While my previous article explored why the customer information system is vital in increasing revenue, reducing costs, and mitigating risks in an increasingly competitive and compliance-centric market, this article focuses on the strategic implications of artificial intelligence and how it might provide the foundations for future-proofing the solution.
With customer expectations soaring, contact centres struggle to deliver seamless, round-the-clock support across multiple channels. Surging call volumes, escalating service demands, and the necessity for after-hours support present formidable hurdles to maintaining service quality.
Agents must now handle more complex queries; achieving first-call resolution is harder, driving up operational costs. Meeting service expectations becomes challenging without the right tools, impacting customer satisfaction and agent performance.
High-pressure environments contribute to burnout and staff turnover, making recruitment and training costly and time-consuming. Lengthy onboarding processes and inconsistent service quality further complicate operations, leading to inefficiencies and customer frustration.
Legacy systems often cannot keep up with evolving business needs, lacking the integration and flexibility required for modern customer engagement. However, adopting advanced technology, such as artificial intelligence (AI), presents a promising solution. Businesses striving to remain competitive are turning to AI to boost efficiency, enhance agent support, and create seamless customer experiences. This transition to AI heralds a promising future for customer service in the energy and utilities sector.
The Shortcomings of First-Generation Chatbots
The emergence of chatbots was expected to revolutionise customer service by offering instant, automated support. However, early-generation chatbots often failed to meet expectations, leading to user frustration and a lack of significant improvements. These limitations underscore the need for more advanced and intelligent solutions to foster a more natural interaction and, ultimately, satisfy the customer’s needs. AI steps into this gap, offering the potential to overcome these limitations and deliver a more effective customer service experience.
Robotic and Unnatural Interactions
First-generation chatbots could not engage in human-like conversations. Their responses were often mechanical, missing the emotional intelligence needed to build customer rapport. They frequently misinterpreted queries without understanding context or nuance, leading to irrelevant or unhelpful responses.
Rigid and Linear Conversations
Early implementations followed pre-defined scripts with little flexibility. They struggled to handle deviations from expected inputs, forcing users to restart interactions if they strayed from the structured path. This rigidity made conversations unnatural and often frustrated customers when they couldn’t get the needed answers.
Limited to Basic Use Cases
Early chatbots could only handle simple, repetitive tasks such as answering basic FAQs, perhaps extending to checking account balances and resetting passwords; when faced with complex, multi-step inquiries, they often fail, requiring customers to escalate their issues to human agents, ultimately defeating the purpose of automation.
No Cross-Use Case Memory
A significant drawback of early chatbot implementations was their inability to retain or transfer information across different channels or interactions. Forcing customers to repeat their details every time they engage with the bot leads to inefficient and frustrating experiences. Without continuity, these chatbots failed to create the seamless, connected service customers expect today.
Lack of Personalisation
First-generation bots operated with a one-size-fits-all approach, offering generic responses that lacked personal relevance; they were deployed as hammers, viewing every situation as a nail. They did not consider customer history, preferences, or past interactions, making conversations feel impersonal and transactional rather than helpful and engaging.
Integration Challenges
With many poorly connected to backend systems, these chatbots could not pull in real-time data or execute actions effectively. This lack of integration led to incomplete or inaccurate responses, forcing customers to switch to other channels for assistance.
The Need for a Smarter Approach
To truly transform customer service, chatbots must evolve beyond the limitations of early implementations. The next generation of conversational systems must offer dynamic, context-aware interactions, integrate seamlessly with enterprise systems, and personalise responses based on individual customer journeys. By addressing these challenges, businesses can create intelligent, responsive, and human-like digital assistants that enhance – not hinder – the customer experience. The urgency of this need for a quantum leap in capabilities is why organisations are considering AI-driven solutions.
The But: The EU’s AI Act and its Implications for AI-driven Customer Engagement in Europe
As AI becomes increasingly integrated into customer engagement strategies, organisations in the energy and utilities sector must navigate new regulatory landscapes, particularly with the European Union’s Artificial Intelligence Act. This pioneering legislation aims to ensure the ethical deployment of AI, balancing innovation with compliance, data protection, and customer trust. The AI Act creates significant implications for AI-driven customer engagement in Europe, including usage within the energy and utilities sector. It introduces new regulatory requirements and oversight for AI applications, such as the need for transparency and user consent, which will impact the design and implementation of AI-driven customer service solutions.
