Robotics landscape: From the lab to the field, where innovation meets execution

Robotics technology may be inherently complex, but its success depends on pragmatic execution and a clear roadmap for scaling innovation into sustainable, real-world solutions, write Krish Kupathil and Sridhar Solur.

Balancing cutting-edge innovation with practical execution is a constant challenge in robotics—a field where progress hinges as much on operational viability as it does on technological breakthroughs. It also offers pragmatic insights into critical, often overlooked aspects such as training datasets, deployment strategies, and cost optimization. Together, these insights offer a roadmap for navigating the challenges and opportunities shaping the robotics landscape.

In a conference room packed with roboticists and engineers, the Bill of Materials (BOM) was under scrutiny. Every component, from sensors to chipsets, was evaluated for reliability, cost reduction, and implications of chaos engineering. These discussions extended beyond designing cutting-edge technology to delivering reliable, cost-effective products capable of consistent performance in real-world conditions.

Fast forward to a boardroom, where metrics tied to BOM cost reduction, reliability improvements, and scalability dominate discussions. Questions arise about chaos engineering's role in bolstering product robustness and whether cost optimization targets are being met. These scenarios underscore a critical truth: robotics technology may be inherently complex, but its success depends on pragmatic execution and a clear roadmap for scaling innovation into sustainable, real-world solutions.

Key Insights for Robotics Success

1. Cost and reliability are foundational

Delivering reliable products at sustainable costs is critical for competitiveness. Reducing BOM costs by 40–50% within three years often determines long-term viability. Techniques like HALT (Highly Accelerated Lifetime Testing) and chaos engineering ensure durability and resilience.
Takeaway: Cost-efficiency and reliability must guide every stage of development.

2. Addressing corner cases is essential

Robotics success hinges on addressing rare but impactful scenarios:

• Consumer Robotics: Navigation challenges, like avoiding "lobster traps" or adapting to seasonal lighting, directly affect user satisfaction.
• Industrial Robotics: SKU entitlement in distribution centers presents a bottleneck. Even 60% SKU coverage, while impressive, can lead to operational nightmares if the remaining 40% is handled manually.

Takeaway: Ignoring corner cases undermines scalability and feasibility.

3. Infrastructure costs often exceed robot costs

In industrial robotics, civil engineering and integration costs often surpass the cost of the robots themselves.
Takeaway: Factoring in infrastructure costs is crucial for accurate ROI calculations.

4. Avoid unsustainable pilots

Heavily subsidized pilots may grow initial revenue but often mask product-market fit issues. Scaling these pilots at a loss can result in financial strain.
Takeaway: Validate long-term business models before scaling pilots.

Bridging the gap: Deployment challenges in robotics

Deploying robotics solutions presents unique hurdles. Successful projects address these challenges by focusing on:

• Zero or low integration scenarios: Starting small with minimal integration proves value and simplifies scaling.
• Comprehensive user training: Tailored training programs reduce adoption barriers and enhance success rates.
• Realistic expectations: Stakeholder alignment is essential, with a focus on progress over perfection.

Emerging technologies such as AI and advanced materials are increasingly helping overcome these barriers. AI-driven predictive maintenance reduces downtime, while modular robotics designs lower infrastructure requirements. In addition, AI-powered training simulators accelerate operator proficiency, bridging knowledge gaps.

The investor perspective: Scaling robotics with AI

Investors today scrutinize how robotics companies leverage AI to drive scalability and efficiency. Key areas of focus include:

1. AI utilization: Companies using AI for dynamic behavior adjustments and real-time decision-making at customer sites stand out.
2. Training data strategy: Investors assess whether companies rely on real-world or synthetic data and whether they can generate training data on the job to reduce costs.
3. BOM cost reduction: A clear roadmap for achieving a 40–50% reduction in BOM costs is vital, with AI playing a role in design optimization.
4. Size of the prize: Investors prioritize automation solutions addressing the largest share of customer budgets. For instance, in distribution center automation, ASRS systems and AMRs/AGVs dominate the budget, while picking solutions represent a smaller fraction.

Robotics success hinges not just on technological breakthroughs but also on operational excellence. Companies that prioritize cost efficiency, reliability, and scalable deployment are best positioned to deliver impactful solutions. Similarly, investors are increasingly drawn to teams that demonstrate innovative use of AI, robust data strategies, and clear cost-reduction roadmaps.

(The original article was first published in 'Business India')