A guide to navigating the next-generation robotics wave

The generative AI movement may dominate today’s headlines, but another technological transformation is gaining momentum under the radar—the proliferation of autonomous service robots.

Their surge in popularity continues to be fueled by their ability to improve efficiency and reduce the need for manual labor in repetitive, hazardous tasks, as well as more recent advancements in sensor fusion and software-defined processes that improve their price-performance. In addition, the COVID-19 pandemic has underscored increasing demand for operational and supply chain resilience.

Reaching a critical tipping point

Many autonomous robotic solutions today have reached a critical adoption tipping point due to improved price-performance. Customer business cases are pointing to obvious ROI, driving adoption outside the confines of R&D labs to pivotal roles in the enterprise.

Demand for these robotic solutions is expected to balloon 15x by 2030, representing a $200bn market opportunity and comprising nearly three-fourths of all robotic systems. This rise isn’t limited to one sector — leading names in warehousing and logistics have already showcased the ROI of commercial service robots. Instead, it’s making its mark in retail, agriculture, healthcare, construction, and more.

The future: modular architectures & generative AI 

On the technology front, robotics solutions are transitioning from integrated to modular software and hardware architectures, paralleling trends observed in many technology categories, such as payments and cloud services. Software layers are now well-abstracted, enabling companies to hone their unique offerings.

This shift erodes the entry barrier relied upon by full-stack robotics providers the way it did for web 1.0 companies that built their own payment infrastructure. With commoditized hardware and modular software, businesses in this industry can rapidly iterate on behalf of their customers and grow.

The fusion of AI and robotics promises a transformative wave for industries venturing into large-scale automation. Rich data is pivotal for optimizing fleets and enriching AI models, heralding advanced automation capabilities. The synergy between AI, machine learning, and real-world applications amplifies the potential of robots.

The role of data streaming, edge processing, and AI model training cannot be overstated. The ability to collect and manage extensive data generated by robots and seamlessly integrate the latest AI advancements is poised to amplify the growth of physical automation and other service capabilities.

Success heuristics for founders scaling robotics companies

Even though the rapid pace of innovation in this industry can render any advice quickly obsolete, the startups that are succeeding most in this field currently blend the following:

  • Modular architecture: Gone are the days when full-stack solutions reigned supreme. Today’s competitive advantage lies in the speed, consistency, and flexibility to generate business value. Leveraging modular architectures and commodity hardware can allow for more focus on your unique value proposition and avoid expensive, non-value-added activities.
  • Hardware isn’t forever: While hardware innovations can provide an initial edge, their value often diminishes compared to advancements in software, data, and integration. Use hardware as your initial entry point, but plan for its eventual commoditization.
  • Prioritize quick time-to-value: Lengthy customer conversion and implementation cycles are often the bane for robotics startups. Ensure your solutions offer immediate and measurable ROI. This not only accelerates revenue but also builds client trust. Applications that customers can pilot without halting their day-to-day operations can drive quicker sales and implementation cycles.
  • Adopt software-first strategies: Given that hardware costs continue to comprise a smaller percentage of the total value created by these solutions, robotics firms increasingly resemble their pure-play software company cousins. Adopt proven go-to-market strategies like “land and expand” and “layer cake” features to boost your stickiness and share of wallet. An easily integrated, initial solution can fast-track adoption and pave the way for more comprehensive solutions. Automate pilot-to-paid contract transitions for efficiency.
  • Deepen customer relationships: Successful autonomous service robot implementation requires buy-in from top brass and ground-level operators. Missing either can lead to poor in-field performance and customer attrition. Direct customer engagement helps you gain essential feedback, handle unforeseen challenges post-implementation, and refine your customer profile. Only rely on intermediary channels if your solution is entirely hands-off.
  • Positive unit economics: It’s crucial to grasp the full spectrum of your unit cash flows and not just the obvious upfront ones. This includes the timing, magnitude, and persistence of all unit cash flows, including post-sale services like implementation, maintenance, and warranty servicing. Failing to address underlying product issues early on will compound exponentially as your robotic fleet size scales linearly, amplifying costs. Equip your solution with remote diagnostic capabilities and real-time operational and log data streaming to enhance scalability.
  • Consider Robotics-as-a-Service (RaaS): RaaS models are becoming increasingly lucrative. Offering robots as a service can lead to more predictable revenue streams and appeal to a broader customer base, especially among small- to medium-sized businesses. Furthermore, emerging alternative financing options can optimize capital costs (vs. pure venture equity alone) and accelerate cash-to-equity conversions.
  • Consider novel GenAI-native architectures & training: AI is eating software, and leveraging the latest innovations in generative AI can potentially enable new robot use cases through improved inference or model performance, more generalizable and semantic understanding of the operating environment, and more efficient model training.

A final word

Today, modern autonomous service robots enhance mission-critical and operational support functions across diverse industries and supply chains. But as more startups enter the field, it is important to take a smart look at strategies, adopt integrated modular software and hardware architectures, and fuse AI and robotics. There has never been a more opportune moment for innovation in this domain.

Brian Wei is an Associate at BMW i Ventures, where he focuses on early to growth stage investments in B2B application and infrastructure software, industrial automation, mobility, and sustainability. Scott Walbrun is a principal venture capital investor at BMW i Ventures, where he focuses on Series A and B investments around the future of mobility, energy, robotics, and AI. In this capacity, Scott has served on the board of eight companies. He also serves as a mentor and workshop leader for hundreds of startups through various accelerator programs.

BMW i Ventures is an independent venture capital fund managed on behalf of BMW Group based in Silicon Valley, with an office in Munich.

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