AI governance refers to the set of policies, processes, and responsibilities through which an organization ensures that AI systems are used in a reliable, transparent, and ethical manner. It is essential for customer service because AI directly impacts the customer experience, privacy-sensitive data, and employee decisions. Read on for concrete answers to the most frequently asked questions about AI governance in the context of customer service, ranging from risks to implementation steps. Also check out our Agentic AI for customer service solutions to see how governance and technology come together.
What risks arise in customer service without AI governance?
Without AI governance, you run the risk of AI systems making unreliable, discriminatory, or legally non-compliant decisions, with no one held accountable. In customer service, this means, specifically: customers receiving incorrect information, complaints being unjustly rejected, or personal data not being properly protected.
The risks are broader than you might think. A chatbot that operates without supervision can provide customers with inconsistent answers that differ from what employees say. An AI system that scores conversations or prioritizes customers may unintentionally disadvantage certain groups. And if something goes wrong, without proper governance, it’s unclear who is responsible and how to resolve the issue.
Added to this is the legal dimension. As of February 2025, the prohibited practices outlined in the EU AI Act will take effect. Systems that use manipulative techniques or exploit vulnerable groups are prohibited. Organizations that fail to monitor this not only risk reputational damage but also face fines of up to 35 million euros or 7% of their global annual revenue.
What does the EU AI Act say about customer service applications?
The EU AI Act (Regulation (EU) 2024/1689) classifies AI applications based on risk. Most standard customer service AI, such as chatbots and automatic routing, falls under the “limited risk” or “minimal risk” category, but that does not mean there are no obligations.
Transparency requirements are particularly relevant to customer service. If a customer is communicating with an AI system, this must be clearly indicated. Systems that recognize emotions in the workplace or create customer profiles to inform decisions about access to services may be classified as high-risk, especially when they fall under Annex III domains such as access to essential services.
Specifically, this means the following for organizations that use AI in their customer service:
- Customers need to know when they are communicating with an AI system, not a human
- Systems that profile customers are always high-risk and require additional documentation and human oversight
- Deployers (the organizations that use AI, not just the developers) must assign human oversight to qualified employees
- Logs of AI interactions must be retained for at least six months
- Employees must be informed about AI systems that affect them before they are put into use
The compliance requirements for high-risk systems will become fully enforceable on August 2, 2026. This gives organizations some time now to get their governance in order, but that time is limited.
How does AI governance differ from general IT policy?
AI governance differs from general IT policy in that it is not only about technical security and availability, but also about the quality, fairness, and accountability of automated decisions. IT policy governs how systems operate; AI governance governs what systems are allowed to do and how that is monitored.
Traditional IT policies focus on issues such as access control, backups, and incident response. While these are also relevant to AI, they fall short in a number of areas specific to AI systems:
- Data quality and bias: AI models learn from historical data. If that data is biased, the models will make biased decisions. IT policies do not address this.
- Explainability: Employees and customers can ask why an AI system made a particular decision. IT policies do not provide an answer to that question.
- Continuous monitoring of model behavior: An AI model’s performance may change over time as the input changes. This requires specific monitoring, separate from standard system availability.
- Ethical frameworks: AI governance raises questions such as: Which decisions can an AI make autonomously, and when should a human intervene?
In short: IT policy is a necessary foundation, but AI governance adds a layer that specifically addresses the societal and organizational responsibilities of automated systems.
Who is responsible for AI governance within an organization?
AI governance is not the responsibility of a single department. The responsibility is shared among executive management, IT, operations, legal, and the employees who work with AI systems on a daily basis. Without a clear division of ownership, governance remains a mere formality.
In practice, we see three levels of responsibility:
Strategic level: executive board and management
Senior management sets the parameters: which AI applications are permitted, which values are central, and how is AI usage reported? They are also ultimately responsible for compliance with legislation such as the EU AI Act. It is considered best practice for a member of the management team or the Chief Digital Officer to serve as the owner of the AI governance framework.
