Major retailers like Amazon have been using advanced technologies like artificial intelligence (AI) seemingly since the beginning. So when brands set out to go the direct-to-consumer route, they have to quickly master cutting-edge retail tactics and adopt new technologies to challenge, or at least keep up with, these established competitors as quickly as possible.
There’s an especially steep learning curve for brands that choose to execute on these new strategies in-house, using artificial intelligence to scale their team and reach rather than relying solely on an agency.
This shift is not limited to retail brands. Financial institutions, telecommunications companies and other traditional industries are also assuming the role of seller — and increasingly, of agency — to take a more direct approach to customer experience and acquisition.
My company recently commissioned Forrester Consulting to understand brands’ increasing adoption of artificial intelligence in this changing climate. Forrester spoke with 156 marketing decision-makers in retail, CPG, food and beverage, financial services, telecommunications, software and travel and hospitality to take a closer look at their use and applications of the technology.
Their research revealed an opportunity gap between how marketers are currently using AI versus how they could and should be using it. Here are three ways brands can bridge that gap:
1. Redefine the division of labor between humans and machines
In 2016, Forrester conducted a similar study (via MarTech Today) that revealed over 40% of marketers were using AI. This number has now more than doubled, with 88% using either assistive AI or autonomous AI technologies. The distinction between these two types is important as it highlights an important part of the AI opportunity gap — or, where brands are versus where they have the potential to be.
Where brands are: Seventy-four percent of respondents report using assistive AI technology, which surfaces insights for marketers to consider during manual decision making. The remaining 26% are using autonomous AI, which can act on its own insights and work collaboratively with marketers.
Marketers using AI in an assistive capacity are experiencing similar complexities in their processes and operations as they were before AI adoption. While an assistive approach may speed up certain campaign-oriented tasks, it’s limited by its reliance on humans to make decisions and manually complete tasks.
Where they have potential to be: AI has far more to contribute if we start thinking about it as a collaborative and autonomous system for scaling marketing campaigns rather than cool technology that exists solely to help brands make semi-informed decisions. Even equipped with machine-generated insights, marketers don’t have all the information they need to make the same number of decisions with the same level of clarity and act on them at the same scale.
Marketers must redefine the division of labor between humans and intelligent technology. Humans will tackle all things creative, strategic, intuitive and emotional. AI will take on all things data gathering and analysis and then act autonomously on the insights it surfaces.
This shift is inevitable. According to a global study by Pegasystems and Marketforce, “Sixty-nine percent [of marketers] said they expect the term ‘workforce’ to eventually encapsulate both human employees and intelligent machines. “
2. Treat AI as a fundamentally different kind of technology
Today’s direct-to-consumer marketing is all about creating personalized, high-touch experiences for consumers. As the lines between our digital and physical worlds blur, brands need to work at the speed required to provide consumers with the experiences they want, at the cadence they need, across online and offline channels, wherever they are in their journeys.
Brands need to understand where they are in their own AI journey as well. Based on our report, only 43% of marketers said that their technology was helping improve customer experience. Thirty-nine percent said it’s helping increase customer retention, and only 33% credited it with increasing customer acquisition.
These are not just nice-to-haves; they’re make-or-break brand and business objectives. If brands are using AI but not winning in these places, it’s often because they’re hanging onto manual approaches or are struggling with the idea of relinquishing control to a “robot.”
Though some marketers fear working with an autonomous machine, AI can’t do its job on its own. A machine will never have a marketer’s deep knowledge of a particular brand, nor understand the subjective factors that influence a customer. Its job is to translate customer insights into actionable marketing outcomes; collect, integrate and manage data; and operate fast enough to keep up with the rapid pace of interactions.
This shifts the role of humans so that we do what we do best while pulling us out of the data weeds once and for all.
3. Consider whether it makes sense for a brand to ‘become an agency’
Marketers’ relationships with their agencies are changing. In 2016, 37% of marketers reported feeling overly reliant on their agencies for driving marketing strategy. This year we’re seeing a complete shift, with 42% of marketers saying that they’re exploring the potential of “in-sourcing,” or taking part of their digital media and/or creative in-house, and 24% already intending to do so.
Brands like Unilever are leading this new trend toward in-sourcing select marketing functions and making it look very appealing by sharing numbers such as the €500 million it saved in 2018. As Unilever discovered, there are, of course, challenges that come along with replicating agency teams — namely, hiring, retaining and organizing staff, and scaling operations.
AI is one of the strategies brands are deploying to make in-sourcing possible. Insourcing is not just about adopting AI, though; it’s about restructuring internal and external teams, bringing in an AI operator to act as a go-between between the machine and creative teams, and collaborating with the technology in an autonomous capacity rather than simply using it.
Moving from an assistive solution to an autonomous one is the first step in bridging the AI opportunity gap. From there, it’s about rethinking the division of responsibilities between human and machine, adopting a forward-thinking approach to working with this new technology, and deciding which capabilities are absolutely strategic to in-source and which are better left outsourced.
Article previously featured in Forbes, Three Ways Brands Can Bridge The Opportunity Gap, June 24, 2019