Thinking Machines named OpenAI’s first APAC partner

Data science thinking machines join Openai to help more companies around Asia and the Pacific Ocean to convert artificial intelligence into measurable results. Cooperation makes thinking machines the first partner of Openai’s official services in the region.

The partnership comes as artificial intelligence continues to adopt APAC. IBM’s study found that 61 % of institutions already use artificial intelligence, but a lot of struggle to overcome experimental projects and provide a real commercial impact. Thinking machines and Openai aim to change this by providing executive training on Chatgpt Enterprise, supporting the building of customized AI applications, and instructions on including artificial intelligence in daily operations.

Stephanie C, founder and director of thinking for thinking machines, framing the partnership on capacity -building: “We are not only bringing new technology, but we are helping organizations to build skills, strategies and support systems they need to benefit from artificial intelligence.

Converting artificial intelligence pilots into results with thinking machines

In an interview with Artificial Intelligence NewsSye explained that one of the largest obstacles to institutions is how to framing the adoption of artificial intelligence. Often, institutions see it to acquire technology rather than transform business. This approach leads to pilots who stop or fail to expand.

“The main challenge is that many organizations deal with Amnesty International as a technology gain rather than transforming business,” she said. “This leads to pilots who never expand because three missing essentials: clear driving is the value of the value to create and redesign the workflow to include artificial intelligence in how to do work, investing in the workforce skills to ensure adoption. Obtaining these three rights – vision, process, and people – are expanding to influence.”

Driving at the center

Many CEOs still deal with artificial intelligence as a technical project rather than a strategic priority. Sye believes that the panels and Suites C need to adjust the tone. Their role is to determine whether artificial intelligence is a growth driver or just a oriental risk.

She said: “The paintings and Suites C puts the tone: Is Amnesty International a strategic growth driver or managed risks? Its role is to name some priority results, determine the appetite of risk, and appoint a clear ownership.” Thinking machines often begin with executive sessions where leaders can explore where to tools such as ChatgPT add value, how to control them, and when to expand. “This clarity from top to bottom is what transforms artificial intelligence from experiment to the ability of the institution.”

Human cooperation-AA in practice

SY often talks about “re -invention of the future of work through human cooperation.” She explained how this appears in practice: a “human being in a leader” where people focus on judgment, decision -making, and exceptions, while artificial intelligence deals with routine steps such as recovery, formulation or summary.

She said: “The inner person means redigctioning the work so that people focus on judgment and exceptions, while artificial intelligence takes retrieval, formulation, and routine steps, with transparency through auditing paths and source links.” The results are measured in preserved time and quality improvements.

In the workshops run by thinking machines, professionals who use Chatgpt often install an hour to two hours a day. Research supports these results-SSY to the study of the Massachusetts Institute of Technology showing a 14 % production increase for the communication center agents, with the largest gains seen among the least experienced employees. She added: “This is a clear evidence that AI can raise human talents instead of removing them.”

Artificial intelligence agent with the degrees of thinking machines

Another field of focus for thinking machines is Agentic AI, which goes beyond individual queries of dealing with multiple -step processes. Instead of answering a question, the agents can manage research, fill out forms, make API calls, and coordinate the entire workflow with a person who is still responsible.

“The agents can take the work from” ASK-AD-SSWER “to multi-step implementation: coordination of research, browsing, filling models, and API calls, so that the difference is faster with a person in driving.” The promise is implementation and productivity faster, but the risks are real. “The principles of internal man and scrutiny remain extremely important to avoid the lack of appropriate handrails. Our approach is to pair the elements of institutions control and scrutinize the agent’s capabilities to ensure tracking, treat them, and align the policy before expansion.”

Governance that builds confidence

While adoption is accelerating, governance often fails to knee. SY warned against the failure of governance when it is dealt with as papers instead of part of the daily work.

“We keep human beings in driving and make governance visible in daily work: Use the approved data sources, impose roles -based access, and maintain auditing paths, and requires human decision points to obtain sensitive procedures,” she explained. Thinking machines also apply what he calls “reliability + control”: restricting retrieval to reliable content and return answers with categories. Then the workflow tasks are adapted to local rules in sectors such as financing, government and health care.

For SY, success in the volume of policies is not measured but in scrutiny and exception rates. And she said: “The rational judgment speeds adoption because the teams trust what is shipped.”

Local context, regional scope

The cultural and linguistic diversity in Asia and the Pacific represents unique challenges to expand the scope of artificial intelligence. One model that suits everyone does not work. SY emphasized that the correct play book is building locally first and then deliberately expanding.

“Global templates fail when you ignore how local teams work. Playing book is built locally, and deliberately expanded: fit artificial intelligence with local language, models, policies, and escalation paths; then unifying parts that travel such as governance pattern, data connections, and effects of effect.”

This is the approach taken by thinking machines in Singapore, the Philippines and Thailand – the value with local teams first, then put the area according to the region. The goal is not a uniform chatbot but a reliable pattern that respects the local context while maintaining the ability to expand.

Skills on tools

When asked about the most important skills in the workplace that is enabled from artificial intelligence, Sye indicated that the scale comes from the skills, not just tools. I stormed this into three categories:

• Executive literacyThe ability of leaders to determine the results and handrails, and know when and where to expand the scope of artificial intelligence.
• Workflow design: Human-Ei, clarifying who prevails, who agrees, and how exceptions are escalating.
• Practical skills: Claim, evaluation and retrieval from reliable sources so that the answers are verified, not just reasonable.

She said: “When the leaders and teams participate in this institution, the adoption from the experiment is transmitted to repetitive results at the production level.” In thinking programs, many professionals are to save an hour to two hours a day after a one -day workshop. To date, more than 10,000 people have been trained through roles, and Sye has noticed that the pattern is fixed: “skills + the opening scale.”

Industry shift forward

Looking at the next five years, SY, Amnesty International, sees the drafting to full implementation of critical business functions. It expects great gains in software development, marketing, service operations, and supply chain management.

She said: “For the next wave, we see three concrete patterns: assistants who are aware of politics in financing, the supply chain in the field of manufacturing, and the character of CX’s allocation in the field of retail-all that were built with human inspection points and resources that can be verified so that the leaders can expand with confidence.”

A practical example is the thinking machines in the system designed with the bank of the Philippines. It is called Beai, a generation (RAG) system that carries its retrieval that supports English, Philippine and Taglish. It restores the answers associated with sources of pages and understands excessive policy, and transforms complex policy documents into daily guidelines for employees. “This is what” AI NATIVE “seems to be in practice,” Ci said.

Thinking machines expand artificial intelligence via APAC

The partnership with Openai will start with programs in Singapore, the Philippines and Thailand through the regional offices of thinking machines before expanding all over APAC. Future plans include sewing services for sectors such as financing, retail and manufacturing, where Amnesty International can face specific challenges and open new opportunities.

For SY, the goal is clear: “AI adoption is not only related to the experience of new tools. It is related to building vision, processes and skills that allow institutions to move from pilots to influence. When leaders, teams and technology meet, when AI offers permanent value.”

See also: X, Xai Sue Apple and Openai on Monopoly AI claims

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