When IBM CEO Arvind Krishna, recently estimated that, "30% of [back office roles] could easily be replaced by AI and automation over a five-year period” it appeared the oxygen had suddenly been sucked out of the room. While much of the media reacted with fear and hand wringing, many business leaders admired Krishna’s candor.
With major corporations announcing plans to integrate AI across back-office functions, many Chief Executives are wondering exactly what cost savings the deployment of AI will actually create for their business. Beyond the media hype, many are left speculating on which areas of business can, should and will be outsourced to an AI.
Perhaps no other business function is more poised to be disrupted by these technologies than Marketing. Already many of the core functions of the marketing department are being both augmented and challenged by generative AI. Everything from copy writing to the modeling talent industry would appear up in the air.
While in many ways, marketing budgets serve as the lifeblood of a company's success, enabling businesses to reach, engage, and convert customers; they can also represent a relatively opaque cost center for a business from which ROI can be difficult to quantify.
It is perhaps no surprise then that Marketing is often the first target of any cuts with a shift in the economic winds and the CMO is the shortest tenured position amongst the C-Suite.
As CEOs consider their next moves, understanding the potential benefits of AI and embracing it as a strategic tool for marketing is essential for driving future growth. Company leaders should be helping to foster open conversations with CMO’s, CROs and other marketing leaders within the organization to best navigate the path ahead.
In the following paragraphs we break down the core marketing functions and evaluate how each might and might not be replaced with AI tools.
Market Data Analysis and Customer Insights
AI technology has the potential to revolutionize the data analysis and customer insights function of marketing. Data analysis that may have taken costly teams of experts sometimes weeks to perform is increasingly able to be delivered in real time by AI’s trained to analyze such datasets.
The cumbersome Marketing Mix Models of yesterday often required significant investment - $1 Million or more - for a few key conclusions each year, putting such models out of reach for all but the biggest brands, but real-time platforms with automated data analysis promise to dramatically reduce the barriers to entry.
Through machine learning and advanced algorithms, AI can quickly and accurately analyze customer data, providing valuable insights into consumer behavior and preferences. This allows marketing teams to identify trends, patterns, and opportunities for targeted marketing, ultimately improving campaign performance.
Of course, this trend toward data driven audience targeting is nothing new. What is new is the next generation of campaign management tools that are able to automatically draw conclusions from data and provide recommended optimizations.
Meanwhile human input remains important for interpreting data and making strategic decisions, as AI may overlook nuances or context that are crucial for understanding the bigger picture. Additionally, human expertise is necessary for ensuring the ethical use of customer data and maintaining trust with consumers.
While keeping humans in the loop to ensure quality and contextual relevance, we can anticipate that automated MMM type platforms will quickly obviate large data analysis teams that currently operate the manual models meaning brands will be able to achieve better results with lower cost.
Advertising and Media Buying
As we’ve already witnessed with the advent of algorithmic bidding in digital advertising markets, AI-driven advertising and media buying platforms have shown great promise in improving the efficiency and effectiveness of marketing campaigns.
By analyzing performance metrics in real-time, AI can make automated adjustments to ad campaigns, ensuring optimal ad spend allocation and maximizing ROI. This level of automation can significantly reduce labor costs associated with traditional advertising and media buying processes.
The main area where human expertise is still required will be for creative decisions, such as ad design and messaging. As algorithmic campaign optimization has taken over in certain social channels we’ve already witnessed a shift in the market from social agencies focusing on media targeting - now handled by the machine - to “creative strategy” ensuring that messaging is resonating and running rapid testing of different combinations of creative.
If we look out a few years into the future we see a time where media buying will be largely automated with a few highly skilled media strategists and brand owners dictating strategy and testing protocols to be executed by AIs. Not only will these human-in-the-loop systems reduce advertising operations expenses will likely result in more efficient media strategies and less wasted media dollars.
Content Creation and Distribution
While creative control will likely remain a very human task and we cannot imagine a time when iconic luxury brands like Chanel or Mercedes Benz would trust their brand to a machine - AI has made significant strides in content creation and distribution, with algorithms capable of generating content ideas and automating distribution across multiple channels.
This automation - most visible in the current wave of generative AI tools dominating the media - can reduce labor costs and streamline the content creation process. Moreover, AI can optimize headlines and recommend content based on user behavior, further improving campaign performance.
