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Trusted AI

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Over the past year, adoption of generative AI has grown significantly across industry domains and functions, such has customer operations, marketing and sales, software engineering, and research and development. In a world where 50 percent of business or organizational decisions could be taken by AI, companies must work hard to build trust.

Generative AI is pervading organizations. According to our latest research, 97 percent of global organizations allow employees to use generative AI in some capacity. While large language models and agentic AI systems shows incredible potential, questions arise about bias in their training data and the robustness of safety constraints.

The rise of foundation models comes with associated trust issues. And neglecting to address them leads to financial losses and business risks.

Incoherent execution of evaluation and tests, lack of content monitoring, or no consistent benchmarks can lead to untrustworthy solutions.

To be able to run GenAI at scale effectively, organizations need to envision guardrails from an operational, rather than solution-specific, perspective while ensuring setting a specific framework to business context and domain.

“Trust is bigger than one question, it’s a multi-dimensional problem, so you need to think about trust in a specific context.”

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What we do

Everyone has a role in Trusted AI, from the chief procurement officer (CPO) to operations support, but each have their own specific challenges, therefore frameworks must be specific to their challenges and criteria.

Capgemini believes every role within a business has a responsibility to ensure Trusted AI, this is why our multi-framework approach doesn’t attempt to be a “one size fits all” solution, but instead contains frameworks that are tailored to different roles and business scenarios.

Ensuring an organization has trusted, compliant, ethical and responsible AI means each participant playing their individual part of a coherent overall approach. This only happens when Trusted AI is applied to business governance, technical execution, and operations.
 For example, our Trusted AI for Procurement Framework helps CPOs with the background assessments of model providers and financial management tools required to correctly validate AI providers, their methods and data sources helping to ensure that only validated providers are allowed within the organization. Our Trusted AI for Cybersecurity focuses on the threat assessments associated with AI adoption, covering the threats and responses to trojan horse attacks as well as the security updates required on user identification and delegated authority to prevent both deployed AI issues and the leveraging of AI in industrialized social engineering attacks.
Our Trusted AI Business Model and Strategy looks at the organization change management required to achieve a new business model where managers are able to include AI as team members and be accountable for the outcomes of those AI and ensure that AI drives the corporate and their personal career success.
Underpinning all of this is the industries widest “Trusted AI for Purpose” experience, including Trusted AI for Safety Critical Systems and Trusted AI for Enterprise and package and technology specific variations which each need different proscriptions and details to ensure you can trust AI no matter what platform it is deployed within.
For AI to be trusted everywhere, the whole business has a role ensuring you can trust AI.

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      Client stories

        Eneco eMobility supercharges its customer care with generative AI

        Working with Capgemini, the charging services provider boosted agent productivity and enhanced the client service experience by introducing Dynamics 365 Contact Center – a Copilot-first cloud contact center, powered by Gen AI.

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          Research and insights

          Capgemini Research Institute

          Generative AI in organizations 2024
          Point of view

          Taking control of generative AI
          Capgemini Research Institute

          Gen AI in software

          Expert perspectives

          Data and AI

          ChatGPT and I have trust issues

          Tijana Nikolic
          Mar 30, 2023
          Data and AI

          Deep stupidity – or why stupid is more likely to destroy the world than smart AI

          Steve Jones
          Jun 7, 2023
          Data and AI

          RAISE helps organizations move from exploration to results

          Weiwei Feng
          Jul 4, 2024

          Meet our experts

          Steve Jones

          Expert in Big Data and Analytics
          Steve is the founder of Capgemini’s businesses in Cloud, SaaS, and Big Data, a published author in journals such as the Financial Times and IEEE Software. He is also the original creator of the first unified architecture for Big Fast Managed data, the Business Data Lake. He works with clients on delivering large-scale data solutions and the secure adoption of AI, he is the Capgemini lead for Collaborate Data Ecosystems and Trusted AI.

          David Hughes

          Head of Technical Presales, Capgemini Engineering Hybrid Intelligence
          David has been working to help R&D organizations appropriately adopt emerging approaches to data and AI since 2004. He has worked across multiple domains to help deliver cutting edge projects and innovative digital services.

          Nishant Kapoor

          Senior Manager – GenAI and Digital Continuity Solutions
          Nishant Kapoor leverages extensive experience in Engineering, Manufacturing, and Quality to drive digital transformation for diverse customers. As a Solution Architect and Digital Transformation lead, he excels in cross-functional teamwork. His expertise in emerging technologies like GenAI and collaborative platforms ensures effective solutions across industries.

          Victoria Madalena Otter

          Lead for Cybersecurity and AI
          Victoria Madalena Otter leads Capgemini’s Group Cybersecurity AI division. She bridges policy, cybersecurity and innovation, leveraging her background in startup ecosystems and a Master’s in Public Policy for Digital Technology from Sciences Po Paris.

          Sarah Engel

          Director AI Strategy & AI Governance
          Sarah empowers organizations to confidently deploy AI by providing an organizational safety net for Gen AI. This enables them to manage risks, foster employee reliance on AI, and build customer trust in AI-supported solutions. She drives innovation and transformation through AI, governance, and trust.

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