Interest in emerging technologies such as artificial intelligence (AI), machine learning (ML), and cloud computing is growing across organisations, with a strong appetite for experimentation. However, successfully exploring, testing, and implementing these technologies—and achieving meaningful outcomes—requires a well-defined methodology, a solid strategic approach and good governance. Many organisations, however, struggle to prioritise this.
AI as a key business priority
AI is a top priority for many organisations, as highlighted in CGI’s annual Voice of our Clients survey, which gathers insights from business and IT leaders. The survey reveals that AI-related initiatives rank among the top five priorities for both business and IT. A striking 83% of organisations agree or strongly agree that generative AI (GenAI) models—particularly those leveraging their own business data—will provide them with a significant competitive advantage. Additionally, 32% report that GenAI is already impacting their business.
Nearly 80% of CGI’s clients are exploring AI, but very few have progressed beyond the initial phases of implementation. This signals exciting near-term opportunities for innovation and transformation – but also the need for an immediate focus on strategy and governance.
Aligning cloud and AI strategies
The growing focus on AI extends beyond directly involved departments, influencing entire organisations. A crucial aspect of success is ensuring that cloud and AI strategies are aligned.
For many organisations, cloud strategies have primarily focused on migrating applications and systems from on-premise data centres to the cloud. However, with AI adoption accelerating, organisations must now consider how to leverage AI effectively while managing IT budgets.
To explore how organisations should approach AI strategy—and its broader IT and business implications—we spoke with CGI’s AI expert, Alexander Lepp , and CGI’s cloud evangelist, Christian Dahlgren. They highlight key focus areas:
“A cornerstone of both cloud and AI strategies is data management,” says Christian Dahlgren. “To create value with AI, organisations need structured, well-managed data—and the same applies to cloud strategy. The level of protection and governance your data requires is a crucial factor influencing many strategic decisions. Data management is not a minor issue—it should be a board-level discussion.”
Concerns about responsible AI implementation
Discussions with organisations reveal growing concerns about protecting sensitive data. Additionally, 27% of organisations report having low confidence in their ability to use AI responsibly.
“This is an issue that must be taken seriously,” says Alexander Lepp. “Building internal trust is essential. Organisations that establish strong governance frameworks for AI adoption will find it easier to implement and scale AI-driven solutions effectively.”
The impact of regulation on AI initiatives
While AI development is advancing rapidly, regulatory frameworks are also evolving. As an example, the European Union (EU) is expanding data protection regulations, requiring organisations to review and adjust their IT environments to maintain compliance.
At the same time, public and business expectations around data security and transparency are rising. These factors significantly influence AI and cloud initiatives, shaping how organisations develop, deploy, and govern AI solutions.
As part of its commitment to responsible AI, CGI has signed the EU AI Act Pledge, a key component of the European Commission’s AI Pact. This aligns with CGI’s global ambition to uphold high standards, adopt best practices, and ensure ethical AI use.
Balancing AI innovation with regulatory compliance
AI models require vast amounts of data for training, but this often includes sensitive or proprietary information that should not be used in public AI models.
“To train public language models, it can be beneficial to use synthetic data, which does not contain personal or sensitive information,” says Christian Dahlgren. “Once the model is trained, a version can be deployed within the organisation—creating a ‘Private AI’ that applies real business data, minimising risks.”
CGI is actively conducting AI research in collaboration with Karlstad University, focusing on synthetic data. Currently, no widely accepted method exists for assessing the quality of synthetic data, and this research aims to develop better evaluation techniques and improve synthetic data generation.
AI strategies must reach the executive level
To support organisations in their AI journey, CGI has identified three critical success factors:
1. Corporate-wide strategy development
2. Change management—engaging employees and stakeholders
3. Risk assessment—ensuring organisations can manage AI-related risks
By addressing these areas, organisations can better integrate AI initiatives, enhance operational resilience, and gain a competitive advantage.
“A well-defined AI strategy is essential,” says Alexander Lepp. “70% of the effort is related to people, culture, and organisational change—not just technology. AI projects require a structured approach to risk management, governance, and workforce readiness.”
The challenge of moving AI initiatives into production
Many organisations rush into AI adoption, yet a key challenge remains: moving AI initiatives beyond proof-of-concept into full-scale production.
“When I meet with organisations, I see a growing focus on ensuring AI delivers measurable value and return on investment (ROI),” says Alexander Lepp. “A significant number of AI projects never progress beyond pilot phases due to cost concerns or unclear business objectives.”
Common pitfalls include:
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Underestimating costs—such as infrastructure, scaling, and ongoing maintenance
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Lack of alignment with business strategy—AI projects treated as isolated experiments
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Failure to integrate AI into existing processes—leading to limited adoption and impact
Combining change management with technology
One of the main reasons AI initiatives fail is insufficient focus on people and culture. Successful AI adoption requires a high level of AI literacy and readiness among employees.
CGI has experienced this first-hand through its global internal Copilot project and its Australia and UK ChatGPT and GitHub Copilot trials.
“Many colleagues were highly enthusiastic,” says Christian Dahlgren. “We approached the rollout systematically, with training, coaching, structured follow-ups, and cultural change initiatives. The impact was clear: users gained efficiency (in the ChatGPT trial 60% of users reported significant productivity gains), but the benefits varied across job roles, demonstrating the need for tailored AI adoption strategies.”
Read more about CGI’s learnings and predictions drawn from our experiences implementing our own AI projects.
The need for strategic leadership in AI implementation
To succeed with AI, organisations must ensure AI governance and strategy are prioritised at the leadership level.
“Knowledge and technology are essential, but they are just one part of the equation,” says Alexander Lepp. “Strategic vision and innovation frameworks are equally important. Without them, AI adoption becomes fragmented and ineffective.”
CGI has long supported organisations in AI strategy development and digital transformation. Success in AI requires a structured, human-centred approach—one that balances innovation, governance, and business outcomes.
By investing in people, culture, and responsible AI adoption, business leaders can navigate the AI revolution with confidence and integrity.
