Over the course of 150 years, manufacturing evolved from relying on skilled artisans and craftsmen to automated, computer-driven, flexible manufacturing systems. Marketing will do the same in less than 50 years as AI radically shifts the paradigm for markers. However, if marketers fail to adjust their overall marketing systems, productivity gains from AI can easily be lost.
Let’s explore the legacy of marketing systems, how AI turns these systems on its head, and suggest new ways to approach marketing in the age of AI.
The legacy of marketing systems
Modern-day marketing often works like an apprentice system, where individuals learn their trade through a combination of on-the-job training and formal education under the tutelage of experienced professionals who provide guidance, supervision and feedback. Marketing relies on highly skilled people in areas like copywriting, design and media management to create winning campaigns that drive business outcomes.
As a result, most marketing systems are designed with multiple layers of approval from the mentors to the apprentice (think senior copywriter to junior copywriter) to ensure quality and consistency. Workflows are set up to hand off from one specialty to another, with the assumption that each specialty has unique skills that they apply to the marketing work. These systems worked well enough in a mostly manual, slower-paced world.
The introduction of marketing automation and digital marketing started to mechanize the delivery of certain marketing assets, but, in general, the creation of the assets and the marketing systems reflect a traditional approach.
How AI changes marketing systems
However, as marketing becomes more automated through AI, traditional systems that include multiple handoffs and manual approvals stymie productivity gains. What use is it for a graphic designer to create images 10 times faster if it still takes a week to get through the approvals process? What do we gain if each test and budget adjustment has to route through management for approval?
Just like the introduction of the assembly line radically increased the speed and efficiency of automobile production, AI can potentially increase the speed and efficiency of producing marketing outputs.
Repetitive tasks such as content and ad creation can be automated and tested at a scale previously unimaginable. Data can be created, processed and analyzed in near real-time. Machine learning can continually optimize campaigns, taking advantage of always-on feedback loops.
Instead of debating button colors and placements for hours, marketers can now run real-time tests with many options. The need for multiple layers of approval flattens dramatically as templates and programmatic brand guidelines ensure standardization and quality. Workflows become automated.
Marketing leaders must consider productivity gains and technology as well as the entire marketing system, including the impact on people, processes and roles.
Dig deeper: AI in marketing: Examples to help your team today
Impact on marketing team organization
With AI in its marketing infancy, most marketers are focused on the power of generative AI. The leaders in the field, however, are starting to move beyond generative AI into using AI to drive workflows and feedback loops. Some companies, like Tomorrow.io, have marketers owning multiple specialties such as email, events and social media. As this happens, we see the rise of the marketing generalist, a person who owns multiple specialties.
Initially, these marketing AI specialists will be responsible for stitching together individual AI solutions to create a workflow. As the marketing team starts to flatten, skill sets become broader, and marketers will need to upskill their data and technology skills.
Marketing teams’ focus shifts toward managing automation streams, streamlining processes and running tests to deliver faster results and improved outcomes. Coordination with data teams, quality teams and marketing ethics/policy teams has become the norm.
We will start to see the collapse of strategy and execution where AI notifies the marketing team of a new strategy to try. When marketers approve that strategy, AI executes it.
For example, MasterCard’s Digital Engine analyzes billions of online conversations to spot new micro-trends. This alerts the marketing team, who then uses existing content to create relevant social media posts and targeted ads.
In another few years, AI might notify the marketing team of the opportunity and, when approved, select which assets to draw from the library of content or even develop hyper-personalized content on the fly.
How marketing jobs are set to change
How does this impact the role of the marketer? Just as automation shifted the number and type of jobs in manufacturing, the number and type of jobs in marketing are set to change. In their book “Marketing Artificial Intelligence, AI, Marketing and the Future of Business,” Paul Roetzer and Mike Kaput say that future marketers need proficiency in data, technology and communications.
Marketers of the future will need technical expertise, problem-solving skills and an eye for process optimization. Marketing leaders must anticipate these changes and create environments for continuous improvement and cross-functional collaboration.
A lot has been said about disappearing roles. However, AI in marketing is set to create new opportunities. Automations need to be conceptualized, built, managed and maintained, all within a robust framework of guardrails and guidelines. AI technology in marketing must be chosen, implemented and maintained.
Dig deeper: AI transformation: How to prepare your marketing team
How to respond now and in the future
Examples of how to respond already exist in companies like Netflix, Nike and Amazon. Nike currently uses AI technology to analyze the emotional intelligence and traits of particular audience segments to create compelling narratives that offer the best ROI. How do they do it?
Small teams collaborate to achieve specified outcomes with pre-defined metrics. The teams look for ways to consolidate or automate processes. Leaders look for ways to decentralize decision-making within well-defined policy parameters. Teams are entrusted to act on AI insights.
If you are just getting started, look for ways to start stitching AI tools together to automate workflows. Start thinking about how you can automate approval processes. Consider the impact on other processes, including ways processes can be consolidated with the power of technology.
If you are in an advanced organization, start to look for additional ways to use your data and automations to create feedback loops that continuously optimize for pre-defined outcomes. The goal is to align with other teams on shared outcomes and program the systems to optimize for those outcomes.
As with any fast-moving market, stay flexible, assume variability and preserve your options.
Dig deeper: How brands like Klarna and Mars are using AI in marketing operations
AI and the marketing ecosystem: Transforming people, processes and roles
It’s easy to get distracted by the newest way to create content with AI and forget that marketing operates in a system. Current marketing systems are based primarily on manual processes where one person hands work off to another.
In the age of AI marketing, these manual processes have the potential to kill productivity gains from AI. Marketers need to think about how marketing workflows through the system with an eye toward automation, much like how manufacturing now automates many processes formerly done by hand.
Marketing leaders need to think through the fundamental way work gets done, including people, processes and roles, to leverage the real-time decision-making and rapid execution AI enables.
Dig deeper: How to do an AI implementation for your marketing team
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