Generative AI is taking the world by storm, and many companies are already investing in AI for marketing. But even though many marketers in APAC intend to use AI, preparation, use, and investment are still in the early stages.
According to WE Communications' latest Brands in Motion report, only 46% of companies are investing in upskilling their employees with AI learning and programs, highlighting the gap between vision and action. In fact, only 26% of respondents in Singapore said their company encourages personal experimentation and the adoption of AI in work and personal use cases.
Similarly, a 2024 Forrester study titled “The State of Generative AI in Marketing in Asia-Pacific” found that most brands in APAC are still in the exploration stage of applying Gen AI to marketing..
The survey found that agencies are more advanced, with 46% already using Gen AI, compared to 24% of brand marketers. moreover,Marketing Gen AI investment remains low in APAC. Brands and agencies typically spend less than $50,000 on Gen AI in the past 12 months.
According to a McKinsey article titled, “Generative AI Reset,” this is why a Gen AI reset is needed as brands find the potential value of Gen AI harder than expected and their initial enthusiasm for the new technology paves the way for a rethink and recalibration: Rewiring to turn potential into value in 2024.”
First
First, industry insiders MARKETING-INTERACTIVE spoke to believe brands should consider how to measure the return on investment of AI.
Vincent Kang, Head of Digital and GBA at PHD Hong Kong, said: said cCompanies and brands should measure themselves against key performance indicators set based on the goals they want to achieve with AI, such as customer acquisition, retention, streamlining business and marketing operations, and achieving positive results like increased sales.
“Although AI adoption requires a learning curve, it is important to continually monitor and evaluate the cost-benefit ratio to understand the overall return on investment,” Kan added. Ta.
We have also witnessed success in clients leveraging LLM for social buzz monitoring and insight generation in marketing planning and performance marketing.
“Clients are leveraging AI-driven solutions with first-party data to enhance new customer acquisition, and agencies are adopting AI internally for faster, more intelligent reporting,” he said. added.
Nathan Petralia, former managing director of Merkle Hong Kong, echoed his thoughts, saying that by continuously improving AI models, optimizing processes, and leveraging AI-driven insights, companies can improve operational efficiency, improve customer He said it can improve satisfaction and overall performance.
However, it is important to note that specific results may vary depending on industry, use case, and effectiveness of AI implementation.
What else should companies invest in to get the best results with AI?
Many people tend to calculate the value of AI in terms of the human time it saves on existing tasks. But that provides an incomplete picture and creates the risk of treating technology only as a means to cut costs and reduce headcount, Ruben says. Schreurs, Chief Strategy Officer, Ebiquity.
“Existing staff can significantly improve the quality of their deliverables, but this is often overlooked as a financial benefit beyond simple cost savings,” he added.
In addition to skilled AI engineers and machine models, Petralia said companies should also look to invest in data infrastructure, including: Data storage, processing power, data governance, and data cleaning, validation, and augmentation techniques must be employed to ensure that the data used for AI is accurate and representative.
Companies should also invest in ethical and responsible AI practices, such as 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 continuous model training, monitoring, and updating. “This ensures that the AI model remains up-to-date, accurate, and effective in delivering the desired results,” he said.
In terms of talent development, PHD HK's Kang said that in addition to investing in technically-minded talent, companies should provide comprehensive training at all levels of the organization to deepen understanding of the process and value of AI applications.
Moreover, he added that it is essential to create channels where cross-functional teams, especially frontline teams, can provide feedback on AI implementation from a business perspective.
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