Enterprise AI strategy 2026 and beyond: Five priorities for scalable value creation

The short answer: The next AI advantage is orchestration across the enterprise 

Enterprise AI has entered a new phase. Access to models and tools is no longer the main constraint. The strategic challenge for 2026 and beyond is to connect AI across systems, data, workflows, people, and decision rights.

The OMMAX AI Trends Report 2026 shows both progress and fragmentation. Fifty-eight percent of 250 European decision-makers report a fully defined AI strategy, but only 44% have a fully implemented end-to-end operating model. Fifty-two percent remain partly implemented. Integration complexity is the leading barrier to stronger impact at 40%. AI execution is owned primarily by IT and engineering in 48% of organizations, while only 7% place ownership in business units. 

At the same time, AI is producing material results. Eighty-four percent report improved operational efficiency, 75% report revenue impact, and agentic AI use cases are already in production across most surveyed organizations. The opportunity is not to add use cases without limit. It is to turn isolated applications into an orchestrated enterprise capability. 

The report identifies five leadership priorities: think business AI-first, distribute ownership, align the operating model with strategy, fix data and integration before scaling agents, and treat enablement as infrastructure. 

What does AI orchestration mean for an enterprise?

AI orchestration is the coordinated use of models, agents, data, systems, workflows, controls, and human decisions to deliver an end-to-end business outcome. It moves the focus from individual tools to the value chain. 

A stand-alone assistant can help an employee draft an answer. An orchestrated service workflow can identify the customer, retrieve relevant history, interpret intent, recommend or execute an action, update core systems, communicate through the right channel, and escalate exceptions - while preserving an audit trail. 

Agent-to-agent workflows extend this model. Specialized agents can coordinate research, decisions, and actions across functions. The promise is significant, but so is the control requirement. As workflows become more autonomous, organizations must be able to see and govern every action. 

Orchestration is therefore both a technology and operating-model challenge. It requires integration, shared data, explicit ownership, reusable platforms, lifecycle governance, and employee adoption. 

Priority 1: Think business AI-first

Being business AI-first means starting with the enterprise outcome and redesigning the workflow around what AI makes possible. It does not mean adding AI to every process or allowing technology to define the agenda. 

The report's ownership data shows why this shift is necessary. With 48% of AI execution located in technical functions, infrastructure and efficiency naturally dominate. To unlock more commercial and customer value, every initiative should be tied to a measurable outcome such as conversion, retention, margin, speed, quality, or risk. 

The global food-processing case demonstrates the value-pool approach. OMMAX analyzed the full value chain, identified more than 250 AI use cases, and prioritized those with clear ROI, uncovering EUR 64 million in value potential over six years. The result was not merely an innovation backlog; it was an economically sequenced transformation agenda. 

Leaders should define a small number of AI value domains, allocate funding against outcomes, and review the portfolio as business investment rather than technology experimentation. 

Priority 2: Distribute AI ownership

Only 7% of surveyed organizations place AI ownership in business units. This limits the organization's ability to change customer journeys, commercial processes, products, and functional behavior. 

Distributed ownership does not mean decentralized technology without standards. It means that business functions own the outcomes while shared technical and governance capabilities make delivery safe and reusable. 

A useful structure includes: 

• Executive sponsors for enterprise value domains 
• Business AI leads responsible for functional outcomes 
• Cross-functional product teams for priority workflows 
• A central platform and governance capability 
• Communities and champions that spread learning 

Decision rights should be explicit. Business leaders prioritize outcomes and own adoption. Technology leaders own architecture and operations. Risk leaders define control requirements. Finance validates the value case. The center enables; the business transforms.

This model also supports revenue growth. AI productivity gains will not automatically become commercial impact unless sales, marketing, service, and product leaders direct released capacity and redesign customer-facing processes.

Priority 3: Align the AI operating model with strategy 

The 14-point gap between fully defined strategies and fully implemented operating models is one of the report's clearest findings. Organizations know where they want to go, but many have not installed the roles, processes, and capabilities to execute repeatedly. 

Leaders should conduct an operating-model gap assessment across: 

• Value and portfolio management 
• Business and technical ownership 
• Funding and capacity allocation 
• Product delivery and stage gates 
• Data and platform architecture 
• Governance and risk 
• Talent, adoption, and change 
• Performance and value tracking 

The objective is not to create bureaucracy. It is to remove ambiguity and rework. A strong operating model helps high-value cases advance, weak cases stop, and reusable capabilities compound.

The scaling data makes this urgent. Seventy-nine percent of failures occur during the pilot or before production, and 65% of completed projects exceed budget. A mature operating model tests integration, adoption, governance, and economics early rather than discovering them after the prototype.

