Introduction
Generative AI is transforming conversational applications in the EU, from customer service chatbots to virtual assistants. These AI-driven solutions enhance user engagement, streamline operations, and offer personalized experiences. However, businesses must navigate strict EU regulations like GDPR and the EU AI Act, focusing on data privacy and ethical AI practices.
This article aims to help you choose the right generative AI provider for chat solutions within the EU’s regulatory framework. We will cover key factors such as AI model quality, data privacy and security, customization, scalability, cost, support, and ethics.
Understanding conversational AI
Conversational AI is a type of generative artificial intelligence designed to interact with humans through natural language, visual elements, and on-screen interactions. It can understand and generate human-like text, allowing it to hold conversations, intervene with other systems, or use visual means to answer questions.
These AI systems learn from large amounts of company data and use complex algorithms to predict and generate responses that make sense in a given context. They are typically based on Large Language Models (LLMs). Conversational AI is used in many ways, from customer service to marketing and internal company operations.
Conversational AI platforms
Conversational AI platforms are software services that enable businesses to create and manage conversational AI agents. These platforms simplify the development process by providing user-friendly interfaces, pre-built models, and integration capabilities with existing systems. They allow businesses to design, train, and optimize AI models without extensive coding, making it easier to implement and scale AI solutions to improve customer service, marketing, and internal operations.
Why a conversational AI solution is different in the EU
Choosing a conversational AI for use in the EU involves unique challenges compared to other regions. The EU has very strict data privacy laws, especially the General Data Protection Regulation (GDPR), which sets clear rules on how personal data should be collected, stored, and used. The new EU AI Act sets additional standards for AI technologies, focusing on transparency, accountability, and safety. The EU also has 24 official languages and many regional dialects, requiring advanced language processing skills. Additionally, cultural sensitivity, strict data storage rules, and ethical AI practices are crucial considerations.
Key considerations for choosing a solution in the EU
Each section includes a checklist of questions to consider when choosing a service provider. For your convenience, we’ve also created a downloadable spreadsheet to assist with your product selection. Please note that this article was written by Unless, and we have included ourselves in the spreadsheet as a benchmark example.
Set goals and determine ROI
Before you begin, it’s important to set clear goals. Whether you want to improve customer support, boost sales, or gather user insights, your objectives will guide your choice. Clear goals help you prioritize features, measure the platform’s effectiveness, and align the tool with your business strategy.
To help you get started, here are some key factors that determine the return on investment (ROI) of a conversational AI solution:
- Self-help support on public websites: This reduces the number of support tickets, easing the workload of your support team.
- Contextual help in client portals or dashboards: Similarly, this feature can deflect support tickets by providing timely assistance.
- Virtual assistants for your support team: These can lead to faster ticket resolution and shorter response times.
- Additional benefits: Improved conversion rates, higher Net Promoter Scores (NPS) and Customer Satisfaction Scores (CSAT), and increased customer retention.
By considering these factors, you can better understand the potential ROI of implementing a conversational AI solution. Once you determined what your preferred application is going to do, it’s important to assess its risk level. The EU AI Act categorizes AI systems into four risk levels:
- Unacceptable risk: AI systems, such as those involving social scoring or manipulation, are banned due to their potential for significant harm.
- High-risk AI systems, which include applications in critical sectors like healthcare, transport, and law enforcement, are subject to strict regulations and must meet specific requirements for data quality, transparency, and human oversight.
- Limited risk AI systems, such as chatbots, must comply with basic transparency obligations, like informing users they are interacting with an AI.
- Minimal risk AI systems, which pose little to no risk, face minimal regulatory requirements, allowing for more innovation with fewer restrictions.
Checklist:
- Did you set clear goals with a quantified outcome?
- Does your platform of choice support your business case?
- Does your platform of choice support your industry specifically?
- Are you aware of the risk level of your intended solution?
- Does your platform of choice explicitly support the risk level?
Training the AI
Training an AI for business purposes usually involves using your organization’s data. There are several ways to train an AI:
- Training an AI model yourself: Starting from scratch but can be time-consuming and expensive.
- Using a pre-trained model: Quick and easy but may not be perfectly suited to your needs and is prone to hallucination.
- Fine-tuning an existing model: Adjusting a pre-trained model to better fit your requirements.
- Using retrieval-augmented generation (RAG): Combining generative AI with a retrieval system for better data governance and privacy compliance.
Additionally, the quality of business resources is crucial for the effectiveness of your AI. Ensure your platform provider helps assess the training data and creates a feedback loop to improve these resources.
Checklist:
- Does your platform of choice provide ways to strictly curate training resources?
- Can it handle multi-lingual ingestion?
- Does it integrate with your knowledge base and other sources of training data?
- Does it support enough resource formats to suit your needs?
- Does it support strict logical topic organization in the training database?
- Does it let you manage training data in real-time?
- Does it offer audience-based access and delivery?
- Does your platform of choice provide resource quality evaluation?
- Does it offer response quality evaluation without relying on user feedback?
Quality of the AI solution
Natural Language Understanding (NLU) and Natural Language Generation (NLG) are essential for effective conversational AI, especially in the diverse linguistic landscape of the EU. Ensure the models are of recent generation and capable of multilingual support.
