State of AI for Organizations in 2026: From Experiment to Scalable Value Creation

Where organizations will truly stand in 2026

Current surveys show that AI use has become mainstream. In global surveys, 78-87% of companies report that they already use AI, and most use it in multiple functions such as operations, customer service, or analytics. At the same time, around two-thirds of organizations are not yet in a true scaling mode, but rather in a phase of pilot projects and isolated projects.

The field of generative AI and AI agents is particularly dynamic. In a recent McKinsey study, nearly 90% of respondents said they use AI in their daily work, and over 60% are actively experimenting with agentic systems; about a quarter are already scaling agents in at least one function. Nevertheless, structural change—i.e., translating tools into changed processes, roles, and governance—often lags behind the technology.

Key figures on enterprise AI adoption

Several reports paint a consistent picture: Enterprise AI is widespread, but uneven in depth. Operations, IT, and customer service are particularly strong drivers.

• 87% of large companies use AI solutions in at least one area.
• 76% use AI for process automation, achieving an average of 43% shorter processing times.
• 75% of employees in an OpenAI study report that AI improves the speed or quality of their work.

At the same time, an analysis of the "GenAI Divide" shows that up to 95% of organizations with GenAI investments have not yet achieved a clearly measurable return—mostly because projects are not deeply enough integrated into processes and systems.

The most important AI trends for organizations

2026 is characterized by three interrelated trends: the rise of agentic AI, the focus on domain-specific solutions, and the professionalization of governance and ROI management.

Agentic AI and autonomous workflows

Agentic AI systems go beyond traditional assistance functions and can plan, make decisions, and execute multi-step workflows independently. In recent enterprise studies, 62% of companies report that they are experimenting with AI agents, and 23% are already scaling such systems.

Forecasts for 2026 predict networks of collaborating agents that orchestrate complex processes in IT, HR, finance, supply chain, and customer operations—including monitoring, escalation, and documentation. This shifts the human role from operational execution to control, governance, and continuous improvement.

"Agentic AI becomes an invisible layer that executes processes in the background—turning humans into orchestrators rather than executors." - ELE Times News

Domain-native and vertical AI

Parallel to the hype surrounding generic foundation models, the trend toward domain-specific, vertical solutions is gaining ground. Analysts expect to see a growing number of "domain-native" models trained on industry-specific data, ontologies, and regulatory rules.

This applies in particular to areas such as:

• Financial process and regulations
• Healthcare and Life Sciences
• Industrial production and maintenance
• Process Management and Operations

For organizations, this means that the focus is shifting from "Which model is the best?" to "Which combination of model, data, integration, and process knowledge generates the greatest added value in a given context?" Process expertise and data quality are thus becoming critical assets.

Investment momentum and economic effects

From a financial perspective, the importance of AI is growing rapidly. According to estimates, companies will spend around $30-40 billion on generative AI in 2025; another report puts spending on enterprise GenAI alone at $37 billion – more than three times the figure for 2024.

AI adoption studies also show that:

• On average, organizations see 34% efficiency gains and 27% cost reductions within 18 months of implementing AI solutions.
• Three out of four executives already report positive returns on GenAI investments; at the same time, there is increasing pressure to measure ROI systematically.

For process owners, this means that AI will increasingly be measured by how clearly it influences business objectives such as throughput times, quality, or customer satisfaction—not by how impressive individual demos are.

Challenges and risks when scaling AI

Despite widespread use, organizations struggle with a number of recurring hurdles: from data quality and talent issues to governance and acceptance.

Data, infrastructure, and fragmentation

The most common obstacle is fragmented data landscapes and inadequate infrastructure. In enterprise studies, around 73% of companies cite data quality as the biggest challenge for successful AI projects.

Typical patterns:

• Data is stored in isolated systems (ERP, CRM, ticket system, file shares) without a uniform data model.
• Processes are documented, but not linked to event logs, KPIs, or responsibilities.
• There is a lack of end-to-end pipelines to automatically provide process data for analysis and AI agents.

As a result, AI often remains limited to specific use cases and cannot realize its potential in end-to-end process optimization.

Skills, organizations, and the "AI divide"

A second bottleneck lies in organizations and skills. While some of the workforce uses AI productively on a daily basis, others feel overwhelmed or left out.

• 82% of executives use GenAi at least weekly, and nearly half use it daily.
• At the same time, 43% warn of a possible decline in skills if AI takes over tasks without targeted further development of skills.
• Reports on the "GenAI Divide" show that only a small proportion of teams really work with AI in a structured way, while many organizations generate hardly any measurable benefits despite high investments.

The result: a growing gap between "frontier teams" that deeply integrate AI into their work and the rest of the organization. For process owners, this makes it a core task to consistently consider skills, roles, and responsibilities.

