Thinking Machines Named OpenAI's Premiere Services Partner in Asia-Pacific Region

Thinking Machines Data Science has partnered with OpenAI to enable more businesses in the Asia Pacific to achieve measurable outcomes through artificial intelligence. This partnership designates Thinking Machines as the first official Services Partner for OpenAI in the region.

AI use is growing in APAC, with 61% of enterprises already incorporating it, according to an IBM study. However, many find it challenging to extend beyond pilot projects and have a tangible business impact. Thinking Machines and OpenAI aim to transform this by providing executive training on ChatGPT Enterprise, constructing tailored AI applications, and guiding companies in integrating AI into daily operations.

Stephanie Sy, Founder and CEO of Thinking Machines, emphasized that the partnership focuses on capability development: We0re not just introducing new technology but helping organizations develop the skills, strategies, and support systems necessary for leveraging AI effectively. Our aim is to innovate the future of work by fostering human-AI collaboration, making AI truly beneficial for people across the Asia Pacific.
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In a conversation with industry experts, Sy highlighted that one of the significant obstacles for companies is misinterpreting AI adoption. Often, AI is seen as a technology acquisition rather than a business transformation, resulting in pilot projects that fail to scale.

cThe main challenge is treating AI as a technology acquisition and not as business transformation,d she stated. cThis results in pilots that don't scale due to three missing fundamentals: alignment in leadership on value creation, workflow reforms to incorporate AI, and investment in enhancing workforce skills for adoption. By getting these right
4vision, process, people
4the projects scale into impactful results.

Many executives still regard AI as a technical endeavor rather than a strategic focus. Sy believes leadership is crucial in setting the agenda. They decide whether AI is a growth driver or merely a managed risk.

cBoards and executive teams set the agenda: Is AI a strategic growth lever or just a managed risk? Their role includes identifying priority outcomes, defining risk parameters, and delegating clear responsibilities, she remarked. Thinking Machines typically initiates with executive sessions to explore how tools like ChatGPT bring value, how they can be governed, and when to scale. cTop-down clarity transforms AI from an experiment into an enterprise asset.

Sy frequently discusses dreimagining the future of work through human-AI synergy. In practice, this means a chuman-in-command model where humans focus on judgments and decisions, while AI tackles routine tasks such as data retrieval and summarization.

cHuman-in-command signifies altering work dynamics so that people concentrate on making judgments and exceptions, while AI manages retrieval and routine tasks, ensuring transparency through audit trails and source links, she explained. This leads to time savings and quality enhancements.

In workshops conducted by Thinking Machines, professionals report liberating one to two hours daily using ChatGPT. Research backs these findings
4an MIT study cites a 14% productivity rise for contact center agents, with the greatest benefits accruing to less-experienced staff. cIt0s definitive proof that AI can amplify human abilities, not replace them, she concluded.

Another focus for Thinking Machines is agentic AI, which manages complex process chains instead of handling individual queries. Beyond merely answering questions, agentic systems can perform research, fill forms, and execute API calls, orchestrating complete workflows with a human still in command.

cAgentic systems shift work from ask-and-answer to executing multi-step tasks: managing research, form-filling, and API calls, thereby speeding up project completion with humans overseeing, Sy stated. The benefits include quicker execution and heightened productivity, but risks also exist. cHuman oversight and transparency are vital to avoid inadequacies. Our method is to couple enterprise controls with auditing processes to ensure traceability, reversibility, and policy compliance before scaling.

AI implementation is advancing, but governance is often lagging. Sy warned against reducing governance to mere documentation instead of integrating it into everyday responsibilities.

dWe position humans at the helm and make governance integral to daily operations: using authorized data sources, enforcing role-based access, keeping audit records, and ensuring human decision points for critical actions, she stated. Thinking Machines also practices what it terms dcontrol + reliability:0 limiting retrievals to trusted material and providing answers with references. Workflows are then adjusted to fit industry-specific standards such as finance, government, and healthcare.

For Sy, the effectiveness isn0t measured by the number of policies but rather by auditability and exception occurrence. dRobust governance enhances adoption as teams have confidence in their output, she noted.

The Asia Pacific0s varied cultural and linguistic landscape presents distinct hurdles for AI scalability. A one-size-fits-all tactic is ineffective. Sy stressed that the most effective strategy is identifying local solutions prior to scaling them.

cGlobal solutions fail when they disregard local context. The strategy involves building locally and then scaling intentionally: customizing AI to local languages, forms, policies, and escalation processes; then unifying scalable elements like governance, data integration, and impact assessments, she said.

This is the model Thinking Machines has employed in Singapore, the Philippines, and Thailand
4establishing value with local teams first before expanding regionally. The aim isn0t for a uniform chatbot but to create a reliable model while honoring local specificities and ensuring scalability.

When asked which competencies are most vital in AI-enabled workplaces, Sy emphasized that scale arises from enhancing skills, not just acquiring tools. She delineated it into three categories:

cWhen leadership and teams share this foundational understanding, adoption evolves from trials to reproducible, operational-level results, she asserted. In Thinking Machines0 initiatives, numerous professionals reported reclaiming one to two hours a day after just a single-day seminar. Over 10,000 individuals have undergone training, aligning with the conclusion that dskills + governance unlock scaling potential.

Looking to the future, Sy foresees AI moving from drafting to executing essential business functions over the next five years. She anticipates significant advancements in software development, marketing, service operations, and supply chains.

cIn the forthcoming wave, we envision three clear models: policy-aware assistants in finance, supply chain copilots in manufacturing, and customized but compliant customer experiences in retail
4each with human checkpoints and verified sources, enabling leaders to scale with assurance, she said.

An illustrative example is a system Thinking Machines designed for the Bank of the Philippine Islands known as BEAi. It0s a retrieval-boosted generation (RAG) platform that supports English, Filipino, and Taglish, providing answers sourced from reliable documents while comprehending policy intricacies. cThis is what dAI-native looks like in action, Sy explained.

The collaboration with OpenAI will commence with programs in Singapore, the Philippines, and Thailand through Thinking Machines0 regional offices, then broaden across the APAC. Future initiatives will customize services for sectors like finance, retail, and manufacturing, where AI can address unique challenges and open new prospects.

For Sy, the mission is explicit: cAI adoption isn't just about experimenting with burgeoning tools. It's about cultivating the vision, processes, and competencies that allow institutions to graduate from experimental phases to impactful results. When leadership, teams, and technology converge, that0s when AI offers enduring value.