Global Companies and a New Era of Growth | Global Investors' Symposium Hong Kong 2025

Core Strategic Focus:

• Panelists identify Artificial Intelligence (AI) as a foundational enabler rather than the sole driver, emphasizing that successful adoption requires robust data infrastructure first.
• Consumers in Southeast Asia are driving a "leapfrog" adoption of technology, bypassing legacy systems (e.g., physical check-ins, cash payments) in favor of mobile-first solutions.
• Bombardier is leveraging AI to accelerate aircraft product development, aiming to reduce the multi-year research and certification timeline to gain a competitive first-mover advantage.

Data & Infrastructure Fundamentals:

• Hans (IBM): Identifies five fundamental enterprise challenges: exploding data volume, heterogeneous infrastructure environments, lack of automation, skills shortages, and resulting security vulnerabilities.
• Tony (AirAsia): States AI effectiveness is directly dependent on data quality; the airline is prioritizing data cleaning and governance before scaling AI applications.
• Eric (Bombardier): Highlights the complexity of managing data across 30–35 aircraft models, each containing 50,000–100,000 unique parts, necessitating automated forecasting over manual Excel processes.

Specific AI Use Cases & Outcomes:

• AirAsia Operations:
  • Targeting 3–4% fuel savings through predictive analytics, which could amount to hundreds of millions in cost reduction.
  • Implementing predictive maintenance to anticipate part failures before they occur, improving aircraft availability.
  • Planning to enhance on-time performance by integrating weather prediction models.
• Bombardier Logistics:
  • Achieved a 93% success rate in delivering spare parts to customers within 24 hours globally, setting an industry benchmark.
  • Using connected airplane health monitoring to dispatch technicians with required parts prior to landing, minimizing downtime.
  • Utilizing digital twins to simulate optimal assembly sequences when supply chain parts are unavailable.
• Enterprise & Sales:
  • AirAsia is cleaning up historical complaint data to train chatbots for improved customer interaction.
  • Bombardier employs AI algorithms to analyze customer data and guide sales teams in identifying high-probability prospects for aircraft purchases.

Regulatory & Certification Challenges:

• Certification Complexity: Aircraft software lines of code have grown from ~300,000 (20 years ago) to ~100 million (current flagship models), significantly complicating certification with authorities like the FAA and EASA.
• Strategy: Partners must collaborate proactively with regulators to validate AI-driven testing and certification processes, avoiding the delays seen by industry peers between 2008–2012.

Risk Management & Vendor Selection:

• Hype vs. Reality: AirAsia notes a pattern of "over-promising and under-delivering" by major AI vendors, citing delays in expected capabilities as a reason for a cautious, "baby steps" approach.
• Data Sovereignty: Panelists advocate for enterprises to retain control of their data and models rather than relying solely on closed public models, citing the need for 100% accuracy in safety-critical industries.
• Architectural Requirements: Success depends on three pillars: flexibility (portability of data/apps), openness (interoperability across ecosystems/models), and resiliency (ability to migrate infrastructure amidst geopolitical shifts).

Workforce & Talent Dynamics:

• Cultural Pivot: The primary implementation challenge is organizational culture, requiring a shift where human staff "delegate" tasks to AI while retaining oversight of "black box" decisions.
• Talent Availability: AirAsia remains unconcerned about talent shortages in Southeast Asia, attributing success to inclusive hiring (e.g., pioneering female pilots) and strong internal training; Bombardier notes a generational shift in engineers' comfort with delegating tasks to machines.
• Human Element: Emphasis remains that human resilience and spirit were critical during the pandemic and will continue to complement AI, rather than be replaced by it.

Forward-Looking Statements:

• Asia-Specific Growth: Technology adoption in Asia is expected to outpace the West due to higher consumer readiness, enabling faster cost reductions and friction removal (e.g., barcode-based immigration).
• Individualization: The future of enterprise AI involves horizontal integration and individualized solutions, moving beyond generic services to tailored models for specific clients.
• Defense & Geopolitics: Bombardier anticipates increased demand for rapid technology adaptation in defense sectors driven by geopolitical tensions and a desire for national autonomy.

Key Decisions & Directives:

• Enterprises must prioritize foundational data management over immediate AI application to avoid wasted investment.
• Organizations should adopt a "less is more" project strategy, focusing on high-impact, well-briefed use cases rather than broad experimentation.
• Companies must maintain internal control over core processes and data governance while selectively engaging third-party partners.