Campaign Asia-Pacific teamed up with Forrester to understand how marketers and agency professionals are applying AI in their work now and plan to in the future. The final results feature over 158 senior level respondents across the region, largely from marketing and advertising agencies and brands. The study focused on larger enterprises with more than 100 employees, including multi-national agencies, non-for-profit companies and central government or public sector entities. In a three-part series, Campaign unpacks the results of the survey, its key findings, and speaks to experts on the challenges of investing in, adopting and utilizing Gen AI.
Part One: Investing in AI
In the swiftly transforming landscape of technology today, brands are finding themselves perched on the edge of a supposed contemporary gold rush: The all-encompassing realm of Generative Artificial Intelligence (AI). Since 2020, it seems the rise of Gen AI has sparked a revolution of sorts—enabling consumers to unlock a whole new perspective on the everyday value of machine learning on their lives, as well as offering businesses an unprecedented opportunity to innovate and streamline their operations.
This modern equivalent to a promising yet uncertain treasure mine draws parallels with the early days of cybersecurity and information and cloud computing technology. Similar to those earlier industrial waves, AI's potential may be enormous, but demands a profound understanding, specialized skills, and the necessary infrastructure to explore its vast territories both effectively and securely.
As such, whilst praise has boomed for popular Gen AI conversational bots such as ChatGPT, Bard, Jasper Chat, Bing AI and others in the free-to-use consumer arena, a palpable vacillation seems to remain amongst brands when it comes to fully embracing this new way of artificial intelligence.
A recent Forrester and Campaign Asia survey of more than 150 brands, advertising and agency respondents across the Asia-Pacific region found that industry professionals are still hesitant to truly part with their hard-earned profits and invest in the hype—and when they do, are only really willing to spend on content generation at this point in time.
In fact, of the surveyed sample, only 4% of brands and advertisers across APAC said they'd anticipate more than a 100% increase in investment across their marketing organizations on Generative AI. The highest, 21%, indicated a 21% to 30% increase in investment, whilst 14% said they expect to increase investment between 11% to 20%.
The figures showed little improvement when it came to marketing agencies. Only 4% expected an increase of more than 100%, while 21% of respondents indicated they'd be anticipating investment to go up between 1% to 10% or 11% to 20%. Only 3% of those surveyed expected to increase spend on Gen AI by up to 50%. Combined, 42% of all respondents said that current spend by their marketing functions on Gen AI was anticipated to be US$50,000 and under for the next 12 months. Only 2% said they anticipate to spend more than US$10 million or more.
So, what factors are contributing to the persistently pessimistic investment outlook for brands and agencies, and preventing them from embracing the supposed gold rush of the Generative AI revolution beyond mere hype?
Finding the business case for Gen AI
At the heart of the hesitation is the conundrum of defining a clear business case for Gen AI. While the technology showcases remarkable capabilities in generating new content, simulating human-like interactions, and predicting outcomes, its application is far from universal. This creates perplexity for organizations trying to decipher its integration into their unique business models. Adding to the complexity: There's a myriad of artificial intelligence types, each tailored for specific tasks, spanning machine learning, natural language processing, computer vision, and expert systems. This diverse landscape adds to the confusion, especially as generative AI takes centre stage in current discussions, overshadowing the broader spectrum of AI benefits.
For businesses, particularly those whose core operations are not centred around technology such as those in the Fast-Moving Consumer Goods (FMCG) sector, they face unique challenges when considering spend in generative AI. For example, an FMCG company might struggle to see how generative AI could improve the formulation of its products or enhance the physical distribution networks to reach more customers, which ultimately is central to its success.
Similarly, services-led businesses such as marketing or advertising agencies might also find limited applications for Gen AI that significantly outperform existing processes, or justify the cost and effort of implementation. Especially when the uniqueness of their people’s skillsets and talents are at the core of their distinctive business value proposition.
"Many companies are grappling with the ambiguity surrounding AI,” says Leon Cooper, partner and chief technology officer of Australian advisory and investments firm, Sayers Advisory. “The major hurdle lies in a pervasive lack of understanding regarding the array of tools, potential benefits, and risks associated with AI, contributing to substantial confusion about its business value and the crucial prerequisites for developing capabilities.”
