In 2018, we saw the emergence of a new ecosystem of AI-related tools for marketers. Coming to market alongside new machine learning development platforms, these tools afforded new opportunities for marketers of all varieties. From enhanced DCO campaigns to smart DAM solutions. The worst were shown as gimmicks or AI-washed, while the best oftentimes left us wondering if the hype was all for naught.
In reality, the most important contribution of new AI tools in 2018 was merely our beginning to consider how AI will change the nature of creative work. After all, it’s one thing for new tools to come to market — it’s a whole other endeavor to build the same processes that built those tools into a creative agency setting.
That’s why in 2019, the onus will be on both tech companies and creative agencies to work closer together, facilitating both the operationalization of these tools and even the future of creative work itself. In this landscape, the future belongs to those who can find viable opportunities for both brands and agencies to work /with/ tech companies, proactively operationalizing these new ML technologies and taking creative, inspired stabs at the future of work.
The ‘Big AI’ Antagonism Fallacy
Due to their role in the global economy, it may seem that Google, Amazon and Facebook have an intractable upper-hand in the development of these tools. With unmatched access to consumer data, hordes of AI/ML expertise, and some of the deepest coffers in business today, it’s tempting to view Big Tech as the ultimate and unjust winners in the competition for AI. Leading our societal shift away from the paradigm of the past (toward one with attributes more like endless choice, digital technology, data, automation and artificial intelligence), tech companies are simply best positioned to succeed in this new age — while the rest of us, slow to pivot to harnessing the arcane wizardry of data science, risk the resultant inevitability of wildly diminished employment prospects.
To be clear, it is of course a positive development that tech companies are using AI to build more than just more accurate recommendation/personalization engines, (catering to the attention economy in ways that provoke outrage, partisanship and addiction)… but the notion that tech companies alone will lead AI innovation betrays what we all increasingly realize about technology’s own glaring limitations.
Rather, in working closer with Big Tech on AI-related projects, agencies can begin to augment and improve existing processes, perspectives, and even value creation modalities. Specifically, agencies and brand marketers are wise to continue bringing novel activation concepts to tech companies, letting them work out the technical specifics. But by that same logic, tech companies would be wise to re-examine their agency relationships, ensuring that their agencies are aware of the utility of cloud-based ML tools. In particular, platforms such as Google’s Machine Learning Suite, AWS Rekognition, Neurala’s Brain Builder, and Adobe’s Sensei will allow agencies to experiment with new modes of value creation that correspond with their existing unique and highly nuanced key value propositions.
Our Infinite Game
Perhaps the biggest misunderstanding embodied by the Big AI Antagonism Fallacy is the notion that AI-related projects are a finite game, an instrumental activity in which there are clear rules, boundaries, winners and losers. In reality, part of what’s so exciting about the emergence of these AI tools is that they empower us to take part in a new, bold, (and possibly more fulfilling) /infinite/ game, requiring participants to act in the present toward a ubiquitous, protean, and impactful goal.
Just because AI can be understood as an infinite game does not mean that there literally no rules, boundaries, or winner and losers. This is still business, after all. There are, of course, new opportunities and trade-offs in this environment — but while the arrival of creative AI operations in advertising shifts some aspects of how traditional agencies function, it will also enhance how agencies have traditionally thought audiences, branding, and engagement. Through new modes of collaboration between agencies and tech companies, we can
Training Machines and Humans with New Tools and Processes
The broad promise of artificial intelligence is that it will teach us new things about the nature of reality. For marketers in 2018, that meant entertaining new notions and prospects of what advancements in machine learning may hold for our respective industries and the future of work. Business press prognosticators pitched us AI-driven startups; tech barreled on in the AI arms race; and incumbent martech firms began to pivot toward offering “AI-driven” variants of their existing value propositions.
One approach to considering this new dynamic is through the lens of actually building a machine that demonstrates some aspect of general artificial intelligence. What sorts of questions would it have to answer, and how would it get there? In order to build machine that can help us answer questions about the performance our own work—and in doing so, help us learn new things about our work—we have to harness the power of the crowd (read: our coworkers and colleagues) to build work into a platform. This platform will also therefore serve as a tool for data annotation, and iteration.