Introduction to AI Adoption
Thinking Machines Data Science is teaming up with OpenAI to help businesses in the Asia Pacific region turn artificial intelligence into measurable results. This collaboration makes Thinking Machines the first official Services Partner for OpenAI in the region. The partnership aims to address the challenge of AI adoption in APAC, where 61% of enterprises already use AI, but many struggle to move beyond pilot projects and deliver real business impact.
The Challenge of AI Adoption
According to Stephanie Sy, Founder and CEO of Thinking Machines, one of the biggest hurdles for enterprises is how they frame AI adoption. Too often, organisations see it as a technology acquisition rather than a business transformation. This approach leads to pilots that stall or fail to scale. Sy explained that three fundamentals are missing: clear leadership alignment on the value to create, redesign of workflows to embed AI into how work gets done, and investment in workforce skills to ensure adoption.
Leadership at the Centre
Many executives still treat AI as a technical project rather than a strategic priority. Sy believes that boards and C-suites need to set the tone. Their role is to decide whether AI is a growth driver or just a managed risk. Thinking Machines often begins with executive sessions where leaders can explore where tools like ChatGPT add value, how to govern them, and when to scale. This top-down clarity is what turns AI from an experiment into an enterprise capability.
Human-AI Collaboration in Practice
Sy often talks about “reinventing the future of work through human-AI collaboration.” She explained what this looks like in practice: a “human-in-command” approach where people focus on judgment, decision-making, and exceptions, while AI handles routine steps like retrieval, drafting, or summarising. The results are measured in time saved and quality improvements. In workshops run by Thinking Machines, professionals using ChatGPT often free up one to two hours per day.
Agentic AI with Thinking Machines’ Guardrails
Another area of focus for Thinking Machines is agentic AI, which goes beyond single queries to handle multi-step processes. Instead of just answering a question, agentic systems can manage research, fill forms, and make API calls, coordinating entire workflows with a human still in charge. The promise is faster execution and productivity, but the risks are real. Thinking Machines’ approach is to pair enterprise controls and auditability with agent capabilities to ensure actions are traceable, reversible, and policy-aligned before scaling.
Governance that Builds Trust
While adoption is accelerating, governance often lags behind. Sy cautioned that governance fails when it’s treated as paperwork instead of part of daily work. Thinking Machines keeps humans in command and makes governance visible in daily work: using approved data sources, enforcing role-based access, maintaining audit trails, and requiring human decision points for sensitive actions. Workflows are then adapted to local rules in sectors such as finance, government, and healthcare.
Local Context, Regional Scale
Asia Pacific’s cultural and linguistic diversity poses unique challenges for scaling AI. A one-size-fits-all model doesn’t work. Sy emphasised that the right playbook is to build locally first and then scale deliberately. Thinking Machines has taken this approach in Singapore, the Philippines, and Thailand—proving value with local teams first, then rolling out region by region.
Skills over Tools
When asked what skills will matter most in an AI-enabled workplace, Sy pointed out that scale comes from skills, not just tools. She broke this down into three categories: executive literacy, workflow design, and hands-on skills. When leaders and teams share that foundation, adoption moves from experimenting to repeatable, production-level results.
Industry Transformation Ahead
Looking to the next five years, Sy sees AI shifting from drafting to full execution in critical business functions. She expects major gains in software development, marketing, service operations, and supply chain management. Thinking Machines is working on building policy-aware assistants in finance, supply chain copilots in manufacturing, and personalised yet compliant CX in retail—each built with human checkpoints and verifiable sources so leaders can scale with confidence.
Conclusion
The partnership between Thinking Machines and OpenAI aims to help businesses in the Asia Pacific region turn artificial intelligence into measurable results. By addressing the challenges of AI adoption, providing leadership at the centre, and focusing on human-AI collaboration, agentic AI, governance, and skills, Thinking Machines is poised to make a significant impact in the region. As AI continues to evolve, it’s essential for businesses to stay ahead of the curve and invest in the skills and technologies that will drive success in the future.
FAQs
Q: What is the main challenge for enterprises in AI adoption?
A: The main challenge is that many organisations approach AI as a technology acquisition rather than a business transformation.
Q: What is the role of leadership in AI adoption?
A: Leadership sets the tone and decides whether AI is a growth driver or just a managed risk.
Q: What is human-AI collaboration in practice?
A: Human-AI collaboration is a “human-in-command” approach where people focus on judgment, decision-making, and exceptions, while AI handles routine steps like retrieval, drafting, or summarising.
Q: What is agentic AI?
A: Agentic AI goes beyond single queries to handle multi-step processes, managing research, filling forms, and making API calls, coordinating entire workflows with a human still in charge.
Q: What skills will matter most in an AI-enabled workplace?
A: Executive literacy, workflow design, and hands-on skills will be essential for success in an AI-enabled workplace.
