$74B+ market by 2029. $50T addressable. Only 12% deployed beyond pilot. Foundation models enable natural language robot instruction. Value strongest where variability defeats automation. Safety is the absolute prerequisite. Sim-to-real gaps require validation. Workforce transition planning essential.
72% use GenAI regularly. Only 18% have dependency policies. Trust escalation reduces verification. Skills atrophy through disuse. AI anchoring prevents independent analysis. Junior employees skip foundational development. Think-first-then-AI preserves reasoning. Cognitive metrics must complement productivity.
$50.3B market by 2030. 15% of decisions autonomous by 2028. Only 14% scaled to production. 40%+ face cancellation. 130 genuine vendors among thousands. 30-35% success on complex tasks. Agent washing is rampant. Success requires evaluation infrastructure and graduated autonomy.
The AI correction is here. $2.5T spending with 95% seeing zero P&L impact. $1.9M per initiative with under 30% CEO satisfaction. 85% stuck in pilot. GenAI in Trough of Disillusionment. 71% expect budget cuts. Winners fix data first, upskill for 2.7x ROI, and target high-value use cases.
Only 28% of AI projects fully succeed. 95% saw zero GenAI P&L impact. 48% reach production. 85% fail from poor data. 63% start from FOMO. Successful organizations redesign workflows first (2x more likely), build data foundations (10.3x ROI), and establish AI operations ownership (5.7x fewer rollbacks).
Developer hiring has bifurcated into junior oversupply and senior scarcity. Time-to-hire jumped to 95 days. Senior salaries hit $235K. 3x more ML engineers needed. Junior postings down 34%. The shortage is 40% worse than 2025. AI amplifies senior output but reduces junior demand. Leaders must upskill, hire globally, and maintain pipelines.