A former Accenture and PwC technology senior executive, Cooper says the reluctance to allocate substantive budgets to generative AI is not surprising, given so many in the industry are still trying to unpack how the technology actually contributes to the ultimate goal for most businesses and agencies: Driving profits.
"Business leaders still need assurance that Gen AI aligns with their core objectives—aspects such as enhancing customer value, generating revenue, and expanding market share. Without a clear demonstration of its utility in achieving these goals, companies are right to approach AI investments cautiously, ensuring alignment with their overarching business strategy,” shares Cooper.
Results from the survey concur with Cooper’s insights. When asked what outcomes they expect from the application of generative AI for marketing in the next 12 months within their respective organizations, 39% of all respondents indicated ‘not much’ for revenue increase, whilst 33% stated a 1% to 50% anticipated increase in revenues. However, 57% anticipated an increase in efficiency, while 13% cited they were unsure yet as to how much efficiency could be unlocked with generative AI.
The survey results also unveil a significant contrast between brand/advertiser responses and those from marketing agencies, particularly regarding the anticipation of revenue increase through generative AI. While 32% of brand/advertiser respondents foresee 'not much' impact on revenue, this figure rises to 41% for marketing agency professionals. Intriguingly, both groups converge at 64% for expected benefits in productivity and 57% in efficiency over the next 12 months.
"It's not unexpected that both brands and agencies are navigating similar uncertainties regarding the value of generative AI. Amidst various use cases and a range of technological options, the ambiguity surrounding costs, coupled with uncertainties about its actual impact on efficiency and revenue, serves as a unifying factor for both companies and agencies. This shared uncertainty is emblematic of the nascent stage of the corporate ecosystem around Gen AI,” says Cooper.
"Ultimately generative AI's key purpose lies in serving three fundamental company goals: Magnifying value systems, enhancing efficiency, and reducing risk. If it doesn't distinctly contribute to growth, efficiency, or stability, businesses need to determine if it’s really necessary and why they want to adopt it.”
The true cost of investing in Gen AI
Another major factor contributing to the reluctance of investment in Gen AI by brands and agencies is the concern over the cost of infrastructure required to support it and its associated returns on investment (ROI).
Joshua Kennedy-White, an APAC-focused cyber-security expert and director at CyberX, emphasizes that implementing Gen AI is not a trivial endeavour. Rather, he says, it requires substantial financial resources, not only for the acquisition of the technology, but also for the protection of data, security and adapting existing systems to be compatible with generative AI enhancements. All of this before knowing what the clear ROI on all the money spent will be.
“Brands are cautious about pouring funds into something whose financial benefits are not guaranteed or might take a long time to materialize. Just as in the early days of cybersecurity, there are new risks and uncertainties to consider: Ethical considerations, data privacy, and regulatory challenges. Brands are like the first early miners, eager but cautious, aware of the potential but wary of the unknown and untested grounds.”
He continues, “I think companies generally struggle with the security aspect of AI because it inherently involves opening up their systems and data. How safe is their data? Are they enriching a model which may help a competitor? Anyone who cracks AI model security will make a lot of money.”
The challenge for chief marketing officers (CMOs) is to weigh the immediate costs against the potential long-term benefits. For instance, deploying Gen AI for targeted advertising campaigns or content creation involves upfront investment in technology and talent. While the long-term gains—increased customer engagement and sales—may justify the expense, the initial financial outlay can be daunting for many businesses.
This is especially true when considering scale and size. For large corporations, the scale of the deployment amplifies the challenge, necessitating comprehensive changes to existing systems. Smaller agencies on the other hand, may lack the resources to build the necessary infrastructure from the ground up.
"Businesses often strive for simplification rather than embellishment in their processes,” says Cooper. “While Gen AI has the potential to help in this pursuit by automating tasks and enhancing productivity, it introduces a layer of complexity in managing systems and infrastructure. The successful integration of it also pose challenges in terms of system compatibility, data management, and overall infrastructure maintenance. That’s why the deployment of AI requires a careful balance to ensure that you’re not prioritizing perceived innovation over actual efficiency.”
