FPT Corporation (FPT) is a globally leading Vietnam-headquartered technology and IT services provider, with operations spanning more than 30 countries and territories. Over more than three decades, FPT has consistently delivered impactful solutions to millions of individuals and tens of thousands of organizations worldwide. With a strong focus on mastering strategic technologies, FPT continues to drive innovation across industries. As an AI-first company, FPT is committed to elevating Vietnam’s position on the global tech map and delivering world-class AI-enabled solutions for global enterprises. In 2025, FPT reported a total revenue of USD 2.66 billion and a workforce of over 54,000 employees across its core businesses.
For years, enterprise AI discussions have centered on one question: How do we get AI into the business?
That is no longer the right question.
Most large organizations have already crossed the adoption threshold. AI assistants are helping developers write code. Customer service teams use generative AI to resolve cases faster. Finance departments automate reporting. Manufacturing organizations use predictive models to optimize operations. AI has moved from isolated experimentation into day-to-day business processes.
Yet despite this progress, many executives remain frustrated. AI initiatives continue to multiply, but enterprise-wide transformation often feels just out of reach.
The reason is straightforward: technology is no longer the primary constraint. Operating models are.
In our work with global enterprises, we’ve found that organizations rarely struggle because today’s AI solutions are inadequate. They struggle because the business itself was never designed to absorb AI at scale. Legacy systems, fragmented governance, disconnected data, and siloed decision-making prevent promising pilots from becoming enterprise capabilities.
Recent research conducted by Forrester Consulting on behalf of FPT reinforces this shift. While AI adoption has become widespread across large enterprises, relatively few organizations have successfully scaled AI across the entire business under a coordinated strategy. The gap is no longer between companies using AI and those that are not. It is between organizations deploying AI tactically and those redesigning how work gets done.
That distinction matters.
Many organizations still evaluate AI through the lens of individual use cases. They measure how many copilots employees use, how many support tickets were automated, or how much time developers save writing code. Those metrics demonstrate activity, but they do not necessarily indicate transformation.
Boards and executive leadership teams increasingly expect something different. They want to know how AI improves business resilience, accelerates product delivery, strengthens customer relationships, reduces operational risk, and creates new opportunities for growth.
Those outcomes require more than successful models. They require new ways of operating.
One of the most common obstacles we see is legacy technology. Many organizations continue to devote the majority of their IT budgets to maintaining existing environments. That limits both the resources and organizational capacity needed to modernize at the pace AI demands. Adding AI on top of outdated infrastructure rarely produces transformational results. Instead, it often increases complexity.
Data presents a similar challenge. Enterprise data remains fragmented across applications, business units, and regions. AI systems are only as effective as the information they can access, and disconnected data inevitably limits their value. Before organizations pursue increasingly sophisticated AI capabilities, they must first establish trusted data foundations and governance that allow those systems to operate consistently across the enterprise.
Security and governance have become equally important. As AI becomes embedded in customer interactions, financial decisions, software delivery, and operational processes, organizations can no longer treat governance as a compliance exercise completed after deployment. It must become part of how AI systems are designed, deployed, and managed from the outset.
This represents an important evolution in the CIO’s role.
Historically, technology leaders focused on selecting platforms, implementing systems, and maintaining reliable infrastructure. Today, CIOs are increasingly responsible for orchestrating how people, AI, data, and business processes work together. Success depends less on deploying another AI tool and more on creating an environment where human expertise and intelligent systems continuously reinforce one another.
That shift also changes how organizations think about the workforce.
Rather than replacing employees, many leading enterprises are redesigning work around collaboration between people and AI. Software engineers spend less time on repetitive coding tasks and more time solving complex architectural problems. Customer service representatives use AI to resolve issues faster while focusing their attention on higher-value conversations. Analysts spend less time gathering information and more time interpreting insights that guide strategic decisions.
In each case, AI augments human capability and speed, instead of simply automating existing work.
We’ve seen similar patterns emerge across industries. Organizations that generate the greatest business value rarely begin with the most advanced AI models. Instead, they establish clear governance, modernize critical technology platforms, improve data quality, and align AI initiatives with measurable business objectives. Once those foundations are in place, AI becomes part of the organization’s DNA, and scaling is significantly more achievable.
Conversely, organizations that treat AI primarily as a technology deployment often struggle to move beyond isolated successes. Individual departments realize productivity gains, but those improvements remain disconnected from broader enterprise transformation.
The next generation of enterprise leaders will distinguish themselves not by deploying more AI applications, but by building organizations capable of continuously adapting as AI capabilities evolve.
For CIOs, that means shifting the conversation from experimentation to execution.
A few practical principles can help guide that transition:
- Modernize the operating environment alongside AI investments rather than layering new capabilities onto aging infrastructure.
- Build governance into every stage of AI adoption, making security, transparency, and accountability foundational instead of reactive.
- Prioritize enterprise data quality and integration before expanding AI deployments.
- Measure AI success using business outcomes such as revenue growth, customer experience, operational resilience, and productivity rather than technology metrics alone.
- Invest in workforce readiness by helping employees learn how to collaborate effectively with AI instead of viewing automation as a replacement strategy.
The organizations that lead the next decade will not necessarily be those with access to the newest AI models. They will be the ones that redesign their operating models to take full advantage of them.
AI adoption is rapidly becoming table stakes. Enterprise transformation is what will separate tomorrow’s leaders from everyone else.
For more information, Visit: https://fpt.com/en
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