As generative AI revolutionizes industries, organizations are eager to harness its potential. However, the journey from production-ready solutions to full-scale implementation can present distinct operational and technical considerations. This post explores key insights and lessons learned from AWS customers in Europe, Middle East, and Africa (EMEA) who have successfully navigated this transition, providing a roadmap for others looking to follow suit.
Building a solid business case: Operational excellence drives customer experience
The foundation of successful generative AI implementations are business cases with clear value propositions that fit with organizational goals, for example, improving efficiency, cost savings, or revenue growth. Typical examples include enhancing customer experience, optimizing operations, maintaining compliance with legal standards, improving level of services, or increasing employee productivity.
Companies in EMEA have used AWS services to transform their operations and improve customer experience using generative AI, with their stories illustrating how a strong business case can lead to tangible results across various industry verticals.
Il Sole 24 Ore, Italy’s leading multimedia publishing group, partnered with AWS Professional Services to boost the efficiency of a historic service, L’Esperto Risponde, where users can ask fiscal questions and receive responses from a team of experts. Il Sole 24 Ore leveraged its vast internal knowledge with a Retrieval Augmented Generation (RAG) solution powered by AWS. This solution maintained over 90% accuracy in responses and reduced the time spent by experts in searching and processing information, empowering them to focus on more strategic tasks. Additionally, the company is continuously incorporating end-user feedback to keep the service tailored to customer needs. For more information, you can watch the AWS Summit Milan 2024 presentation.
Booking.com, one of the world’s leading digital travel services, is using AWS to power emerging generative AI technology at scale, creating personalized customer experiences while achieving greater scalability and efficiency in its operations. Booking.com uses Amazon SageMaker AI to provide highly personalized customer accommodation recommendations.
“One of the things we really like about AWS’s approach to generative AI is choice. We love open source, and we feel it will play an important role in the evolution of generative AI,”
– Rob Francis, Chief Technology Officer of Booking.com.
With AWS support, Booking.com is enhancing its generative AI capabilities and positioning itself for future growth in the travel and hospitality industry. For more details, you can watch Booking.com’s keynote at AWS re:Invent 2023, their presentation on generative AI from idea to production on AWS at AWS London Summit 2024, and read the case study on how Booking.com helps customers experience a new world of travel using AWS and generative AI.
ENGIE is a global power and utilities company, with 25 business units operating worldwide. ENGIE’s One Data team partnered with AWS Professional Services to develop an AI-powered chatbot that enables natural language conversation search within ENGIE’s Common Data Hub data lake, over 3 petabytes of data. The solution complements traditional keyword-based search by allowing users to discover datasets through simple conversational queries, making it easier to find relevant data among tens of thousands of datasets. This dual approach to data discovery has accelerated the development of data-driven products and enhanced data assets sharing across the organization.
These examples demonstrate how companies across various sectors have successfully used AWS generative AI capabilities to address specific business challenges.
Getting ahead of implementation challenges
Though essential, a solid business case is only the first step. As organizations move their generative AI initiatives forward, they often encounter new challenges related to making the solution scalable, reliable, and compliant. Let’s explore what it takes to successfully advance generative AI projects from the preproduction phase, making sure that the original value of the business case is then fully realized in real-world application.
Achieving scale, reliability, and compliance
Factors to consider in transitioning to full-scale production include scalability, data governance, privacy, consistent and responsible AI behaviors, security, integration with existing systems, monitoring, end-user feedback collection, and business impact measurement. As organizations in EMEA have discovered, success in this transition requires a holistic approach that goes beyond mere technological considerations. With a multitude of customer learnings, paired with AWS expertise, we can identify key strategies for implementation.
Production-ready infrastructure, applications, and processes in the cloud
With the increase in scope, number, and complexity of generative AI applications, organizations have an increased need to reduce undifferentiated effort and set a high-quality bar for production-ready applications. Standard development best practices and effective cloud operating models, like AWS Well-Architected and the AWS Cloud Adoption Framework for Artificial Intelligence, Machine Learning, and Generative AI, are key to enabling teams to spend most of their time on tasks with high business value, rather than on recurrent, manual operations. Such an approach should include established industry standards such as infrastructure as code (IaC), continuous integration and continuous delivery (CI/CD), monitoring and observability, logging and auditing, and solutions for scalability and high availability.
