Turning AI ambition into scalable, sustainable business impact, end-to-end

Lolita Tijunaitiene: “The biggest challenge isn’t the technology - it’s whether the organization is ready to use it.”

Artificial intelligence has become important enabler for strategic priorities, yet many organizations still struggle to move beyond pilots and isolated experiments. CGI’s 2026 Voice of Our Clients research points that while AI adoption continues to accelerate, only 43 percent of organizations have a comprehensive, enterprise-wide AI strategy. For business leaders, the challenge is no longer whether AI can deliver value, but how to scale that value across the organization. Lolita Tijūnaitiene, Vice President at CGI Lithuania and CGI Latvia, shares her insights on the gap between ambition and execution, the barriers to scaling, and why sustainable impact depends on far more than technology.

Almost every organization today has AI ambitions. Where do businesses currently stand on the journey from experimentation to scalable adoption?

The shift is real, but uneven. Last year was largely about experimentation - organizations were running pilots, testing ideas, exploring what technology could do. This year, the conversation is changing. We are seeing more clients move from asking, „Can we use AI?’ to asking, „Where does AI actually create value, and how do we scale it?’ This is an important shift.

CGI’s Voice of Our Clients research shows that only 43 percent of organizations have a comprehensive, enterprise-wide AI strategy. Why does that gap matter?

Investment without strategic clarity does not scale. Many organizations run multiple AI initiatives in parallel, each with its own goals and metrics. What they often lack is a framework that connects those initiatives to business objectives and defines enterprise-wide success. Without it, you accumulate pilots rather than build capability. The difference between organizations that scale AI and those that do not is rarely the technology itself, but the quality of the strategy behind it.

What are the most common reasons AI initiatives fail to generate sustainable business value?

Organizations often apply AI to processes that do not generate enough value to justify the investment, or they have no clear objectives and no way of measuring whether the initiative is delivering anything. The question should always start with the business: what outcome are we trying to achieve, what does success look like, and how will we know when we have got there? If those questions are not answered before technology is selected, the initiative is already at risk.

What does CGI's research tell us about the biggest barriers to scaling AI?

CGI's 2026 Voice of Our Clients research, tracking business and technology priorities across hundreds of organizations globally, consistently highlights the same obstacles: legacy technology environments, siloed teams, regulatory requirements, budget pressures, and talent shortages. What's striking is how these barriers compound each other: fragmented data, misaligned business and IT teams, and absent leadership sponsorship can limit even the most capable AI models. 

We often hear that not every process needs AI. What do you think about that?

It sounds obvious, but organizations need to hear it now. Pressure to find AI use cases, prove innovation, and move quickly is intense. Yet the strongest business cases for AI are usually repetitive, high-volume processes where efficiency gains can be measured. If a task is occasional or already optimized, the investment may not be justified. The same applies to transformation initiatives and legacy modernization, where technology delivers the greatest value only when it addresses clear business needs and operational challenges. Discipline about where to apply AI - and where not to - is what separates real capability from impressive pilots that never scale.

What role does leadership play in turning AI initiatives into enterprise-wide impact?

Leadership matters more than most people assume. Transformation depends on people choosing to change, not just deploying new tools. Without executive support, AI initiatives often remain isolated experiments, delivering local improvements but not broader transformation. Change management is equally important and often underestimated. Every AI transformation is ultimately human. Employees need to understand why change is happening, how it affects their work, and how they can develop the skills to succeed. Organizations that treat AI adoption as a technology project rather than an organizational transformation almost always struggle to scale.

You have spoken about the importance of AI ambassadors inside organizations. Who are they, and why do they matter?

They are people who are genuinely curious, actively exploring new AI capabilities, experimenting on their own initiative, and helping colleagues apply technology in practice. In our experience, these individuals become catalysts for change. They accelerate adoption by building understanding and confidence where the work actually happens.

Where do you see the clearest AI impact in the public sector?

The public sector is further along than many people assume. CGI’s selection to develop and maintain Studyinfo, Finland’s largest public education service, with around 14 million annual visitors, using an AI-enabled delivery model, is a recent example. That is AI on critical national infrastructure - a very different conversation from a pilot - and a relevant reference point for Baltic organizations. This progress is also reflected in regional AI readiness data. The strongest public-sector impact appears in governance, digital government delivery, and policy capacity, where the Baltic countries score highly. Estonia stands out with near-leading scores in government digital policy and e-government delivery, showing how AI is being embedded into public services, while Lithuania and Latvia also show strong adoption foundations.

If you could give one piece of advice to business leaders looking to turn AI ambition into business impact, what would it be?

Start with the business problem, not the technology. Define success before choosing tools, and build the data and governance foundations needed to scale. Just as important, choose a partner like CGI, which combines global expertise, industry knowledge and a learning mindset to help organizations turn AI ambition into lasting business value.