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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.”

The Trusted AI Orchestra

Building trust in an AI agent world

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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