{"id":17354,"date":"2026-04-24T14:47:42","date_gmt":"2026-04-24T12:47:42","guid":{"rendered":"https:\/\/www.kickmaker.fr\/blog\/?p=17354"},"modified":"2026-04-24T14:47:42","modified_gmt":"2026-04-24T12:47:42","slug":"ai-is-going-physical-thats-where-most-challenges-begin","status":"publish","type":"post","link":"https:\/\/www.kickmaker.fr\/blog\/ai-is-going-physical-thats-where-most-challenges-begin\/","title":{"rendered":"AI Is Going Physical. That’s Where Most Challenges Begin."},"content":{"rendered":"\n

<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

AI hardware mass production\u00a0is entering a new era.\u00a0The global humanoid robotics market is projected to reach $4.9 billion in 2025, growing at over\u00a050% annually. GM is embedding AI-driven “eyes-off” driving into its 2026 lineup. EightSleep adjusts mattress temperature in real time via machine learning. Waymo operates fully autonomous taxis across US cities.<\/p>\n\n\n\n

AI is no longer confined to the cloud. It is moving into physical products, and this changes everything.<\/p>\n\n\n\n

The challenge is not intelligence. It is making it work.<\/h2>\n\n\n\n

A model can perform perfectly and still fail once deployed, because at that point, it must deal with reality: unpredictable environments, physical constraints, system integration, safety requirements. You are no longer “only” building software, you are building something that has to function reliably, over time, in conditions you don’t fully control.<\/p>\n\n\n\n

At the leading parcel delivery carrier in France for example, dedicated teams focus on integrating AI into operational systems, ensuring it connects properly, runs continuously, and delivers value in production. Once hardware is involved, complexity compounds. Each layer introduces new dependencies and new failure points.<\/p>\n\n\n\n

Before the current AI wave, robots like Pepper<\/strong>, NAO<\/strong> (Aldebaran\/SoftBank), or HEASY<\/strong> (Hease Robotics) could perceive and act, but their autonomy collapsed in unpredictable environments. AI opens powerful new possibilities: predictive maintenance reducing downtime, synthetic data generation as a faster alternative to real-world data collection, and digital twins powered by LLMs accelerating prototyping.<\/p>\n\n\n\n

The market is accelerating, with caution<\/h2>\n\n\n\n

Asia-Pacific already accounts for over 40% of the global robotics market. China held more than 190,000 valid robotics patents by mid-2024, roughly two-thirds of the global total.<\/p>\n\n\n\n

Investment patterns already reflect this shift. Capital is increasingly directed toward specialized robotic systems designed for specific tasks, rather than general-purpose humanoids. Focused systems integrate faster, deliver measurable results, and provide a clearer return on investment.<\/p>\n\n\n\n

As AI becomes physical, it becomes heavier. More hardware, more integration, more constraints. Development cycles get longer, costs rise, and execution becomes significantly more demanding. This is why many projects fail after the prototype phase: because the level of complexity required to make it work was underestimated.<\/p>\n\n\n\n

3 Common examples where AI hardware projects fail<\/h2>\n\n\n\n

Most AI hardware projects don’t fail because the AI doesn’t work. They fail because the system around it wasn’t engineered for reality.<\/p>\n\n\n\n

1\/ Proving the wrong thing<\/strong><\/p>\n\n\n\n

Concrete use case: A humanoid robot client focused on developing a walking demo. Compelling on stage, but it added months of development without demonstrating what actually mattered: understanding the environment, interacting with objects, and operating safely.<\/p>\n\n\n\n

Problem: Engineers optimize for what looks impressive instead of what proves real value.<\/p>\n\n\n\n

Kickmaker recommendation: First think about the objective and the usage, then prove what creates value. We advised removing the walking constraint and using wheels instead. Not a shortcut, a sharper demonstration of value.<\/em><\/p>\n\n\n\n

2\/ Applying the wrong development logic<\/strong><\/p>\n\n\n\n

Concret use case: Many AI projects follow a pure software mindset: move fast, iterate, adjust later. This works when changes are cheap. It breaks down when each iteration impacts mechanics, electronics, and system integration.<\/p>\n\n\n\n

Problem: You don’t start with the walls and figure out the foundations later. If the base is wrong, everything built on top becomes unstable.<\/p>\n\n\n\n

Kickmaker recommendation: Complex AI hardware projects require both speed and structure. The right approach combines a strong systems framework with the ability to iterate across disciplines without losing coherence.<\/em><\/p>\n\n\n\n

3\/ Scaling before sequencing<\/strong><\/p>\n\n\n\n

Concrete use case: A humanoid robotics program nails its AI model on synthetic data, then discovers during field testing that actuator heat dissipation degrades sensor accuracy after 45 minutes.<\/p>\n\n\n\n

Problem: Skipping Engineering Validation Test (EVT), Design Validation Test (DVT), or Production Validation Test (PVT) milestones means safety-critical defects surface too late.<\/p>\n\n\n\n

Kickmaker recommendation: Sequence decisions properly. Prototype, EVT, DVT, and PVT. Answer different questions on different timelines, respect the process.<\/em><\/p>\n\n\n\n

Where Kickmaker stands<\/h2>\n\n\n\n

Kickmaker does not build AI models. We build the systems that make AI work in the real world.<\/p>\n\n\n\n

This is already our reality:<\/p>\n\n\n\n

\u00b7 Sanctuary AI:<\/strong> Development of advanced touch sensors and integration support for the Phoenix robot in industrial environments.<\/p>\n\n\n\n

\u00b7 Symbotic:<\/strong> Synthetic data generation studies for the Symbot platform in logistics.<\/p>\n\n\n\n

\u00b7 Confidential project:<\/strong> Industrialization of a humanoid hardware platform integrating AI pre-trained on hundreds of millions of public internet videos, a fundamental shift from traditional robotics.<\/p>\n\n\n\n

The question is no longer who builds the best AI, but who can turn it into a product that works, reliably, safely, and at scale.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

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