What is AI Talent Strategy?
AI Talent Strategy is a comprehensive plan for identifying, recruiting, developing, and retaining the human skills and expertise required to execute an organization's AI initiatives, encompassing technical roles like data scientists and ML engineers as well as AI-literate business professionals across the company.
What Is an AI Talent Strategy?
An AI Talent Strategy is your plan for building and maintaining the human capabilities needed to succeed with AI. It goes beyond hiring data scientists to encompass the full spectrum of talent needed — from technical specialists who build AI models to business professionals who identify opportunities, manage AI products, and drive adoption across the organization.
The global shortage of AI talent makes this one of the most challenging aspects of any AI initiative. Companies that approach AI talent strategically — rather than reactively posting job listings when a project demands it — have a significant advantage in execution speed and quality.
Why AI Talent Strategy Is Critical
Technology alone does not deliver AI value — people do. The most sophisticated AI platform is useless without skilled professionals who can:
- Identify high-value business problems suited for AI
- Prepare and manage the data that AI systems learn from
- Build, test, and deploy AI models
- Monitor and maintain AI systems in production
- Drive organizational adoption and change management
AI talent is also the scarcest and most expensive resource in most AI initiatives. Global demand for AI professionals far exceeds supply, and this imbalance is particularly acute in Southeast Asia. Without a talent strategy, companies find themselves unable to execute their AI plans or, worse, hiring expensive talent that they cannot effectively utilize.
Key Roles in an AI Organization
Technical Roles
- Data Engineer — Builds and maintains the data pipelines and infrastructure that feed AI systems. This is often the most urgently needed role because AI cannot function without reliable data
- Data Scientist — Explores data, develops hypotheses, builds and evaluates models. Requires strong statistics and programming skills
- Machine Learning Engineer — Takes models from development to production, managing deployment, scaling, and monitoring. Bridges the gap between data science and software engineering
- MLOps Engineer — Specializes in the infrastructure and processes for deploying and managing ML models at scale
- AI/ML Research Scientist — Pushes the boundaries of what AI can do through original research. Only needed for organizations pursuing cutting-edge AI development
Business and Management Roles
- AI Product Manager — Translates business needs into AI projects, manages priorities, and ensures solutions deliver business value
- AI Program Manager — Coordinates multiple AI initiatives, manages dependencies, and tracks progress across the portfolio
- Data Analyst — Interprets AI outputs and translates them into business insights and recommendations
- Change Management Lead — Drives organizational adoption of AI-powered workflows
Leadership Roles
- Chief AI Officer / VP of AI — Sets AI vision and strategy at the executive level
- Head of Data — Oversees data governance, quality, and infrastructure
- AI Ethics Lead — Ensures responsible AI development and compliance
Building Your AI Talent Strategy
Step 1: Assess Current Capabilities
Inventory the AI-relevant skills you already have. Many organizations are surprised to find existing employees with data science backgrounds, programming skills, or deep domain expertise that can be redirected toward AI work.
Step 2: Define Talent Needs
Based on your AI roadmap, identify the roles and skills you will need over the next 12 to 36 months. Be specific about technical requirements, experience levels, and timing.
