Generative AI stands as one of today's most promising technologies, holding the potential to revolutionize creativity, productivity, customer satisfaction and efficiency.

Balancing the desire for growth, efficiency, and enhanced customer satisfaction with data privacy is a challenge that CRIF, a global data-driven company, has already taken, integrating these considerations into its DNA, thoroughly analyzing privacy concerns in every disruptive change.In this challenge, InnovEcoS, the Global Open Innovation unit of CRIF, has taken the lead in setting up and professionally scaling the Venture building GEN AI laboratory in 2023. The Innovation hub is actively working on and experimenting with the most promising use cases, elevating CRIF’s operational, business, technical, analytical, and partner management knowledge, and scaling this innovative approach throughout the Group. This series of interviews delves into responsible adoption and experimentation, addressing questions of ethics, data privacy, and security to three key leaders from CRIF who share their insights, challenges, and achievements in responsibly adopting generative AI. Their experiences guide the company's journey, aiming for moderate, steady, and thorough advancement to the next level.

Interview 1
Carlo Gherardi - Founder and CEO

What is your vision and strategy for using generative AI in our company?

My vision is to position our company as a leader and pioneer in leveraging generative AI for transformative impact in our industry and society. Recognizing generative AI as a game-changer, my aim is to create new value for our clients, employees, and stakeholders. Through strategic deployment, I see the potential to address substantial challenges such as climate change, healthcare, and education.

My strategy involves ingraining generative AI into our culture and operations. Encouraging and empowering our teams to experiment and innovate with generative AI is paramount. Concurrently, adherence to best practices and standards is non-negotiable. Collaboration with external entities, including universities, research institutes, and startups engaged in generative AI, is integral. Learning from their experiences and insights, and reciprocally sharing ours, fosters a collaborative environment.

How do you measure the success and impact of using generative AI?

While it's too early for definitive statements, initial results from delivered experiments are promising. Measurement of success and impact involves a dual focus on quantitative and qualitative indicators. Quantitatively, metrics such as revenue growth, cost reduction, customer satisfaction, employee engagement, and social responsibility are considered. Qualitatively, indicators encompass creativity, innovation, diversity, and ethics. Soliciting feedback from clients, employees, and stakeholders provides valuable insights into their perceptions and the value attributed to our use of generative AI. Customer satisfaction is a central component of our evaluation.

What are some of the lessons learned or best practices that you can share with other leaders who are interested in using generative AI?

A crucial lesson learned is that using generative AI extends beyond technical and operational considerations; it is also cultural and organizational. It necessitates a mindset shift from technology consumers to creators and producers, engaging all levels of the organization, including myself and Board members. A corresponding skillset shift from specialists to generalists is essential, as is a leadership shift from directive to facilitative and collaborative.

A best practice worth sharing emphasizes that using generative AI is not solely an opportunity or benefit but also a responsibility or duty. Ensuring respect for the rights and interests of our clients, employees, and society is paramount. The ethical use of generative AI is contingent on promoting fairness, transparency, and accountability. Ultimately, our approach should enhance human dignity, autonomy, and overall well-being, aligning with our commitment to customer satisfaction and societal welfare.

Interview 2
Natalia Shchelovanova - Global Open Innovation and Ecosystem Lead

What is your role and responsibility in leading the generative AI initiative?

As the Head of Global Open Innovation Hub, my role is pivotal in fostering a culture of innovation and experimentation across the company via engagement, collaboration and research. I oversee the generative AI initiative, a strategic priority for this year. Our pragmatic approach includes creation, growth and scale of multi-disciplinary AI-Lab, leveraging past successful initiatives, best in class professionals and all strengths of being a global company within a strong ecosystem of partners, customers, academic world and strong Innovation communities. We've collected waves of ideas and use cases from across the company, executing proof-of-concept and experiments to measure and deliver tangible results. This unique journey has been empowered by the Global innovation team. Additionally, we emphasize knowledge sharing, empowering, and accelerating the scaling of successful initiatives, all aimed at increasing process efficiency and enhancing our products and services. Customer satisfaction is at the core of our endeavors.

