How to Kickstart your AI journey

AI can accelerate the development of business processes on a scale unseen since the Industrial Revolution and companies are racing to incorporate the tech into their operations. 

However, while many companies may agree on the need to incorporate AI into their systems and processes, managing the construction of a company's AI strategy and rollout can be overwhelming.   

We at Sopra Steria have a profound understanding of how to support companies throughout all stages of their AI journey.   

As so many of the concerns and challenges facing businesses seeking to launch their AI initiatives are similar, we wanted to share our responses to some of these frequently asked questions.  

We have gathered all the key questions asked most often of our experts from across our European offices combined with our responses. Here is a short recap of the questions and answers, but for a more detailed breakdown, download our dedicated report, AI for Business. 

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AI is transforming how humans and machines interact. Initially seen as a tool for tasks such as analysis and design, AI is rapidly evolving into an integral part of business operations, reshaping both processes and decision-making. It is no longer simply a technological resource but a driver of competitive advantage, enabling companies to gain market advantage. Although there is much hype surrounding AI, its benefits are already apparent – and its impact is likely to be just as transformative as past innovations such as electricity and the Internet. Once integrated, AI becomes indispensable, with most people unable to imagine working without it. 

To successfully leverage AI, organisations need a multi-disciplinary team that combines both technological and business expertise . This team should include:  

  • Business leaders to align AI initiatives with organisational goals.  

  • Data scientists to analyse data and develop reliable AI models.  

  • Software engineers to integrate AI into existing systems. 

  • User researchers and designers to create intuitive experiences. 

  • Legal, commercial, and security experts to ensure compliance and security. 

  • Ethics and data privacy experts to address ethical concerns. 

  • Business change experts to drive adoption and smooth integration of AI solutions. 

Together, this diverse team ensures AI is implemented effectively and responsibly.  

Any company seeking to adopt AI faces three key barriers: cognitive understanding, access to quality data, and lack of best practices.  

  1. Cognitive understanding : Overconfidence in AI’s short-term capabilities often overshadows its long-term potential. Businesses need to raise AI literacy across all levels, from employees to board members.  

  2. Data access and quality : Different AI technologies (machine learning, computer vision, NLP, and generative AI) face challenges such as data quality, volume, and structure. Success requires reliable, well-structured data. 

  3. Lack of best practices : Scaling AI projects can be difficult due to flawed implementation. Developing best practices, ensuring proper AI ownership, and learning from experience are critical to success. 

AI should not be handled through a standalone strategy but fully integrated into a company’s overall technology and business strategies. While AI doesn’t require its own strategy, using it as a competitive advantage needs a distinct, actionable plan to avoid slow progress and added costs. Organisations must align AI with existing strategic priorities  – whether growth, operational efficiency, asset utilisation, sustainability, or customer experience. By embedding AI across initiatives, companies can capitalise on its opportunities and develop a robust AI operating model. 

A Chief AI Officer is unnecessary, much like how companies do not appoint Chief Wifi or Knowledge Officers. Instead, AI responsibility should be spread across the organisation. AI often begins with small cross-functional teams (“Tiger Teams”) developing proof-of-concepts. However, as AI matures, companies need well-defined ambitions, as well as aligned technology, and data strategies. 

The best approach is to establish an AI Centre of Excellence (CoE), which centralises expertise, resources, and best practices. Leadership of the CoE will depend on the strategic importance of technology to the business. Although the CoE is a temporary entity, its goal is to fully embed AI within the company, but this may mean it is many years before the centre can be dissolved.  

The key to adopting AI is to start small with agile “Tiger Teams” experimenting within flexible guidelines. Use existing innovation and development frameworks, and prioritise learning objectives — whether related to technology, data, or governance. Focus on projects that are easy to implement and deliver quick value, such as digital assistants, which can streamline tasks and improve efficiency. 

Next, create criteria to evaluate and prioritise projects, ensuring the right initiatives are pursued. Decide whether to buy or build AI solutions – starting with purchasing is recommended to reduce risk. Incremental improvements, especially those enhancing internal processes, are a solid starting point. 

 

Scaling AI in large corporations requires a structured approach using an AI operating model that aligns AI initiatives with business goals for efficiency and scalability. Key steps include setting clear objectives, implementing strong data governance, and building scalable infrastructure. Collaboration between data teams and business stakeholders, fostering a culture of continuous learning, and using agile methodologies are crucial for success. Ethical AI practices, such as mitigating bias and maintaining transparency, are essential. The operating model standardises processes, balancing innovation with operational stability, and ensuring long-term value. It differs from a business model, which focuses on revenue generation and value creation. 
AI maturity is a journey that requires integrating AI into the core of an organisation. Managers and executives should start by assessing their current AI capabilities, defining clear AI goals, and ensuring robust data infrastructure. Effective AI governance, ethical use, and seamless integration into business processes are essential for success. Building an AI-ready workforce with the right skills and fostering a data-driven culture are key steps. Measuring success through KPIs, managing change effectively, and identifying risks are crucial for continuous improvement. Lastly, staying ahead requires continuous learning, innovation, and adapting to new AI trends. 
Preparing data for AI is essential to remaining competitive. Businesses must place AI and data at the core of their operations, focusing on data governance, quality, and management. Key actions include ensuring clean and accurate data, investing in master data management (MDM), and building a scalable, secure data architecture. Organisations should also remain patient and proactive, investing in R&D and preparing employees through data and AI literacy training. A scalable data platform — cloud-based or on-premises — is critical for integrating, analysing, and leveraging data effectively for AI-driven decisions.