Governance, risk, and trust

With growing prevalence, governance is becoming a central issue. Institutions such as Stanford HAI, the OECD, and other research and policy actors emphasize that questions of security, transparency, and fairness must no longer be answered experimentally, but operationally.

Companies address, among other things:

• Handling confidential data in cloud- and API-based AI services
• Traceability of decisions in highly regulated areas
• Responsibility for errors or bias in AI-supported recommendations
• Requirements imposed by legislation and standards (e.g., EU AI Act)

Current enterprise studies show that around 72% of companies are now attempting to systematically measure the ROI of GenAI – meaning that governance is increasingly being linked to specific business KPIs.

Best practices: How to develop your AI roadmap for 2026

Against this backdrop, the question arises: How will you strategically address the topic of AI in your organization in 2026—especially if you are responsible for processes and operations?

1. Clear starting point: Process and data inventory

Before introducing new tools, you should clarify the basics. Studies show that successful AI programs align their use cases closely with business processes and available data.

Meaningful steps:

• Identify critical core processes (e.g., order-to-cash, purchase-to-pay, incident management).
• Merge process documentation and real event data (system logs, tickets, timestamps).
• Check data quality, completeness, and accessibility throughout these processes.

Process intelligence platforms such as aiio can bridge this gap by combining modeling, event data, and AI analytics in a single workspace, providing the basis for AU agents that not only analyze processes but also actively help control them.

2. Business-driven use case selection

Most reports recommend starting with a few clearly measurable use cases rather than launching dozens of initiatives in parallel.

Typical high-value use cases in operations:

• Reduce throughput times: Automated prioritization and routing of processes.
• Improve quality: AI-supported review of applications, orders, or tickets.
• Creating transparency: Generative explanations of process bottlenecks and their causes.

It is important that you define specific targets for each use case—such as reducing processing time by X%, fewer escalations, or higher first-time-right rates—and track them continuously.

3. Embed agentic AI in processes

Given the developments toward agentic AI, the logical next step is to use AI not only as an analysis or chat layer, but as an active part of your processes.

This can mean, for example:

• AI agents that continuously monitor process data and generate automatic alerts or suggestions for action in the event of deviations.
• Agents that perform standard tasks independently (e.g., data entry, status updates, reminders) and only escalate exceptions to humans.
• Integration of AI analyses directly into process models (e.g., in aiio) so that optimization suggestions are visible where processes are designed and decisions are made.

This shifts the use of AI from a "tool alongside the process" to an "intelligent layer within process management."

4. Consider governance, skills, and change right from the start

Successful programs combine technology and change perspectives. Best practices from current enterprise reports emphasize, among other things:

• Clear guidelines: What is AI allowed to do, and what is it not allowed to do? Which data sources are approved?
• Skill programs: Empowering employees to use AI pragmatically in their everyday work.
• Cross-functional teams: Business, IT, Data/AI, and Compliance work together from the outset.

Many companies are establishing AI councils or centers of excellence for this purpose, which define standards, prioritize use cases, and pool experience.

Common mistakes—and how to avoid them

Current studies reveal recurring pitfalls that slow down organizations as they move from pilot to scale.

Common mistakes

• Technology without a process focus: Tools are introduced without it being clear which process metrics they are intended to improve.
• Too many parallel pilots: Resources are spread across many small experiments without a clear scaling path.
• Unclear responsibilities: No one feels truly responsible for AI results in a process.
• Lack of ROI measurement: AI is treated as an "innovation topic" but is not linked to business KPIs.
• Ignored data problems: Poor data quality is not addressed, meaning AI recommendations remain unreliable.

Some reports explicitly point out that it is precisely these patterns that mean that, despite investments of US$30-40 billion in GenAI, the majority of organizations are not seeing any demonstrable return.

How to proceed more effectively

Instead, the following approaches in particular will prove their worth in 2026:

• Start small, but with clear scaling prospects: one use case per core process, which will be rolled out if successful.
• Hold process owners accountable: They define goals, data sources, and performance measurement.
• Closely link AI experts and specialist departments. Make joint decisions on models, data, and integration.
• Use a platform approach: A central environment (e.g., aiio) for process modeling, data connection, and AI functionality, instead of isolated individual solutions.

"In 2026, the bottleneck will rarely be AI—it will be processes, data, and responsibilities." —McKinsey

The state of AI in 2026 for organizations can be summed up in one sentence: AI has become part of everyday life, but real, scaled value creation depends on how consistently you align processes, data, and governance. Studies show high adoption rates and growing investment, but also a clear "GenAI divide" between pioneering teams and the rest of the organization.

If you want to develop your organization in a targeted manner, you should not treat AI as an isolated technology, but as an integral part of your process management—ideally with Agentic AI, which works directly on your end-to-end processes. Use 2026 to turn scattered pilots into a structured AI roadmap that is clearly linked to business goals, process metrics, and robust governance.