What are some of the use cases and experiments that you have conducted or are planning to conduct with generative AI?

We've explored a range of use cases and domains where generative AI adds value to our business. Potential is always hiding in one of 3 pillows: revenues, profitability and customer satisfaction. For instance, we've utilized generative AI to craft new content for marketing campaigns, including slogans, headlines, and images. Generating new product ideas, such as features, names, and designs, has also been a focus. Experimentation extends to improving internal processes, such as generating reports, summaries, and presentations. Our constant and most strategic and complex quest is to identify new opportunities and challenges where generative AI can make a meaningful difference within our offering, ultimately enhancing customer satisfaction. `’Meaningful’ is a key word here, requiring time to measure, prove and scale. The key case which is a real growing piece of Gold is the experiences we are getting in the domain since months via a very practical approach to the topic: by doing, testing, partnering, discovering and tuning, winning but sometimes also failing.

What are some of the benefits and challenges that you have encountered or anticipate in adopting generative AI?

Generative AI brings substantial benefits by augmenting human creativity and intelligence, generating fresh ideas, innovative solutions, and diverse perspectives. It streamlines operations, automating repetitive tasks and saving time and resources. Importantly, it contributes to elevating customer experiences through personalized and engaging content and products. Navigating generative AI does come with challenges, such as ensuring the quality and reliability of the output, meeting our standards, and exceeding client expectations. Verifying the accuracy of the data used or generated is crucial, and ethical considerations, including legal implications and data privacy, must be addressed. We are committed to using generative AI in a fair, transparent manner without bias, placing customer satisfaction at the forefront.

Emphasizing the need for a robust data foundation—a comprehensive data lake with high-quality data and standardized processes is paramount. This foundation is essential for effectively training tools, minimizing the risk of hallucination. Human expertise is equally critical; the right people in the lab, training tools with the appropriate data, inject a creative element into the process. This collaboration optimizes the use of generative AI while mitigating associated risks, all in service of delivering exceptional value and satisfaction to our clients.

Interview 3
Marco Berti - Head of Legal and corporate affairs

What is your role and responsibility in overseeing the legal aspects of generative AI?

As the Head of Legal, with my team, I ensure compliance with the laws and regulations governing our business activities, with a particular focus on data protection, intellectual property, and consumer rights. I provide guidance to the management and teams on legal risks and opportunities arising from the use of generative AI.

What are some of the legal issues or concerns that you have encountered or anticipate in using generative AI?

Data privacy compliance is a significant legal concern in our use of generative AI, given the diverse jurisdictions we operate in, each with distinct rules such as GDPR in Europe. Obtaining necessary consent from our clients and employees before collecting or processing their personal data is paramount. Equally critical is safeguarding their data from unauthorized access or misuse, making data privacy compliance a top priority for our team, actively involved in validating this aspect during every experiment. Intellectual property rights present another legal challenge. Clarifying ownership rights to content or products generated by generative AI—whether by us, our clients, or third parties—is crucial. Respecting the rights of others when using their data or content as inputs for generative AI is imperative to avoid copyright or trademark infringements.

How do you balance the legal risks and benefits of using generative AI?

Our approach involves a meticulous balance of legal risks and benefits through a risk-based assessment. We evaluate the potential impact and likelihood of each legal risk, implementing measures to mitigate or prevent them. Keeping abreast of legal developments and trends in generative AI, we adapt our policies and practices accordingly. Simultaneously, we acknowledge the considerable benefits and opportunities generative AI offers, such as innovation, efficiency, and competitiveness. Leveraging these advantages responsibly and ethically aligns with our values and mission, ensuring that our clients interests and satisfaction remain at the forefront of our endeavors. Conclusion Making effective use of AI across all areas of our enterprise is our goal, to unlock its potential and ensure that it is used in a way that benefits everyone and that matches our ethical standards at all times will continue to be our top priority and responsibility.

Conclusion
Making effective use of AI across all areas of our enterprise is our goal, to unlock its potential and ensure that it is used in a way that benefits everyone and that matches our ethical standards at all times will continue to be our top priority and responsibility.