Chances are, you are no longer flattered—or a little unnerved—when a Starbucks barista offers you your favourite beverage or when Amazon recommends books that are on your wish list. By now, you may have grown accustomed to companies sending you personalised recommendations, each competing to deliver the right product—and unparalleled customer delight. It’s no secret that behind the scenes, complex artificial intelligence algorithms are at work, predicting customers’ needs and enabling brands to ensure a seamless end-to-end customer journey.
Indeed, organisations, across industries, have exploited the prowess of AI for various business functions. Yet, current trends indicate that the cutting-edge technology has proved to be particularly beneficial to the marketing domain. The implications are far-reaching for marketers. AI is used to segment customers to identify the best target audience, hyperpersonalise content, automate marketing campaigns, undertake sentiment analysis, drive sales through digital ad purchases, deploy real-time predictive analytics and use chatbots to become the face of brands. The uses of AI in marketing are varied and seemingly endless.
New research on the impact of AI technologies in marketing turns a global lens on the topic. The study by Manish Gangwar, Associate Professor, Marketing, Executive Director – Institute of Data Science, Indian School of Business (ISB), along with his co-authors, examines how AI has made inroads in marketing by focusing on three levels of analysis: country, company and consumer.
The three-part global lens study also explores two significant dimensions of AI’s role in the marketing function: 1. The synergy between human–machine interactions and 2. The insights gathered from the automated analysis of text, audio, images, and video.
This narrative attempts to articulate the essence of the research that has enriched existing scholarship in this domain. It also calls attention to the larger issue of the challenges and risks in implementing AI, a technology that Google CEO Sundar Pichai has described as “as important or more than fire and electricity.” With a recent forecast projecting the value of AI in marketing to touch more than USD107.5billion by 2028, it’s becoming increasingly clear that the promising technology has emerged as an indispensable tool for marketers.
Machines are taking over humans! The oft-repeated remark has become as commonplace as the ubiquitous automated algorithms deployed by organisations for varied purposes. The reality is, heightened customer expectations have led companies to delegate critical marketing tasks to machines.
A gamut of AI technologies, including machine learning, natural language processing and neural networks, among others, have upped the game for marketers. Big tech firms deeply rely on AI for their varied marketing functions. For example, Cisco Systems and IBM have built tens of thousands of “propensity to buy” models that predict customers’ shopping behaviour. Cisco generated about 60,000 propensity models leveraging machine learning that proved invaluable to its sales and marketing teams. Verizon employs cognitive technology to gain customer intelligence that will sharpen its marketing strategy.
Besides firms, the race for AI adoption is gaining momentum among countries seeking to establish their digital competitiveness. According to a study published by The Brookings Institution, the US, China, the UK, France, Japan and Germany are leading in AI adoption. India occupies a slot among the top 10 nations in the world in terms of spending and investments in AI. It is projected that India’s AI spending will touch USD880.5 million in 2025.
Given the global popularity of the technology, Prof. Gangwar’s study analyses AI via a three-part global perspective. The researchers propose that AI in marketing is strongly influenced by the “three Cs”-country, company and consumer.
Economic inequality at the country-level: The first-level of the study showed that today’s savvy marketers are cognisant of the heterogeneity in economic inequality among nations that may result in slower AI adoption by underdeveloped and developing economies. At the country-level, while the benefits of AI (e.g. customised remote learning due to high-speed internet in developed countries) may not be transferred to the poorer nations, the technology has notably been used for the larger good in such markets (e.g. affordable quality education, improved healthcare access).
It’s clear that advanced economies (USA, Singapore, Germany) have an inevitable edge in swifter AI adoption than emerging economies (India) or underdeveloped ones (sub-Saharan Africa, the Carribean) due to their per capita GDP and plentiful economic resources. Poorer nations lack largescale computing infrastructure to implement AI technologies that come at a hefty price tag. While the Industry 4.0 technology can solve many of the problems developing nations face, it can also widen the gap between the rich and poor nations, leading to a digital divide and the potential creation of “hubs of wealth and knowledge”.
On the more positive side, the study proposed that AI technologies in marketing have the potential to serve economically disadvantaged consumers. For example, Amazon India’s algorithmic-driven marketing strategies are beneficial to small entrepreneurs who lack the generous marketing and advertising budgets that bigger players have at their disposal. Marketers need to build a more focussed HMI strategy to cater to the underserved consumer base.
Glocalisation at the company-level: The second facet of the study underscored the importance of a “glocalised” approach to AI deployment. While AI technologies are global by nature, they are implemented across geographies, cultures and consumer segments. It’s imperative that marketers are strategic in adapting AI, remaining sensitive to the needs of the local markets.
