Monday, December 17th, 2018 | 17 min read
We’re on the edge of a civilization-changing collection of new technologies, so disruptive that some have begun to discuss a Fourth Industrial Revolution. Klaus Schwab, the executive chairman of the World Economic Forum, wrote:
There are three reasons why today’s transformations represent not merely a prolongation of the Third Industrial Revolution but rather the arrival of a Fourth and distinct one: velocity, scope, and systems impact. The speed of current breakthroughs has no historical precedent. When compared with previous industrial revolutions, the Fourth is evolving at an exponential rather than a linear pace. Moreover, it is disrupting almost every industry in every country. And the breadth and depth of these changes herald the transformation of entire systems of production, management, and governance.
The possibilities of billions of people connected by mobile devices, with unprecedented processing power, storage capacity, and access to knowledge, are unlimited. And these possibilities will be multiplied by emerging technology breakthroughs in fields such as artificial intelligence, robotics, the Internet of Things, autonomous vehicles, 3-D printing, nanotechnology, biotechnology, materials science, energy storage, and quantum computing.
On the supply side, many industries are seeing the introduction of new technologies that create entirely new ways of serving existing needs and significantly disrupt existing industry value chains. Disruption is also flowing from agile, innovative competitors who, thanks to access to global digital platforms for research, development, marketing, sales, and distribution, can oust well-established incumbents faster than ever by improving the quality, speed, or price at which value is delivered.
Major shifts on the demand side are also occurring, as growing transparency, consumer engagement, and new patterns of consumer behavior (increasingly built upon access to mobile networks and data) force companies to adapt the way they design, market, and deliver products and services.
In this post, I will be zooming in on the impact of AI – probably the biggest wild card in the deck – and in particular on the domain of marketing. Nonetheless, we should remain acutely aware that the changes in marketing and the unrolling of AI across our world are inextricably linked to other functional areas in work, as well as the other technologies that Schwab mentioned. And others that he doesn’t explicitly name, like 5G.
A shorter quotation that builds on the central thread of Schwab’s observation comes from Carlota Perez:
Each technological revolution brings with it, not only a full revamping of the productive structure, but eventually a transformation of the institutions of governance, of society, and even of ideology and culture.
We should brace ourselves for the roller coaster ride ahead of us.
We are still at a very early stage of the impact that AI will have on the world. We’re hearing every day of new contexts in which AI is being applied, ranging from AI outperforming the best physicians in diagnosing disease, to more adept and autonomous warehouse robots, to faster chipsets designed to allow AI to run at the edge of our computing networks, such as mobile phones, retail kiosks, cars, and voice computing devices like Amazon Echo and Google Home. A world animated by AI.
Estimates of the impact of AI on the economy can set some context: Gartner calculates that AI is expected to generate a total of $1.2 trillion in global business value in 2018, up 70% from 2017, with AI-derived business value forecast to reach $3.9 trillion by 2022.
But recall the exponential aspect. Stanford maintains an AI Index, last updated in 2017, which can be considered a speedometer for AI, consolidating measures like the volume of scientific papers on AI, the number of students attending AI courses, the number of startups involved in AI systems, how many job postings call for AI skills, and so on. While they haven’t come up with a single consolidation of all these growth curves, the AI Vibrancy Index might be a proxy for one.
Over the period shown, AI’s impact on industry, academia, and the media is up 7 times. My bet is that the curve from 2010 to 2015 is indicative of what is to come in the next five years: nearly vertical growth as AI finds its way into every niche in every industry in every business.
Like in marketing.
If we could take a snapshot of the changes in marketing today, it would be a blurry image, like a shot from the window of a speeding car.
The ideas of digital transformation and the wholesale disruption of the traditional premises of pre-digital communication and connection with customers has led to an industry in disarray, as some companies have adopted radically new approaches to deal with increasingly digital consumers, and others are lagging behind. It’s the marketing realization of William Gibson’s line:
The future’s here already, it’s just not evenly distributed.
And that’s before AI has made much of an inroad.
McKinsey has a lot to offer marketers and business leaders considering their path into the future of marketing, in their report Artificial Intelligence: The Next Digital Frontier, such as this:
Companies cannot delay advancing their digital journeys, including AI. Early adopters are already creating competitive advantages, and the gap with the laggards looks set to grow. A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture.
And, once again, McKinsey discovered Gibson was right:
Few companies have incorporated AI into their value chains at scale; a majority of companies that had some awareness of AI technologies are still in experimental or pilot phases. In fact, out of the 3,073 respondents, only 20 percent said they had adopted one or more AI-related technology at scale or in a core part of their business. Ten percent reported adopting more than two technologies, and only 9 percent reported adopting machine learning.
New analysis of the challenges associated with building the necessary foundation in skills and and technologies suggests that those that delay a push into AI may never catch up, according to Vikram Mahinder and Tom Davenport:
It may, then, take a long time to develop and fully implement AI systems, and there are few if any shortcuts to the necessary steps. Once they have been successfully undertaken, scaling —particularly if the company has a plentiful supply of data and the knowledge engineering mastered— can be very rapid. By the time a late adopter has done all the necessary preparation, earlier adopters will have taken considerable market share — they’ll be able to operate at substantially lower costs with better performance. In short, the winners may take all and late adopters may never catch up.
