Can AI Generate ROI? Why Frontier Companies Achieve Four Times More Value from AI

Reasons Why AI Does Not Deliver ROI

There can be several reasons why integrating AI into organizations fails to generate ROI.

• AI is treated as a technology project rather than integrated into strategy: In many organizations, AI is still viewed as an isolated IT project. Pilot projects are launched within individual departments without a clear connection to overarching business strategies or measurable business objectives. As a result, technically functional solutions may emerge, but they often lack a direct link to concrete business value.
• Insufficient data maturity: Data is considered the foundation of every AI initiative — yet this foundation is often fragile. According to IDC³ fewer than⁴ out of 10 organizations are confident about their data-readiness for current AI priorities. Typical issues include data silos between departments, inconsistent data quality, and a lack of governance for data access.
• Lack of skills, organizational overload, and underestimated change management: Another bottleneck is people. IDC reports⁴ that most workers are acknowledging AI's impact on their roles, with 75% believing their roles will change. However, AI does not just change tools — it changes processes, roles, and decision structures. Without change management, typical symptoms emerge such as shadow AI outside official policies — meaning employees use AI tools outside the official framework, increasing security risks — and low adoption despite available solutions, or resistance due to uncertainty and fear of losing control.
• Difficult integration into existing systems and processes: Many AI PoCs work — but often only in isolation. Real business value only emerges when AI is integrated into existing business processes, enterprise data, and ways of working. This is where many organizations encounter structural barriers: legacy systems, complex IT landscapes, and missing end-to-end processes make integration difficult and prevent AI solutions from moving from pilot projects into productive operations.
• AI remains stuck at the copilot stage: Many organizations primarily use AI as an assistance system — for example, for text generation, analytics, or simple automation of individual tasks. These so-called copilots can increase productivity but often remain limited to supportive functions. Decisions are still made entirely by humans, processes remain fragmented, and AI is not deeply embedded in operational workflows. This creates localized benefits but no structural transformation. The next step — agentic AI that independently analyzes data, prepares recommendations, and initiates processes — is often not implemented. Companies therefore remain in a phase of assistance rather than true automation and decision support.

In summary, organizations that continue to launch isolated pilot projects, ignore data challenges, underestimate change management, and treat AI merely as an efficiency tool risk stagnating ROI results.

What Do Successful Companies Do Differently?

As stated in the IDC InfoBrief, sponsored by Microsoft⁵, Frontier firms are seeing a return of 2.84 times on AI investments versus a return of 0.84 times for laggards. Overall, Frontier companies achieve up to four times better outcomes in growth, efficiency, and customer experience than other organizations.⁶ Further, 76% of Frontier firms describe their organizations’ overall adoption of GenAI as scaling (delivering both incremental and new value across the organization) or realizing (achieving consistent GenAI value across the organization and in multiple business units) compared to 21% of laggards.⁷ So what do Frontier companies do differently to be so successful?

• AI is a business strategy — not an IT project: Frontier companies treat AI not as a technology initiative but as a strategic core capability. AI goals are directly linked to revenue growth, risk mitigation, time to market, and operational excellence. Management involvement is critical. AI is discussed at the executive level — not developed solely within IT teams.
• Data foundations instead of isolated dashboards: Many organizations invest in new platforms or dashboards without harmonizing their underlying data architecture. Yet even the best visualizations create no value if the underlying data is fragmented, inconsistent, or difficult to access. Frontier companies take the opposite approach. They first invest in consistent data models, system integration, and clear data ownership. They also promote data literacy and decision literacy. Data literacy refers to the ability to understand, interpret, and use data effectively. Decision literacy goes one step further — it describes the ability to make sound decisions based on data. Dashboards still play an important role, but not as isolated reporting tools. Instead, they are part of an integrated decision architecture that makes data transparent, connects information from different systems, and enables leaders to understand developments in real time and make data-driven decisions. IDC predicts⁸ that by 2026, 30% of large organizations will evolve their hybrid clouds into integrated digital business stacks with federated data fabrics to realize business value, doubling AI production success through smooth data access and unified governance.
• Change management as a success factor and governance as a scaling accelerator: Technology changes processes. But AI transformation is not a technology project — it is an organizational project that must address people, governance, and culture equally. Frontier companies invest not only in systems but also in people. They provide structured training, emphasize transparent communication, and actively involve business units. According to IDC, over 75% of surveyed organizations rate transparency as very important. This figure jumps to 88% for Frontier firms.⁹ Governance therefore becomes a prerequisite for scaling — not an obstacle.
• AI is deeply integrated into systems and processes: Frontier companies understand that AI only creates real business value when it is seamlessly embedded in existing systems, data flows, and business processes. Instead of developing isolated applications, they integrate AI directly into operational workflows — for example in customer service processes, supply chains, IT operations, or management decision processes. AI accesses enterprise data, connects with core systems such as ERP, CRM, or content platforms, and supports employees exactly where decisions are made. This close integration makes it possible to translate insights from data directly into action and to continuously optimize processes. AI thus becomes not only an analytical tool but a core component of operational value creation.
• From copilot to agentic AI: Another difference lies in the use of agentic AI. While many organizations use AI as an assistance system, Frontier companies take a step further. They deploy AI agents that analyze data, evaluate scenarios, prepare recommendations, and trigger processes. This shifts the focus from pure analysis to active decision support. The future of AI lies not in reporting but in action.

In summary, Frontier Companies do not simply use AI — they strategically and systematically embed it across the organization. According to IDC¹⁰ these organizations integrate human expertise, data, technology, and governance to drive AI-powered innovation, productivity, and long-term business leadership. They view AI not as a tool but as a leadership instrument. While many organizations see AI primarily as an efficiency measure, Frontier companies use it to accelerate decision-making, strengthen organizational resilience, and create structural competitive differentiation. 

The same IDC research¹¹ found that only 22% of organizations worldwide are Frontier firms and that on average, Frontier firms are currently using GenAI in seven business areas. Additionally, among Frontier firms, over 70% are currently using GenAI in customer service, marketing, IT, product development, and cybersecurity.

Conclusion: The era of AI experiments is over — AI can deliver ROI

Organizations in EMEA on average carried out 40 GenAI pilots and PoCs in 2023-24. Faced with often modest productivity gains, those organizations are now shifting away beyond broad experimentation, towards a more structured, directed and scalable approach to uncovering, prioritizing, implementing and governing AI use cases that deliver measurable business value.¹²  

Conclusion: AI can generate ROI — but only if the right strategic and organizational measures are in place. Technology alone does not create successful AI initiatives. In 2026, the question is no longer who is using AI, but who is using it effectively. Organizations that approach AI strategically and invest in data, governance, people, and agentic AI will continue to pull ahead. Companies that treat AI as a strategic capability will achieve faster decision cycles, greater organizational agility, more resilient structures, and sustainable competitive advantages.