An important note on compliance: in the EU, models trained on anonymized data might face legal challenges if they used Personally Identifiable Information (PII) without consent and don’t allow data removal. This could disrupt many existing AI solutions. To stay ahead of these changes, it’s important to choose a platform that allows quick switching between different Large Language Models (LLMs). This flexibility ensures long-term stability of the solution.
Checklist:
- Does your platform offer multilingual capabilities for its responses?
- Are the models of a recent generation?
- Does it avoid hallucination, or the wrongly mixing of similar topics?
- Is sensitive data isolated from the LLM?
- Can it switch to other LLMs if the regulatory landscape changes?
- Does it provide automated response, coherence, and relevance evaluation over time?
- Does it provide mechanisms and insights to assess individual user feedback over time?
- Does it provide a mechanism to assess the effectiveness of the AI agent components, like analytics data or A/B testing?
Data privacy and security
Compliance with GDPR and the EU AI Act is crucial. Ensure your platform collects only necessary data, uses anonymization techniques, and obtains explicit user consent. Data security measures like strong encryption and access controls are essential, along with regular audits and reviews.
Specifically, one major challenge with large language models is their inability to selectively delete specific data, like a person’s name or date of birth. This is a problem because privacy laws, such as Europe’s “right to be forgotten,” require the ability to erase such data.
As a result, public APIs from large LLM providers like OpenAI pose significant privacy risks due to jurisdictional hosting and lack of data control. Self-hosting LLMs offers more data control but is resource-intensive and still struggles with data governance. Cloud-based solutions from providers like AWS and Google Cloud offer a middle ground with privacy warranties, but face similar data governance challenges. A better approach is combining cloud infrastructure with data privacy vaults to isolate and protect sensitive data, ensuring compliance with GDPR and other regional laws.
Checklist:
- Does the platform support data minimization and anonymization?
- Are there filters in place to remove incidental PII input by the end user?
- Is there a mechanism in place to tokenize contextually relevant PII before sending it to any LLM (like a privacy vault)?
- Does it obtain explicit user consent?
- Are there mechanisms for data access, correction, and deletion?
- Does it use strong encryption and strict access controls?
- Is there a response plan for data breaches?
- Does it comply with GDPR residency requirements?
- Are third-party services GDPR compliant?
- Does it have data processing agreements (and list sub-processors)?
- Are risk management protocols in place?
- Is there transparency in AI decision-making processes?
- Are regular audits and reviews conducted?
Customization and flexibility
Ensure the system can recognize specific user intents and generate tailored responses. Look for robust APIs and seamless integration with existing chat platforms and customer service systems. Advanced personalization using CRM data can enhance the user experience.
Checklist:
- Can it recognize specific user intents?
- Can you adjust the tone-of-voice for different audiences?
- Is it easy to integrate with existing support platforms and systems?
- Does it allow handover to a live agent?
- Can it integrate with CRM data for personalization?
- Does it ensure GDPR compliance in data handling?
- Does it allow for customizing the AI agent in terms of design and layout?
- Is it developer-friendly (documentation, APIs, and so on)?
- Can you build your own AI agent using an API?
- Is there a standard framework for allowing the AI to interact with other systems?
Scalability and performance
The provider should handle high volumes of interactions and ensure low latency and fast response times. Rapid deployment is crucial for competitive advantages and compliance with EU regulations on data processing.
Checklist:
- Is it suitable for real-time chat applications?
- Can it handle high volumes of interactions?
- Is it scalable with business needs?
- Does it ensure low latency and fast response times?
- Does it comply with EU data processing regulations?
Cost and pricing structure
Understand the cost and pricing structure, comparing subscription-based, pay-as-you-go, or interaction-based pricing. Evaluate overall cost-effectiveness, including additional costs for EU regulatory compliance.
Checklist:
- Compare different pricing models.
- Find a model that aligns with your budget.
- Evaluate overall cost-effectiveness.
- Consider additional costs for regulatory compliance.
Support and documentation
Reliable technical support and comprehensive documentation are crucial. Ensure the provider offers 24/7 support, knowledgeable staff, and clear implementation guides that cover EU regulations.
Checklist:
- Is 24/7 support available?
- Are the support staff knowledgeable?
- Are response times quick for troubleshooting?
- Are the instructions comprehensive and easy to follow?
- Do the guides cover EU-specific regulations?
- When asked, do they offer references to validate their claims?
Ethical considerations
Ensure the AI models are trained to minimize bias and comply with EU ethical guidelines. Transparency in AI model development and deployment is crucial for building trust and ensuring users are well-informed.
Checklist:
- Are AI models trained to minimize bias?
- Do they comply with EU ethical guidelines?
- Is there transparency in AI model development?
- Is information on handling sensitive topics clear?
Evaluating providers
Conduct thorough research on potential providers, focusing on their compliance with EU regulations. This downloadable spreadsheet may help you organize the results.
Also, look at customer reviews and case studies from other EU-based companies, and ask for references. Take advantage of free trials and demos to evaluate capabilities. Consider establishing a long-term partnership for ongoing support and compliance with evolving regulations.
Conclusion
Selecting the right conversational AI provider involves evaluating several key factors, including scalability, performance, cost, support, documentation, ethical considerations, and regulatory compliance. Prioritize providers that adhere to EU regulations to avoid legal issues and ensure data security. Use customer reviews, case studies, trials, and demos to thoroughly assess each provider’s capabilities. By following these guidelines and focusing on your specific needs, you can confidently choose a provider that will support your long-term success.
