In the first phase of our AI Transformation series, Frost & Sullivan explored the strategic business considerations organizations must address before embarking on their AI journey.

1. AI Transformation: A Business Imperative, Not Just a Technology Shift
2. Continuing the Journey to Unlock the ROI of AI Transformation
3. Adopting AI: Moving Beyond the Hype to Practical Implementation

Artificial intelligence (AI) is reshaping how economies and enterprises operate, offering transformational potential for innovation, productivity, and customer experience. As organizations shift AI implementation from the proof-of-concept stage to enterprise deployment, employing an enterprise-wide strategy and roadmap is crucial to prevent delays. Therefore, it is important to analyze organizations' AI readiness or maturity in terms of technology deployment. Source

We asked the foundational questions:

• What is the business need?
• What real-world problem are we aiming to solve?
• How will AI drive tangible outcomes for our customers and stakeholders?

Phase 2: From Strategy to Execution - Laying the Technical Foundation

Now, as more organizations identify their first wave of AI use cases, the transformation journey enters a new phase: technical readiness.

We had the opportunity to speak with Ker Yang TONG, ASEAN CTO at Fujitsu, to delve deeper into the technical and operational aspects businesses must now confront. Together, we outline the next steps organizations must take to turn AI ambition into operational reality.

1. People: Building or Buying Your AI Capabilities

AI is not a plug-and-play solution. It requires specialized skillsets-from data scientists and ML engineers to AI governance leads and security experts.

Ask yourself:

• Do we have the internal capabilities to manage model training, deployment, and monitoring?
• Or do we need to collaborate with AI vendors, system integrators, or managed service providers?

As Ker Yang explains, "AI transformation requires cross-functional teams that understand both the business challenge and the technological solution."

In many cases, a hybrid approach-internal ownership supported by external experts-can accelerate deployment while maintaining long-term control.

1. Process: Governance, Security, and Operations for AI

With Gen AI and large language models (LLMs) now part of the enterprise toolkit, governance becomes mission-critical.

Key questions include:

• What are our internal policies for LLM usage?
• How do we secure and monitor model outputs to prevent hallucinations, bias, or compliance risks?
• Who is accountable for AI decision-making in the organization?

Fujitsu's Ker Yang notes that companies must adopt a secure-by-design approach. "How good is the model? Is it transparent and explainable? We must understand the shortfall of each LLM as well."

Successful AI transformation demands not just the technical, but also the safety and security aspects as well. For additional guidance, refer to globally recognized frameworks like the NIST AI Risk Management Framework and the OECD AI Principles.

1. Technology: Choosing the Right AI Stack for Your Use Case

Once the business use case is clear, it's time to select the technical architecture that best fits the job.

Here are key considerations:

Model Selection

• Is a general-purpose model (like OpenAI's GPT-4 or Google Gemini) sufficient, or do you require a domain-specific LLM (e.g., for legal or medical terminology)?
• Do you need support for multilingual inputs, especially in diverse regions like Southeast Asia? For example, the SEA-LION model is designed specifically to support Southeast Asian languages such as Bahasa Indonesia, Thai, Vietnamese, and more.

Functionality

• Will you be deploying a Q&A chat agent, a document summarizer, or an OCR solution for digitizing hand-written forms?

Infrastructure & Deployment

• Can this run fully on the cloud with scalable compute, or must it be deployed at the edge, where compute and connectivity are constrained?

Security & Transparency

• Is the model explainable and auditable? (Explainable AI - Google Cloud)
• How will you protect sensitive data, prevent prompt injection attacks, and enforce access controls?

These technical questions are not just the domain of IT and cybersecurity-they are executive-level decisions, especially as AI touches customer experience, legal exposure, and brand trust.

Looking Ahead

The move from strategy to execution is where many AI projects stall. But it doesn't have to be that way. With the right people, processes, and technology in place, organizations can ensure that their first use cases not only deliver ROI but also lay a sustainable foundation for future AI scale.

As Ker Yang summarizes, "AI transformation is a journey. You need business vision at the top, technical depth at the core, and operational discipline across the board."

Whether you're refining your first AI use case or scaling deployment across the enterprise, Frost & Sullivan provides actionable insights and strategic guidance. Connect with us to learn how we can support your AI transformation journey.

About Kenny Yeo

Kenny Yeo currently leads Frost & Sullivan's cyber security practice across Asia Pacific. A current topic of interest is analysing how vital cyber security is today to enterprise digital transformation efforts to achieve secure DX outcomes. With 20 years of research, consulting, advisory, team management and business development experience, Kenny has expertise spanning cyber security, IoT, smart retail, industrial and e-government.

View all posts by Kenny Yeo

Kenny Yeo

Kenny Yeo currently leads Frost & Sullivan's cyber security practice across Asia Pacific. A current topic of interest is analysing how vital cyber security is today to enterprise digital transformation efforts to achieve secure DX outcomes. With 20 years of research, consulting, advisory, team management and business development experience, Kenny has expertise spanning cyber security, IoT, smart retail, industrial and e-government.