Thinking Machines becomes OpenAI’s first services partner in APAC
Thinking Machines Data Science is becoming a member of forces with OpenAI to assist extra companies throughout Asia Pacific flip synthetic intelligence into measurable outcomes. The collaboration makes Thinking Machines the first official Services Partner for OpenAI in the area.
The partnership comes as AI adoption in APAC continues to rise. An IBM examine discovered that 61% of enterprises already use AI, but many battle to maneuver past pilot initiatives and ship actual enterprise impression. Thinking Machines and OpenAI intention to vary that by providing govt coaching on ChatGPT Enterprise, help for constructing customized AI purposes, and steerage on embedding AI into on a regular basis operations.
Stephanie Sy, Founder and CEO of Thinking Machines, framed the partnership round functionality constructing: “We’re not simply bringing in new know-how however we’re serving to organisations construct the talents, methods, and help programs they should make the most of AI. For us, it’s about reinventing the way forward for work by way of human-AI collaboration and making AI really work for folks throughout the Asia Pacific area.”
Turning AI pilots into outcomes with Thinking Machines
In an interview with AI News, Sy defined that one of many greatest hurdles for enterprises is how they body AI adoption. Too typically, organisations see it as a know-how acquisition relatively than a enterprise transformation. That method results in pilots that stall or fail to scale.
“The most important problem is that many organisations method AI as a know-how acquisition relatively than a enterprise transformation,” she stated. “This results in pilots that by no means scale as a result of three fundamentals are lacking: clear management alignment on the worth to create, redesign of workflows to embed AI into how work will get carried out, and funding in workforce expertise to make sure adoption. Get these three proper—imaginative and prescient, course of, folks—and pilots scale into impression.”
Leadership on the centre
Many executives nonetheless deal with AI as a technical mission relatively than a strategic precedence. Sy believes that boards and C-suites must set the tone. Their position is to resolve whether or not AI is a development driver or only a managed threat.
“Boards and C-suites set the tone: Is AI a strategic development driver or a managed threat? Their position is to call a couple of precedence outcomes, outline threat urge for food, and assign clear possession,” she stated. Thinking Machines typically begins with govt classes the place leaders can discover the place instruments like ChatGPT add worth, the way to govern them, and when to scale. “That top-down readability is what turns AI from an experiment into an enterprise functionality.”
Human-AI collaboration in follow
Sy typically talks about “reinventing the way forward for work by way of human-AI collaboration.” She defined what this seems like in follow: a “human-in-command” method the place folks deal with judgment, decision-making, and exceptions, whereas AI handles routine steps like retrieval, drafting, or summarising.
“Human-in-command means redesigning work so folks deal with judgment and exceptions, whereas AI takes on retrieval, drafting, and routine steps, with transparency by way of audit trails and supply hyperlinks,” she stated. The outcomes are measured in time saved and high quality enhancements.
In workshops run by Thinking Machines, professionals utilizing ChatGPT typically liberate one to 2 hours per day. Research helps these outcomes—Sy pointed to an MIT study displaying a 14% productiveness increase for contact centre brokers, with the largest good points seen amongst less-experienced workers. “That’s clear proof AI can elevate human expertise relatively than displace it,” she added.
Agentic AI with Thinking Machines’ guardrails
Another space of focus for Thinking Machines is agentic AI, which fits past single queries to deal with multi-step processes. Instead of simply answering a query, agentic programs can handle analysis, fill kinds, and make API calls, coordinating whole workflows with a human nonetheless in cost.
“Agentic programs can take work from ‘ask-and-answer’ to multi-step execution: coordinating analysis, shopping, form-filling, and API calls so groups ship sooner with a human in command,” Sy stated. The promise is quicker execution and productiveness, however the dangers are actual. “The rules of human-in-command and auditability stay essential; to keep away from the dearth of correct guardrails. Our method is to pair enterprise controls and auditability with agent capabilities to make sure actions are traceable, reversible, and policy-aligned earlier than we scale.”
Governance that builds belief
While adoption is accelerating, governance typically lags behind. Sy cautioned that governance fails when it’s handled as paperwork as an alternative of a part of day by day work.
“We preserve people in command and make governance seen in day by day work: use authorised information sources, implement role-based entry, keep audit trails, and require human resolution factors for delicate actions,” she defined. Thinking Machines additionally applies what it calls “management + reliability”: limiting retrieval to trusted content material and returning solutions with citations. Workflows are then tailored to native guidelines in sectors comparable to finance, authorities, and healthcare.
For Sy, success isn’t measured in the quantity of insurance policies however in auditability and exception charges. “Good governance accelerates adoption as a result of groups belief what they ship,” she stated.
Local context, regional scale
Asia Pacific’s cultural and linguistic variety poses distinctive challenges for scaling AI. A one-size-fits-all mannequin doesn’t work. Sy emphasised that the fitting playbook is to construct domestically first after which scale intentionally.
“Global templates fail after they ignore how native groups work. The playbook is construct domestically, scale intentionally: match the AI to native language, kinds, insurance policies, and escalation paths; then standardise the components that journey comparable to your governance sample, information connectors, and impression metrics,” she stated.
That’s the method Thinking Machines has taken in Singapore, the Philippines, and Thailand—show worth with native groups first, then roll out area by area. The intention just isn’t a uniform chatbot however a dependable sample that respects native context whereas sustaining scalability.
Skills over instruments
When requested what expertise will matter most in an AI-enabled office, Sy identified that scale comes from expertise, not simply instruments. She broke this down into three classes:
- Executive literacy: the power for leaders to set outcomes and guardrails, and know when and the place to scale AI.
- Workflow design: the redesign of human-AI handoffs, clarifying who drafts, who approves, and the way exceptions escalate.
- Hands-on expertise: prompting, analysis, and retrieval from trusted sources so solutions are verifiable, not simply believable.
“When leaders and groups share that basis, adoption strikes from experimenting to repeatable, production-level outcomes,” she stated. In Thinking Machines’ applications, many professionals report saving one to 2 hours per day after only a one-day workshop. To date, greater than 10,000 folks throughout roles have been skilled, and Sy famous the sample is constant: “expertise + governance unlock scale.”
Industry transformation forward
Looking to the following 5 years, Sy sees AI shifting from drafting to full execution in essential enterprise capabilities. She expects main good points in software program growth, advertising, service operations, and provide chain administration.
“For the following wave, we see three concrete patterns: policy-aware assistants in finance, provide chain copilots in manufacturing, and personalised but compliant CX in retail—every constructed with human checkpoints and verifiable sources so leaders can scale with confidence,” she stated.
A sensible instance is a system Thinking Machines constructed with the Bank of the Philippine Islands. Called BEAi, it’s a retrieval-augmented technology (RAG) system that helps English, Filipino, and Taglish. It returns solutions linked to sources with web page numbers and understands coverage supersession, turning complicated coverage paperwork into on a regular basis steerage for workers. “That’s what ‘AI-native’ seems like in follow,” Sy stated.
Thinking Machines expands AI throughout APAC
The partnership with OpenAI will begin with applications in Singapore, the Philippines, and Thailand by way of Thinking Machines’ regional places of work earlier than increasing additional throughout APAC. Future plans embrace tailoring services to sectors comparable to finance, retail, and manufacturing, the place AI can deal with particular challenges and open new alternatives.
For Sy, the purpose is evident: “AI adoption isn’t nearly experimenting with new instruments. It’s about constructing the imaginative and prescient, processes, and expertise that allow organisations transfer from pilots to impression. When leaders, groups, and know-how come collectively, that’s when AI delivers lasting worth.”
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