How brands can yield better results during their exploration phase of AI

Generative AI has taken the globe by storm and more companies are already investing in it for marketing. However, despite many of the APAC marketers intending to use AI, their readiness, usage and investment are still in the early stages.  

According to WE Communications’s latest ‘brands in motion’ report, only 46% of companies are investing to upskill their employees in AI learning and programmes, revealing a vision-action gap. In fact, only 26% of respondents in Singapore say their companies are encouraging personal experimentation and the adoption of AI for work and personal use cases. 

Similarly, in APAC, most brands are still in the exploration phase of applying Gen AI in marketing, according to a 2024 Forrester survey titled “The State of Generative AI For Marketing in Asia Pacific”.  

The survey found that agencies are more advanced, with 46% already using Gen AI compared to 24% of brand marketers. Furthermore, marketing’s Gen AI investment is still low in APAC. Brands and agencies typically spent less than US$50,000 on Gen AI in the past 12 months. 

That's why a Gen AI reset is needed, as the initial passion for the new technology is paving way to second thoughts and recalibrations as brands realise that capturing Gen AI’s potential value is harder than expected, according to a Mckinsey article named “A generative AI reset: Rewiring to turn potential into value in 2024.” 

First things first

To begin with, industry players MARKETING-INTERACTIVE spoke to believed that brands should look at how they can even start to measure the return on investment of AI.  

Vincent Kan, head of digital and GBA practice for PHD Hong Kong, said companies and brands should measure themselves against key performance indicators set based on the objectives they aim for AI to achieve, including improving efficiency in customer acquisition, retention, business and marketing operations, or achieving positive outcomes such as increased sales.  

“While adopting AI may involve a learning curve, continuous monitoring and evaluating the cost-benefit ratio are crucial to understand the overall return on investment,” Kan added. 

He has also witnessed successful cases where clients leverage LLM for social buzz monitoring and insight generation in marketing planning, performance marketing. 

“Clients utilise AI-driven solutions with first-party data to enhance new customer acquisitions, and agencies internally employ AI for faster and more intelligent reporting,” he added.  

Echoing his thoughts was Nathan Petralia, former managing director, Merkle Hong Kong, who said by continuously improving AI models, optimising processes, and leveraging AI-driven insights, companies can enhance operational efficiency, customer satisfaction, and overall business performance.  

However, it is important to note that the specific results may vary depending on the industry, use case, and the effectiveness of AI implementation. 

What else should companies invest in to yield the best results in AI? 

Sometimes, the instinct for many people is to calculate the value of AI in terms of human-hours saved on existing tasks. However, it provides an incomplete picture and creates the risk of treating the technology purely as a way to save costs and initiate redundancies, said Ruben Schreurs, chief strategy officer, Ebiquity. 

“Existing staff can improve the quality of their output significantly, and this is often overlooked as a financial benefit, beyond the mere cost saving,” he added. 

Beyond skilled AI technicians and machine models, Petralia said companies should also look to invest in data infrastructure, including data storage, processing capabilities, and data governance. Additionally, data cleaning, validation, and augmentation techniques should be employed to ensure the data used for AI is accurate and representative. 

Companies should also invest in ethical and responsible AI practices, including ensuring fairness, transparency, and accountability in AI algorithms and decision-making processes, he added. 

He added that companies should invest in processes and resources for ongoing model training, monitoring, and updating. “This ensures that AI models stay up-to-date, accurate, and effective in delivering the desired outcomes,” he said. 

In terms of talent development, PHD HK’s Kan said in addition to investing in tech-focused individuals, companies should offer comprehensive training across all levels of the organisation to enhance understanding of the AI application's processes and value. 

Furthermore, creating channels for cross-functional teams, especially those on the frontline, to provide feedback on AI implementation from a business perspective is essential, he added. 

Join us this coming 26 June for Content360 Hong Kong, a one-day-two-streams extravaganza under the theme of "Content that captivates". Get together with our fellow marketers to learn about AI in content creation, integration of content with commerce and cross-border targeting, and find the recipe for success within the content marketing world! 

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