How the era of AI experimentation has ended—and why leaders must act now or risk irrelevance
The market is experiencing a tectonic shift that most executives are underestimating. After years of cautious experimentation, artificial intelligence has crossed the threshold from technological curiosity to strategic imperative. The question is no longer “does this work?” but “how fast can we scale it?” As a consequence, the investments in AI infrastructures reach unprecedented levels, with a recent article of the Wall Street Journal calculating that the amounts invested this year by the US Big Tech in AI represent 10 times the ones of the Apollo program targeting to send human on the Moon. The companies moving fastest on AI adoption are creating competitive gaps that may prove insurmountable—and the window for others to catch up is closing rapidly.
The Great Divide: From Pilots to Production at Scale
A clear bifurcation is emerging in the industrial sector. While some organizations remain trapped in “proof-of-concept purgatory,” forward-thinking industrial giants—Veolia, Thales, Siemens, and others—are already deploying AI at scale and reaping measurable returns. The gap between experimenters and executors is widening daily, and industry observers believe 2026 represents a critical inflection point.
In the industrial sector, for example, the data from early adopters paints a compelling picture:
- 30-40% reduction in downtime through predictive maintenance
- 15-25% energy optimization via AI-driven process control
- 50% faster time-to-market for new products through accelerated R&D
- Mass customization without cost penalties, enabling personalized products at industrial scale
In the IT industry, agentic factories may have an even more disruptive impact on application development and maintenance, with up to 90% efficiency gains while enhancing quality.
These aren’t projections—they’re operational realities today. And they represent advantages that competitors may never recover from.
The Employment Paradox: AI as Job Creator
Perhaps most striking is recent Nobel Prize-winning economic research showing that companies most advanced in AI adoption are also creating the most jobs. This counterintuitive finding shatters the dystopian narrative of AI-driven unemployment.
The reality is more nuanced and optimistic. AI doesn’t eliminate work—it transforms it, elevates it, and creates entirely new categories of value-adding activities. The companies attracting top talent today are precisely those demonstrating AI mastery, not those avoiding it out of misplaced caution. From a human resources’ perspective, recruiting AI-skilled talents able to create and manage agents has become a critical competitive advantage.
Small Models, Big Impact: The Technology Paradigm Shift
A critical technical evolution is reshaping industrial AI strategy: the era of bigger-is-better is ending. Small Language Models (SLMs) are now outperforming their massive cousins for company applications, offering:
- Superior speed and efficiency with lower computational costs
- Local deployment via edge computing, eliminating cloud dependency
- Enhanced security as sensitive data never leaves the company perimeter
- Domain specialization through training on industry-specific datasets and vocabularies
Combined with edge computing, SLMs enable resilient, autonomous AI systems that operate even in connectivity-challenged environments—mines, remote facilities, offshore platforms—where industrial operations have historically struggled with digital transformation.
This technological shift represents a strategic opportunity: companies no longer need to compete on infrastructure scale. A well-architected SLM deployment can outperform massive cloud-based systems for specific use cases while maintaining complete data sovereignty.
Sovereignty Through Application, Not Just Infrastructure
Europe’s GPU infrastructure lags significantly behind the US and China: only 4% of GPU capabilities are in Europe, against 70% in the US—a hardware reality unlikely to change soon. But a more promising truth is emerging: the battle for AI sovereignty will be won through models and applications, not just processing power.
Open-source AI is democratizing access to foundational technologies. European AI champions prove that world-class models can emerge outside Silicon Valley. The real opportunity lies in vertical AI applications—industry-specific agents and workflows that intimately understand for instance mining operations, chemical formulation, logistics orchestration, or manufacturing optimization.
Industrial leaders who invest now in proprietary AI capabilities tailored to their core operations won’t just reduce dependence on American hyperscalers—they’ll create defensible competitive moats that generic AI vendors cannot easily replicate. This is particularly relevant for new champions who can leapfrog traditional technology adoption curves by building sovereign AI capabilities from the ground up.
The Coming Wave: AI Democratization
Within 3 years, creating AI agents will be as common as building spreadsheets. Every professional will have the tools to automate tasks, analyze data, and orchestrate processes through custom AI assistants.
This democratization demands preparation today:
- Massive upskilling initiatives to build universal AI literacy across all roles and levels
- Organizational redesign around new functions—AI orchestrators, agent architects, agent supervisors, ethics officers
- Process reimagination to leverage intelligent automation end-to-end
- Governance frameworks to ensure ethical, secure, compliant AI deployment at scale
The companies preparing their workforces now for this agent-rich future will dominate tomorrow’s market landscape. Those waiting until the technology fully matures will find themselves competing against organizations where every employee is augmented by purpose-built AI assistants.
From Strategy to Execution: The Velocity Imperative
The ultimate lesson from industrial AI leaders is simple but profound: velocity matters more than perfection. The industrial AI revolution rewards bold action over cautious deliberation.
Every month spent in planning and piloting is a month that competitors are scaling, learning, and building advantages. The first movers in industrial AI aren’t necessarily the most technologically sophisticated—they’re the fastest at translating capability into operational value.
The path forward requires:
- Ending the pilot phase: Transition from isolated experiments to coordinated, enterprise-wide deployment programs
- Investing in industry-grade applications: Focus on mature technologies delivering measurable ROI today—for instance digital twins, predictive maintenance, AI-assisted R&D
- Building sovereign capability: Develop proprietary models and partnerships that reduce external dependencies while maintaining cutting-edge capability
- Preparing the workforce: Launch comprehensive AI training programs across all functions, not just technical roles
- Establishing trusted ecosystems: Forge partnerships with technology providers aligned on values—sovereignty, transparency, security, and long-term capability building
The Window Is Closing
The market stands at an inflection point. AI adoption is no longer a technology initiative—it’s a business transformation imperative that determines market leadership for the next decade.
The choice is stark: companies that industrialize AI in 2026 will create performance advantages their competitors may never overcome. Those that wait risk permanent relegation to second-tier status as the gap between AI leaders and laggards becomes insurmountable.
The question business leaders must answer isn’t whether to embrace AI at scale. It’s whether they’re moving fast enough—and whether their organizational velocity matches the speed of market transformation.
The coming years may be the last ones when catching up is still possible. By 2030, the winners and losers of the AI revolution may already be determined.
This analysis draws on conversations with industrial AI practitioners, recent economic research on AI’s employment impact, and observations of enterprise AI deployment patterns across global market leaders.
Article by Philippe Mareine, Partner, PMP Strategy
February 2026
