Fast AI, Slow IT? Why Companies Urgently Need a Shared Target Vision

Artificial intelligence has arrived in companies. A recent McKinsey study shows that nearly all organizations are already using AI. At the same time, most are still in the experimentation or pilot phase. Almost two thirds have not yet started scaling AI across the enterprise. 

This is exactly where a central tension emerges. While business units increasingly want to leverage AI to drive efficiency gains, reduce costs, and unlock new business opportunities, IT is facing a significantly more complex reality. Promises of plug-and-play solutions, automation, and rapid productivity improvements increase the pressure to implement use cases quickly, even when essential prerequisites are still missing. 

How is IT supposed to introduce AI securely when business units are already implementing their own solutions? Requirements emerge in a decentralized way, budgets are used independently, and initial tools or applications are introduced without overarching alignment. Without shared strategic guidelines and a clear target vision, this leads to shadow solutions, isolated applications, and new data silos. Operational effort increases, costs rise, and IT can no longer ensure security or scalability in a holistic way. 

At the same time, it is undisputed within organizations that AI can only deliver sustainable impact if infrastructure, data foundations, governance, and the organization itself are prepared accordingly. This is where the gap between expectations and technical feasibility becomes particularly evident — and IT is the first to feel it. 

From our experience supporting AI and transformation initiatives, a similar pattern emerges again and again. When business and IT operate at different speeds and key prerequisites are not clarified early on, even well-intentioned AI efforts lose impact. Instead of scaling, isolated solutions emerge, complexity increases, and frustration grows on both sides. 

In many organizations, the root cause runs deeper. IT often works with limited resources, complex approval processes, and in some cases missing technical foundations to reliably meet AI requirements. At the same time, existing operating models, roles, and responsibilities are being questioned as AI is introduced. Established structures are usually not designed to enable innovation quickly or to continuously evolve. This creates a clear need to review IT operating models in a targeted way and further develop them so that they can enable innovation and the productive use of AI in the first place. 

While business units are already bringing in concrete use cases, IT faces the task of rethinking architecture, data flows, application and data security, and the integration of AI into existing systems and processes all at once. AI thus becomes a litmus test for IT maturity — and for its ability to evolve its own way of working and strengthen its role as a strategic partner to the business. 

To close the gap between business expectations and technical feasibility, companies first need transparency about their actual level of AI maturity. From our experience, one thing becomes clear again and again: Only when IT and business share a common, realistic view of the status quo can discussions about priorities, investments, and operating models be conducted constructively. 

The AI Readiness Check provides a structured entry point. It makes visible where a company truly stands today — not only technologically, but also organizationally. The assessment shows which structures, processes, and roles support or slow down AI initiatives, where decision paths are unclear, where governance is missing, and which capabilities still need to be built within the organization. On this basis, next steps can be derived in a targeted way. The result is a prioritized roadmap that fits the organization’s actual maturity level and outlines realistic development paths. 

This roadmap also forms the foundation for a shared vision and a sustainable AI strategy. IT and business develop it together, with a clear focus on functional objectives, organizational feasibility, and technical conditions. The strategy must not be created in isolation. It needs to be understood, supported, and integrated into the existing organization by the people within the company. 

The productive use of AI requires an evolution of existing IT operating models and ways of working together. Traditional role distributions, linear handoffs, and clearly separated responsibilities reach their limits when AI applications need to be continuously developed and operated. What is needed are organizational models in which business units, AI experts, and IT jointly take responsibility for use cases and collaborate closely in interdisciplinary teams. 

At the center is the question of how responsibility, governance, and ongoing operations are concretely organized — with clear ownership, short decision-making paths, and tight alignment between business requirements and technical implementation. IT plays a shaping role by considering architecture principles, security requirements, and stable operations early on and integrating them into the collaboration. A modern operating model creates transparency around responsibilities, facilitates collaboration across organizational boundaries, and ensures that innovation does not fail due to organizational friction. 

On this organizational foundation, the technical prerequisites can then be built effectively. This includes a resilient data architecture, clear data flows, and defined interfaces, as well as consistent identity and access management concepts. Questions of scalability, performance, model integration, versioning, and the secure operation of AI applications must also be addressed early. Without an appropriate platform architecture, aligned governance, and clearly defined operational processes, AI often remains limited to isolated pilot projects. Only an integrated technical foundation enables stable, secure, and scalable productive deployment. 

Once companies have developed a shared vision, clear technical foundations, and an aligned operating model, the role of IT in execution can be defined more deliberately. In practice, three models have become established. They differ primarily in how strongly IT co-owns the development of use cases, how much governance is defined centrally, and how closely implementation happens in joint teams. The chosen model should be defined consciously and firmly anchored within the organization. 

For AI to deliver reliable impact within an organization, IT and business need a shared starting point. Once the actual maturity level of data, infrastructure, processes, and organizational setup is understood, use cases can be prioritized meaningfully and implemented securely. Transparency about the status quo is therefore the critical first step to ensure that AI is not approached in isolation, but anchored in a structured way across the enterprise. 

The AI Readiness Check creates clarity on technical and organizational prerequisites, highlights concrete areas for action, and provides the foundation for a roadmap aligned with the organization’s maturity level.