And who is doing that adoption? The internet tech giants (Google, Amazon, Microsoft), telecom players, financial services firms, large retailers. Large companies tend to invest in AI faster, with small and mid-sized companies lagging in investments. Those who do invest adopt multiple technologies and work it into their core business operations. And the clincher:
Early adopters that adopt at scale tend to be motivated as much by the upside growth potential of AI as they are by cutting costs. AI is not only about process automation, but is also used by companies as part of major product and service innovation. This has been the case for early adopters of digital technologies and suggests that AI-driven innovation will be a new source of productivity and may further expand the growing productivity and income gap between high-performing firms and those left behind.
McKinsey’s report emphasizes the critical role of strong executive leadership in getting on the AI bandwagon, with those that report deploying successful ‘AI technology at scale tended to rate C-suite support nearly twice as high as those from companies that had not adopted any AI technology’.
As noted earlier, those who successfully invest in AI do so in their core operations, and some core parts of the business are more core than others. McKinsey found that:
Customer service functions such as sales and marketing, as well as operations and product development, all tend to use the most commonly cited AI applications. General and financial management, by contrast, lag well behind. A similar pattern is found in big data. The literature shows that the most frequent big data applications originate in sales and marketing functions.
So marketers in those companies most aggressively moving onto an AI footing are going to find themselves at the forefront of the AI revolution.
AI will transform marketing in several ways:
One example of the shifting ground for marketing is applying AI to offer the right price and the right messages to the right target, in real time. AI will be invaluable to marketers trying to reach hyperconnected consumers who continuously redefine value by comparing prices online—even, and particularly when browsing in a non-digital store.
To get there, companies will need to both capture large amounts of data about customer behavior and develop the algorithms for this sort of pitch, while continuously refining the micro-segmentation involved, finding which offers work, and locating the price boundaries.
And it doesn’t stop there, and this is where AI starts to meet the marketers and where the title of this post comes into play. Marketers must learn to dance with the robots. What do I mean by that?
Consider the scenario above, where a company is testing and retesting approaches to deal with dynamically-generated prices and offerings for customers. The impact of changes arising from such experimentation could include — and is likely to include — rethinking workflows in the business. These changes could include breaking down siloed organizational groups so that teams of developers, marketing, manufacturing, and retail management staff might be pulled into ad hoc working groups around the development and operations of an AI-driven project.
The difficulties of change management mean that those involved have to buy into the theory and practice of cooperative work, and work hard to become more flexible in a context that may be evolving rapidly. This means learning new skills, new tools, and new ways of thinking. And we will have to think of AI as another colleague, not just a black box.
The dancing part: the interaction between humans and machines is critical. Sometimes that will mean parts of the job of ‘marketing’ will be automated, and the handoffs have to be orchestrated well. Sometimes it means that new data will be captured, and that has to find its way to the right person, and determining who is ‘right’ may be challenging, because the company may not have had such data in the past. And sometimes it means that marketers will have to learn a new dance step, something that doesn’t feel like or look like work that marketers did a year ago, or even a week ago. But we will have to learn new steps, new rhythms, and new songs. And fast.
Consider how adopting AI is changing work. In the world of journalism, AI has been applied to create content that is indistinguishable from that produced by human reporters. For example, The Washington Post uses Heliograf to write stories that the paper would otherwise be unable to create:
It’s been a year since The Washington Post started using its homegrown artificial intelligence technology, Heliograf, to spit out around 300 short reports and alerts on the Rio Olympics. Since then, it’s used Heliograf to cover congressional and gubernatorial races on Election Day and D.C.-area high school football games.
In the marketing context, Narrative Science’s Quill can generate human-sounding narratives. Indix, one of their partners, has built a solution based on Quill that can write product copy, like this blurb for Elmer’s Glue-Al, based on existing product information:
It’s always good to have glue around when things get broken, and this 4-oz liquid white glue from Elmer’s is sure to do the trick. This white glue dries clear, so you don’t have to worry. It is a versatile adhesive working with cardboard, cloth, leather, papers, and wood. This glue is recommended to use for school projects, crafts, and household repairs. It is tear-resistant, showing what a strong product this is. Elmer’s glue is also ACMI AP non-toxic certified and can be used by all ages. This liquid glue has a 30-day warranty.
In similar creative domains, companies are using AI to generate music for ads and videos. JukeDeck, for example, has used AI to generate a library of royalty-free, editable music to act as a starting point for videos, ads, or other creative marketing work.
Computer-generated models – I mean the human models that grace the pages of fashion magazines – are being used to cut the costs of photo ‘shoots’ and replacing photographers, make-up artists, and supermodels with programmed reality. Irmaz is an ‘imagined reality’ model agency that has a stable of imagined models like Carl and Kenzy, below:
These imagined models never get sick, never arrive late, and never grow old.
These practices and tools are being used to augment the staff in marketing organizations, allowing companies to meet and exceed their goals without breaking the budget. Albert.ai goes so far as to tout its Albert technology as the ‘first fully-autonomous digital marketer’, and its just one of 14 companies to get an honorable mention in Gartner’s 2018 Magic Quadrant for Ad Tech.
At some point, inevitably, the trend of augmenting work may become automating workers. For a marketing professional specializing in creating the very best content to market shoes, dental floss, or software, it may be a good time to start learning how to work with the tools that are encroaching on your specialization, or rethink specialization altogether.
Finally, we may find that we need new ethics: new principles that guide and channel the goals of the company and balance them with the rights of customers, the emerging regulatory boundaries, and an expanding sense of morality to match an exponential shift into AI.
It would be very easy to skirt this step, but I bet those that are most committed to pushing the boundaries of AI in business will also be the first to encounter the boundaries of our ethics, as well. As I said at the outset, we’d better brace ourselves for the ride ahead.
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