Operational level: IT, legal, and operations
IT manages the technical infrastructure and handles logging and monitoring. Legal translates legislation into policy and assesses risk classifications. Operations, including customer service managers, ensures that employees know when they are working with AI and how they can intervene. The EU AI Act explicitly requires deployers to assign human oversight to competent and trained individuals, which entails specific HR and training responsibilities.
What specific steps does an organization take to implement AI governance?
Implementing AI governance starts with understanding: know which AI systems you’re using, understand the risks, and only then develop policies. A governance framework that isn’t based on the reality of your AI usage won’t work.
Follow these steps as a starting point:
- Create an AI registry: Document which AI systems your organization uses, who the provider is, what the intended purpose is, and what role your organization plays (provider, deployer, or user). This is also a requirement under the EU AI Act.
- Classify the risks: Determine for each system whether it is low-, moderate-, or high-risk. Systems that profile customers or make decisions about access to services are automatically considered high-risk.
- Assign ownership: For each AI application, designate who is responsible for oversight, maintenance, and incident response.
- Establish a transparency policy: Ensure that customers know when they are interacting with AI, and that employees understand how AI systems work and when they can override them.
- Organize training and promote AI literacy: The EU AI Act has required organizations to promote AI literacy since February 2025. Employees who work with AI must understand what the system does and what its limitations are.
- Set up monitoring: Determine how you will track model behavior and outcomes, and how you will identify and correct deviations.
- Document and evaluate: Keep records of decisions, incidents, and changes. Schedule periodic evaluations to assess whether the governance framework still aligns with actual practice.
How do you measure whether AI governance is effective in practice?
AI governance is effective if you can demonstrate that AI systems are performing as intended, that risks are being identified and addressed, and that employees and customers can trust them. You measure this using concrete indicators, not simply by the existence of policy documents.
Relevant metrics for customer service AI include:
- Accuracy of AI Outputs: How often does the system provide correct answers or make the right decisions? Compare this regularly with human judgment.
- Intervention Rate: How often do employees intervene in an AI decision? A high rate may indicate unreliable results; a low rate may indicate insufficient human oversight.
- Incidents and complaints: Track how many complaints are directly related to AI behavior, and how quickly they are resolved.
- Compliance Status: Are all required documents, logs, and training courses up to date? Are there any open findings from internal audits?
- Employee Satisfaction with AI Tools: Employees who work with AI on a daily basis serve as an early warning system for problems that aren’t visible in dashboards.
Effective AI governance is not a one-time project but an ongoing process. As AI systems evolve and regulations become more stringent, the governance framework must adapt accordingly. Organizations that embed this approach structurally build a reliable foundation for the responsible use of AI in the long term.
How Pegamento Helps with AI Governance in Customer Service
We understand that AI governance can feel like a complex puzzle for many organizations, especially when you’re also dealing with fragmented systems, staffing shortages, and rising customer expectations. Pegamento helps you deploy AI responsibly and effectively, without having to manage ten different vendors.
What we offer specifically:
- Agentic AI assistants that not only follow instructions but also take the initiative independently in customer interactions, with built-in escalation mechanisms to ensure that human oversight is always maintained. This is what we mean by Agentic AI: an evolution from executive bots to self-thinking assistants that operate within clear governance frameworks.
- Customized solutions built with standard building blocks, allowing you to scale up quickly without costly and risky development projects.
- Everything under one roof: from implementation to management and compliance support, with a single point of contact for the complete package.
- ISO 27001-certified information security as a foundation, supplemented by ISO 9001 for quality management and ISO 26000 for corporate social responsibility, ensuring that governance is not only sound on paper but is also adhered to in practice.
Would you like to know how your organization can implement AI governance in practice while improving customer service? Check out our AI-driven intelligence solutions or contact us directly for a no-obligation consultation.
Frequently Asked Questions
Hoe begin ik met AI governance als mijn organisatie al AI-systemen in gebruik heeft maar nog geen beleid?
