On December 11th, I had the privilege of moderating the Momentum AI Singapore 2024 panel discussion, “Assessing AI: Sorting the Myths from Reality,” held during this year’s Momentum AI Conference, organized by Thomson Reuters. 

As the Founder and CEO of AI Business Asia, it was an absolute delight to lead a conversation that dived into the transformative potential and current realities of AI in enterprise settings.

Panelists included Prerit Mishra, Head of Data & Analytics at DHL; Miao Song, Global Chief Information Officer at GLP; and Jason Tamara Widjaja, Executive Director of AI Singapore Tech Center, MSD. Their insights offered a compelling blend of strategic foresight, real-world applications, and reflections on the challenges and opportunities AI presents.

The session brought together industry leaders and five topics that were prevalent throughout the discussion on the day: 

• Enterprise AI adoption
• AI governance and ethics
• Data quality and infrastructure
• Emerging AI trends
• Regional and global perspectives

I opened the discussion by addressing the “deafening hype” surrounding AI over the past 18 months. I then asked, “Where exactly are we with enterprise deployments now, and where are we heading?” This question framed a candid exploration of the gaps between expectations and reality.

Miao Song reflected on GLP’s journey, stating, “AI is a tool to solve business problems, not an end in itself. While Generative AI (GenAI) has created immense enthusiasm, its adoption has required balancing expectations with clear, scalable use cases.” She shared examples, including the automation of GLP’s contract management processes using AI for cognitive search and summarization, which significantly reduced manual workloads and improved accuracy.

Let’s look at some of my favorite moments from the discussion with the panelists:

The Impact of Generative AI on Industries

When asked about the role of GenAI in logistics, Prerit Mishra shared DHL’s pragmatic approach: “Logistics is a data-heavy and operationally intensive industry. Traditional AI remains critical for applications like routing optimization and demand forecasting. However, GenAI has transformed customer service through chatbots and text-to-insights tools, enabling faster responses and improved client interactions.”

He emphasized that the logistics industry benefits significantly from AI in route optimization and predictive maintenance but noted that broader enterprise adoption requires alignment between technical teams and business leaders.

Prerit also reflected on DHL’s journey, stating, “We’ve seen immense promise in AI, but the reality often comes down to integration challenges. While AI tools are increasingly sophisticated, they are only as good as the data ecosystems and processes they are embedded within.” Jason Tamara highlighted GenAI’s transformative potential in biopharma: “We’re seeing large language models (LLMs) accelerate drug discovery processes, optimize clinical trial designs, and enhance manufacturing workflows. The pre-trained nature of these models allows us to fine-tune them for specific applications, reducing time-to-market for new therapies.”

Distinguishing Substance from Hype

A recurring theme was how enterprises differentiate genuine AI capabilities from inflated marketing claims. Miao Song shared, “One of the biggest misconceptions I encounter is that AI will solve all problems out of the box. The reality is that AI requires careful calibration, continuous learning, and human oversight. At GLP, we rigorously pilot solutions in isolated environments before scaling.”

Jason Tamara Widjaja added, “It’s critical to have internal AI literacy at all organizational levels. This prevents us from falling for buzzwords and ensures that investments are directed toward tools that align with our long-term strategic goals.”

Governance Challenges in AI

The panelists emphasized the importance of data quality and governance in AI’s successful adoption. Miao stated, “Every AI project is a data project. Without solving data engineering and quality issues, scalable results are unattainable.”

Prerit added, “AI adoption also requires a cultural shift. At DHL, we’ve invested in training programs to upskill employees—from technical experts to business leaders—ensuring alignment on AI’s strategic value.”

Jason discussed AI governance, noting, “In regulated industries like biopharma, robust governance frameworks are essential. We’ve established clear protocols to ensure AI use aligns with safety, security, and ethical standards.”

He highlighted the intersection of AI and governance, particularly in China’s regulatory environment: “China’s emphasis on purpose-built models and detailed regulations fosters innovation while maintaining safety. This approach provides a blueprint for global AI adoption.” 

“Rapid advancements in AI models are forcing us to rethink long-term investments. For example, generative AI is opening new avenues in content creation and knowledge management. However, its transformative potential also brings regulatory and ethical considerations that require careful navigation,” added Jason.

Debunking the Misconception Around AI Usage

As AI evolves from narrow applications to more sophisticated systems, enterprises face strategic planning challenges. One of the biggest misconceptions regarding the usage of AI is that it can replace human strategic thinking.

Miao flipped this common misconception on its head, saying, “AI isn’t magic. It’s a tool. Many executives mistakenly believe it can replace strategic decision-making. In reality, AI’s role is to enhance human capabilities, not eliminate them.”

AI tools can help executives avoid biases in decisions, pull insights out of oceans of data, and make strategic choices more quickly. And that’s just the beginning

Prerit also highlighted, “In logistics, AI has transformed operations. Predictive analytics in maintenance, for instance, has reduced downtime by 20%. However, areas like customer experience remain more complex, as personalization demands a nuanced understanding of consumer behavior.”

Prerit echoed this sentiment, noting, “The pace of innovation necessitates agility. Businesses must not only anticipate the opportunities AI presents but also the risks of misalignment with operational goals.”

Emerging Trends and the Future of AI

Looking ahead, Miao shared her optimism about “agentic workflows”—integrated systems combining GenAI, RPA, and machine learning to automate complex processes. She gave an example of GLP’s utility bill processing workflow, which uses AI to extract, structure, and analyze data in real time, saving significant manpower and enabling predictive analytics.

Prerit underscored the potential of GenAI in augmenting human capabilities: “We’re moving beyond chatbots to AI-driven insights that push information to users, enabling faster and more informed decision-making.”

Jason concluded, “What excites me most is AI’s role in democratizing access to knowledge. Tools like generative AI have the power to bridge gaps in education and training, empowering individuals and organizations alike.”

Key Takeaways

The panel concluded with a unified vision for the future of AI:

• Miao Song: “AI’s role is to assist, not replace. Its value lies in enhancing human capabilities and driving business efficiency.”
• Prerit Mishra: “Adoption requires cautious optimism. By managing expectations and focusing on practical applications, businesses can unlock AI’s full potential.”
• Jason Tamara: “The intersection of technology, governance, and human-centric design will define AI’s success in enterprise settings.”

The session left the audience with actionable insights and a clear message: while AI’s hype is undeniable, its real value lies in thoughtful, ethical, and strategic implementation. As businesses prepare for 2025, the focus will remain on using AI to drive innovation, efficiency, and sustainable growth.