For industrial and service companies alike, generative AI is fast becoming a strategic engine for change, influencing how organisations design processes, manage risk, and pursue sustainability goals. Beyond quick productivity wins, the real challenge now lies in turning rapid technological advances into coherent, scalable transformation. Julien Jacqué, Partner Digital Transformation and AI Champion for Industries & Services at PwC Luxembourg, outlines where GenAI is already delivering business value, and explains how companies can build the data foundations, governance models, and execution roadmaps needed to convert experimentation into lasting performance.

In Industry and Services, where do you see AI and GenAI creating the most value today — purely in efficiency gains, or increasingly as a driver of deeper business transformation?

The greatest value of AI and generative AI lies in deeper business transformation rather than isolated efficiency gains. In practice, however, most organisations follow an iterative journey. They typically begin with productivity-focused use cases before progressively moving toward end-to-end process redesign.

Today, many organisations start by deploying enterprise-wide GenAI platforms embedded into everyday tools such as email, spreadsheets, presentations, and documents. This delivers immediate productivity improvements, faster responses, and better-quality outputs. The next step usually involves enhancing broader workflows by introducing AI agents into selected stages of existing processes. The most significant value is ultimately unlocked when organisations redesign entire business processes with GenAI at the core, although this stage remains less common today, particularly in Luxembourg.

Given your role at the intersection of sustainability and digital transformation, how can AI and generative AI help organisations reconcile performance, resilience, and sustainability objectives?

AI and GenAI can act as a common lever across performance, resilience, and sustainability. From a performance perspective, they enhance steering capabilities by improving financial analysis, cost transparency, and decision-making speed through better data ingestion and analytics.

From a resilience standpoint, AI strengthens risk management by enabling organisations to identify and anticipate financial, operational, geopolitical, and supply-chain risks more effectively, supporting faster and more informed responses.

On sustainability, while AI does consume energy, its ability to significantly increase productivity and resource efficiency can offset this impact. AI can support sustainability reporting, regulatory compliance, and the identification of inefficiencies in energy, water, and material usage. In this way, GenAI helps organisations optimise resource consumption, reduce environmental footprints, and align sustainability objectives with operational performance.

Many organisations see GenAI as a shortcut to transformation yet struggle with data quality and architecture. In your experience, what level of data maturity is truly required to unlock value from GenAI?

There is no strict minimum level of data maturity required to begin extracting value from GenAI. Data quality has always been a challenge in any digital transformation, and GenAI is no exception. Rather than waiting for perfect data, organisations should start small with clearly defined use cases where data quality can be controlled.

Beyond data quality, integration, architecture, and knowledge management are equally important. GenAI delivers the most value when it can access structured system data as well as unstructured content such as documents and knowledge bases. A pragmatic, iterative approach — testing, learning, and scaling — is far more effective than large, multi-year transformation programmes, especially given the rapid pace at which GenAI technologies evolve.

As AI systems become embedded in core operations, how should organisations in Industry and Services rethink governance, risk management, and accountability?

Once GenAI becomes embedded in core business processes, it must be treated as a critical operational system. This requires production-grade governance, including clear ownership, access controls, change management, and continuous monitoring.

Organisations must also ensure compliance with regulatory frameworks such as the EU AI Act, including risk classification, documentation, transparency, and human oversight. Governance models should evolve from one-time validation toward continuous monitoring, ensuring that deployed AI systems remain reliable, compliant, and aligned with ethical standards over time.

What generative AI strategy do you typically recommend to achieve a rapid and measurable return on investment?

A successful GenAI strategy typically follows a two-speed approach. The first is bottom-up: empowering employees with training and access to enterprise-grade GenAI tools so they can improve day-to-day productivity and identify practical use cases. This drives adoption, builds habits, and enables internal capability.

The second is top-down: leadership teams jointly identifying high-impact, cross-functional processes where GenAI can deliver the greatest return on investment – specifically linked to performance KPIs. These strategic use cases often require end-to-end process redesign and benefit from a transversal view across departments. Combining bottom-up adoption with top-down prioritisation enables organisations to move quickly from experimentation to scalable value creation.

Looking ahead, what concrete actions should leaders in Industry and Services be taking today to prepare for the next phase of AI and GenAI adoption?

Leaders should first establish a secure governance and compliance framework, ensuring data protection, architectural readiness, and alignment with regulatory requirements. Upskilling employees and fostering hands-on experimentation with GenAI tools is equally critical to drive adoption.

At the same time, leadership teams should identify and prioritise the most valuable business use cases, balancing bottom-up insights with top-down strategic objectives. This creates a clear roadmap for implementation. Continuous learning, iterative deployment, and close collaboration between business and IT functions are essential to sustain momentum.

As a Partner at PwC Luxembourg, how are you leveraging AI and GenAI internally, and how do you ensure these initiatives remain value-driven, scalable, and embedded into day-to-day operations?

Within PwC Luxembourg Advisory, we apply the same principles we recommend to clients. We have combined bottom-up enablement — training thousands of professionals and providing access to GenAI tools — with top-down prioritisation of high-value use cases.

Our focus areas include enhancing client experience, improving service delivery quality and efficiency, and streamlining internal processes such as compliance and financial management. These initiatives are governed through a clear operating model, with centralised oversight, prioritisation frameworks, and phased releases.

By embedding GenAI into a shared, secure ecosystem and continuously monitoring adoption and impact, we ensure that AI is fully integrated into daily operations rather than remaining a collection of isolated experiments.

 Find out more about the GenAI Revolution by contacting us!