Marketing Implementation of AI using the Low to High Involvement Framework
Artificial intelligence (AI) has only recently been feasible to implement within business. While the term was originally coined by Dartmouth math professor John McCarthy in 1950, technological limitations made AI largely useless for many years. The initial spur of research done on artificial intelligence gave way to a period named the “AI Winter” during the 1970’s.
Now, a myriad of factors has contributed to AI becoming feasible for business use. Rapid innovation in graphics processing units (GPUs), lowered costs for computing technologies, increased access to massive amounts of data, and a heightened level of interest amongst companies and investors across many industries have led to galvanization of AI’s potential.
For a perspective of the rate of progress, the recent excitement around AI has resulted in $27 billion in venture funding going toward AI startups (3X more in 2017 compared to 2016). Of this, the marketing industry has received the fourth most funding.
Marketing implementation of AI varies widely. AI can be implemented within an organization at a basic level for retargeting ad campaigns, and it can be implemented at a complex level with robust customer relationship management systems (CRMs) and omnichannel integration.
As such, understanding the current state of artificial intelligence in marketing can be tricky. Part of my undergrad honors thesis research led to me creating a scale to understand marketing implementation of AI better.
The Involvement Framework
for analyzing marketing implementation of artificial intelligence.
A key factor for differentiating the various degrees of marketing implementation of AI is what I termed “involvement.” The level of involvement that an application of AI exhibits reveals key details of how it’s integrated into the company.
Resource involvement: the amount of resources required to implement the AI. Low involvement entails low resources required, whereas high involvement entails a high amount of financial requirements. Similarly, low involvement solutions require less data or make use of collective pools of data, but high involvement solutions typically require large amounts of proprietary company data.
Labor involvement: the labor requirements for implementing the AI. Low involvement AI solutions can be used by entry-level employees, but high involvement AI solutions require experts to implement and manage the systems. Hiring top-quality AI talent is both difficult and expensive at the moment, creating a barrier to entry for smaller companies.
Business involvement: the degree to which the AI plays a critical role in the day-to-day and high-level business operations. Low involvement AI offers a limited competitive advantage and is not a determinant factor in the brand’s value proposition. Contrastingly, high involvement AI is deeply rooted in the brand’s core product or service offerings.