AI trends for companies: the 5 newest and most important
Current innovations in operations and process management (outlook for 2026)
AI is no longer an experiment. In 2026 it becomes the strategic operating system with which leaders automate processes radically, accelerate decisions and open up new business models.
These are the five most important trends and innovations of recent weeks.
1. Hyperautomation and AI agents establish themselves as the standard
Hyperautomation describes the combination of RPA, machine learning, generative AI and process orchestration used to automate not merely individual steps but complete end-to-end processes. Where RPA long focused on clearly structured, rule-based tasks, AI agents now take on unstructured, knowledge-intensive work too, and make independent decisions within defined boundaries.
Over 2025, agentic AI moved from experimental pilots to systems in productive use that classify requests, gather information, prepare decisions and then execute them directly in operational systems. International analysts estimate that by the end of the decade such agents will support or partly automate a considerable share of operational decisions, particularly in recurring, high-volume scenarios.
For companies this means:
- efficiency gains through sharply reduced manual routine work, for example in master data maintenance, ticket routing or standardized approvals.
- markedly shorter cycle times, because AI agents work around the clock, bring together context from several systems and trigger escalations automatically.
- lower error rates and better compliance, because rules are applied consistently and documented automatically.
Concrete use cases range from automated purchase-to-pay processes through AI-supported service workflows to intelligent escalation chains in incident management. Industrial companies and global service providers already report measurable efficiency gains where agentic automation is embedded directly into production, service and back-office processes.
For these systems to work safely and at scale, however, operations teams need robust foundations: uniform process and data standards, clear permission concepts, clean role models and central monitoring that watches both performance and compliance. Organizations that lay this groundwork in 2025 can integrate AI agents step by step, and ever more deeply, into daily business in 2026 and beyond.
2. Generative AI transforms process design and improvement
Where AI agents focus above all on executing workflows, generative AI plays to its strengths in designing, documenting and continuously improving processes. Studies by global consultancies show that a large share of leading companies already use generative AI for product innovation, knowledge management and operational support — and that share keeps growing.
In process management this opens up new possibilities:
- generative models draft alternative process variants, for onboarding journeys, service flows or internal approval processes, for instance.
- they automatically produce process descriptions, work instructions and training materials, generated directly from diagrams, logs and policies.
- they support teams with impact analyses (“what happens if this check step is dropped?”) and simulate the effects on cycle times or error risks.
Current surveys show that around 60% of companies already use generative AI productively in at least one business function — from development and marketing through to service, HR or finance. In parallel, self-service automation is taking hold: business units configure their own micro-workflows or document processes using prompts, without having to master complex low-code platforms.
For process automation to be carried this widely into an organization, however, clear guardrails are needed:
- role and permission concepts that define who may create, approve and change which automations.
- minimum standards for data quality, so that generated content and decisions rest on reliable information.
- governance rules for approving models and monitoring outputs, to avoid shadow automation, security gaps and contradictory process logic.
Set up properly, the result is a controlled framework in which business units can experiment and become productive quickly, while central teams safeguard standards, compliance and the stability of core processes.
3. Process Intelligence as a success factor for AI implementation
“It doesn't work without Process Intelligence.” That statement is gaining weight in the boardroom, because it is becoming ever clearer that AI only delivers value where it genuinely understands the context of business processes. Process Intelligence combines process mining technologies, data integration and AI analytics to make real workflows more transparent and to uncover room for improvement systematically.
Current research shows that a large majority of leaders intend to use AI over the coming 12 months specifically to improve business processes — not merely to automate individual tasks here and there. So far, though, many companies have bet either on classic RPA automation or on isolated AI use cases, without building the bridge to an end-to-end view of the process.
The real competitive advantage arises where the two worlds are brought together:
- Process Intelligence first makes visible how processes actually run, which variants dominate, and where bottlenecks and rework loops occur.
- On that basis, target processes are developed, priorities set and business cases quantified.
- Automation, AI agents and generative AI are then applied deliberately to realize the levers identified.
Companies that establish this cycle consistently — understand, improve, automate — report significantly faster improvement cycles, greater transparency and closer interlocking of business units, IT and data teams. Process Intelligence is thereby developing from a niche topic into the strategic foundation for AI at scale.
