AI trends for 2026: the five most important ones for businesses

AI trends for businesses: the 5 latest and most important trends

Current innovations in operations and process management (outlook for 2026)

AI is no longer an experiment, but will become a strategic operating system in 2026, enabling executives to radically automate processes, accelerate decision-making, and tap into new business models.

These are the five most important trends and innovations of recent weeks.

1. Hyperautomation and AI agents are becoming the norm

Hyperautomation describes the combination of RPA, machine learning, generative AI, and process orchestration, which automates not only individual steps but entire end-to-end processes. While RPA has long focused on clearly structured, rule-based tasks, AI agents now also take on unstructured, knowledge-intensive activities and make independent decisions within defined limits.

By 2025, agentic AI will have evolved from experimental pilots to productive systems that classify requests, gather information, prepare decisions, and then execute them directly in specialist systems. International analysts estimate that by the end of the decade, such agents will support or semi-automate a significant proportion of operational decisions, particularly in high-volume, repetitive scenarios.

For companies, this means:

• Efficiency gains through significantly reduced manual routine work, for example in master data maintenance, ticket routing, or standardized approvals.
• Significantly reduced turnaround times because AI agents work around the clock, consolidate context from multiple systems, and automatically trigger escalations.
• Lower error rates and better compliance, as rules are applied consistently and automatically documented.

Specific use cases range from automated purchase-to-pay processes and AI-supported service workflows to intelligent escalation chains in incident management. Industrial companies and global service providers are already reporting measurable efficiency gains when agentic automation is embedded directly into production, service, and back-office processes.

However, for these systems to operate securely and scalably, operations teams need a robust foundation: uniform process and data standards, clear authorization concepts, clean role models, and centralized monitoring that tracks both performance and compliance. Organizations that lay this foundation in 2025 will be able to gradually integrate AI agents more deeply into their day-to-day business in 2026 and beyond.

2. Generative AI is revolutionizing process design and optimization

While AI agents focus primarily on executing processes, generative AI plays to its strengths in the design, documentation, and continuous improvement of processes. Studies by global consultancies show that a large proportion of leading companies already use generative AI for product innovation, knowledge management, and operational support—and that proportion continues to grow.

This opens up new possibilities in process management:

• Generative models design alternative process variants, for example for onboarding routes, service flows, or internal approval processes.
• You automatically create process descriptions, work instructions, and training documents that are generated directly from diagrams, logs, and guidelines.
• They support teams in impact analyses ("What happens if this test step is omitted?") and simulate effects on throughput times or error risks.

Current surveys show that around 60% of companies are already using generative AI productively in at least one business function—from development and marketing to service, HR, and finance. At the same time, self-service automation is becoming established: departments use prompts to configure their own micro-workflows or document processes without having to master complex low-code platforms.

However, clear guidelines are needed to ensure that process automation can be widely implemented throughout the organization in this way:

• Role and authorization concepts that define who is allowed to create, approve, and modify which automations.
• Minimum standards for data quality, so that generated content and decisions are based on reliable information.
• Governance rules for model approval and output monitoring to prevent shadow automation, security gaps, and conflicting process logic.

When implemented correctly, this creates a controlled framework in which specialist departments can quickly experiment and become productive, while central teams ensure standards, compliance, and stability of core processes.

3. Process intelligence as a success factor for AI implementation

"You can't do without process intelligence!"—this statement is gaining traction in executive suites because it is becoming increasingly clear that AI only unleashes its full potential when it truly understands the context of business processes. Process intelligence combines process mining technologies, data integration, and AI analytics to make real-world processes more transparent and systematically uncover optimization potential.

Recent studies show that a large majority of executives intend to use AI specifically to improve business processes in the coming 12 months—not just for the selective automation of individual tasks. However, many companies have so far relied either on traditional RPA automation or isolated AI use cases without bridging the gap to a consistent process view.

The real competitive advantage arises where both worlds come together:

• Process Intelligence first reveals how processes actually run, which variants dominate, and where bottlenecks and rework loops occur.
• On this basis, target processes are developed, priorities are set, and business cases are quantified.
• Subsequently, automation, AI agents, and generative AI are used in a targeted manner to implement the identified levers.

Companies that consistently establish this cycle—understand, optimize, automate—report significantly faster improvement cycles, greater transparency, and closer integration between departments, IT, and data teams. Process intelligence is thus evolving from a niche topic to a strategic foundation for scalable AI deployment.

4. Explainable AI (XAI) is gaining importance

With the EU AI Act and similar regulations worldwide, the focus is shifting from "What is technically feasible?" to "What is comprehensible, safe, and legally permissible?" In the European context in particular, companies will in future have to be able to explain in detail how AI models arrive at their decisions, especially in high-risk areas of application.

Explainable AI (XAI) addresses precisely this requirement. It provides methods that make model decisions transparent—for example, through feature weightings, visual explanations, or natural language that describes the main reasons for a result. The goal is for users to understand why a transaction was flagged as suspicious, a production run was adjusted, or a request was prioritized.

