Global business services leaders confront ai's strategic imperatives and adoption challenges

Strategic clarity: The foundation of AI success

The roundtable commenced with a critical examination of a common pitfall: the absence of a defined AI strategy. Many organizations, despite the widespread interest in AI, often find themselves without a clear vision for its application, oscillating between a desire for mass usage of general models and the need for unique, tailored solutions for specific business problems. This strategic vacuum often leads to fragmented efforts and unrealized potential, failing to translate excitement into tangible business value.

Establishing clear governance emerged as a paramount concern. Discussions highlighted that defining ownership, policies, and operational models for AI implementation often precedes the actual technological rollout. Several participants noted the formation of dedicated AI councils, typically comprising IT, compliance, and automation experts, tasked with laying the groundwork for a cohesive strategy and responsible AI usage. These councils are crucial for addressing complex questions like data ownership and ethical AI deployment.

“Looking back, many organizations failed to define a clear AI strategy, either seeking mass usage without purpose or attempting to solve everything with a single tool. The fundamental question of 'what do we want to achieve with AI?' was often overlooked.” – said Eszter Lukács, GBS Advisory and Finance Transformation Director at Deloitte Hungary.

Navigating implementation: Approaches and tangible gains

The dialogue explored diverse implementation approaches, ranging from top-down, CEO-level mandates seen in financial institutions and technology companies, to more organic, bottom-up initiatives focused on empowering employees. Some companies, for instance, are redefining internal operations with AI solutions, while others are leveraging internal "AI accelerators" and hackathons to foster adoption and generate innovative ideas from the ground up. These initiatives aim to demystify AI, reduce employee apprehension, and cultivate a culture of experimentation.

Success stories shared included optimization of pricing and promotions, and a system for detecting fraudulent product returns. Other examples highlighted automated processing of incoming emails and documents, AI-driven simulations for process optimization, and intelligent inventory management. These cases demonstrate AI's potential to drive operational efficiency and enhance customer experience, offering tangible returns on investment within typical payback periods of 18 to 24 months for prioritized projects.

However, participants also acknowledged pitfalls. A critical insight was the danger of framing AI initiatives solely around cost savings, potentially overlooking opportunities for revenue generation, capability enhancement, or addressing existing gaps. The discussion underscored the importance of distinguishing between Large Language Models (LLMs), which excel at language understanding but can "hallucinate" information, and more specialized Small Language Models (SLMs) or traditional machine learning for applications requiring high accuracy, such as financial forecasting or contract analysis.

“While AI offers immense capabilities, we must understand its probabilistic nature and the need for human expertise in validation and content curation. Misaligned expectations and inadequate user understanding can lead to significant setbacks, underscoring the vital role of skilled subject matter experts.” – added Zoltán Páll, Technology & Transformation AI Manager at Deloitte Hungary.

Cultivating talent and an AI-ready ecosystem

A recurring theme was the urgent need for talent development and upskilling. While Hungary boasts a strong foundation in analytical, data science, and engineering talent, the rapid pace of AI evolution outstrips current educational and internal training efforts. Organizations are increasingly investing in in-house programs, focusing not only on technical skills like Python but also on essential soft skills such as resilience, problem-solving, and proactivity, which are crucial for navigating an AI-driven environment.

An often underutilized enabler discussed was access to R&D incentives and governmental support. In Hungary, AI‑related development, particularly initiatives involving experimentation, uncertainty, or novel combinations of existing technologies can frequently qualify for R&D tax incentives and cash grants, potentially allowing organizations to recover 20–50% of eligible costs. Participants noted that these mechanisms can materially improve business cases and enable projects that might otherwise struggle to secure internal funding.T

he human factor, including potential resistance and the need for "inclusive AI," was also critically discussed. Misplaced trust in AI, or conversely, a complete refusal to engage, can undermine even well-conceived projects. The roundtable emphasized that AI is a tool that augments human capabilities, not replaces them entirely. The focus should shift from "AI taking jobs" to "colleagues using AI to excel", necessitating robust SME (Subject Matter Expert) involvement in validating AI outputs and continuously refining systems.

Furthermore, a solid data strategy and robust data governance are foundational. While modern AI, especially LLMs, can process unstructured data, standardized, high-quality data significantly enhances accuracy and reliability. The integration of AI strategy with existing data management frameworks, often under separate leadership, presents an ongoing challenge that requires greater cross-functional collaboration, ensuring data is not just "AI-ready" but truly fit for purpose across the enterprise.

Looking ahead: Adaptability and continuous learning

The consensus among GBS leaders was that the journey towards optimal AI integration is dynamic and requires continuous learning and adaptability. The speed of AI development necessitates an infrastructure that can evolve, accommodating future advancements rather than solely addressing current needs. Organizations must foster a mindset that embraces change, encourages experimentation, and views AI as a powerful enabler for strategic growth and innovation, well beyond its immediate cost-saving potential. This collective effort, marrying strategic vision with agile execution and human-centric development, will define success in the evolving AI landscape