Realizing AI’s potential requires a thoughtful, responsible approach. Prioritizing explainable AI could be the key.

As artificial intelligence (AI) continues to reshape the business landscape across Asia, organizations face a dual challenge: harnessing the transformative power of AI while ensuring its responsible and ethical implementation. Praveen Thakur, SVP of Growth Markets, Asia, LATAM and MEA at Teradata, offers valuable insights into this complex terrain, during an email interview.

The Trust Imperative

At the heart of successful AI adoption lies trust. As Praveen emphasized, “When we use extraordinary tools like AI, we have to take extraordinary measures to make sure we are protecting the people these tools touch.” This sentiment underscores the critical need for ethical AI practices, particularly as we witness AI bias creeping into company strategies across Asia.

Praveen Thakur

The proliferation of AI systems trained on biased data sets has already led to concerning outcomes, such as job recommendation systems inadvertently excluding women from certain opportunities. These incidents highlight the urgent need for explainable AI, especially in heavily regulated industries like banking and finance.

Data Quality: The Foundation of AI Success

In today’s AI-driven world, data is king. However, the sheer volume of data being generated – projected to reach 120 zettabytes by 2025 – presents its own challenges. Thakur notes, “Finding valuable information in this sea of raw data means filtering through a lot of unusable or polluted content. We still need human discernment to find the rivers of clean and reliable data that should inform AI training models.”

When we use extraordinary tools like AI, we have to take extraordinary measures to make sure we are protecting the people these tools touch.

This observation underscores a crucial point: while AI can process vast amounts of data, human judgment remains indispensable in ensuring data quality and relevance. Organizations must invest in robust data governance frameworks and cultivate a culture of data literacy to truly capitalize on AI’s potential.

Praveen also shared the example of a customer Chugai Pharmaceuticals that is working to develop breakthrough therapeutics. It takes more than 10 years to develop a new drug, the success rate of new drug development is falling each year and development costs are steadily rising. The company also realized that much more time was spent preparing data, rather than on creating business value from AI projects.

“By leveraging cutting-edge AI, on our platform that enables AI tools to access and run highly accurate validation using all data, Chugai has been able to improve the momentum of data utilization within the company and drive much more efficient clinical trials and applications of drug approval.”

Overcoming Adoption Hurdles

Despite AI’s enormous economic potential in Asia, with the market projected to reach $136 billion by 2025, many organizations struggle with implementation. Common challenges include a lack of necessary technology, resources, and talent to drive optimal AI strategies.

To overcome these hurdles, Praveen advocates for a focus on three core principles: people, transparency, and value-creation. By prioritizing these elements, businesses can foster a new era of creativity and confidence in AI-driven decision-making across Asia.

One trend that Teradata has observed is the AI bias that is starting to creep into company strategies in Asia when analysis comes from an AI system that was trained on a slanted collection of data. For example, an AI-based job recommendation system in Indonesia inadvertently excluded women from certain job opportunities due to historical biases in the hiring data.

No doubt, it is critical that AI users in Asia start to understand the lineage of the data behind the AI systems they use, especially now that we are in an AI-driven explosion of data, Praveen opined.

“Rather than looking to address roadblocks to AI adoption, we first need to be mindful of the broader underlying issues, that technology has no moral compass. When we use extraordinary tools like AI, we have to take extraordinary measures to make sure we are protecting the people these tools touch,” he added.

The Path Forward: Balancing Innovation and Responsibility

As AI deployment scales across enterprises, new challenges emerge. Praveen said, “As the scale of AI deployed across the enterprise increases, there is also greater risk of losing control and oversight over intellectual property. Asian organizations need to ensure that they deliver accountability, security, and trust, and ensure and maintain a tidy data estate.”

This cautionary note serves as a reminder that the AI revolution isn’t just about technological advancement – it’s about creating a sustainable, trustworthy ecosystem that benefits businesses and society alike. Organizations must prioritize explainable AI, offering complete visibility into data usage and decision-making processes.

Moreover, as data sharing becomes increasingly prevalent in Asia, businesses must navigate complex regulatory landscapes while maximizing the benefits of collaborative innovation. This requires a delicate balance between data utilization and compliance with data sovereignty laws.