For instance, Iveco Group, a global automotive leader active in the Commercial and Specialty Vehicles, Powertrain, adopted a structured cloud-operating model, leveraging IaC via Terraform for consistent and repeatable deployments across environments. A DevOps environment, via CI/CD pipelines, allows for frequent updates and testing of generative AI models and applications, allowing the developers to focus on improving and expanding the solutions rather then spending time on manual operations. This also helps make sure that generative AI solutions are optimized for performance, security, and cost-efficiency. This integrated approach not only accelerates the path from pre-production to full-scale implementation, but also enables them to adapt quickly to new generative AI advancements, manage complex dependencies, and scale resources as needed, ultimately driving innovation and competitive advantage in the rapidly evolving field of generative AI. See the re:Invent 2024 session for more information.
Accor Group, a major hospitality company that developed a generative AI-powered booking application, showcased how, even when working with new technologies like generative AI, fundamental software development principles remain a must-have. They implemented a three-layered comprehensive testing strategy. First, unit tests verify that the prompts consistently generate acceptable responses from the chatbot, even upon prompt modifications. Second, integration tests verify the end-to-end flow of the REST API and the chatbot’s interaction with the large language model (LLM). The final step is functional testing with predefined scenarios for manual testing and validation. They also implemented feedback systems, essential for the improvement flywheel of customer-facing applications, in the form of in-app surveys, instant feedback options (thumbs-up or thumbs-down), and a dedicated feedback portal for detailed user input. Finally, to measure the effectiveness of the solution and its business impact, they established a system to track room bookings made through the generative AI application.
Danske Bank, a leading Nordic bank, transitioned from a container-based on-premises setup to Amazon Elastic Container Service (Amazon ECS) with AWS Fargate. This allowed them to quickly move their API-based backend services to a cloud-native environment. This decoupled architecture, designed to be provider-agnostic, set them up for flexibility in leveraging different cloud-based generative AI tools and services as needed. The integration with Amazon Bedrock was seamless and impactful, as it provided faster access to multiple foundational models from more providers. This allowed the customer to rapidly experiment, iterate, and evaluate different models for their specific use cases. This case demonstrates how the combination of generative AI services and a cloud-native, API-driven architecture allowed this customer to iterate faster, and keep the focus on business value rather than integration of technologies.
The Schaeffler Group has been driving forward groundbreaking inventions and developments in the field of motion technology for over 75 years. The company developed a comprehensive generative AI framework, which establishes enterprise-grade governance and security guardrails for generative AI use case roll-out at scale with infrastructure blueprints. A generative AI inference gateway is integrated within the solution, offering centralized access to numerous foundational models while tracking usage and costs. Going forward, Schaeffler envisions to further integrate these capabilities into their wider generative AI and data landscape, including more fine-grained access controls to data assets and the adoption of generative AI agents.
These examples highlight a key theme for organizations across industries: Success in generative AI goes beyond developing standalone applications. A thorough cloud-based operating model is crucial for enterprises looking to keep pace with the rapidly evolving technology, with minimal operational overhead.
Security, compliance, and responsible AI
As an organization’s generative AI applications expand to handle increasingly sensitive data, security, compliance, and governance must be prioritized accordingly. This includes implementing authentication and access control, encrypting data at rest and in transit, monitoring and auditing of system access and usage, maintaining compliance with regulations (such as GDPR and the recent EU AI Act), as well as establishing clear policies for data handling and model usage.
Here are some examples of customers who have successfully navigated these critical requirements.
Il Sole24 Ore implemented a code of self-discipline for ethical AI application. It prescribes retention of high-quality standards and the centrality of trustworthy data. The principles include regulatory compliance, maintaining data provenance and reliability, incorporating human oversight via human-in-the-loop, inclusivity and diversity in data usage and algorithm adoption, responsibility and accountability, and digital education and communicative transparency. By adhering to these principles, Il Sole 24 Ore Group demonstrates its commitment to leveraging innovative technologies like generative AI in a safe and responsible manner, particularly in sensitive areas such as providing expert legal and tax advice. This approach allows them to harness the benefits of AI while mitigating potential risks and maintaining the trust of their users.
For Accor Group, the implementation of their next-generation booking application required direct customer interaction, emphasizing the critical need for responsible AI practices. To make sure the chatbot would deliver effective customer service while operating within strict ethical boundaries, they established specific safeguards to minimize misuse:
- Blocking responses to discriminatory queries
- Withholding responses to illegal activities
- Implementing guardrails to keep conversations within appropriate business context
- Installing protections against role-switching or tone-changing attempts during conversations
- Implementing robust technical defenses against prompt injections
Conclusion
The transition from preproduction to full-scale implementation for generative AI applications presents new challenges and opportunities. It requires identifying a solid business case, maintaining high standards for infrastructure and processes, strategic thinking in choosing an efficient cloud operating model, robust data governance, security, compliance, ethical AI practices, and more.
