Thinking Machines positions itself as the *first service partner of* OpenAI in the Asia-Pacific, revolutionizing the integration of artificial intelligence into the operational fabric of businesses. The collaboration aims to overcome major challenges related to *AI adoption*, often hindered by ineffective pilot projects. Establishing itself as a catalyst, Thinking Machines focuses on strategic and personalized training to assist organizations in optimizing their processes and improving *operational performance*.
Strategic Partnership Between Thinking Machines and OpenAI
Thinking Machines Data Science has recently established an official partnership with OpenAI, thus becoming the first service partner of OpenAI in the Asia-Pacific region. This collaboration aims to help a greater number of businesses in Asia transform artificial intelligence into measurable outcomes. This close relationship comes as AI adoption in this region is experiencing significant growth.
An IBM report revealed that 61% of companies are already using AI. Unfortunately, many of them struggle to transcend pilot projects and generate concrete business impact. Through this partnership, Thinking Machines and OpenAI aim to address this issue by offering executive training on ChatGPT Enterprise, support for developing custom AI applications, and guidance on integrating AI into daily operations.
Strengthening Organizational Capacities
Stephanie Sy, founder and CEO of Thinking Machines, expressed that this partnership is focused on capacity building. “We are not just introducing new technologies; we are helping organizations develop the necessary skills and strategies to leverage AI,” she stated. The goal lies in reinventing the future of work, thereby fostering collaboration between humans and AI within the Asia-Pacific region.
Challenges Related to AI Adoption
In an interview with AI News, Sy identified the major reasons why companies fail to effectively adopt AI. Many organizations view AI as a mere technology acquisition, ignoring its potential to transform their operations. This perspective often leads to stagnant pilots. To overcome these obstacles, three fundamental elements are necessary: a clear alignment among leaders, a redefinition of workflows, and an investment in employee skills.
Focusing on these three aspects – vision, process, and people – allows pilots to transform into concrete outcomes.
Leadership at the Core of AI Integration
Many leaders still perceive AI as a technical project rather than a strategic priority. Sy argues that it is up to management to state whether AI represents a growth engine or a risk to be managed. By establishing clear priority objectives and defining risk appetite, C-suites can catalyze the integration of AI into business capabilities.
Thinking Machines often begins with executive sessions, allowing leaders to explore the added value of tools like ChatGPT, discussing governance rules and scaling opportunities. This clarity at the top of the hierarchy shifts AI from experimentation to an institutional skill.
Human and AI Collaboration
Sy describes “human-AI collaboration” as an approach where humans retain control, focusing on judgment and decision-making, while AI manages routine steps. Implementing this model leads to significant time savings and an improvement in the quality of outcomes.
AI Agentic and Execution Control
The concept of AI agentic constitutes another priority for Thinking Machines, allowing systems to process complex multi-step processes. These systems can coordinate research, fill out forms, and execute API calls, all while keeping a human in charge. This architecture enables rapid execution and increased productivity without sacrificing human control.
Thinking Machines ensures that the principles of “human governance” and auditability are applicable within this framework, guaranteeing that every action is traceable and compliant with company policies.
Empowerment of AI Governance
With the acceleration of AI adoption, it is ensured that governance does not lag behind. Sy states that good governance must be integrated into daily routines. This involves using approved data sources, enforcing access controls, and requiring human validation for sensitive actions.
Sy emphasizes the implementation of indicator governance models to build the necessary trust among teams adopting AI, thereby enabling more expansive and faster adoption. Transparency of processed data helps establish this trust.
Local Adaptation and Regional Scaling
The cultural and linguistic diversity of the Asia-Pacific region presents unique challenges for scaling AI solutions. A one-size-fits-all model is insufficient. Sy recommends first “building locally then deliberately expanding,” adapting AI to local specifics before standardizing certain aspects.
Thinking Machines has implemented this strategy in various countries, including Singapore, the Philippines, and Thailand, proving value with local teams before rolling out solutions on a regional basis.
Prioritizing Skills Over Tools
Sy emphasizes that scaling up lies in skill development and not just in acquiring tools. Three categories of skills stand out: executive literacy, workflow design, and practical skills. This educational framework helps teams substitute experimentation with recurring production results.
The Future of Industries Transformed by AI
From a five-year perspective, Sy anticipates that AI will evolve into the complete execution of major business functions. Expected gains will emerge in software development, marketing, and service operations.
Thinking Machines does not stick to a uniform strategy. Each project, like BEAi, a system developed for the Bank of the Philippines, serves as a glaring example of this approach. This model and others should align with local capabilities while delivering quantifiable results, thereby contributing to a strengthened integration of AI technologies within the economy of the Asia-Pacific region.
Frequently Asked Questions
What is the collaboration between Thinking Machines and OpenAI?
Thinking Machines has partnered with OpenAI to help businesses in the Asia-Pacific region leverage artificial intelligence to achieve measurable outcomes. It is the first official service partner of OpenAI in this region.
How does Thinking Machines help businesses adopt AI?
Thinking Machines offers executive training on ChatGPT Enterprise, support for creating custom AI applications, and guidance for integrating AI into daily business operations.
What are the main challenges faced by companies when adopting AI?
Companies often view AI as a technological acquisition rather than a business transformation, leading to stagnant pilot projects. A strategic approach is therefore necessary to succeed in AI integration.
What is the importance of leadership in AI adoption?
Leadership plays a key role in determining whether AI is viewed as a growth engine or a managed risk. Clear direction is crucial for defining objectives, risk appetite, and accountability for AI projects.
How is the human-AI collaboration put into practice by Thinking Machines?
This is a “humans in charge” model where humans focus on judgment and decision-making, while AI manages routine tasks, such as research or writing, thereby allowing for time and efficiency gains.
What is AI agentic and how is it used by Thinking Machines?
AI agentic goes beyond simple queries to drive multi-step processes. Thinking Machines uses these systems to coordinate complete tasks while keeping the human at the center of the decision-making process.
How does Thinking Machines manage AI governance?
Thinking Machines integrates governance directly into daily tasks by using approved data sources, enforcing access controls, and maintaining audit trails to ensure safe and reliable AI adoption.
What model does Thinking Machines adopt for AI adoption in the Asia-Pacific region?
Thinking Machines prioritizes a local-first approach to adapt AI to the specific contexts of teams before expanding initiatives regionally, ensuring that solutions remain relevant and effective.
What skills are essential for succeeding in an AI-enabled work environment?
Key skills include executive literacy for defining AI outcomes, workflow design for human-AI interactions, as well as practical skills such as the ability to formulate queries and assess reliable data.
In which sectors does Thinking Machines plan to expand its AI services?
Thinking Machines plans to expand particularly in the sectors of finance, retail, and manufacturing, developing AI solutions that address specific challenges and open new opportunities.