The AI Act uses a risk-based regulatory framework to govern AI applications across various industries, including energy and utilities. This framework classifies AI systems into four distinct risk categories – unacceptable, High, Limited, and minimal – each with different levels of regulatory oversight. For instance, AI systems used for social scoring or manipulating human behaviour are strictly prohibited, falling under the ‘unacceptable’ risk category. AI systems classified as ‘High-risk’ are subject to stringent regulatory requirements, as they could play an essential role in critical infrastructure, employment decisions, credit scoring, and other sensitive domains. Understanding this framework is crucial for ensuring that AI applications are used responsibly and ethically, explicitly targeting high-risk applications that could threaten fundamental rights and freedoms.
The AI Act’s regulatory focus is on transparency for AI applications falling under the limited risk category. This classification typically applies to AI-driven customer service solutions, such as chatbots, where organisations must inform users that they are interacting with AI and provide appropriate disclosures regarding its functionality. The AI Act requires that users be aware of when they interact with AI and understand the capabilities and limitations of the AI system they are engaging with. This transparency is crucial for building and maintaining customer trust in AI-driven solutions.
The drive for automation in the energy and utilities sector is not new; it’s leveraged increasingly for customer support, demand forecasting, billing automation, and energy efficiency recommendations. While these innovations improve customer experience and operational efficiency, they introduce new compliance challenges under the AI Act and have considerable privacy considerations.
GDPR Compliance & Privacy Protections
The General Data Protection Regulation (GDPR) remains the foundation of data governance in the EU, and AI-driven customer engagement solutions must align with its core principles. Organisations must ensure that customers provide informed consent when AI systems process their personal data. This legislation is not just a recommendation but a necessity. Transparency is key, as users have the right to understand how AI-driven decisions are made, including dynamic pricing models, automated customer support, or energy efficiency recommendations. The gravity of GDPR compliance cannot be overstated, as it is crucial for building and maintaining customer trust in AI-driven solutions.
Additionally, GDPR grants individuals control over their personal data. Customers have the right to access, rectify, delete, or restrict the use of their data when processed by AI systems. Companies must also prioritise fairness and avoid bias in AI decision-making. AI-based pricing models, for example, must not discriminate against specific customer segments, ensuring equitable access to energy services. Non-compliance with these GDPR requirements can lead to substantial fines and reputational damage, reinforcing the need for strict adherence to data protection standards.
Data Sovereignty & Retention
Under the AI Act, data sovereignty principles ensure customer data is processed and retained within appropriate jurisdictional boundaries. Organisations must store personally identifiable information (PII) within the EU or in jurisdictions that provide equivalent data protection. Furthermore, AI-driven customer engagement solutions should follow the principle of data minimisation, collecting only the necessary information required for AI functionalities while avoiding excessive data accumulation.
Companies should implement well-defined retention policies to align with GDPR’s storage limitation principle, ensuring that AI models do not indefinitely store customer interactions. Instead, organisations must define specific timeframes for data storage, after which data should either be deleted or anonymised. Automated techniques can further enhance data privacy by ensuring that personal identifiers are removed or anonymised, thereby reducing the risk of identity exposure.
Data Sharing & Third-Party Risks
As AI adoption grows, many energy and utility companies rely on third-party vendors, cloud-based AI models, or external data sources to power their AI-driven solutions. However, the AI Act introduces stricter obligations regarding sharing customer data with these third parties. Companies must establish robust data processing agreements that ensure AI vendors comply with EU data protection laws and regulatory frameworks.
To maintain compliance, organisations must prohibit the unauthorised sharing of PII with external AI models unless customers explicitly consent to such data usage. Additionally, the AI Act emphasises AI supply chain accountability, meaning organisations using third-party AI solutions must conduct thorough due diligence on their vendors. This requirement includes evaluating security protocols, assessing risks of data leaks, and ensuring that AI providers meet ethical and legal standards. These requirements are particularly relevant for automated energy management solutions that rely on predictive analytics, where customer data must remain protected and used solely for its intended purpose.