Priority 4: Fix data and integration before scaling agents 

Integration complexity is the top barrier to AI impact at 40%, while data quality is the leading reason initiatives fail to scale at 28%. These constraints become more serious with agentic AI because agents may act on information, not only summarize it. 

The report recommends a clear sequence: integrate first, automate second, orchestrate third. 

Integration means connecting the authoritative sources and tools required for the outcome, with clear identity, permissions, semantics, and monitoring. Automation means improving a bounded task or decision. Orchestration means connecting multiple automated components into an end-to-end flow. 

The national transport-provider case shows how foundational work creates economic value. OMMAX implemented a data excellence and governance program that achieved more than 99% data quality, enabled automation of over 25 processes, and contributed several million euros to EBIT through improved performance tracking. 

Leaders should resist two extremes: waiting for perfect enterprise data before starting, or scaling agents across fragmented systems. A use-case-led data roadmap allows priority deployments to justify shared data products, connectors, governance patterns, and observability. 

Data sovereignty must be part of the roadmap. Eighty percent of surveyed organizations share sensitive data with external AI providers, while 54% use flexible, use-case-based sovereignty models. Clear classification and provider rules can preserve speed without turning local flexibility into enterprise inconsistency. 

Priority 5: Treat enablement as infrastructure 

Technology platforms receive budgets, owners, roadmaps, service levels, and governance. AI enablement needs the same seriousness. 

The report finds that business-impact tracking is fully implemented in 53% of organizations, support for AI champions in 50%, knowledge sharing in 46%, and non-technical employee enablement in only 39%. These are not secondary communication activities. They determine whether AI products are used, challenged, improved, and translated into value. 

For a global sportswear brand, OMMAX upskilled more than 1,000 employees, developed over 50 AI prototypes, and introduced a company-wide engagement framework. The case shows how broad participation and structured coordination can build organizational learning. 

Enablement for 2026 should be role-based and outcome-led. It should include executive literacy, practitioner training, AI champions, communities, office hours, reusable patterns, feedback, workflow redesign, and clear data and governance guidance. Adoption should be measured through active use and business outcomes, not attendance alone. 

How should leaders build an AI portfolio for 2026? 

A resilient portfolio should balance three horizons. 

Horizon 1: Scale proven value 

Industrialize use cases with demonstrated economics, adoption, and manageable risk. Reuse platforms, data products, and delivery patterns. Efficiency cases in IT, service, and operations can fund further investment. 

Horizon 2: Convert capability into growth 

Build customer-facing and commercial use cases on the same foundation. For a large private university, OMMAX deployed a self-learning agentic system designed to send more than 1.3 million personalized messages in 100 days, equivalent to the output of more than 100 sales representatives. The strategic value is scalable personalization, not volume alone. 

Horizon 3: Orchestrate the value chain 

Experiment with connected agent workflows in bounded domains where data, controls, and ownership are mature. Focus on outcomes that require coordination across multiple systems or functions. Build traceability and human intervention into the architecture from the start. 

Capital and leadership attention should shift among horizons as evidence changes. A portfolio is a decision system, not a fixed list. 

What should the board ask about enterprise AI? 

Boards do not need to manage model choices, but they should test whether AI is becoming a controlled source of value. 

Boards should ask which value pools the portfolio targets; how much net value has been realized; where business, technology, and risk ownership sits; which data constraints limit scale; why pilots do or do not reach adopted production; what controls apply to agentic workflows; and how the workforce is being prepared. 

These questions shift oversight from activity to enterprise capability. 

Outlook: Build the enterprise that runs on AI

The leadership question has moved beyond how to adopt AI. It is how to build an enterprise that can run on it - visibly, responsibly, and economically. 

Models will continue to improve, and individual automations will become easier to create. Competitive advantage will come from the system around them: connected data, reusable integration, distributed ownership, operational governance, and a workforce capable of redesigning work. 

The OMMAX AI Trends Report 2026 shows that most organizations are still in the scaling phase. Those that act on the five priorities can move from fragmented success to coordinated value creation. The goal for 2026 and beyond is not more AI everywhere. It is an enterprise in which AI is deliberately orchestrated where it creates the greatest value.

About the research

The OMMAX AI Trends Report 2026 is based on a quantitative online survey of 250 decision-makers conducted in April and May 2026 across France, Germany, Italy, the Netherlands, and the UK. Statista conducted the survey on behalf of OMMAX, Ibexa, and Make. Download the full report here.  

About OMMAX

OMMAX is a leading AI-first consultancy and AI-engineering platform specializing in AI strategy, business transformation, transaction advisory, and value creation in the age of AI. Founded in Munich in 2011, OMMAX serves large corporates, mid-sized companies, and private equity firms across Europe and the US, with more than 4,000 completed projects and a Net Promoter Score of 90.