Exploring Infosys' Essential Steps to AI Readiness

European organisations are lagging behind the US when it comes to AI adoption – not because of a lack of ambition, but because too many initiatives are being launched without the foundations needed to scale.  

Here, Rajan Padmanabhan, AVP and Unit Technology Officer for Data Analytics and AI at Infosys, offers a comprehensive AI strategy that encompasses workforce upskilling, data infrastructure and a culture of innovation.

How should organisations adopt and scale AI towards AI-readiness?

Organisations should begin by developing a comprehensive AI strategy that focuses on identifying and prioritising the most impactful AI opportunities aligned with business objectives.

Developing a comprehensive AI opportunity assessment methodology covering market sizing, competency mapping, competitive analysis and a viability scorecard helps ensure optimal resource allocation and measurable outcomes. They must also invest in solid data infrastructure, responsible AI practices, workforce upskilling, and a culture of innovation to ensure AI can be scaled responsibly, sustainably, and at pace.

Europe’s AI market is growing fast. What is really holding European organisations back?

Like the rest of the world, Europe is taking to AI technologies. The AI market in Europe, estimated at €42.66bn (US$49bn) in 2024 (Statista), is expected to cross €191bn (US$219bn) by 2030.

However, European organisations are 45-70% behind US companies in AI adoption (McKinsey), owing to lower investments in IT infrastructure, a shortage of AI talent and complex regulation. Additional barriers include the absence of a strategic AI roadmap and concerns around data security and ethics.

In Europe, a lack of preparedness is compromising the progress and outcomes of AI initiatives. To close the gap, organisations must provide the right data infrastructure, adopt a responsible by design approach built on trust, ethics, privacy, compliance and security (TEPCS), upskill the workforce and cultivate a culture of tech-powered, human-centric innovation.

According to Infosys, what does an effective AI strategy look like?

An effective AI strategy must focus on identifying and prioritising the most impactful AI opportunities aligned with business objectives. Developing a comprehensive AI opportunity assessment methodology to uncover growth potential, enhance efficiencies and fostering ecosystem alignment is key.

To adapt to the evolving technological landscape, organisations must prioritise workforce upskilling. This involves reimagining work processes and leveraging AI-driven innovations like AI twins, which can augment human capabilities by providing role-specific intelligence, enhance job performance and decision-making.

To keep pace with these advancements, organisations must invest in continuous learning programmes focused on specialised skills such as language understanding, prompt engineering, enhanced contextual comprehension and nuanced response generation.

How can organisations embed responsibility into AI development from the start?

Responsible AI must be established from the beginning using a responsible‑by‑design approach built on TEPCS principles

Trust requires checks for explainability, fairness, transparency, safety and adherence to standards. Ethical practices demand scrutiny for bias and alignment with core organisational values and corporate social responsibility. Robust security ensures protection of data and systems from potential threats.

Further to instituting governance, organisations need to establish an AI Ethics Council and prioritise data completeness, consistency, accuracy and unbiasedness. Developers need to be trained in transparent and explainable AI, and their work should undergo rigorous fairness and accountability checks. Even business and citizen developers must be taught responsible AI practices, for example, using personal data with consent, and avoiding subjective labelling.

Addressing data security, privacy, trust and ethical concerns requires robust protection measures, compliance with security and privacy regulations – especially EU’s advanced laws – and the strategic use of synthetic data. Maintaining human oversight throughout the AI development process is essential.

Beyond strategy and skills, what role does an organisations’ culture play in achieving AI?

While data-readiness, skills training and technology are crucial, a ‘build and learn’ mindset is equally important. It is essential to establish responsible AI practices and governance structures to protect both employees and the organisation. Tech tools should orchestrate learning and human oversight.

To facilitate this culture, organisations need to implement robust data and AI literacy programmes to empower employees and enable them to navigate the AI-enhanced workplace effectively.