Dec 10, 2025 Sina Schäfer
ShareHow will the use of artificial intelligence (AI) evolve in industrial environments in 2026? And what impact will this have on core areas such as production, logistics, and supply chain management? Here is a concise overview of five developments that will be particularly influential in the coming year and a look at where AI is heading.
Few years have accelerated the advancement of AI as visibly as 2025. AI became more widely accessible, more powerful, and more systematically adopted. Many companies took their first concrete steps towards automation, generative assistance, or data-driven process optimization. In 2026, this momentum will continue. AI is becoming an integral part of operational value creation and a foundational component of modern manufacturing and logistics environments.
Trend 1: AI agents increasingly take over operational tasks
In 2026, AI agents will play an even greater role in day-to-day operations. These agents are advanced software components that:
- analyze data streams in real time
- adapt dynamically to changing conditions
- derive proactive recommendations
- operate autonomously and directly interact with users
As their adoption grows, companies will begin using not just a single assistant but multiple specialized agents working in parallel on different subtasks and connecting their analyses. This creates a dynamic ecosystem in which several digital helpers monitor different aspects of a process simultaneously and jointly contribute to well-informed decision-making.
For example: A supplier informs a manufacturing company in the afternoon that a shipment will be delayed. A critical component will arrive three hours later than planned. Without intelligent support, planners might assess this information too late or without full context. With AI agents, the situation becomes more streamlined. A material-availability agent detects the deviation immediately and evaluates possible scenarios, another agent analyzes available machine capacity, and a third assesses supply chain alternatives, such as partial quantities from other sites. Together, these perspectives produce a prioritized recommendation displayed directly in the system, which decision-makers can adjust or approve with a few clicks.
INFORM trend report on AI agents
Our INFORM Trend Study 2025 shows that AI agents are no longer viewed as a futuristic concept but as a highly relevant and concrete technology:
- 89 percent of the 114 logistics and supply chain professionals surveyed see strong potential in AI agents
- 12 percent already use AI agents productively, 25 percent are planning pilot projects, and 33 percent are actively discussing their introduction
- Top application areas include demand planning, inventory management, risk assessment, and transport and production management
- The greatest tangible benefits cited are reductions in manual routine work, improved forecasting accuracy, and increased efficiency in operational processes
Trend 2: AI becomes the core of modern software architectures
While AI features have often been added as complementary modules to existing systems, 2026 marks a shift in the fundamental logic of software development. Applications are increasingly designed from the outset to incorporate learning, data analytics, and automated decision-making mechanisms as central architectural elements. AI is no longer viewed as an add-on but as a structural component that continuously shapes how software behaves.
For developers, this means an expanding toolbox:
- AI-generated code segments support routine functions as well as complex logic
- Automated tests become smarter, learning from real usage patterns where errors occur or adjustments are needed
- Deployment pipelines evolve by monitoring application behavior after new releases and optimizing their workflows automatically when certain steps repeatedly cause issues or delays
Software architecture is becoming more dynamic, more adaptive, and increasingly geared toward continuous evolution.
Trend 3: AI systems become more specialized and modular
As companies broaden their use of AI, demand grows for systems that deeply understand a specific domain or industry. Universal models provide a solid foundation but often fail to capture the nuances of production processes, material characteristics, or sector-specific workflows. Domain-specific AI systems therefore gain further importance in 2026. They are trained with specialist and historical data, recognize typical patterns, seasonal effects, and company-specific variations, and deliver significantly more accurate forecasts and assessments, for example, in demand planning, inventory management, or risk evaluation.
At the same time, modular AI architectures are becoming more common. Individual models take on clearly defined functions and can be combined flexibly. Companies can choose the components that address their specific needs without rebuilding their entire system landscape. For example, demand-planning components can be paired with inventory-optimization modules and extended with models for production planning or transport optimization. This building-block approach creates an adaptable model ecosystem that can be expanded, exchanged, or refined as needed, delivering greater precision and efficiency across well-defined areas.
Trend 4: Transparent and compliant AI systems become mandatory
As AI-based applications grow increasingly complex, the need to understand their behavior at all times becomes essential. Transparency is now a key quality criterion. The concept of AI observability aims to make the full lifecycle of an AI system comprehensible, including:
- how models are trained
- what data they use
- how they derive decisions
- how stable their performance is in different situations
In parallel, major European regulatory frameworks such as the AI Act, the NIS2 Directive, and the Cyber Resilience Act are entering new implementation phases. They require companies to document in detail how their AI systems operate, what risks exist, and which security measures are in place. For industrial companies, this means they must provide clear evidence of decision paths, data flows, and control mechanisms. Transparency and compliance are no longer optional, they are prerequisites for reliable and productive AI use.
Trend 5: New roles and competencies shape how companies work with AI
In 2026, the interaction between employees and AI systems will move further into focus. As AI systems more frequently provide recommendations, prepare scenarios, or support operational decisions, the need rises for roles that interpret and evaluate these results. New responsibilities emerge at the intersections of business domains, data management, and AI applications, while traditional roles such as dispatchers, planners, or shift supervisors evolve. Instead of spending time on manual data research, they assess proposed scenarios, evaluate potential impacts, and make informed approvals. Their roles become more analytical, more communicative, and more interconnected with other departments, while routine tasks shift toward automation.
At the same time, skills development and change management become increasingly important. Companies must ensure that employees are well prepared to work with AI and see new tools not as a burden but as support. Training, clear guidelines, and transparent communication about AI decisions are essential to ensure that systems can be used reliably in daily operations.
2026 strengthens the interaction between people, processes, and AI
As a result, 2026 will be a year in which AI not only achieves greater technological maturity but also improves the interplay between people, processes, and systems. At INFORM, we believe that those who actively shape this transformation will create the foundation for AI to become a trusted, natural part of their value creation.
What experiences do you want your organization to have with AI in 2026, and what steps do you plan to take next?
About our Expert
Sina Schäfer
Corporate Communications Manager
Sina Schäfer has been working as Corporate Communications Manager in Corporate Marketing at INFORM since 2021. Her focus is on external communication in the areas of inventory & supply chain, production and industrial logistics.