4. Explainable AI (XAI) is gaining importance
With the EU AI Act and similar regulation worldwide, the focus is shifting from “what is technically possible?” to “what is traceable, safe and legally permissible?”. In the European context in particular, companies will in future have to set out in detail how AI models arrive at their decisions, especially in high-risk fields of application.
Explainable AI (XAI) addresses exactly this requirement. It provides methods for making model decisions transparent — through feature weightings, visual explanations, or natural language describing the main reasons for an outcome. The goal is that users can understand why a transaction was flagged as unusual, a production run adjusted, or a request prioritized.
Practical projects show how XAI works in an industrial setting. In the process industry, for instance, AI models for optimizing plants are extended so that operators can follow the reasons behind recommended set-point changes, detected anomalies or divergent patterns. That not only raises trust in the systems; it also improves collaboration between domain experts and data science teams, because discussions take place on a shared explanatory footing.
For companies this means:
- anyone using AI in safety-critical, regulated or reputation-sensitive processes — in healthcare, in finance or in industrial operations, say — has to plan XAI capabilities in from the start.
- documentation, audit trails and governance processes should be designed to withstand scrutiny by regulators, internal audit or customers.
- XAI is increasingly becoming a differentiator in the market: vendors that deliver explainable models and comprehensible interfaces make productive adoption easier for decision-makers and lower the barriers to adoption.
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 considerably more intuitive. Users can, for example, upload a process diagram, add a spoken description, and formulate change requests by voice or text that the system translates directly into a new model or a simulation.
New generations of models don't merely process information, they also produce multimedia outputs: instructions with explanatory graphics, automatically annotated dashboards, or training videos generated from process documentation. For operations and training teams this creates a markedly more efficient way to update and distribute knowledge.
In parallel, edge machine learning is gaining relevance. An ever greater share of company data is processed directly where it arises — in machines, sensors, vehicles or production lines — instead of first being transferred to central data centres. That makes decisions with very low latency possible, which is decisive in time-critical environments such as manufacturing, energy supply, healthcare or autonomous systems.
For process management this combination opens up new room to manoeuvre:
- process data from plants, IoT sensors or shop-floor systems is pre-processed locally, anomalies are detected and countermeasures triggered immediately.
- only condensed, relevant information flows back into central systems, where it feeds Process Intelligence platforms and supports long-term improvement.
- multimodal models can bring together data streams from text logs, image inspection and machine signals to track down quality deviations faster and more precisely.
Fields of application close to practice: from quality management to industry
The greatest effects of these trends show up where AI is used not as a technology experiment but as a building block of clearly defined use cases. A few current examples illustrate the breadth of application.
In quality management, AI-based assistance systems already support the automatic detection of deviations — through image and sensor data analysis, for instance — as well as context-aware search across documentation and standards. Employees get fast answers to detailed questions, can generate inspection records, and feed suggestions for improvement straight into their workflows.
In industry, pilot projects demonstrate how process data, edge intelligence and explainability work together to optimize complex production steps. In continuous plants, for example, set points are adjusted so that yield and energy efficiency rise, while operators can follow at any time why the system makes a particular recommendation.
Such examples make one thing clear: what matters is less which specific technology is in use than whether it is clearly tied to business and process goals — from throughput and quality through safety and compliance to customer satisfaction and sustainability.
AI as a strategic operating system for processes
The greatest effects show up where AI is used not merely as a technology experiment but as part of clearly defined use cases.
- In quality management: Microsoft Copilot already supports the automatic detection of deviations, context-aware search across documentation, and the 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.
In 2026, AI develops from an individual pilot project into a strategic operating system for business processes — from agentic AI through generative AI to Process Intelligence, XAI and edge-based solutions.
Companies that invest now in transparent, well-monitored and process-aware AI solutions build the foundation for real-time efficiency gains, lower risk and entirely new data-driven business models in operations and process management.
Sources
Celonis, ‘Ohne Prozessintelligenz kein erfolgreicher KI‑Einsatz: 89 % der Führungskräfte sehen Process Intelligence als Erfolgsfaktor’, 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, ‘Sechs KI‑Trends, von denen wir 2025 noch mehr sehen werden’, Microsoft Newsroom Deutschland, https://news.microsoft.com/de-de/sechs-ki-trends-von-denen-wir-2025-noch-mehr-sehen-werden/