We leverage AI to drive measurable value creation.
More than 95 percent of all companies have already worked with Artificial Intelligence (AI). Yet only a small fraction achieve measurable impact on earnings and value creation through AI. The challenge we see: For many organizations, the step from isolated AI pilot projects to sustainable economic value remains unclear. What’s missing is a systematic approach that consistently aligns initiatives with financial targets, develops processes holistically, and enables the entire organization. Horváth.AI integrates strategy, execution, and performance management into a holistic end‑to‑end approach, positioning AI as a value creation program rather than an IT experiment.
When we implement AI in your business processes, we align it directly with your value creation. With our AI-to-EBIT approach, we systematically connect technological possibilities with your financial objectives. Our modular framework consists of four components: strategy development, potential assessment, sourcing and capability building, as well as piloting, scaling, and organizational embedding. Supported by end-to-end data and change management, we ensure that your leaders and employees effectively adopt and apply AI solutions. Whether you are planning your first steps, implementing specific use cases, or pursuing enterprise-wide scaling, we support you throughout your entire AI roadmap, meeting you exactly where you are.
What sets Horváth.AI apart? Our AI Value Management becomes the central control framework for your entire AI roadmap. We measure every initiative consistently and transparently based on its EBIT impact. The result: AI transformation becomes a true driver of business success. We work with a strong, vendor-independent partner ecosystem, enabling us to select the best solution for your organization in a neutral and objective way. Our experience from numerous AI projects across industries such as automotive, financial services, energy, and logistics, combined with our library of more than 500 industry-specific use cases, directly benefits your AI transformation.
are piloting AI
see the impact reflected in EBIT
AI goals, decision scopes, and value levers vary significantly across business functions. That is why we do not address AI in an abstract way, but tailor it to the specific requirements of each area. We provide clear answers to the relevant steering questions and enable the shift from AI activity to measurable business impact.
AI only unlocks its full value when it is consistently aligned with earnings and value creation. With our AI-to-EBIT approach, we ensure that every initiative makes a measurable contribution to business success.
AI increases profitability when it is deployed in value-generating business processes, such as Finance, Supply Chain, Procurement, and Sales. In these areas, it can accelerate decision-making, improve forecasting accuracy, reduce costs, and identify new revenue opportunities.
The greatest economic value of AI does not come from isolated pilot projects, but from its systematic integration into an organization’s core processes. Companies achieve measurable results when AI initiatives are consistently aligned with business performance, growth, and efficiency objectives.
The business value of AI can be measured when AI initiatives are directly linked to specific business objectives, such as revenue growth, cost reduction, productivity improvement, or risk mitigation. The key is not to evaluate the technology itself, but to assess its contribution to value creation.
Reliable performance measurement requires clear targets and continuous tracking of the results achieved. Companies are therefore increasingly evaluating AI initiatives based on metrics such as EBIT contribution, processing times, forecast quality, degree of automation, or realized cost savings. This makes it clear which use cases create the greatest value and where further investments are likely to yield the highest economic benefits.
AI delivers the greatest impact in processes that are not purely rule-based but require judgment. In complex, cross-functional environments, information from multiple sources must be consolidated, alternatives assessed, and recommendations derived. These judgment-based processes offer particularly strong value creation potential for AI.
Among the most impactful use cases are emerging agentic AI systems that can not only analyze information but also execute substantial parts of business processes autonomously. Humans typically remain responsible for final decisions, prioritization, and approvals, while AI handles a significant share of operational tasks.