AI Ethics & Transparency in Customer Interactions
Transparency is a fundamental pillar of the AI Act, particularly for AI-driven customer engagement solutions. Energy and utility companies must disclose AI usage in customer interactions, ensuring they proactively advise when users interact with AI-powered chatbots, virtual assistants, or automated decision-making systems. This transparency fosters trust and enables customers to make informed decisions about their usage and interactions with AI technologies.
In addition to transparency, high-risk AI applications – such as those affecting billing, energy access, or contract approvals – must include mechanisms for human oversight. Customers should always be able to escalate AI-driven decisions to a human representative when necessary. Furthermore, AI models used in customer engagement must avoid manipulative practices, ensuring they do not exploit consumer vulnerabilities or nudge users toward behaviours that may not be in their best interest. For example, AI-powered recommendations should promote energy efficiency rather than incentivise excessive consumption.
Sector-Specific Impacts for Energy & Utilities Providers
Understandably, the energy and utilities sector is evaluating the potential for AI in applications beyond customer engagement, including demand forecasting, grid optimisation, fraud detection, and smart metering. While these innovations enhance efficiency, they also introduce regulatory considerations under the AI Act. For example, AI-driven personalised energy pricing models must ensure fairness, preventing discriminatory rates based on customer profiling. Similarly, automated billing and AI-powered customer support systems must include safeguards allowing customers to request human assistance.
Likewise, AI-based fraud detection and anomaly detection solutions must be designed with non-discrimination principles, ensuring that surveillance mechanisms do not unfairly target specific demographics or communities. If AI-driven applications significantly impact consumers’ financial standing or energy access, they may be classified as high-risk under the AI Act, triggering additional compliance obligations.
More generally, predictive maintenance and AI-driven grid management also require careful consideration. While AI can improve infrastructure reliability, it must not compromise data security or result in unfair prioritisation of service availability.
How Organisations Can Prepare
While continuing to innovate, energy and utility companies must take proactive steps toward compliance to navigate these evolving regulatory requirements. AI impact assessments are essential to determine whether solutions fall under high-risk or limited-risk classifications. Strengthening data governance policies ensures that AI applications align with GDPR and AI Act standards, particularly concerning data minimisation and transparency.
Engaging with regulatory authorities and industry bodies can help organisations stay ahead of legislative changes and adapt their AI strategies accordingly. Internal AI ethics frameworks can further support responsible development, ensuring fairness, explainability, and security in AI-driven customer engagement solutions; additionally, enhancing customer communication and transparency by educating users about AI processes and their rights fosters trust and compliance.
Finally, companies must exercise caution when selecting AI vendors, ensuring third-party providers comply with the EU’s AI governance frameworks and maintain robust data protection measures. By taking these proactive steps, energy and utility companies can embrace AI innovation while maintaining compliance with evolving regulatory landscapes.
Conclusion
This article introduces the EU AI Act and discusses the challenges and opportunities for AI-driven customer engagement in the energy and utilities sector.
Organisations can build customer trust by proactively addressing compliance, privacy, and ethical considerations while harnessing AI’s full potential for service innovation and operational efficiency. With a well-structured approach to AI governance, energy and utility companies can leverage AI to enhance customer interactions, streamline operations, and uphold the highest data protection and transparency standards.
Now, safe in the knowledge that AI is a viable and valuable alternative, organisations can plan a path forward. In my next article, I’ll introduce you to Hansen’s scalable and cost-effective AI-powered contact centre solution that empowers businesses to elevate customer experiences, streamline operations, and drive long-term efficiency by combining industry-specific training, omnichannel support, and seamless integration.
Don’t hesitate to contact us to learn more about Hansen’s regionally optimised customer billing and engagement solutions for the energy and utilities market.
Lina is the Product Director for Hansen CIS in the Nordics and leverages over 15 years of experience in the energy industry; she drives innovative software solutions for market participants across the energy value chain.
Reference and Further Reading:
EU framework: The Artificial Intelligence Act – Privacy Rules