Thinking Machines Data Science has formed a partnership with OpenAI to help more companies across the Asia Pacific translate artificial intelligence into measurable business outcomes. The agreement names Thinking Machines the first official Services Partner for OpenAI in the region and will roll out programs aimed at moving AI projects from pilots into production.
The move comes as adoption of AI in APAC continues to accelerate. An IBM study cited by the companies found that 61% of enterprises already use AI, yet many organisations struggle to convert early experiments into lasting impact. Thinking Machines and OpenAI plan to offer executive training on ChatGPT Enterprise, hands-on support for building custom AI applications, and advisory services to embed AI into routine operations.
Stephanie Sy, Founder and CEO of Thinking Machines, framed the tie-up around capability building. “We’re not just bringing in new technology but we’re helping organizations build the skills, strategies, and support systems they need to take advantage of AI. For us, it’s about reinventing the future of work through human-AI collaboration and making AI truly work for people across the Asia Pacific region,” she said.
In an interview with AI News, Sy said one of the biggest obstacles for enterprises is how they frame AI adoption. She argued that many organisations treat AI as a technology purchase rather than a wider business transformation, a mindset that leaves pilots stranded.
“The main challenge is that many organisations approach AI as a technology acquisition rather than a business transformation,” she said. “This leads to pilots that never scale because 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 so teams actually use the tools. Get those three right—vision, process, people—and pilots scale into impact.”
Sy described the role of senior leadership as decisive. “Boards and C-suites set the tone: Is AI a strategic growth driver or a managed risk? Their role is to name a few priority outcomes, define risk appetite, and assign clear ownership,” she said. “That top-down clarity is what turns AI from an experiment into an enterprise capability.”
Her preferred operating model puts people at the center of automated systems. Sy uses the phrase “human-in-command” to describe work redesign that keeps humans responsible for judgment, decision-making, and handling exceptions, while AI performs routine tasks such as retrieval, drafting, and summarizing. “Human-in-command means redesigning work so people focus on judgment and exceptions, while AI takes on retrieval, drafting, and routine steps, with transparency through audit trails and source links,” she said. The gains are tracked in time saved and quality improvements.
Thinking Machines reports that participants in its workshops who use ChatGPT free up about one to two hours per day. Sy cited independent research to support those claims, pointing to an MIT study that found a 14% productivity increase for contact center agents, with the largest gains among less-experienced staff. “That’s clear evidence AI can elevate human talent rather than displace it,” she added.
The company is also focused on agentic AI, which goes beyond single-response models to coordinate multi-step processes. Agentic systems can manage research, complete forms, make API calls and orchestrate workflows while a person remains accountable for the outcome.
“Agentic systems can take work from ‘ask-and-answer’ to multi-step execution: coordinating research, browsing, form-filling, and API calls so teams ship faster with a human in command,” Sy said. She warned that those capabilities raise genuine risks, and that strong controls are required before scaling: enterprise controls and audit trails must sit alongside agent capabilities so actions are traceable, reversible, and aligned to policy prior to wider deployment.
Adoption has been moving faster than governance in many organizations. Sy argued that governance fails when it becomes a box-ticking exercise rather than part of daily operations. Her prescription is practical controls that are visible in everyday workflows: use approved data sources, apply role-based access, keep audit trails, and require human decision points for sensitive tasks.
“We keep humans in command and make governance visible in daily work: use approved data sources, enforce role-based access, maintain audit trails, and require human decision points for sensitive actions,” she explained. Thinking Machines also follows a “control + reliability” approach that limits retrieval to trusted content and returns answers with citations. Workflows are then adjusted to comply with local rules in sectors such as finance, government, and healthcare.
“Good governance accelerates adoption because teams trust what they ship,” she said.
Sy stresses that Asia Pacific’s cultural and linguistic diversity demands local-first design rather than one-size-fits-all templates. She urged teams to prove value with local users and then expand methodically, adapting models to local languages, documents, policies, and escalation paths while standardizing the elements that can be shared, like governance patterns, data connectors, and impact measurements.
“Global templates fail when they ignore how local teams work. The playbook is build locally, scale deliberately: fit the AI to local language, forms, policies, and escalation paths; then standardize the parts that travel such as your governance pattern, data connectors, and impact metrics,” she said.
That approach has been used in projects in Singapore, the Philippines, and Thailand, where Thinking Machines first validates solutions with local teams before broader regional rollout. The result is not a single chatbot rolled everywhere, but a repeatable pattern that respects local context while preserving scalability.
Sy outlined three skill areas she sees as decisive for adoption at scale:
- Executive literacy: leaders setting outcomes and guardrails, and choosing when and where to expand AI use.
- Workflow design: redesigning human-AI handoffs, clarifying who drafts, who approves, and how exceptions are escalated.
- Hands-on skills: prompting, evaluating outputs, and retrieving from trusted sources so answers can be checked, not just plausible.
“When leaders and teams share that foundation, adoption moves from experimenting to repeatable, production-level results,” she said. Thinking Machines’ programs show early returns: many participants report saving an hour or two each day after a one-day workshop. To date the firm has trained more than 10,000 people across roles, and Sy described a clear pattern where skills and governance drive meaningful scale.
Looking ahead five years, Sy expects AI to move from assisting with drafting to taking on full execution across key functions. She anticipates notable productivity gains in software development, marketing, service operations, and supply chain management.
“For the next wave, we see three concrete patterns: policy-aware assistants in finance, supply chain copilots in manufacturing, and personalized yet compliant CX in retail—each built with human checkpoints and verifiable sources so leaders can scale with confidence,” she said.
She offered a concrete example from the region: a system developed with the Bank of the Philippine Islands called BEAi. The tool is a retrieval-augmented generation (RAG) system that works in English, Filipino, and Taglish. BEAi returns answers linked to source documents with page numbers, understands when newer policy overrides older guidance, and converts complex policy material into practical daily guidance for staff.
“That’s what ‘AI-native’ looks like in practice,” Sy said.
The initial phase of the OpenAI collaboration will run programs in Singapore, the Philippines, and Thailand through Thinking Machines’ regional offices, with plans to expand across APAC. Services will be adapted to sector needs in finance, retail, and manufacturing where AI can address specific operational challenges and open new opportunities.
Sy summed up the objective plainly: “AI adoption isn’t just about experimenting with new tools. It’s about building the vision, processes, and skills that let organizations move from pilots to impact. When leaders, teams, and technology come together, that’s when AI delivers lasting value.”