Both Kennedy-White and Cooper also highlight the necessity to restructure an organization's data management chain in integrating Gen AI. The size and scale of data plays a pivotal role in its effective utilization, as generative AI relies on vast datasets to train and refine its models. Cooper stresses a robust data infrastructure, efficient data collection, storage, and processing are paramount to truly deriving value out of the technology.
Adapting to Gen AI also requires substantial investments in up-skilling personnel and potentially restructuring the entire people strategy within an organization, presenting a considerable financial commitment. As the technology evolves, equipping the workforce with the requisite skills becomes imperative for effective integration. This might include the creation of comprehensive training programmes, hiring of specialized talent, and in some cases, and is quickly emerging, redundancies and a reduced headcount. The associated costs of up-skilling, coupled with potential restructuring to align with evolving roles, can be a significant financial undertaking for businesses and agencies alike.
The results of the survey showcase the above. When asked, to the best of their knowledge how the adoption of generative AI has affected the allocation of staffing budgets in APAC so far, 67% respondents from agencies said their creative allocations had remained the same. This number surged to 86% for account servicing, and 75% for media planning. However, only 5% of respondents from agencies indicated no change for technology-focused agency practices, inferring a significant allocation of the investment of agency money on AI being focused here.
For brands, a similar picture: 54% of brands said their media planning or buying budgets remain the same, while 79% anticipated no change to their headcount allocations. 43% also expected no change in marketing budgets for investments in technology.
Despite the obvious challenges noted above, Kennedy-White believes the parallel between the hesitancy in Gen AI investments and the early stages of information technology and cybersecurity can be drawn. And much like those earlier periods, he believes brands and agencies will eventually navigate and capitalize on the potential of Gen AI. But overcoming inertia (especially for larger brands) may take time.
"AI is the modern gold rush for brands—full of potential, but fraught with uncertainty. Think of the early days of cybersecurity and ICT. These technologies were like a newly-discovered gold mine when they first emerged. The potential was enormous, but more understanding, skills, and infrastructure were needed to mine this gold effectively and safely."
Coming together is the key to success
Diana Malcomess, a former Digital and Emerging Technology partner at EY, says the integration of generative AI into a business goes beyond mere technological adoption; it also requires a strategic overhaul that touches upon every aspect of the organization. For such a significant transformation to succeed, the collective effort and decision-making of the entire C-suite from CEO to CDO and CMO and other top executives—is essential.
Malcomess shares that to truly achieve alignment across departments, diverse perspectives within the C-suite help to tailor AI strategies that not only advance technological capabilities, but also reinforce the company's market position, financial health, and competitive edge. Without this alignment, AI initiatives risk becoming siloed projects that fail to contribute to the broader business objectives.
“You have to come together. The decision-making process for AI implementation cannot be solely delegated to a CIO, CMO, or even the CEO. It requires a collective effort, where every stakeholder within the organization plays a role. I like to say: ‘Are we all rowing in the same direction?' Because you might be putting in a lot of hard work, time, effort and money with your individual paddles, but if you’re not rowing in the same direction, you’re ultimately going in circles.”
Malcomess warns the risks of not adopting a collaborative approach are significant. Without the collective buy-in and support from the C-suite, AI projects may suffer from a lack of direction, insufficient funding, and resistance to change within the organization. Moreover, disjointed efforts can lead to missed opportunities for innovation and cost savings, potentially leaving the company at a disadvantage in a competitive market.
“Ultimately to succeed, especially during the cusp of an economic downturn, brands and agencies should prioritize their customer understanding and their emotional needs. They should also assess internal capabilities and leverage Gen AI to bridge gaps, clearly identifying areas where it can enhance and accelerate processes. Finally, for long-term success, there is no alternative but to collaborate with key stakeholders—CISO, CDO, CIO, CEO—for a collective decision-making approach. This will always ensure alignment and synchronized efforts.”
This story first appeared on Campaign Asia-Pacific.
