Why AI adoption is everybody's priority and nobody's plan

The priority paradox

AI is everywhere today. It’s impossible to open a newspaper, scroll through LinkedIn, attend an industry event, or sit in a meeting without hearing about AI, its potential impact, and the need to apply it within your organization. The message is clear: AI is no longer optional. Organizations feel increasing pressure to embrace it, driven by a fear of being left behind if they fail to keep pace with the rapid evolution of technology.

Yet despite the widespread enthusiasm and experimentation, only a limited number of organizations have a clear roadmap for AI adoption. Many are exploring use cases and running pilots, but few have developed a coherent strategy to maximize AI’s impact across the business. As a result, AI often remains a collection of isolated initiatives rather than a transformative force that delivers measurable value.

Clear ownership of AI initiatives

Ownership of AI initiatives varies significantly across organizations. In larger enterprises, leadership is typically driven by roles such as the Chief AI Officer, Chief Digital Officer, Chief Technology Officer or Transformation Officer. In mid-sized organizations, however, ownership tends to be more decentralized, with responsibility often determined by the company's structure, individual roles, personal interest in AI, and available technical expertise.

IT departments are rarely the primary drivers of AI adoption. This makes sense, as AI implementation is fundamentally a business transformation effort rather than a pure technology project. While strong involvement from IT is important given the technological components, leadership often sits elsewhere in the organization.

Establishing clear ownership is critical. Without it, AI risks becoming everyone's initiative but no one's responsibility, leading to fragmented efforts, limited accountability and solutions which are not scalable.

The pilot trap

Regarding AI readiness, many organizations are launching small AI initiatives to improve productivity. These efforts often begin without a clear strategic framework, resulting in isolated projects rather than a coordinated transformation. This approach is understandable. AI is evolving rapidly, making it difficult to predict which use cases will create the most value. As a result, experimentation remains an essential way to learn, build capabilities, and identify opportunities.

Early AI pilots can deliver quick wins and valuable insights. However, staying in the experimental phase for too long creates risks. When projects develop independently across teams, efforts become fragmented, resources are diluted, and scaling successful initiatives becomes challenging.

A clear AI roadmap helps connect individual projects to broader business goals. It aligns investments, prioritizes high-value opportunities, and creates a pathway from experimentation to enterprise-wide adoption. While pilots are often the starting point, long-term success depends on moving beyond isolated use cases and scaling AI across the organization.

Concerns and bottlenecks

Besides the importance of ownership, we see other elements that will impact the adoption of AI:

• Lack of strategic, end-to-end vision and roadmap. AI initiatives are often fragmented, tool-focused, and driven by individuals rather than business strategy. Organizations struggle to connect AI adoption to enterprise-wide objectives, scalable operating models, and clear transformation roadmaps.
• Governance, AI maturity, and realistic value creation. Many organizations lack the AI maturity and governance needed to make informed AI decisions. This leads to rushed investments, unrealistic expectations, fragmented experimentation, and difficulty distinguishing genuine business value from AI hype.
• Leadership, workforce transformation, and change management. Beyond AI literacy, leaders face fundamental questions about role redesign, organizational structures, performance management, compensation models, and managing employee fears of obsolescence. AI adoption requires structured change management across people, processes, and systems.
• Skills erosion, judgment, and human expertise. As AI automates operational work, organizations risk losing opportunities for employees to develop expertise, critical thinking, and professional judgment. A key challenge is ensuring people continue to build and maintain the skills needed to oversee, challenge, and guide AI outputs.
• Culture, mindset, and navigating uncertainty. Organizations must develop a culture that embraces continuous learning, rapid technological change, and increasing business complexity. Success requires adapting to accelerating innovation while avoiding paralysis caused by uncertainty and the overwhelming number of AI options.

The role of Finance in AI adoption

The role of finance in AI adoption varies across organizations, but finance functions are more often followers than pioneers. In many cases, departments such as Sales, Marketing, Compliance or Product Development take the lead in driving AI initiatives. As a result, the AI maturity within finance remains relatively low.

Current AI applications in finance are primarily focused on improving efficiency. Typical use cases include invoice processing, credit management, and supporting initial analyses in controlling and reporting. These initiatives are largely aimed at automating routine tasks and increasing productivity.

One of the biggest barriers to adopting AI in finance is the strong emphasis on accuracy and reliability. Finance professionals operate in an environment where mistakes can have significant consequences, making the fear of getting it wrong particularly pronounced. Additional challenges include poor data quality, fragmented data across silos, insufficient data governance, a lack of process standardization, limited forecasting and scenario-planning capabilities, and the complexity of legacy systems and manual processes.

Cultural factors also play an important role. Finance professionals often value control, precision, and trust, which can make them hesitant to rely on algorithm-driven recommendations. Moreover, regardless of how advanced AI becomes, CFOs and finance teams remain ultimately accountable for financial controls, compliance, and the accuracy of reporting.

However, there are signs of change. Finance functions are becoming increasingly involved in AI initiatives, particularly in areas such as productivity enhancement, report generation, data preparation, and data cleansing. While adoption is still at an early stage, growing familiarity with AI is gradually increasing its relevance and acceptance within finance organizations.

From AI ambition to AI execution

The AI debate is no longer about whether organizations should adopt AI, but about how they can turn AI ambitions into measurable business value.

In our experience, the organizations that will succeed are those that move beyond experimentation and establish clear ownership, objectives, and governance. Competitive advantage will not come from using AI alone, but from using it purposefully and embedding it into everyday business processes and ways of working.

Our key advice is straightforward: start now, learn quickly, and adapt continuously. Communicate openly about your ambitions, initiatives, and progress to build trust and engagement across the organization. Approach AI as a transformation of workflows, combining automation with human expertise at a pace that teams can confidently embrace.

The most successful organizations strike a balance between structure and experimentation, maintaining a clear roadmap while creating room for innovation. Ultimately, AI success depends not on the technology itself, but on an organization's ability to turn it into sustainable business value.