HELLENiQ ENERGY’s shift from an oil-and-refining heritage to a digital-first, AI-enabled workplace is no longer a boardroom aspiration—it’s a functioning reality that has reshaped how the group manages knowledge, serves employees, and measures productivity across a diverse energy portfolio.
Background
HELLENiQ ENERGY is a major integrated energy group headquartered in Athens, operating across refining, petrochemicals, fuels marketing, exploration & production, power generation, and an expanding Renewable Energy Sources (RES) business with operations in eight countries. The group’s Vision 2025 strategic roadmap and a public multi-year Digital Transformation program set the stage for technology-driven efficiency gains, targeted investments in renewables, and a stated ambition to reach climate neutrality by mid-century.
Digital transformation at HELLENiQ ENERGY was conceived around three corporate pillars:
continuous innovation,
employee-centric design, and
operational excellence. Those pillars guided a program that combined traditional modernization (data governance, process digitization) with a rapid embrace of generative AI productivity tools. The company’s declared results include a multi-year digital investment program and measurable financial benefits that management projects will scale substantially through 2025.
Overview of the PwC partnership and program goals
In a strategic engagement with PwC Greece, HELLENiQ ENERGY moved from pilots into enterprise-scale deployment of Microsoft-powered generative AI capabilities—starting with Microsoft 365 Copilot, and then extending to purpose-built AI agents delivered via Copilot Studio and the Microsoft Power Platform.
The immediate goals were pragmatic and employee-focused:
- Increase everyday productivity of knowledge workers.
- Remove friction in routine HR and administrative tasks.
- Accelerate cultural change toward a digital-first working model.
- Create a sustainable operating model (CoE + community) to scale AI responsibly.
PwC supported the company across strategy, technical architecture, change management, and the rapid development of tailored use cases, including a bespoke HR assistant named
HRaklis.
How the program was structured
A people-first adoption model
Rather than treating Copilot as a top-down tool rollout, the project placed employees at the center:
- A comprehensive Adoption Playbook was created to map use cases, learning paths, and role-based guidance.
- A “Digital Stars” community of roughly 600 employees acted as local champions and co‑creators of use cases.
- A 500+ person training cohort supported initial deployment waves, producing an early adoption rate cited at around 70%.
This human-centric approach emphasized small, measurable wins to build momentum and trust before automating higher-risk tasks.
Governance, operations and enablement
Supporting scale was a dedicated
Centre of Excellence (CoE) and a delivery model that combined PwC consulting, a team of internal “Digital Heroes,” and low-code/no-code tooling to allow business teams to iterate without heavy IT bottlenecks.
Technologies used included:
- Microsoft 365 Copilot for embedded productivity (Word, Excel, Outlook, Teams).
- Copilot Studio for building specialized AI agents.
- Azure Cognitive Services for natural language understanding and retrieval.
- Power Automate and SharePoint for workflows and secure content management.
- Microsoft Teams as the primary user interface with adaptive cards for interactive microflows.
This architecture intentionally favored a
single vendor stack to accelerate integration and reduce complexity—trading off potential multi-cloud or third-party modularity for faster time-to-value.
The HRaklis story: a departmental AI agent
HRaklis is the program’s first purpose-built agent: an HR-focused conversational assistant designed to reduce friction for employees seeking HR information and to offload routine HR tasks.
Key design decisions:
- Convert written HR policies, collective agreements, and FAQs into a structured knowledge base optimized for automated querying.
- Leverage low-code interfaces so HR teams could update the assistant without heavy IT involvement.
- Integrate access control and storage within Microsoft SharePoint and Azure to keep sensitive HR content inside the corporate security perimeter.
- Deliver the interface via Teams to meet users where they already work.
Early operational outcomes reported include a 70% reduction in routine HR queries, freeing up roughly 200 hours per month of HR time—allowing HR staff to focus on talent strategy, learning and development, and higher-value people initiatives.
Measured results and claimed impacts
The combined Copilot + HRaklis rollout delivered a mix of quantitative and qualitative improvements claimed by the program sponsors:
- Approximately 70% of trained users reported productivity improvements.
- Common tasks such as searching, writing and summarizing were reported as 29% faster.
- Time to catch up on missed meetings improved by a factor of four.
- Email processing time reductions of more than 60% were reported in some communications.
- Adoption metrics reached ~70% after initial training of 500+ employees.
- The digital transformation program overall—embracing many initiatives beyond Copilot—was reported to have delivered cumulative financial benefits exceeding €60 million to date, with an investment of around €50 million and projected total benefits rising toward €160 million by the end of the Vision 2025 horizon.
These results illustrate how productivity‑oriented AI, combined with structured change management, can convert theoretical efficiency gains into near-term operational outcomes.
Strengths: why this program works
1. Clear strategic alignment
The AI initiatives were not deployed as isolated experiments. They tie into the corporate Vision 2025, which emphasizes decarbonization, renewables growth, and an integrated energy portfolio. Digital improvements were framed as enablers of competitive resiliency across downstream and new energy businesses.
2. Strong people and change focus
By establishing a deliberate adoption playbook, a community of Digital Stars, and role-specific training, the program reduced the common adoption barriers—fear, opacity, and lack of perceived value.
3. Pragmatic use-case selection
The program started with high-frequency, low-complexity workflows—leave requests, FAQs, meeting catch-up—where automation yields clear returns and risk is low. That led to measurable wins early, which are critical for sustaining sponsorship.
