Responsible AI in Action: Insights from Snowflake World Tour Jakarta

Key Takeaways from the AIBP x Snowflake Executive Programme | Snowflake World Tour Jakarta

The Snowflake x AIBP Executive Programme in Jakarta brought together senior leaders from financial services, consumer goods, manufacturing, telecommunications, and technology to address one of the most pressing questions in digital transformation: how to move artificial intelligence (AI) from experimentation to measurable value. Now in its second year of collaboration with Snowflake’s ecosystem, the programme has established itself as a trusted platform for senior executives to exchange strategies, assess ROI, and explore governance frameworks that ensure AI adoption delivers sustainable impact.

The discussions built on insights from the  AIBP’s From Data to AI: Laying the Groundwork for Indonesia’s Digital Future report, which revealed that more than 80 percent of Indonesian enterprises view AI investment as essential for competitiveness, yet cited barriers such as data quality (81.25 percent), workforce skills gaps (43.75 percent), and data privacy concerns (31.25 percent). Against this backdrop, the expert panel explored how enterprises can address these challenges and unlock AI’s full potential through responsible adoption.

From Experimentation to ROI: Building the Foundations for AI Success

The discussion opened with Rosemary DeAragon, Global Head of Retail and Consumer, Snowflake, who drew on her experience at Walmart and Amazon to emphasise that organisations must carefully evaluate which AI methods are best suited for each business case. She reminded participants that not every use case requires generative AI: “Fraud detection, for example, can be effectively managed with established techniques such as regression or decision trees. The key is selecting the right method and tying it back to measurable value.”

Juan Hasang, Enterprise Solution Architect Manager, Indonesia, AWS, stressed the importance of starting small but impactful: “It does not have to be a major transformation project. Begin with use cases that clearly improve productivity or reduce waiting times, and then scale from there.” He underlined that cost is no longer the primary barrier: with cloud and AI democratisation, organisations can experiment at relatively low entry points, provided their initiatives are anchored to key business objectives.

Adding a consulting perspective, Rio Ricardo, Executive Director of AI and Data, Deloitte, noted the shift in who drives AI conversations within enterprises: “It is no longer just IT. CFOs are now asking how AI can improve efficiency, cut costs, and create tangible business outcomes. This requires moving from small, isolated experiments to enterprise-level adoption supported by governance and measurement frameworks.”

Industry Use Cases: Lessons from the Ground

AI-optimised logistics lifted order fulfilment from 84% to nearly 100%, boosting revenues and market share.
Sami Uddin Ahmad, Chief Strategy and Analytics Officer, XLSmart, shared how his organisation applied AI to optimise distribution routes in telecommunications. “We always start with the problem statement, quantify the impact, and then build the AI solution. ROI must be visible and connected to business outcomes,” he explained.

Real-time data access is empowering CFOs and executives with faster, more reliable decision-making.
Wong Tjin Tjin Tak, Chief Information Officer, Protelindo, highlighted how AI and data are enabling business leaders to gain near real-time visibility of operations. “Our focus is on enabling decision-makers to access data directly, without depending on a small group of specialists. This builds trust and speeds up the adoption of AI-driven insights,” he shared.

Manufacturers are cautiously embedding AI, starting with IT security and predictive maintenance.
Erwin Indrawan, Head of IT, Hino Motors Sales Indonesia, reflected on the company’s deliberate approach. Hino has begun with AI in IT security and is now exploring predictive maintenance through telematics data. “For us, ROI is a critical question. Management wants to see clear financial and operational benefits before scaling. We are building AI literacy alongside data maturity to ensure adoption is sustainable,” he said.

Agriculture enterprises are leveraging data to control costs and predict farmer demand through geospatial analysis.
Robby Setiabudi Madjid, Supply Chain Director, PT Pupuk Indonesia, emphasised that data-driven decision-making is critical for managing raw material costs and anticipating farmer demand. Although direct ROI is complex to measure, he noted that proxies such as efficiency gains and service improvements provide a strong foundation for scaling initiatives.

Data Sharing, Privacy, and Monetisation

A recurring theme was the role of data governance and data sharing in unlocking AI’s value. Rosemary explained how secure data sharing capabilities can flip the ROI equation: “Instead of viewing data platforms as costs, companies can use them to monetise data in a secure and governed way. This transforms the platform into a revenue generator and changes how ROI is evaluated.”

From the consumer goods sector, Dudi Susanto, Head of Process Compliance at Paragon Technology and Innovation, spoke candidly about challenges in balancing compliance and operational needs. He explained that under Indonesia’s Personal Data Protection (PDP) Act, even handling influencer or vendor data required stricter safeguards. “Tax reporting requirements may demand identity data, but at the same time, privacy concerns make sharing difficult. This complexity must be managed with clear governance,” he said.

Parjan Lo, Chief Technology Officer at FKS Group, and Adji, Data Engineer at Paragon Technology and Innovation, both pointed to data quality and access as significant barriers. This echoed findings from AIBP’s ASEAN Enterprise Innovation Market Overview Report 2024/25, where more than three quarters of enterprises across the region cited data quality and availability as the primary obstacles to successful AI implementation. They emphasised that ensuring clean, well governed, and privacy compliant data remains a major challenge, particularly with the enforcement of the PDP Act. They also noted that while organisations recognise the potential of data monetisation, cultural and regulatory hurdles must be addressed to enable responsible collaboration.

The Path Forward: Responsible AI Leadership

In closing, the panel converged on three priorities for enterprises in Indonesia. First, to establish strong data foundations, ensuring quality, governance, and security. Second, to start with high-impact but manageable use cases, building momentum and demonstrating ROI before scaling. Third, to adopt a mindset of responsible innovation, balancing experimentation with accountability and trust.

As Juan Hasang summarised, “AI is already part of our daily lives. The question is no longer if, but when and how organisations will adopt it. Start small, align with business KPIs, and scale responsibly.”

The Snowflake x AIBP Executive Programme underscored that while AI technologies continue to evolve rapidly, sustainable success in Indonesia will depend on trust, collaboration, and the ability to tie AI adoption to measurable outcomes. With the right balance of governance and innovation, Indonesian enterprises are well positioned to lead ASEAN’s next wave of AI-driven growth.