When many marketers in companies that would not exactly call themselves “tech startups” hear about artificial intelligence in marketing, it can sound like science fiction. Sure, there are plenty of algorithmic and automatic features in common programs such as CRM and Marketing Automation suites, but really leveraging “Artificial Intelligence” for game-changing value? It doesn’t seem realistic yet.
Many marketers are accustomed to the real AI headlines being made by tech startups and over the past 20 years those start-ups even include Google (hard to believe Google was founded only 19 years ago) and of course Tesla, Facebook and more.
But in What to do When to Do Machines Do Everything: How to Get Ahead in a World of AI, Algorithms, Bots and Big Data, authors Malcolm Frank, Paul Roehrig and Ben Pring theorize that while those companies may have pioneered AI, incumbent industry leaders—the larger, older companies—may see the real value.
From the book:
While companies such as Facebook, Amazon, Netflix and Google (sometimes knowns as the FANG vendors) seem to have established themselves as the presumptive and eternal winners in this space, history will likely remember them as precursors to a much more momentous and democratic economic shift. The next wave of digital titans probably won’t be characterized by start-ups from Silicon Valley; instead it will be made up of established companies in more “traditional” industries—in places like Baltimore, Birmingham, Berlin, and Brisbane—that figure out how to leverage their longstanding industry knowledge with the power of new machines.
While established companies may not create the AI that benefits their business, they have what’s ultimately more important—the granular, structured data to power AI. Whereas startups often create value in new technology, incumbents may have created longer-term value and are more often more entrenched in their industry with a wealth of relevant data. Traditional data assets sit latent, important for utilization in relevant “departments”, but rarely automated with Artificial Intelligence to create new business models or products.
B2B markets evolve more slowly over time than B2C, and often, with complex specifications, established bid process, massive scale and established distribution channels, we see industry incumbents maintaining their leadership for longer periods of time because markets require established producers.
The downside of such a dynamic is that marketing processes can become stale, with legacy technology and processes delivering satisfactory efficiency, even in challenging markets.
For many marketers in established B2B companies, the idea that the customer, market and product data and knowledge—including research and development— will thrive with new AI should be great news. It means that new technology may create not only valuable efficiencies from latent data, but perhaps entirely new business models for established B2B brands. To achieve this value as AI technology matures, it won’t take a snazzy in-house technology or a startup’s innovative spirit. All it’s going to take is implementation of AI technology as it is infused in existing marketing platforms, and introduced in new applications for customer service, natural language generation and more and the strategic deployment of the firm’s long-term usable data.
And as the authors in our example book point out, this ultimately means greater value and a higher-level of execution for employees in established firms embracing artificial intelligence in their business processes.