Recently, I added my voice to the Marketing Interactive article on the state of generative AI in APAC marketing. The piece explored how businesses in the region are adopting AI, the challenges they’re facing, and what needs to change for companies to realize its full potential. As someone who has worked extensively with AI-driven marketing strategies, I wanted to share my perspective on the insights from the article and expand on the importance of a balanced, strategic approach to AI adoption.
A Snapshot of AI in APAC Marketing
Generative AI is undoubtedly transformative, but in the APAC region, most companies are still in the early exploration phase. According to WE Communications’ “Brands in Motion” report, only 46% of businesses are investing in upskilling employees to use AI, and a mere 26% in Singapore are actively encouraging experimentation with AI for work and personal use.
Even though agencies are slightly ahead—46% of agencies are using generative AI compared to 24% of brand marketers—overall investment is still low. The 2024 Forrester survey found that most brands and agencies spent less than USD $50,000 on AI in the past year. This slow adoption has led to what McKinsey calls a “generative AI reset”, as companies recalibrate their expectations and realize that unlocking AI’s value is more challenging than it initially seemed.
My Perspective on Generative AI Adoption
In my comments for the article, I emphasized that adopting generative AI is not just about acquiring the technology—it’s about building the ecosystem around it. Here’s what I believe companies must prioritize to maximize AI’s potential:
1. Invest in Data Infrastructure
AI is only as good as the data it processes. Companies need to invest in data storage, processing power, and governance to ensure they have the foundation to support advanced AI models. Data cleaning, validation, and augmentation are critical for ensuring the accuracy and representativeness of the data. Without this, AI outputs risk being unreliable or biased.
2. Ethical and Responsible AI Practices
As AI becomes more integrated into decision-making processes, businesses need to prioritize ethical AI practices. This includes ensuring fairness, transparency, and accountability in AI algorithms. Ethical practices not only build trust but also reduce the risk of unintended consequences, such as biased recommendations or unfair customer targeting.
3. Continuous Model Training and Monitoring
AI models are not static—they need ongoing updates and refinements to remain effective. Businesses should invest in processes and resources to monitor and retrain their models regularly, ensuring they stay accurate and aligned with business goals.
4. Focus Beyond Cost Savings
One common mistake is treating AI as a cost-cutting tool. While AI can save time and resources, its real value lies in enhancing creativity, improving decision-making, and delivering better customer experiences. By focusing only on cost reductions, companies risk missing out on its transformative potential.
Key Expert Insights
Other experts featured in the article echoed similar themes. For instance, Vincent Kan of PHD Hong Kong highlighted the importance of setting clear objectives and measuring AI’s ROI through key performance indicators (KPIs), such as customer acquisition or operational efficiency improvements. Ruben Schreurs of Ebiquity warned against viewing AI solely as a way to save on human-hours, pointing out that improving the quality of outputs is often overlooked but equally critical.
What Needs to Change?
To accelerate AI adoption and success in APAC marketing, I believe companies need to take these steps:
- Upskill Teams Across All Levels: AI is not just for data scientists or IT teams. Everyone, from marketers to C-suite leaders, should understand its applications and value.
- Start Small, Scale Fast: Pilot projects focused on specific goals (e.g., content generation or customer segmentation) can help build confidence and demonstrate value.
- Foster Collaboration: Businesses should create channels for cross-functional teams to provide feedback on AI implementation. This ensures that AI strategies are practical and aligned with business needs.
- Think Long-Term: AI adoption is a journey, not a quick fix. Companies must be prepared to invest in the technology, people, and processes needed to sustain it over time.
Additional Thoughts
Beyond the article, I believe there’s room to expand the discussion with:
- Case Studies: Highlighting successful AI implementations by APAC companies to inspire and guide others.
- Future Trends in AI: Exploring emerging applications like hyper-personalized marketing, predictive analytics, and real-time customer engagement.
- Overcoming Barriers: Addressing challenges like cultural resistance, budget constraints, and data privacy concerns.
Final Thoughts
Generative AI is no longer a futuristic concept—it’s here, and it’s transforming how businesses operate. However, as highlighted in the article and my own experiences, success with AI depends on more than just adopting the latest tools. It requires a thoughtful, ethical, and data-driven approach, combined with continuous investment in skills and infrastructure.
The APAC region has immense potential to lead in AI innovation, but it’s clear there’s still work to be done. By prioritizing long-term strategies and focusing on value creation rather than just cost-cutting, companies can unlock the full potential of generative AI and achieve transformative results.
For the full article and additional insights, visit Marketing Interactive.
