Industrialised & Responsible AI

Driving future business performance

In today's rapidly evolving business landscape, Sopra Steria is convinced that the widespread adoption of Artificial Intelligence is no longer just a strategic advantage — it's a necessity. It is the lynchpin of future business performance.

Managing the effective use of AI throughout a large corporation poses significant challenges, which is why we seek to support companies throughout every step of their AI deployment, from initial idea, through proof of concept up to fully-fledged deployment at scale.

Industrialised rollout of AI across the enterprise business lines necessitates extensive technological and regulatory compliance and therefore companies must commit to responsible and ethical AI principles.

That is why Sopra Steria is a key investor in Confiance.ai, a unique French community dedicated to the design and industrialisation of trustworthy critical systems based on artificial intelligence.

Alongside Confiance.ai, we aim to establish a clear and transparent methodology allowing business to qualify the trustworthiness of data-based intelligent systems that companies can integrate into industrial products and services, safe in the knowledge of their reliability.

This covers aspects such as explainability, robustness, monitoring, uncertainty quantification, synthetic data generation, and trustworthy learning.

Combining the technological expertise derived from Confiance.ai with our expertise in AI governance, we help our customers assess, validate and deploy responsible AI at scale while also ensuring they comply with all necessary regulatory requirements.

What is Responsible AI?

As AI systems become increasingly autonomous and capable of making decisions that impact individuals and societies, it becomes imperative to ensure that their deployment is guided by principles that prioritise fairness, transparency, accountability, and human well-being.

Responsible AI promotes equitable treatment, explains algorithmic decisions, and establishes mechanisms for oversight and accountability. By prioritising ethical considerations, it aims to mitigate biases and ensure trustworthiness in AI systems.

Whitepaper

Responsible artificial intelligence

As organisations race to seize AI’s benefits, prioritising responsibility is key. Embracing responsible AI practices is not just about staying ahead but building a sustainable competitive advantage.

Scalability: Industrialising AI rollout allows companies to scale up implementations across various business units and processes, ensuring widespread adoption and maximising the benefits of AI technologies.
Efficiency: Standardising AI implementations streamlines processes and reduces duplication of efforts, leading to increased operational efficiency and cost savings.
Competitive Advantage: Rapid industrialisation of AI enables companies to stay ahead of competitors by leveraging advanced technologies to improve decision-making, optimise workflows, and deliver superior products and services.
Innovation acceleration: Industrialised AI facilitates rapid experimentation and iteration, fostering a culture of innovation within organisations and enabling the development of cutting-edge solutions to complex business challenges.
Regulatory compliance: New legislation, such as the European Union's AI Act, oblige companies to adapt their AI governance to mitigate risks associated with bias, privacy, and security. Companies must be fully compliant within three years.

AI industrialisation challenges

Data Quality & Accessibility

Challenge: Inconsistent or poor-quality data across different departments or sources can hinder AI deployment.

Solution: Establish data governance to ensure data quality, accessibility, and consistency. Invest in data cleaning and preprocessing to enhance dataset reliability.

Talent & Skills Gap

Challenge: Shortage of skilled professionals with expertise in AI development, deployment, and management.

Solution: Invest in training programs to upskill existing employees and attract top AI talent. Foster collaboration between data scientists, engineers, and domain experts.

Infrastructure & Integration Complexity

Challenge: Integrating AI systems into existing infrastructure can be complex and time-consuming.

Solution: Invest in flexible and scalable infrastructure solutions such as cloud computing. Prioritise interoperability when selecting AI tools and platforms.

What is the AI Act?

The European Union’s AI Act is the world’s first comprehensive global regulatory framework governing AI development, deployment, and use within the bloc. It categorises AI systems by risk level and imposes corresponding obligations. High-risk systems face stringent regulations, while transparency requirements apply to lower-risk systems.

Compliance is vital, enabling companies to demonstrate their ethical added value and to reassure end users, not to mention that failing to comply can lead to substantial penalties. These can range from €10 million to €40 million or 2% to 7% of global annual turnover. Proactive assessment and measures enable companies to ensure compliance while upholding ethical standards and user safety.

The EU’s AI Act: How companies turn compliance into competitive edge

How Sopra Steria helps

With profound knowledge and experience in industrialisation of responsible AI, Sopra Steria will help you achieve your digital transformation to enable you to reach your maximum potential.

Expertise

Guidance implementing responsible AI practices, drawing on our vast experience across all key verticals.

Framework development

Construct frameworks to integrate Responsible AI principles into a company’s AI strategy.

Ethical AI design

Conceptualise and realise AI systems with built-in ethical considerations to mitigate biases and ensure responsible decision-making.

Compliance

Audit and assessment of regulatory compliance and implement measures to address any compliance gaps.

Training and education

Development of training programs to empower employees with the skills to understand and implement responsible AI practices.

Test and learn

Ongoing monitoring and evaluation of AI systems to identify and address ethical issues or risks, ensuring continuous improvement.