With that said, it is well known that advertising creative and content remain core drivers of marketing campaign performance. As such, human involvement is indispensable for producing truly engaging content that effectively communicates a brand's message. Tools can be used to produce a greater number of variations and enable creative testing, however a human will still be required to formulate the vision and prompt the AI.
We anticipate that these tools will primarily be used at the production level of brand creative where a brand and their creative director come up with core campaign messaging, visuals and video storyboards but entrust much of the production work to small teams using generative AI to build and test minor variations on the core theme. This will dramatically reduce the costs of production and likely encourage brands to reinvest in either testing more creative versions or investing more heavily in media.
Personalization and Customer Experience
AI can significantly enhance personalization and customer experience in marketing, with the ability to automate content personalization and targeting processes. This level of automation can improve customer engagement and conversion rates, ultimately driving greater success for marketing campaigns.
This customer facing side of the AI question is particularly interesting in that most brands will prefer to keep humans firmly in the driver's seat when it comes to managing customer relationships and experiences. However, with intuitive AI tools the customer experiences, likely to be dramatically enhanced. Imagine a sales force who can automatically access refreshed marketing collateral, customized to their client without spending time adjusting slide decks. Imaging customer service reps who are proactively prompted with the most relevant CRM data on a customer as soon as they walk into the store or onto the dealership floor.
We expect forward looking brands to wholeheartedly embrace AI tools for customer facing roles as a way of dramatically improving the customer experience in ways that a fully human sales and customer service team simply is not equipped to deliver.
Perhaps this function of AI, more than anything, will form the basis for competitive advantage in the age of AI. Brands who can use AI to free up resources to provide exceptional customer experiences will be in pole position for the foreseeable future.
Marketing Strategy and Planning
Like customer facing roles much of marketing strategy will still remain the domain of senior strategists. However AI can greatly enhance the marketing strategy and planning function by providing data-driven insights for better decision-making. These insights can help marketing teams develop more effective strategies and tactics, ultimately aligning their marketing plans with overall business objectives.
Human strategists are still essential for evaluating insights and developing comprehensive marketing plans that take into account various factors, such as market conditions, competitor analysis, and unique selling propositions. Human involvement is crucial for crafting and executing effective marketing plans that resonate with target audiences and adapt to changing circumstances.
This function returns to the common theme across functions that if a job requires analytical skill, data analysis, number crunching or other rote tasks it will likely pass to an AI, however the strategists and leaders who are able to formulate the right strategy and use creativity to see opportunity will remain high value resources for companies.
Marketing Performance Forecasting and Budget Optimization
Finally, bringing it back full circle to Marketing Mix Models, AI has the potential to greatly improve marketing performance forecasting and budget optimization by processing large datasets for accurate forecasting.
With AI-driven data processing, marketing teams can make real or near-real-time data-driven decisions about marketing budget allocation, ultimately ensuring that resources are allocated efficiently and effectively. We imagine a time quite soon when such models will be able to give proactive suggestions to strategy teams about how best to proceed and where to identify incremental opportunities to improve outcomes.
Human input will remain necessary to interpret forecasts and make informed decisions about marketing budget allocation. Human expertise is crucial for understanding the context behind the data, as well as for aligning budget optimization with overall business goals and objectives.
However the details shake out over time the paradigm shift in business operations as a result of AI and automation is inevitable. The question now remains, as posited by IBM's CEO, do marketing teams stand to save up to 30% of staffing costs? This remains to be seen.
The transition will not be without its complexities. It will require a strategic understanding of where AI can add value, and where human ingenuity remains irreplaceable. As we've seen across different marketing functions, ranging from market data analysis, and media buying, to content creation and distribution, AI promises immense potential in enhancing efficiency, reducing costs, and improving outcomes.
Of course a human touch is still necessary. It's needed for the creative spark that fuels effective messaging, the ethical handling of customer data, the strategic vision that can seize market opportunities, and the expertise to interpret the AI-processed data within the broader business context.
The key takeaway is that CEOs must prioritize these conversations now with their sales, marketing, and customer service teams. The marketing value chain must be broken down into digestible modules and decisions made about what can be automated and what must be supported by human teams. This will be essential in navigating the path ahead and ensuring that businesses can fully harness the transformative potential of AI and automation. After all, the future of marketing—and indeed, the future of business—will be shaped by those who can effectively balance the strengths of AI with the unique capabilities that will make their brand stand out.
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