Skip to Content

A Game Changer called “Generative AI” for Data Platforms

Capgemini
January 29, 2023

Generative AI (also known as GenAI) has become a hot topic from the beginning of this year (2023), despite having been around for several years already. This disruptive technology enriches various playing fields with insights that every business will have to contend with. Data-powered organisations have already been using AI and Analytics for several years, mostly well-integrated into their operational systems. Current developments in GenAI are much closer to people, indeed having the potential to help decision- makers in all processes relevant to a company and its customers. New business services will be defined, and new ways of driving enterprises will appear.

What are the capabilities needed to facilitate this? What are the ways to success? Which are the main risks to assess? And how can Capgemini Insights & Data assist businesses in their journey to implement game-changing business services using GenAI?

A new and game-changing technology that organizations are actively exploring is called Generative AI. This technology has the potential to be used in many ways to improve businesses. Whether you’re using a cloud-based data system with a data mesh approach or a more traditional on-site big data warehouse, Generative AI can be applied effectively. This is becoming increasingly common in today’s business landscape.

Capgemini, as a well-established transformation partner, is already well-versed in this field. The company has a track record of successfully implementing Generative AI projects, including recent ones like AI chatbots. This demonstrates the organization’s strong understanding and experience in working with and successfully deploying these types of AI projects on platforms designed for data science.

Helping you to enable Generative AI within your organisation with Google Cloud

Capgemini has launched a key technology partnership with Google Cloud

This opens up a multitude of new Generative AI technical capabilities, ranging from “ready-to-use” solutions to fully customizable options, all made possible through Google Cloud. Additionally, the Global Capgemini Google Generative AI Center of Excellence (CoE) is fully operational, enhancing customer use cases and applying best practices to leverage the Generative AI services available within Google Cloud’s Generative AI model garden.

Inputs to the Generative AI models and Generative AI App Builder on Google Cloud can encompass a variety of data types, including images, audio, text, video, and code. This recognition underscores the pivotal role that platforms will play in the near future.

“Many organisations may need a variety of foundation models, or at least many customized variants of a core model, to accommodate the variety of AI use cases likely to develop in coming years across their teams and operations” (Google, 2023)

A critical consideration when applying Generative AI within Capgemini or Google Cloud is to ensure responsible and well-governed application of any models.

Responsible Approach and Framework

To ensure a responsible approach to Generative AI at Capgemini, we have established clear mandatory group guidelines for projects involving Generative AI, as well as a dedicated Generative AI regulatory office to provide legal advice and guidance. These guidelines are designed to prevent potential pitfalls and promote awareness of quality and liability issues that may lurk beneath the surface. They are not meant to hinder our engagement with this technology but rather to facilitate its use in a responsible and transparent manner.

These mandatory group guidelines and principles form the foundation for the successful implementation of your Generative AI project. They enhance feasibility, success, and the co-piloting of new services generated using Generative AI in collaboration with Google Cloud.

Google’s Commitment to Responsible AI

Google Cloud’s Generative AI services on the Vertex AI platform adhere to seven principles that reflect Google’s commitment to developing technology responsibly in the realm of AI:

  1. Foster social benefit.
  2. Prevent the creation or perpetuation of unfair biases.
  3. Prioritize safety in development and testing.
  4. Be accountable to individuals and communities.
  5. Incorporate principles of privacy by design.
  6. Uphold rigorous scientific excellence.
  7. Ensure availability for uses aligned with these principles.

Simultaneously, Capgemini internally promotes strong corporate values that each consultant brings to various projects and assignments undertaken by Capgemini Insights & Data Nordics.

Capgemini Corporate Values
HonestyLoyalty, integrity, uprightness, a complete refusal to use any underhanded method to help win business or gain any kind of advantage.
BoldnessA flair for entrepreneurship, and a desire to take considered risks and show commitment.
TrustThe willingness to empower both individuals and teams.
FreedomIndependence in thought, judgment and deeds, and entrepreneurial spirit, creativity.
FunFeeling good about being part of the company.
ModestySimplicity, the very opposite of affectation, pretension, pomposity, arrogance and boastfulness.
Team spiritSolidarity, friendship, fidelity, generosity, fairness in sharing the benefits of collective work.

These values, principles, and guidelines create the ideal framework for fostering innovation. Capgemini also maintains clear policies to promote innovation, as open innovation provides benefits for all stakeholders.

Capgemini AI Futures Lab

The AI Futures Lab, specializing in Generative AI, is at the forefront of Capgemini’s AI offerings. It explores the vast landscape of Artificial Intelligence, with a special focus on Generative AI, to identify high-value use cases. These use cases are prioritized, and innovative solutions are developed around them. When necessary, the lab collaborates with external partners and engages in development activities to support the next wave of offerings for the organization.

Internally, the lab conducts research, monitors emerging insights, identifies needs, and explores possibilities. It takes an active role in ensuring internal readiness by enhancing expertise, capabilities, and strategic directions.

Externally, the lab contributes to the discourse on AI through research, analyst approaches, white papers, and thought leadership. It actively engages with customers, seeking early business wins through innovative projects that uncover new areas of opportunity, thereby identifying and facilitating the next wave of offerings for the organization.

A significant part of the lab’s mission involves collaboration with academic institutions and partners to develop new capabilities and gain hands-on experience with technologies that will play a pivotal role in the future. We are eager to embark on this journey with your organization, exploring and adding value to your use cases. We welcome your thoughts on implementing Generative AI projects and scaling them using Google Cloud. We are confident that the comprehensive capabilities provided by Capgemini Insights & Data Nordics will pave the way for success in your use cases. Feel free to reach out to us, and together, we can implement Generative AI projects that benefit your data teams.

About Author(s)

Robert Engels

CTO Insights & Data North-Central Europe
Robert is an innovation lead and a thought leader in several sectors and regions, with a basis in his role of Chief Technology Officer for Northern and Central Europe in our Insights & Data Global Business Line. Based in Norway, he is a known lecturer, public speaker, and panel moderator. Robert holds a PhD in artificial intelligence from the Technical University in Karlsruhe, Germany.

Luis Alberto Farje

Principal Data Solutions ArchitectGoogle Generative AI Lead for NordicsL2 Capgemini Senior Architect Certified
Lucho has worked in different business sectors but in recent years in the financial services, automotive industry and retail as a Principal Data Solutions Architect mainly advising customers on assessing various business use cases to be implemented as data products, including data science, machine learning, AI and Generative AI. This includes evaluation of architectural technical capabilities and feasibility of its technical implementation. He has worked many years under SAFe framework agile methodology, working in cross-functional teams, supporting agile release train architectural roadmap for data foundations. He is member of Capgemini Google Cloud Center of Excellence (CoE) for Generative AI and is passionate about building data foundations for enabling data science products particularly using DevOps, DataOps, MLOps, and LLMOPs for cloud data platforms.