Start met een retroactieve inventarisatie: maak alsnog een AI-register van alle systemen die al draaien, inclusief hun doel, aanbieder en risicoclassificatie. Prioriteer daarna de systemen met de hoogste klantimpact of het grootste risico op niet-naleving van de EU AI Act, en richt daar als eerste toezicht en documentatie voor in. Het is beter om gefaseerd te beginnen met de meest kritieke toepassingen dan te wachten op een volledig uitgewerkt kader voordat je actie onderneemt.
Wat is het verschil tussen een AI-policy en een AI-governancekader, en heb ik beide nodig?
Een AI-policy is een document dat vastlegt wat wel en niet is toegestaan bij het gebruik van AI binnen jouw organisatie. Een AI-governancekader is breder: het omvat de processen, rollen, meetmethoden en controlemechanismen waarmee je die policy ook daadwerkelijk handhaaft en evalueert. Je hebt beide nodig, want een policy zonder uitvoeringsstructuur blijft een papieren tijger, terwijl een governancekader zonder heldere gedragsregels richting mist.
Geldt de EU AI Act ook voor kleine en middelgrote organisaties die AI van een externe leverancier afnemen?
Ja, de EU AI Act maakt onderscheid tussen aanbieders (de partijen die AI bouwen) en deployers (de organisaties die AI inzetten), en legt ook aan deployers concrete verplichtingen op, ongeacht de bedrijfsomvang. Als MKB-organisatie die een chatbot of routeringssysteem van een leverancier gebruikt, ben je deployer en ben je verantwoordelijk voor zaken als transparantie naar klanten, het toewijzen van menselijk toezicht en het bewaren van interactielogs. De omvang van de organisatie beïnvloedt niet of de wet van toepassing is, maar kan wel de complexiteit van de implementatie bepalen.
Hoe ga ik om met medewerkers die weerstand hebben tegen AI-toezichtstaken of AI-gebruik in het algemeen?
Weerstand bij medewerkers is vaak een signaal van onzekerheid over hun rol, niet van onwil. Betrek medewerkers vroeg in het proces door hen te informeren over wat het AI-systeem doet, wat het niet doet, en hoe hun eigen oordeel en ingrijpen expliciet onderdeel zijn van het ontwerp. De EU AI Act verplicht bovendien dat medewerkers worden geïnformeerd vóór ingebruikname van systemen die hen raken, wat een goede aanleiding is om dit gesprek structureel te voeren in plaats van achteraf.
Wat moet ik doen als een AI-systeem in de klantenservice een fout maakt die een klant heeft benadeeld?
Handel het incident in drie stappen af: herstel eerst de directe schade voor de klant door snel en transparant te communiceren over wat er is misgegaan en hoe je het oplost. Documenteer vervolgens het incident gedetailleerd in je AI-register, inclusief de oorzaak en de getroffen maatregel. Evalueer tot slot of de fout structureel is, door het model, de trainingsdata of het toezichtsproces te herzien, zodat herhaling wordt voorkomen en je aantoonbaar kunt laten zien dat je governance-kader werkt.
Hoe vaak moet ik mijn AI-governancekader herzien en updaten?
Plan minimaal één formele evaluatie per jaar in, maar koppel herzieningen ook aan concrete triggers: een significante update van een AI-systeem, een nieuw type klantinteractie, een wetswijziging, of een intern incident. De EU AI Act is een levend regelgevingskader waarvan aanvullende richtsnoeren en technische standaarden nog worden gepubliceerd, dus actief bijhouden van ontwikkelingen via toezichthouders zoals de Autoriteit Persoonsgegevens en de Europese AI Office is aan te raden.
Kan AI governance ook een positieve bijdrage leveren aan klanttevredenheid, of is het puur een compliance-exercitie?
AI governance draagt direct bij aan klanttevredenheid wanneer het goed is ingericht: klanten ervaren consistentere antwoorden, minder fouten en meer vertrouwen doordat ze weten wanneer ze met een AI of een mens spreken. Bovendien zorgt een goed escalatieproces ervoor dat complexe of gevoelige situaties sneller bij de juiste medewerker terechtkomen, wat de klantbeleving verbetert. Zie governance dus niet als rem op innovatie, maar als de fundering die verantwoorde en duurzame AI-inzet mogelijk maakt.
