As the Asia Pacific region emerges as a powerhouse for AI innovation, few are better positioned to guide us through this transformation than Laurent Giraid, a seasoned technologist with deep expertise in artificial intelligence. With a focus on machine learning, natural language processing, and the ethical dimensions of AI, Laurent has been at the forefront of integrating cutting-edge technology into real-world business solutions. In this interview, we explore how strategic partnerships are shaping AI adoption, the challenges of scaling pilot projects, the critical role of leadership, and the importance of human-AI collaboration in redefining work across the region.
How did strategic partnerships in the Asia Pacific region become a catalyst for advancing AI adoption among businesses?
Strategic partnerships have been a game-changer for AI adoption in the Asia Pacific. By teaming up with global leaders in AI technology, local companies gain access to cutting-edge tools and expertise that might otherwise be out of reach. For instance, collaborations allow for tailored solutions that address the unique needs of businesses in this diverse region. It’s about combining global innovation with local know-how to create impactful results, whether through executive training, custom applications, or embedding AI into daily operations. This kind of synergy helps companies move beyond just experimenting with AI to actually seeing measurable outcomes.
What are some of the key challenges businesses face when trying to scale AI pilot projects into full-fledged implementations?
One of the biggest hurdles is that many organizations approach AI as a shiny new gadget rather than a fundamental shift in how they operate. Without a clear vision from leadership, redesigned workflows, or investment in workforce skills, pilots often stall. There’s also a tendency to underestimate the cultural and operational adjustments needed—AI can’t just be plugged in; it requires rethinking processes and ensuring people are equipped to use it. Addressing these gaps is critical to turning small-scale experiments into enterprise-wide impact.
In what ways can AI be seen as a business transformation rather than just a technological upgrade?
AI isn’t just about acquiring a tool; it’s about reimagining how a business functions from the ground up. This means aligning AI with strategic goals, not just tech objectives. For example, it’s about changing how teams collaborate, how decisions are made, and how value is delivered to customers. True transformation happens when AI is woven into the fabric of the organization, supported by a mindset shift that prioritizes long-term impact over short-term fixes. It’s a holistic change, touching everything from culture to operations.
How crucial is the role of leadership in driving successful AI strategies within organizations?
Leadership is absolutely pivotal. When executives and boards treat AI as a strategic priority rather than a side project, it sets the tone for the entire organization. They need to define clear outcomes, establish risk boundaries, and ensure accountability. Without that top-down clarity, AI initiatives can become fragmented or lose momentum. Leaders also play a key role in fostering a culture that embraces change, which is essential for adoption. Their vision turns AI from a tech experiment into a core business capability.
Can you explain the concept of a ‘human-in-command’ approach and its significance in workplace AI integration?
The ‘human-in-command’ approach is about designing work so that people remain at the center of decision-making while AI handles repetitive or data-heavy tasks. Think of humans focusing on judgment, creativity, and handling exceptions, while AI takes care of things like drafting, summarizing, or retrieving information. The significance lies in maintaining transparency and accountability—audit trails and source links ensure trust in AI outputs. This balance not only boosts efficiency but also elevates human talent by freeing up time for higher-value work.
What kind of productivity improvements have you observed when AI tools are effectively integrated into professional workflows?
The productivity gains can be striking. In various workshops and programs, I’ve seen professionals reclaim one to two hours a day by using AI for routine tasks. Research backs this up—studies show significant boosts, especially for less-experienced staff, in areas like customer service. For instance, contact center agents have reported up to a 14% increase in efficiency. These improvements aren’t just about speed; they often come with better quality outcomes as AI helps refine outputs that humans can then build upon.
How do you see governance playing a role in building trust and accelerating AI adoption in businesses?
Governance is the backbone of trust in AI systems. When it’s done right—not just as a checkbox exercise but integrated into daily work—it reassures teams that AI outputs are reliable and aligned with policies. This means using approved data sources, enforcing role-based access, maintaining clear audit trails, and ensuring human oversight for sensitive decisions. Good governance doesn’t slow things down; it actually speeds up adoption because people feel confident in what they’re implementing. Trust is what turns hesitation into action.
Given the cultural and linguistic diversity of the Asia Pacific, how do you tailor AI solutions to fit local contexts while maintaining scalability?
The diversity in Asia Pacific means a one-size-fits-all approach just doesn’t cut it. The key is to start locally—build solutions that respect local languages, workflows, policies, and escalation paths. Once you’ve proven value in a specific context, you can standardize elements like governance frameworks or data connectors for broader scalability. This build-local, scale-deliberate strategy ensures AI feels relevant to local teams while still being efficient to roll out region-wide. It’s about balancing customization with consistency.
What skills do you believe are most essential for professionals and leaders in an AI-enabled workplace?
Skills are the real driver of AI success, more so than the tools themselves. For leaders, it’s about literacy—knowing how to set outcomes, define guardrails, and decide when to scale. For teams, it’s about workflow design, understanding how to hand off tasks between humans and AI, and mastering hands-on skills like crafting effective prompts or verifying AI outputs from trusted sources. When everyone shares this foundation, adoption moves from sporadic experiments to consistent, production-level results.
Looking ahead, what is your forecast for the future of AI in transforming key industries across the Asia Pacific region?
I’m optimistic about the next five years. I foresee AI moving beyond basic assistance to full execution in critical functions like software development, marketing, supply chain management, and customer experience. We’ll see tailored solutions, like policy-aware assistants in finance or personalized yet compliant systems in retail, all built with human oversight and verifiable sources. The potential for transformation is huge, especially as industries leverage AI to address specific pain points and unlock new opportunities. If done responsibly, AI could redefine competitiveness in the region.
