Although experts predict that one-third of our jobs will be replaced by robots by 2025, there’s no reason to worry. It just means it’s time for us to adapt. The only true losers of the AI and machine learning economic shift will be the laggards and the late-adopters.
The transformative effect of AI technology will be far-reaching. Machine learning will drive a whole new wave of software applications and platforms that will revolutionize human-computer interface and - much like the internet and social media waves - it will redefine entire consumer and enterprise markets.
Machine learning is a specific method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Machine learning is the area of AI that will have the biggest impact on marketing in the next 5-10 years.
As we have seen with other disruptive technologies in different sectors of our economy, machines are better suited to perform certain types of tasks than humans. Likewise, humans outperform machines in other areas. Perhaps most interesting, there are process areas where humans and machines can complement each other to optimize productivity. In the chart below, we’ve characterized the type of tasks where machines outperform humans and vice-versa.
In some areas, humans still far outperform machines.
A Framework for Predicting How AI Will Transform Marketing
Today’s marketers need to prepare for the revolution of AI and machine learning, and clearly articulate how they will utilize artificial intelligence to enhance customer experiences, increase ROI, and boost operations efficiency. We’ve developed a framework to help marketers begin to understand how AI and machine learning will disrupt the traditional “marketing value chain”.
Like most processes across the various functions of a business, not all marketing processes are the same. Even within the marketing department, processes can be very different in terms of their basic characteristics. In general, they can be characterized in three ways:
Complexity is the degree of difficulty that marketers experience in collaboration, coordination, and decision-making to get their work done. An example of a low complexity process might be sending out an email. High complexity processes might include things like customer data mining, predictive modeling, strategic planning, and creative design.
Predictability is the degree of difficulty for a marketer to determine in advance the way a process will be executed. Low predictability process examples might include managing customer interactions on social media channels. High predictability processes might include handling marketing budget requests.
Repetitiveness is the frequency that a marketer executes the process. A process executed only once a year has a lower degree of repetitiveness than a process executed every day. Examples of a low repetitiveness process might be developing a brand architecture for a new product. A high repetitiveness process might be managing an online chat with prospective customers.
The figure below provides some guidance for how to think differently about marketing processes across the value chain. By applying the framework, we can begin to identify those processes that are better suited to be performed by humans or machines.
Broadly speaking, marketing processes with high complexity, high predictability, and high repetitiveness are logical targets to be managed by machines. Most marketing execution and marketing analytics processes fit this characterization and we expect that AI will likely replace most human activities in these areas over the next several years.
By contrast, marketing processes with low predictability are not seen as good targets to be managed by machines. It is challenging for a machine to design and adopt new procedures “on the fly”. Low predictability processes require the marketer to exercise judgment and apply originality to define alternative solutions and/or redefine processes, areas in which humans excel.
As AI continues to pervade our everyday lives, the next generation of marketers will be “AI natives” much as the prior generation are known as “digital natives”. They will have a redefined relationship with technology that will remove elements of friction in daily activities and make room for increased productivity and creativity.
“Prediction has always been critical to marketing planning and responsiveness, but this was done by marketers to anticipate what consumers would buy. In the future, consumers will be using predictive tools that will decide what to buy for them”.
– J Walker Smith, Chief Knowledge Officer, Kantar
Are the marketing and advertising industries ready to scale AI? Not quite. But there are signs of disruption. Agencies are building services on top of AI technologies, and there are already some mature AI-based marketing technologies established that go well beyond audience targeting. These early adopters are gaining an advantage through the proper use of the new tools.
While the marketplace is ripe for development and application of AI, there will always be winners and losers.
Oryginaly published by Dave Sutton @ business2community.com on Mar 4, 2019.