Companies must undergo organisational surgery to capture the value of generative AI (gen AI),Upskilling talent, forming centralised teams, and ensuring data quality are crucial for success,Focusing on specific use cases and responsible scaling will drive a competitive advantage.
Introduction: The Gen AI Reset
The time for a generative AI (gen AI) reset is upon us. As initial enthusiasm gives way to recalibration, companies must learn from past digital and AI transformations to build organisational and technological capabilities that drive innovation at scale. With 2024 poised to be the year gen AI proves its worth, firms should focus on rewiring their businesses for distributed innovation.
The Path to Success: Organisational Surgery and Scaling
Companies eager to achieve early wins with gen AI must act quickly, but recognise that the process requires significant organisational change. A Pacific region telecommunications company exemplifies this approach, hiring a chief data and AI officer to drive innovation and implementing cross-functional product teams to develop a gen AI tool for home servicing and maintenance.
In this article, we explore the capabilities necessary for implementing a successful gen AI program at scale, drawing from our experiences and lessons learned.
Finding Your Edge: Gen AI Copilots and Competitive Advantage
To gain a competitive edge, companies must identify where gen AI copilots can enhance their priority programs:
Understand the difference between being a "taker," "shaper," or "maker" of gen AI technology,Focus on productivity improvements as a "taker" while building "shaper" applications for competitive advantage,Target domains where copilot technology can have the most significant impact
Upskilling and Talent Acquisition: Gen AI-Specific Skills
Upskill your workforce with gen AI-specific skills, such as model fine-tuning and prompt engineering, and consider hiring experienced senior engineers to accelerate your efforts. Key practices for building capabilities include apprenticeship, training, and fostering communities of practitioners. For a deeper dive into the skills required, consider exploring what every worker needs to answer: What Is Your Non-Machine Premium?.
Centralised Teams and Responsible Scaling
Establish a centralised team to develop protocols and standards, enabling responsible scaling across the organisation. This team should focus on:
Procuring models and prescribing access methods,Developing data readiness standards,Setting up approved prompt libraries,Allocating resources
Technology Architecture and Scaling
Building a gen AI model is just the beginning; making it operational at scale is the ultimate goal. Focus on three core decisions to simplify and speed up processes:
Reuse technology and create a source for approved tools, code, and components,Enable efficient connections between gen AI models and internal systems,Prioritise testing and quality assurance capabilities
Data Quality and Unstructured Data
Harness the power of your data to fuel gen AI models:
Improve data quality and augmentation efforts targeted at specific AI/gen AI applications,Unlock value from unstructured data by mapping valuable sources and establishing metadata tagging standards,Optimise data infrastructure to lower costs at scale. The importance of this is highlighted in discussions around how AI recalibrated the value of data.
Conclusion: Embracing the Gen AI Revolution
As the gen AI landscape evolves rapidly, companies must adapt and learn to capture its value. By focusing on specific use cases, upskilling talent, and driving responsible innovation, organisations can rewire their businesses for long-term success. This aligns with broader trends discussed in APAC AI in 2026: 4 Trends You Need To Know, emphasizing the region's focus on AI adoption. For insights into executive perspectives on generative AI, consider this report on executives treading carefully on generative AI adoption.
Comment and Share On Generative AI's Potential in Asia
How is your organisation preparing to embrace the generative AI revolution, and what steps are you taking to ensure you stay ahead of the competition? Share your thoughts in the comments below.
Latest Comments (3)
The mentioned focus on “organizational surgery” and internal upskilling for generative AI, while pragmatic for competitive firms, risks deepening the divide for smaller enterprises, particularly in the Global South, that lack such resources. We need broader, more accessible frameworks for AI integration.
Agree on the organizational surgery part. At Tokopedia, setting up dedicated AI teams for specific e-commerce functions, like fraud detection, was key. Just hiring a CDO isn't enough; you need the cross-functional teams to really make these tools useful for daily operations here in Indonesia. The infrastructure for these models is still a big thing to manage though.
interesting point about the "taker, shaper, or maker" framework. for a fintech in HK, the regulatory landscape often forces us more into the "taker" role, relying on established vendors. how does that impact the ability to build truly differentiated "shaper" applications long-term, especially with data residency and compliance overheads?
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