This is where I am coming from: I attended a meeting of the "Digital & Innovation" Committee of the Singapore-German Chamber of Commerce on 22nd of August 2025. Dr. Lovneesh Chanana or the SAP company, the new co-chair of this committee, gave a keynote speech titled "AI in a fragmented world: Rewiring strategy in the Age of Geopolitical Shifts".
When SAP speaks about AI the breadcrumbs in the room are better quiet, and so I kept listening carefully.
The Central Message: Shift from Company-First to Ecosystem-First AI Strategy
Dr. Chanana began by emphasizing a major shift in how companies must approach AI. His core argument was clear:
A company-focused AI strategy is no longer sufficient. Companies must adopt an ecosystem- and context-aware strategy.
This shift is not optional. It is driven by increasing geopolitical pressures, regulatory demands, and divergent national priorities around AI technologies. Dr. Chanana summarized this shift under three imperatives:
- AI has moved from hype to operational integration.
- Traditional "inside-out" strategies are inadequate.
- Ecosystem fragmentation now demands broader strategic thinking.
Put simply, companies must stop building AI tools solely for internal efficiency. Instead, they must account for the environment in which their business operates — including regulations, regional partnerships, and political dynamics.
A Fragmented Geopolitical Landscape
One of the most pressing themes of the keynote was the growing political sensitivity surrounding AI.
AI as a Strategic Asset
Governments no longer treat AI as a neutral tool. It is now viewed as a strategic capability. As a result, companies face:
- Export controls and sanctions on models and infrastructure
- Access restrictions to advanced computing technologies
- Legal requirements for data localization and transparency
For example:
- The United States and China are engaged in a technology race that affects chip exports and AI collaboration.
- Countries such as Australia, Japan, and South Korea are placing limitations on foreign AI tools in sensitive areas.
- Several nations are requiring AI to be trained and deployed using local infrastructure.
These developments have ended the era of a unified, global AI market. The future of AI will be shaped by local politics and regional laws.
Asia's Diverse AI Strategies
Although often viewed as a single region, Asia is not pursuing a uniform AI strategy. Dr. Chanana provided a useful overview of the distinct national approaches:
- South Korea: First in Asia to pass a comprehensive AI law
- Japan: Emphasizes unique principles through its national framework
- Singapore: Takes a community-led, ethics-first approach
- India: Promotes an inclusive model under its "AI for All" strategy
- Philippines: Focuses on infrastructure first, then regulation
These differences further complicate cross-border AI deployment for global businesses.
Practical Implications for Companies
For businesses operating internationally, these regional differences translate into several challenges:
- Technology Sovereignty Pressures: Governments want local control over AI models, data storage, and infrastructure.
- Increased Compliance Burdens: Navigating multiple legal and regulatory frameworks increases operational costs.
- Lack of Regulatory Consistency: What is permitted in one country may be prohibited in another.
The Strategic Consequence of this is that a one-size-fits-all AI strategy will not work. Companies must localize both technology and operations across different regions.
Moving Beyond the Hype: Real Enterprise Value from AI
Despite the complex geopolitical environment, Dr. Chanana reminded the audience that AI continues to offer significant value — especially for companies that move past experimentation and integrate AI into core business functions.
Key Use Cases in the Enterprise
Dr. Chanana shared several examples of how enterprise AI is already transforming operations:
- Manufacturing: AI-based visual systems detect defects and trigger rework orders automatically.
- Human Resources: AI is used to write job descriptions, filter candidates, and create personalized interview questions.
- Finance: Generative AI tools help analyze statements, run forecasts, and summarize key insights through conversational interfaces.
These applications show how AI can reduce manual workloads and enable faster, more accurate decision-making across industries.
Rethinking AI: From a Tool to a Strategic Business Layer
A significant point raised was the need to distinguish between consumer AI and enterprise AI.
| Consumer AI | Enterprise AI |
|---|---|
| Easy to adopt | Requires integration |
| Boosts individual productivity | Enhances business systems |
| Low entry cost | High impact, long-term ROI |
Dr. Chanana emphasized that AI must not be treated as a standalone function. It should be embedded across core systems — including logistics, finance, operations, and compliance.
Turning Insight into Action: Building an Ecosystem-Aware AI Strategy
Dr. Chanana closed his keynote with a strategic framework for how organizations can move forward effectively.
A Garden Analogy for AI Strategy:
- Prepare the soil – Build clean, well-structured data foundations
- Plant the seeds – Launch pilot projects that test value
- Nurture the growth – Scale in controlled environments
- Harvest the results – Realize ROI through enterprise transformation
However, he also noted that traditional return-on-investment models may not fully capture AI's long-term value, which includes resilience, agility, and future readiness.
Actionable Recommendations
Dr. Chanana offered several concrete next steps for companies:
- Align AI initiatives with core business needs, not just technology trends
- Invest in quality data infrastructure — poor data leads to poor AI
- Reskill teams for cross-functional collaboration and AI fluency
- Engage proactively with regulators — influence policies before they become constraints
- Partner with startups, academia, and global platforms to speed up innovation
Final Thoughts
Dr. Chanana's presentation delivered a powerful message. Companies must stop treating AI as an internal tool and start treating it as a strategic response to an evolving global environment.
The next phase of AI strategy is not just about building smarter systems. It is about adapting to geopolitical complexity, working across ecosystems, and building the future your organization will operate in.
Now is the time to move beyond pilots and hype. I am convinced that the companies that succeed will be those that embed AI deeply, govern it wisely, and scale it purposefully.
Martin "AI" Schweiger
IP Lawyer Tools by Martin Schweiger
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
