Integrating IT, OT, and AI for real-world competitiveness

At Hannover Messe 2026, during my session, “AI and Industrial Energy,” and in conversations throughout the event, one message became unmistakably clear: industrial competitiveness is entering a new phase. It will be defined by how effectively companies connect IT, OT, and artificial intelligence to generate measurable value inside operations.

Across energy-intensive sectors and advanced manufacturing, opportunity is no longer theoretical. It is immediate, quantifiable, and already reshaping how leading plants operate. What is changing is not ambition. What is changing is the standard. Results must show up in stability, throughput, quality, and energy performance.

See also: Why IT/OT initiatives fail when executive engagement stops at sponsorship

This year carries additional symbolism. Brazil took center stage as the official Partner Country, and that matters. Hannover Messe is one of the world’s most important industrial stages, in Germany, a country that represents manufacturing excellence. Brazil’s presence signals something in which I strongly believe: Scale matters, but execution matters more.

When I look at industrial artificial intelligence today, I see a clear priority. The biggest gains do not come from buying new machines. They come from extracting more performance from what plants already have, through process optimization, variability reduction, and smarter use of existing data and control systems.

Energy efficiency is not an asset challenge. It’s a data challenge

That is why energy efficiency has become a defining battleground. Not because industry lacks equipment, but because it often lacks operational intelligence. Recent research as reported by the Financial Times highlighted the startling reality that the industrial sector leaves approximately 17% of potential energy efficiency untapped.

This loss isn't due to a lack of physical assets or machinery; it’s due to a lack of operational intelligence and a data challenge. When AI is integrated into process control, it can optimize consumption in real time, adapting to changing conditions faster than manual approaches can. That is where competitiveness becomes tangible: through less waste, more stability, better output, and stronger resilience.

There is plenty of noise around artificial intelligence. In industry, noise does not matter. Value must be measurable. AI becomes real when it is embedded in daily operations, close to the process, improving decisions where performance is actually created. That is where results show up: lower energy use, higher productivity, fewer disruptions, more predictable outcomes.

Stories of AI adoption: Wolfspeed uses ‘three-stage maturity model’ for agent deployment

What remains expectation rather than reality are generic initiatives that sit on top of operations as disconnected digital layers. AI cannot be a side project; it must become part of how the plant runs.

This is where many organizations struggle. Scaling AI is not primarily a technology problem. It’s an integration problem. Most legacy OT environments were not designed for real-time data integration or autonomous optimization.

Modernization is essential, but modernization does not mean rip and replace. It means connecting operational systems to digital intelligence, step by step, with clear ownership and clear KPIs targets.

The triple threat of IT, OT, and AI

True value emerges when IT, OT, and AI work in unison. IT brings architecture, cybersecurity, governance, and scalability. OT brings process truth, control, and operational constraints, while AI brings optimization, prediction, prescription and decision support in real time. When these layers operate together, technology stops being an experiment and becomes part of the plant’s operating culture.

Brazil’s role as Partner Country reflected more than visibility. It reflects an industrial culture that knows how to execute under real constraints: scale, complexity, cost pressure, and constant change.

Brazil combines industrial breadth with need for operational flexibility and a collaborative approach to artificial intelligence. That combination accelerates the path from concept to execution.

See also: Expanding expert decision capacity with AI reasoning agents

Many artificial intelligence projects fail for one reason: they never leave pilot mode. Scaling happens when AI is tied to operational KPIs and has clear ownership inside the plant. Not in a disconnected innovation lab. Not as a proof of concept without accountability. Inside operations, with responsibility, governance, and measurable targets.

Industry’s future belongs to companies that stop treating technology as an isolated initiative and start embedding it into the fabric of operations. Competitiveness will not be won by who talks most about artificial intelligence. It will be won by who integrates IT, OT, and AI to deliver performance, every day.