training or additional offers.
A cobot client can make the most out of a purchase when training content becomes meaningful and relates to real-life roles: Training content is more effective when it is relevant to the learners’ job responsibilities. Creating meaningful information for the right audience can involve scenario descriptions, case studies, and video testimonials. Creating this kind of content is costly! For cobot manufacturers especially since their customers are diverse and cover niche markets. Tailored training content, created with AI, can directly address clients’ real-world needs, enhancing the utility of the purchased robotic systems.
AI can also support the creation of dynamic handbooks.
Smart handbooks give maintenance and repair personnel the experience of talking to an expert technician: Cobots are designed so that customers no longer require integrators for their applications. The manufacturer promises its customers that they will be able to integrate the automation systems into the production line or workstation themselves. The ability for self-service integration depends on the programming capabilities, which in turn depends on the manufacturer’s software and handbooks. Providing a community with handbooks, Q&A web pages and forums for proprietary systems has always been hard. Even when the language is open source, like Python, the underlying system is not. A fine-tuned expert AI can be the much-needed interface for non-software engineers. Expert AI can guide customers to success.
Throughout the customer journey, robotic automation system manufacturers can leverage AI to create more specific information for their customers’ needs.
By addressing specific challenges and needs at each stage, AI facilitates a more informed, efficient, and satisfying purchase experience, laying the foundation for long-term customer loyalty and advocacy.
