Why should brands look beyond AI-based recommendations?

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As in-person experiences and face-to-face interactions reduced in 2020, numerous organizations turned to AI-enhanced solutions to connect with their consumers. The field of artificial intelligence has grown tremendously over the past decade. The global AI market is set to reach a value of USD 16.5 Bn in 2025, with a CAGR of 55.6%. AI-enabled applications are believed to save time and provide efficient business directions.

In a world filled with hyper-personalization opportunities, brands are planning to use AI-based recommendations to improve their overall customer experience. Ranging from shopping to entertainment, brands like Amazon, Nike, Spotify have been providing AI-based recommendations to their consumers for tailoring a relevant experience. But it is not just personalized recommendations for shopping and entertainment. AI-based recommendations have also entered the public sector. The USA Cincinnati Fire Department has developed an AI system that classifies emergency calls based on their urgency. This AI solution prioritized those in need of an ambulance and those who can be treated on-site, reducing overall delays by 22%. Experts are also predicting the rise in AI-based recommendations for personalized education modules to lower drop-out rates in schools.

As we advance towards more relevant, tailored experiences using Artificial Intelligence, consumers are willingly opting into the ‘word-of-machine effect.’ This term is used to describe the times when humans choose the suggestions by a machine over the suggestions by humans. One can only expect that with the growing popularity of AI amongst both consumers and brands, the ‘word of machine’ effect will rise exponentially. But recent studies have shown that there are situations that throw ‘word-of-machine’ out of the picture. There are a few exceptions where consumers willingly opt-out of ‘AI-based recommendations.’ Mapping these exceptions can not only help organizations account for the inherent consumer biases but can also provide an added USP to retain them.

When do consumers go against the ‘word of machine’ effect and why do they do so?

01 The interplay of social influence and experiential choices

How is the rise of socially influenced experiential choices affecting brands that rely on AI-based recommendations?

A recent study based on data from over 3,000 people who took part in 10 experiments talks about the situations where humans would opt out of the ‘word of machine’ effect. The study states that when it comes to experiential and sensory qualities, consumers tend to turn to human recommendations. Whereas functional, utilitarian decisions are better appreciated coming from a machine. For example, in one of the experiments, people opted for the AI-recommended shampoo sample when it came to chemical composition and objective performance. But on the other hand, they opted for a human-recommended shampoo when it came to attributes like the indulgence, scent, etc.

This points to the fact that although AI-based recommendations are fuelling the growth in personalized services, they are not always the go-to option for organizations. In a scenario where emotional experiences are key, it is essential for a brand to understand when to recommend the ‘word of machine’ vs when to provide an opportunity to connect with peers and take a collective decision.

02 Breaking the monotony with a ‘surprise choice’

How is the hunt for contrastive micro-experiences affecting expectations from AI-based recommendations?

One of my colleagues recently chose to listen to songs from the radio once a week, instead of accessing Spotify. Radio had that surprise element where one had no clue on what song would play next. Although most of the entertainment options are point-on with what we actually prefer, it leaves consumers with minimal scope to discover a completely different genre. The recommendations follow a safe loop of diverse options within similar genres. In such cases, consumers are determined to go against the ‘word of machine’ and create a ‘surprise’ genre for themselves.

Given this context, brands must not only hyper-personalize their experience but also ensure that consumers are given the choice of exploring the unexpected. The sense of adventure and delight while discovering a completely new experience cannot be matched with a set of pre-filled recommendations on the screen.

Moving forward, organizations that follow a hybrid model of recommendations instead of loading their consumers with the ‘word of machine’ would have a significant advantage. As we move towards a machine-operated world where humans are left with limited cognitive choices, it is imperative for brands to take a step back and not over-automate experiences. Breaking away from machine-based recommendations for specific experiential decisions and providing micro-surprises through a contrasting option would definitely hit the delight note amongst consumers.