Maria Boldor,
Partner & Managing Director,
Horvath Romania
Artificial Intelligence is no longer a technology topic – it is a boardroom priority. For CEOs, CFOs, and COOs alike, the question is no longer whether to invest in AI, but how to translate that investment into measurable business performance.
And this is where the real challenge begins.
Today, more than 95% of companies are piloting AI, yet only a small fraction of 6% see a tangible impact in EBIT. This gap does not reflect a lack of ambition or investment – it highlights a structural challenge: the difficulty of turning AI into real value creation. Across organizations, AI initiatives are multiplying. Pilots are launched, tools are deployed, and budgets are expanding.
But too often, these efforts remain fragmented, disconnected from business priorities, and insufficiently linked to financial outcomes. The result: activity without impact.
Leading companies are addressing this disconnect by reframing AI from an innovation topic into a disciplined value creation program. The shift is fundamental. It requires moving from isolated use cases to a portfolio mindset, where every initiative is measured against its contribution to growth, efficiency, and ultimately EBIT.
At Horvath Romania, this shift is described within the framework “from AI to EBIT”.
For CEOs, it means using AI to create a forward-looking, integrated view on performance, by linking strategy, growth trajectories, and risk in real time.
For CFOs, it means elevating AI from a reporting tool to a core element of performance steering, by enhancing forecasting accuracy, transparency of earnings drivers, and dynamic decision-making.
For COOs and operational leaders, it means embedding AI into end-to-end business processes to unlock efficiency, resilience, and cost discipline at scale.
However, translating these ambitions into results requires more than technology.
One of the most critical barriers remains the transition from pilot to scale. Proof-of-concepts demonstrate what is possible – but they rarely change how a company operates. Real value is created only when AI is embedded into core processes, decision frameworks, and management cycles.
This is where many organizations fall short.
A structured, end-to-end approach is required: one that starts with strategic priorities, quantifies value potential, builds the right capabilities, and ensures disciplined scaling across the organization.
Equally important is governance: clear accountability, transparent measurement, and consistent steering of AI initiatives based on their financial impact.
Organizations that succeed treat AI not as a collection of tools, but as a managed transformation – integrated into their operating model and actively driven by leadership.
Because in the next phase of competition, AI adoption will no longer be a differentiator. Execution will. The winners will be those organizations that use AI more consistently, scale it more effectively, and connect it more rigorously to business outcomes. Those that close the gap between innovation and performance.
Case Study: From Fragmentation to Data-Driven Performance
A leading German company in the lifestyle and fashion industry faced growing challenges following the introduction of a new data and BI landscape. Despite significant technological investments, the absence of a clear strategy and governance led to fragmented reporting, inconsistent data quality, and declining user trust.
The transformation began with a shift in perspective: from technology-first to strategy-first. A holistic AI, BI, and data strategy was defined – anchored in a clear vision,
supported by structured governance, and translated into a modern data architecture and operating model.
By establishing a unified data foundation, introducing transparent demand management, and investing in capability building, the organization significantly improved data consistency, efficiency, and decision-making. The result was not just better reporting—but a scalable foundation for high-value, data-driven business impact.
In closing, my message is clear: AI does not create value by itself. Value emerges when leadership turns it into a disciplined engine for better decisions, sharper execution, and measurable financial results. In the end, the question is not how much AI a company uses – but how effectively it converts AI into performance.