Step 3: Choose Your Talent Model
Organizations typically use a combination of approaches:
- Hire full-time — For core, long-term AI roles that are strategic to your business
- Upskill existing staff — Train current employees to fill AI roles, especially those with adjacent skills like software engineering, analytics, or domain expertise
- Contract specialists — Engage freelancers or consultants for short-term or specialized needs
- Partner with consultancies — Work with AI consulting firms that provide experienced teams for specific projects
- University partnerships — Build pipelines from local universities for entry-level talent
Step 4: Create a Compelling Employer Brand
AI professionals have many options. To attract top talent, you need:
- Interesting problems — AI talent is drawn to challenging, meaningful work
- Modern technology stack — Outdated tools and infrastructure are a dealbreaker
- Learning opportunities — Budget for conferences, courses, and research time
- Career growth — Clear progression paths for both technical and management tracks
- Competitive compensation — Benchmark against regional market rates regularly
Step 5: Invest in Retention
Replacing an AI professional costs 6 to 12 months of productivity. Retention strategies include:
- Challenging project assignments
- Continuous learning budgets
- Flexible working arrangements
- Technical leadership opportunities
- Competitive compensation reviews every 6 to 12 months
AI Talent in Southeast Asia
The ASEAN talent landscape has distinct characteristics:
- Singapore is the regional AI talent hub with the highest concentration of experienced professionals, but also the highest salaries
- Vietnam and the Philippines have growing pools of software engineers who can be upskilled into data engineering and ML engineering roles
- Malaysia offers a strong balance of talent availability and cost, with good university programs
- Indonesia has a large tech workforce, and Jakarta is emerging as an AI talent center
- Remote work has expanded access to talent across borders, making distributed AI teams increasingly viable
Regional Challenges
- Compensation competition — Global tech companies recruit remotely from Southeast Asia, driving up salaries for top talent
- Experience gaps — While technical graduates are plentiful, experienced AI practitioners with production deployment experience are scarce
- Language barriers — AI training resources and communities are predominantly English-language, which can limit development opportunities for some professionals
- Brain drain — Top talent may leave for opportunities in the US, Europe, or Singapore unless given compelling local opportunities
Upskilling: Your Secret Weapon
The fastest and most cost-effective way to build AI capabilities is often upskilling your existing workforce. Employees who already understand your business, data, and culture can become effective AI practitioners more quickly than external hires who need to learn your domain.
Target employees with:
- Software engineering backgrounds (transition to ML engineering)
- Analytics experience (transition to data science)
- Deep domain expertise (transition to AI product management)
- Project management skills (transition to AI program management)
Invest in structured training programs, mentorship, and on-the-job learning through progressively complex AI projects.
AI talent is the single biggest constraint on AI adoption for most organizations. You can buy the best technology and have the clearest strategy, but without the right people to execute, nothing happens. For CEOs and CTOs, AI talent strategy is not an HR exercise — it is a core business strategy that directly determines whether your AI investments succeed or fail.
In Southeast Asia, the talent challenge is particularly acute. The region's rapid digital growth has created intense competition for AI professionals, while the pipeline of experienced practitioners remains limited. Companies that invest proactively in talent — through strategic hiring, upskilling, and partnerships — will execute their AI strategies faster and more effectively than those who treat talent as an afterthought.
The most important insight for business leaders is that AI talent strategy is not only about hiring data scientists. It requires AI-literate professionals across the organization — product managers who understand AI capabilities, business analysts who can work with model outputs, and executives who can make informed decisions about AI investments. Building this broad organizational AI literacy is just as important as hiring technical specialists.
- Audit existing skills in your organization before hiring externally — you likely have more AI-adjacent talent than you realize
- Invest in upskilling programs for software engineers, analysts, and domain experts who can transition into AI roles
- Build a compelling employer brand with interesting problems, modern tools, and learning opportunities to attract AI talent
- Use a blended talent model combining full-time hires, contractors, and consulting partners to match your needs and budget
- Plan for retention proactively with competitive compensation, career growth paths, and challenging project assignments
- Consider distributed teams across Southeast Asia to access broader talent pools at varied cost levels
- Build AI literacy across the entire organization, not just within the technical team
- Partner with local universities to build a pipeline of entry-level AI talent
Frequently Asked Questions
What is the most important AI role to hire first?
For most organizations starting their AI journey, the first hire should be a data engineer. AI systems depend on reliable, clean data, and a data engineer builds the pipelines and infrastructure that make everything else possible. Many companies make the mistake of hiring a data scientist first, only to find that the data scientist spends 80 percent of their time on data wrangling instead of building models.
How much does AI talent cost in Southeast Asia?
Costs vary significantly by role and location. In Singapore, experienced data scientists command USD 80,000 to 150,000 annually, while ML engineers may earn USD 90,000 to 170,000. In markets like Vietnam, the Philippines, and Indonesia, comparable roles may cost 40 to 60 percent less. Remote work has narrowed these gaps for top talent. Budget for compensation reviews every 6-12 months as the market evolves rapidly.
More Questions
The best approach for most companies is a hybrid. Use a consulting partner to accelerate your first 2-3 AI projects and build organizational capabilities, while simultaneously hiring and upskilling your internal team. Over 12 to 24 months, gradually shift more work in-house as your team gains experience. Pure outsourcing creates dependency, while pure insourcing is too slow for most organizations starting out.
Need help implementing AI Talent Strategy?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how ai talent strategy fits into your AI roadmap.