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Sources:

McKinsey & Company: The State of AI: Global Survey 2025 – Adoption rates, business impact, and maturity of AI in companies.

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations

Second Talent: AI Adoption in Enterprise Statistics & Trends 2025 – Statistics on enterprise AI usage and effects on efficiency.

McKinsey & Company: The State of AI: Global Survey 2025 – Adoption rates, business impact, and maturity of AI in companies

Wharton / University of Pennsylvania: 2025 AI Adoption Report: Gen AI Fast-Tracks Into the Enterprise – Usage Patterns and ROI of GenAI in Enterprises

OpenAI: The State of Enterprise AI 2025 – Report on the use of GenAI and AI systems in companies

Second Talent: AI Adoption in Enterprise Statistics & Trends 2025 – Statistics on enterprise AI usage and effects on efficiency

Second Talent: AI Adoption in Enterprise Statistics & Trends 2025 – Statistics on enterprise AI usage and effects on efficiency

OpenAI: The State of Enterprise AI 2025 – Report on the use of GenAI and AI systems in companies

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations

ISG: State of Enterprise AI Adoption Report 2025 – Challenges and maturity of AI programs in large organizations

Ecosystm: Top 5 Enterprise AI Trends for 2026 – Focus areas such as agentic AI, vertical AI solutions, and governance

Eletimes / other industry sources: Technology trends reshaping operations of enterprises in 2026 – Overview of technology and operations trends.

Eletimes / other industry sources: Technology trends reshaping operations of enterprises in 2026 – Overview of technology and operations trends

IBM: The trends that will shape AI and tech in 2026 – Predictions on AI, automation, and infrastructure.

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations

Second Talent: AI Adoption in Enterprise Statistics & Trends 2025 – Statistics on enterprise AI usage and effects on efficiency.

Wharton / University of Pennsylvania: 2025 AI Adoption Report: Gen AI Fast-Tracks Into the Enterprise – Usage patterns and ROI of GenAI in companies.

ICONIQ Capital: 2025 State of AI: The Builder’s Playbook – Tech/SaaS investors' perspective on AI product and corporate strategies

Second Talent: AI Adoption in Enterprise Statistics & Trends 2025 – Statistics on enterprise AI usage and effects on efficiency

McKinsey & Company: The State of AI: Global Survey 2025 – Adoption rates, business impact, and maturity of AI in companies.

Wharton / University of Pennsylvania: 2025 AI Adoption Report: Gen AI Fast-Tracks Into the Enterprise – Usage patterns and ROI of GenAI in companies.

Wharton / University of Pennsylvania: 2025 AI Adoption Report: Gen AI Fast-Tracks Into the Enterprise – Usage patterns and ROI of GenAI in companies.

Wharton / University of Pennsylvania: 2025 AI Adoption Report: Gen AI Fast-Tracks Into the Enterprise – Usage patterns and ROI of GenAI in companies.

ICONIQ Capital: 2025 State of AI: The Builder’s Playbook – Tech/SaaS investors' perspective on AI product and company strategies.

Stanford Institute for Human-Centered AI (HAI): AI Index Report 2025 – Data and key figures on global AI development.

ISG: State of Enterprise AI Adoption Report 2025 – Challenges and maturity of AI programs in large organizations

McKinsey & Company: The State of AI: Global Survey 2025 – Adoption rates, business impact, and maturity of AI in companies.

ISG: State of Enterprise AI Adoption Report 2025 – Challenges and maturity level of AI programs in large organizations.

Second Talent: AI Adoption in Enterprise Statistics & Trends 2025 – Statistics on enterprise AI usage and effects on efficiency

Ecosystm: Top 5 Enterprise AI Trends for 2026 – Focus areas such as agentic AI, vertical AI solutions, and governance

ICONIQ Capital: 2025 State of AI: The Builder’s Playbook – Tech/SaaS investors' perspective on AI product and company strategies.

ICONIQ Capital: 2025 State of AI: The Builder’s Playbook – Tech/SaaS investors' perspective on AI product and company strategies.

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations

ISG: State of Enterprise AI Adoption Report 2025 – Challenges and maturity of AI programs in large organizations

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations.

OECD: AI adoption by small and medium-sized enterprises – Analysis of AI adoption in SMEs

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations

Ecosystm: Top 5 Enterprise AI Trends for 2026 – Focus areas such as agentic AI, vertical AI solutions, and governance.

MLQ / "The GenAI Divide: State of AI in Business 2025" – Analysis of unequal value creation through GenAI in organizations

Eletimes / other industry sources: Technology trends reshaping operations of enterprises in 2026 – Overview of technology and operations trends