Marketers should exploit AI’s capabilities to get a higher return on ad dollars. The study emphasised that if human-machine interactions (HMI) are not tailored to local conditions, it could potentially spell the death knell for marketers. They should analyse granular predictions to understand the nuances of customer behaviour in local markets. For example, Netflix uses AI to successfully cater to diverse audiences in various international markets, thus, strengthening the company’s local operations. The researchers believe the future of marketing lies in using the global technology to build locally, especially in the area of price optimisation. For instance, construction firm, Dayton Superior, deploys analytics to align its prices with local markets.
Ethics and privacy at the consumer level: The third tier of the study examined how AI applications cause concerns for consumers regarding issues like the ethics of the technology and data privacy.
Companies big and small deploy AI to collect, store and process valuable and sensitive consumer data. Marketers need to be extra vigilant before introducing AI into the workflow to accomplish HMI tasks. Not everyone is enthused about the technology. In a recent survey, nearly one-third of respondents expressed their frustration with chatbots.
The bots’ failure to effectively communicate or resolve issues were cited as the primary reasons for their “complicated” relationship with chatbots. Marketers need to address the faultlines in the complex AI landscape to gain consumer trust and loyalty. Moreover, since data privacy laws are more stringent in some countries (European Union) than in others (China, Russia), organisations should ensure that their AI-embedded systems are transparent about how customer data is collected and used.
Marketers should avoid introducing variables or features in models that may cause bias. For example, advertisers should understand how AI algorithms target potential customers, and more crucially, whether the ads reached the right customers.
In recent years, several marketers have opted for AI-led data collection processes over the more conventional forms and surveys. Today, automated technologies such as natural language processing, autonomous web scraping and computer vision extract rich insights from unstructured and non-numerical data such as text, audio, images, or video. Marketing AI applies lead-scoring algorithms to improve customer conversion rates. It’s indeed a goldmine of data for marketers who can act on data-driven insights gathered from social media posts and customer reviews. Further, audio-based and image-based data also enable marketers to reach the target audience in a more focussed manner.
At the country-level, automated analysis can help economically disadvantaged nations narrow the divide between the rich and the poor. AI technologies such as real-time speech analytics can be used to optimise marketing communication in the local languages, helping companies engage better with customers. For example, Unilever and Telkomsel are working with the conversational platform Kata.ai in Indonesia to categorise and automate more than 95% of customer interactions with minimal human intervention.
On the other hand, AI-powered automated services also lead to concerns about human agency. Low-skilled workers are at risk of being substituted with machines. This might impact economically disadvantaged regions, such as the Philippines, that are preferred geographies for call centers.
At the company-level, text, audio and video analytics are increasingly local in nature. For example, Google’s AI assistant Duplex can make local restaurant reservations for customers based on their residence. Call agents, self-service terminals, chatbots, and voice-based interactions are some of the most popular AI-based automated applications. The technologies are changing the face of retail and gather key insights into shopping behaviour at the local, rather than global level. Localised automated applications have come to play a key role in integrated marketing communications (IMC), creating marketing strategies tailored to diverse cultures and demographics.
Indeed, AI in marketing is moving towards glocalisation, using text mining, sentiment analysis, emotion detection and speech recognition adapted to local languages and markets. AI-based video analytics such as augmented reality (AR) and virtual reality (VR) are popular in retail and enable customer retention and acquisition in the long run.
At the consumer-level, to counter the problem of data privacy, the study’s researchers suggest that consumers can either shift the focus of machine learning from firms to users or engage trusted third parties to help users control their data. However, notwithstanding the stringent data rules governing EU, tech behemoths like Microsoft, Alphabet, Amazon and Facebook might be unwilling to part with proprietary consumer data. Besides, engaging third parties in developing regions is a costly exercise.
Organisations are finding a variety of AI applications across the value chain of marketing. In addition to earlier mentioned benefits, AI in martech is expected to enhance advertising efforts. From writing headlines to push notifications, the technology will enhance brand visibility through targeted ad delivery. Using deeper predictive analytics AI will discover insights for product development. And Large Language Models (LLM) trained on massive datasets are expected to enhance customer engagement.
As AI matures, experts believe it will take the form of “autonomous intelligence”. Machines, robots and other AI technologies will act on their own and augment human analytical competencies. Once this happens, it remains to be seen how the HMI dynamics will play out to accomplish key marketing tasks. CMOs who are aggressive adopters of AI, need to exercise some degree of caution. It’s crucial that the AI solutions they deploy should not only be productive and effective, but also humane.
After all, AI with a heart and high on emotional intelligence is, perhaps, core to acing marketing in the future.