As energy companies accelerate their digital transformation, generative and agentic artificial intelligence are emerging as key enablers of smarter, more resilient, and sustainable operations. From optimizing power grids and improving demand forecasting to supporting predictive maintenance and customer engagement, AI is reshaping the way utilities operate while addressing growing regulatory and cybersecurity requirements.

In this interview with The Diplomat-Bucharest, Kostas Fiakas, Commercial Director, AI & Digital Solutions at AUSTRIACARD, discusses how the company’s GaiaB™ Appliance is helping organizations deploy enterprise AI securely in on-premise and air-gapped environments, the strategic investments driving its AI roadmap, and why trusted data governance will be essential as the energy sector embraces the next wave of intelligent technologies. 

How do you envision generative AI transforming the energy sector in the next 3–5 years, particularly in areas like grid optimization, demand forecasting, or sustainability?

Over the next three to five years, Generative and Agentic AI will move the energy sector beyond basic automation. AI Agents will support Grid Optimization, improve Demand Forecasting, accelerate renewable energy integration, and enable faster decisions in areas such as Predictive Maintenance and Infrastructure Planning.

GaiaB™ Appliance is especially important for energy operators because it brings these capabilities into secure on-premises, private cloud, or fully air-gapped environments. This allows organizations to use advanced AI while keeping sensitive operational data under their own control. The result is greater efficiency, resilience, and progress towards more sustainable energy systems.

Can you share specific examples or use cases where AUSTRIACARD HOLDINGS has successfully implemented generative AI in energy-related operations or customer solutions?

Through GaiaB™ Appliance and our Agentic AI capabilities, we support use cases that improve Operational Efficiency, Customer Engagement, and Sustainability in regulated industries. With respect to Energy related operations, GaiaB™ Appliance is working alongside Smart-Metering Projects and as the Appliance evolves towards Edge AI, selected AI functions can also operate closer to the grid, where low latency, data control, and reliable local processing are essential.

What strategic investments has the company made in the generative AI space — both internally in R&D and externally in partnerships or acquisitions?

Our strategy combines internal product development with selected technology partnerships. Internally, we continue to invest in GaiaB™ Appliance as an enterprise Agentic AI solution that allows organizations to build, manage, and orchestrate AI agents, connect them with business systems, and operate them securely.

The GaiaB™ Appliance turns this capability into a complete, enterprise-ready solution by combining software, local AI models, optimized inference, governance, and infrastructure based on Dell Technologies, to support demanding and mission-critical deployments.

How do you prioritize AI-related investments across the group, especially when balancing innovation with regulatory and cybersecurity concerns in the energy sector?

We prioritize AI investments according to business value, scalability, security, and regulatory readiness. In the energy sector, innovation must be combined with strong data governance, cybersecurity, and operational resilience from the beginning.

This is a core strength of GaiaB™ Appliance. It can operate on-premises, or in an air-gapped environment, helping customers maintain control of their data, models, and infrastructure. We begin with well-defined use cases and scalable pilots and then expand where measurable value and secure deployment have been demonstrated.

Which AI-powered services or tools currently in AUSTRIACARD’s portfolio are driving the most value for clients, and how has generative AI enhanced their performance?

The strongest value comes from Projects around Enterprise Content Management and Content Understanding, Customer-Service Automation and Decision Support. Generative and Agentic AI make these services more intelligent by understanding context, generating content, supporting complex decisions, and automating multi-step tasks.

GaiaB™ Appliance makes these capabilities available as a complete on-premise solution, from Tiny to Large configurations. Customers can start at the scale they need and expand over time, while keeping control of sensitive data and selecting the most appropriate local AI models. This helps reduce manual work, improve response times, and deliver measurable operational benefits.

Given the complexity of energy data, how does the company ensure the quality, security, and ethical use of data when deploying generative AI models?

Responsible AI starts with trusted data and clear governance. Energy data must be validated, standardized, protected throughout its lifecycle, and used only for defined business purposes. Where appropriate, statistically sound synthetic data can support testing and model development without exposing sensitive information.

GaiaB™ Appliance supports this approach through local deployment, controlled access, encrypted environments, model governance, explainability and Predictable Costs behind. Operating on-premises or in an air-gapped environment also reduces exposure to external services. Together with compliance requirements such as GDPR and the EU Data Act, these controls help customers deploy AI securely, ethically, and transparently.

How does your role influence AUSTRIACARD’s long-term AI roadmap, and what major milestones do you foresee in the next few years?

Part of my role is to connect the real market needs, customer priorities, and commercial opportunities with our AI roadmap. This includes identifying high-impact use cases, developing strategic partnerships and ensuring that GaiaB™ Appliance remains relevant for regulated and data-sensitive industries.

The roadmap is not defined only by technology. Feedback from customers, partners, pilots, and commercial teams directly influences which capabilities we prioritize, and which integrations or industry Use Cases we and our value-added partners develop next. This continuous feedback loop helps GaiaB™ Appliance stay ahead of the curve.

Key milestones include broader adoption of GaiaB Appliance, stronger industry-specific solutions, further development of enterprise knowledge and Agentic AI capabilities, more edge use cases, and deeper partnerships that accelerate deployment across the markets.