Organizations across EMEA have demonstrated how using AWS services can help overcome hurdles and accelerate the advantages of generative AI by embracing a holistic approach. By learning from these use cases, more enterprises can achieve successful deployments of generative AI solutions, and benefit from this transformative technology in a reliable, productive, and responsible manner.
Explore more generative AI use cases and customer succcess stories and discover how to accelerate your AI adoption on the cloud with specialized training and the support of AWS Professional Services and the Generative AI Innovation Center.
About the Authors
Dr. Giorgio Pessot is a Machine Learning Engineer at Amazon Web Services Professional Services. With a background in computational physics, he specializes in architecting enterprise-grade AI systems at the confluence of mathematical theory, DevOps, and cloud technologies, where technology and organizational processes converge to achieve business objectives. When he’s not whipping up cloud solutions, you’ll find Giorgio engineering culinary creations in his kitchen.
Daniel Zagyva is a Senior ML Engineer at AWS Professional Services. He specializes in developing scalable, production-grade machine learning solutions for AWS customers. His experience extends across different areas, including natural language processing, generative AI and machine learning operations.
Nicolò Cosimo Albanese is a Data Scientist and Machine Learning Engineer at Amazon Web Services Professional Services. With a Master of Science in Engineering and postgraduate degrees in Machine Learning and Biostatistics, he specializes in developing AI/ML solutions that drive business value for enterprise customers. His expertise lies at the intersection of statistical modeling, cloud technologies, and scalable machine learning systems.
Subhro Bose is a Data Architect in Emergent Technologies and Intelligence Platform in Amazon. He loves working on ways for emergent technologies such as AI/ML, big data, quantum, and more to help businesses across different industry verticals succeed within their innovation journey.
Diar Sabri is a Machine Learning Engineer at AWS who helps organizations transform their business through innovative AI solutions. With experience across multiple industries, he excels at bridging the gap between strategic vision and practical technology implementation, enabling customers to achieve meaningful business outcomes.
Aamna Najmi is a GenAI and Data Specialist at AWS. She assists customers across industries and regions in operationalizing and governing their generative AI systems at scale, ensuring they meet the highest standards of performance, safety, and ethical considerations, bringing a unique perspective of modern data strategies to complement the field of AI. In her spare time, she pursues her passion of experimenting with food and discovering new places.
Anwar Rizal is a Senior Machine Learning consultant for AWS Professional Services based in Paris. He works with AWS customers to develop data and AI solutions to sustainably grow their business.
Amer Elhabbash is a Senior Data & AI Delivery Consultant with AWS Professional Services. With over 25 years of international experience in IT spanning multiple fields and domains; Telecommunication, Software Engineering , Database, Data Analytics and AI. He helps AWS’ customers migrating their legacy data systems and building innovative cloud-native data-driven solutions.
Hassen Riahi is a Delivery Practice Manager Data & AI at AWS Professional Services. He holds a PhD in Mathematics & Computer Science on large-scale data management. He collaborates with AWS customers to build data-driven solutions.
Dr. Marco Guerriero leads Data and GenAI at AWS Professional Services for France and Europe South, holding a Ph.D. in Electrical and Computer Engineering from the University of Connecticut. His expertise spans machine learning, statistical inference, and mathematical optimization, with experience at organizations like NATO, GE, and ABB across defense, manufacturing, energy, and industrial automation sectors. With over 60 publications and five US patents to his name, Dr. Guerriero focuses on leveraging emerging technologies like GenAI and Quantum computing to drive business innovation across industries.
Sri Elaprolu is Director of the AWS Generative AI Innovation Center, where he leads a global team implementing cutting-edge AI solutions for enterprise and government organizations. During his 12-year tenure at AWS, he has led ML science teams partnering with organizations like the NFL, Cerner, and NASA. Prior to AWS, he spent 14 years at Northrop Grumman in product development and software engineering leadership roles. Sri holds a Master’s in Engineering Science and an MBA.
Dragica Boca is Managing Director of Professional Services EMEA at Amazon Web Services (AWS), leading enterprise cloud migration and generative AI transformation initiatives. With 30 years of technology consulting experience across Microsoft and IBM Global Business Services, she specializes in implementing production-ready AI solutions for Public Sector and Financial Services organizations. Dragica currently oversees large-scale GenAI implementations across EMEA, helping enterprises navigate the complexities of responsible AI deployment, scalable architecture, and sustainable adoption patterns.