4. Low-code/no-code enablement
Using Copilot Studio, Power Automate and SharePoint enabled HR and other business functions to iterate independently. That capability reduces IT bottlenecks and speeds the feedback loop between users and product owners.
5. Security-conscious implementation
Keeping knowledge bases and sensitive content within Microsoft-managed services and enforcing SharePoint controls created a clear security perimeter and eased concerns about data exfiltration—an important consideration for regulated corporate environments.
Risks, trade-offs, and areas for vigilant oversight
No enterprise AI program is risk-free. Several risks stand out and require durable mitigation:
1. Model reliability and hallucinations
Generative AI can produce confident-sounding but incorrect answers. For an HR assistant, inaccurate guidance on benefits, contractual terms, or legal entitlements could have legal or reputational consequences. Continuous validation, guardrails, and human oversight remain essential.
2. Data governance and lineage
Sourcing answers from internal documents requires careful management of document versions, policy changes, and authoritative sources. The knowledge base must maintain explicit provenance and escalation paths to ensure users see the most current policy.
3. Privacy and compliance
HR data is sensitive. Although the implementation sits inside corporate Microsoft infrastructure, organizations must ensure appropriate role-based access controls, retention policies, and monitoring consistent with EU data protection laws and internal compliance frameworks.
4. Vendor lock-in
The choice to standardize on Microsoft accelerates deployment but increases reliance on one vendor’s roadmap, pricing, and enterprise policy. Future architectural flexibility may be limited unless interfaces and data are designed for portability.
5. Workforce impacts and expectations
While automation frees HR personnel for higher-value work, employees may expect faster expansion of self-service capabilities. Managing expectations and aligning ROI metrics with human outcomes (e.g., employee engagement, retention) is key.
6. Security posture and attack surface
Introducing AI agents and automated workflows increases the attack surface. Threat models should account for prompt injection, data leakage via third-party connectors, and supply-chain risks in low-code components.
Technical checklist — what enterprises should validate before scaling
- Authentication and authorization: ensure agents respect existing RBAC and conditional access policies.
- Data provenance: each response should be traceable to an authoritative source and show confidence levels or citations where appropriate.
- Version control for policies: implement formal workflows to update knowledge artifacts and invalidate obsolete content.
- Human-in-the-loop escalation: define triggers for routing queries to human agents and audit those handoffs.
- Monitoring and metrics: track usage, error rates, user satisfaction, and business KPIs (time saved, cost avoided).
- Security testing: include adversarial testing for prompt-injection and rigorous review of connectors.
Practical recommendations for enterprises beginning a similar journey
- Start with a small set of high-frequency use cases that have measurable outcomes and low legal risk.
- Build a cross-functional adoption playbook that includes role-based training, a champion network, and measurable targets for adoption and business value.
- Invest in a lightweight CoE that can operationalize governance, accelerate build patterns, and maintain quality across agents.
- Use low-code tools to empower business owners, while keeping IT responsible for security, integration and lifecycle management.
- Measure both productivity and human outcomes—time saved is valuable, but improved employee experience and retention are equally strategic.
- Implement ongoing validation processes—automated testing, human reviews, and feedback loops must be part of any production agent.
Strategic implications for the energy sector
HELLENiQ ENERGY’s experience provides a practical template for energy companies that must coordinate large, geographically-distributed workforces, complex regulatory requirements, and legacy operational systems.
- Cross-functional knowledge sharing: AI agents can break down silos between operations, trading, power generation, and corporate functions—vital in vertically integrated energy groups.
- Rapid rollouts in renewables and e-mobility: As HELLENiQ ENERGY scales its renewables and e-mobility services, AI can accelerate permitting, project handovers, and maintenance scheduling via domain-specific agents.
- Cost-to-serve reduction: Reducing repetitive HR and administrative overhead directly supports the cost base and allows talent to focus on strategic energy transition initiatives.
- Regulatory readiness: A disciplined knowledge management approach aids compliance with evolving ESG reporting and energy market rules.
Where the program can evolve next
The immediate opportunity set beyond HR includes:
- Finance agents for close processes, variance explanations and scenario analysis in Excel.
- Operations agents that synthesize shift reports, maintenance logs and alarm histories for frontline supervisors.
- Customer service bots that integrate billing, usage analytics and renewables procurement for B2B customers.
- Field-worker assistants that pair mobile conversational agents with sensor data and predictive maintenance outputs.
Longer-term, enterprises should consider integrating agents with enterprise systems (HRIS, ERP, EAM) for transactional automation while preserving read-only, auditable paths for policy decisions and compliance.
Final assessment: pragmatic innovation with measurable returns — but not a risk-free sprint
HELLENiQ ENERGY’s collaboration with PwC demonstrates how a structured, human-centered approach to generative AI can produce early productivity wins while building governance and operational structures for scale. The program’s strengths—strategic alignment, adoption planning, accessible tooling, and a CoE-backed delivery model—are classic hallmarks of durable digital transformation.
However, the path to sustained AI-driven value will depend on rigorous governance: controlling for model accuracy, securing sensitive data, avoiding complacency about vendor dependencies, and continuing to measure business outcomes beyond time saved. For energy companies facing regulatory scrutiny and operational complexity, the imperative is clear: move fast, but instrument everything, and retain human oversight where the stakes are high.
HELLENiQ ENERGY’s story stands as a useful case study for enterprises that wish to pair generative AI with business transformation. It shows that with careful planning, targeted use cases, and investment in people and governance, AI can be a potent lever for productivity—and a platform for the broader modernization of the corporate operating model.
Source: PwC
https://www.pwc.com/gr/en/publications/helleniq-energy.html