Practical projects demonstrate how XAI works in an industrial environment. In the process industry, for example, AI models for optimizing plants are being expanded so that operating personnel can understand the reasons for recommended control variable changes, detected anomalies, or deviating patterns. This not only increases confidence in the systems, but also improves collaboration between domain experts and data science teams because discussions take place on a common explanatory level.

For companies, this means:

• Anyone who uses AI in security-critical, regulated, or reputation-sensitive processes—such as in healthcare, finance, or industrial operations—must plan for XAI capabilities from the outset.
• Documentation, audit trails, and governance processes should be designed to withstand scrutiny by regulatory authorities, internal audit, or customers.
• XAI is increasingly becoming a distinguishing feature in the market: providers who deliver explainable models and understandable interfaces make it easier for decision-makers to use them productively and reduce adoption barriers.

5. Multimodal AI and edge computing as technology drivers

Multimodal AI systems can process and combine text, images, audio, and video in a single model—an approach that makes interacting with AI tools much more intuitive. For example, users can upload a process diagram, add a verbal description, and use voice or text to formulate change requests, which the system then translates directly into a new model or simulation.

New model generations not only process information, but also generate multimedia outputs: instructions with explanatory graphics, automatically annotated dashboards, or training videos generated from process documentation. This provides operations and training teams with a significantly more efficient way to update and distribute knowledge.

At the same time, edge machine learning is becoming increasingly relevant. An ever-growing proportion of corporate data is being processed directly where it is generated—in machines, sensors, vehicles, or production lines—rather than first being transferred to central data centers. This enables decisions to be made with very low latency, which is particularly crucial in time-critical environments such as manufacturing, energy supply, healthcare, or autonomous systems.

This combination opens up new possibilities for process management:

• Process data from plants, IoT sensors, or shop floor systems is preprocessed locally, anomalies are detected, and countermeasures are triggered immediately.
• Only condensed, relevant information flows back into central systems, where it is incorporated into process intelligence platforms and supports long-term optimization.
• Multimodal models can combine data streams from text logs, image inspection, and machine signals to detect quality deviations more quickly and accurately.

Practical fields of application: From quality management to industry

The greatest effects of these trends are evident where AI is used not as a technological experiment, but as a building block for clearly defined use cases. A few current examples illustrate the breadth of applications.

In quality management, AI-based assistance systems already support the automatic detection of deviations, for example through image and sensor data analysis, as well as context-related searches in documentation and standards. Employees receive quick answers to detailed questions, can generate test reports, and incorporate optimization suggestions directly into their workflows.

In industry, pilot projects demonstrate how process data, edge intelligence, and explainability interact to optimize complex production steps. For example, in continuous plants, control variables are adjusted to increase yield and energy efficiency, while operating personnel can understand at any time why the system is making a particular recommendation.

Such examples make it clear that what matters is not so much which specific technology is used, but whether it is clearly linked to business and process goals—from throughput and quality to security and compliance to customer satisfaction and sustainability.

AI as a strategic operating system for processes

The greatest effects are seen where AI is used not just as a technological experiment, but as part of clearly defined use cases.

• In quality management: Microsoft Copilot already supports automatic detection of deviations, context-sensitive searches in documentation, and data-driven improvement of workflows.
• In industry: practical projects deliver convincing results: ABB demonstrated AI-supported optimization of flotation processes in the mining industry, enabling greater harmonization and efficiency.

By 2026, AI will evolve from individual pilot projects to a strategic operating system for business processes—from agentic AI to generative AI to process intelligence, XAI, and edge-based solutions.

Companies that invest now in transparent, well-monitored, and process-aware AI solutions are laying the foundation for real-time efficiency gains, reduced risks, and entirely new data-driven business models in operations and process management.

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Sources

Celonis, "No successful AI deployment without process intelligence: 89% of executives see process intelligence as a success factor," Celonis Newsroom, https://www.celonis.com/news/press/celonis-research-unveils-89-percent-of-business-leaders-say-ai-without-process-intelligence-fails-to-deliver-expected-results

McKinsey & Company, ‘The State of AI: Global Survey 2025’, McKinsey Global Institute, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

European Commission, ‘AI Act – Shaping Europe’s digital future’, European Commission, https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

Artificial Intelligence Act, ‘EU Artificial Intelligence Act – Legal Text and Timeline’, artificialintelligenceact.eu, https://artificialintelligenceact.eu/

ABB, ‘ABB‑driven research project EXPLAIN wins prestigious AI innovation award’, ABB News, https://new.abb.com/news/detail/129143/abb-driven-research-project-explain-wins-prestigious-ai-innovation-award

Microsoft, "Six AI trends we'll see more of in 2025," Microsoft Newsroom Germany, https://news.microsoft.com/de-de/sechs-ki-trends-von-denen-wir-2025-noch-mehr-sehen-werden/

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