Recessions force companies to make difficult choices to survive. One of the first places to cut costs is the wage bill. As regulation and costs rise in offshoring countries, there is a new choice. This choice involves a short-term investment but long-term savings in wages, health insurance or breaks: the choice to adopt machine intelligence and automation.
Machine intelligence – a term that encompasses artificial intelligence, machine learning, natural language processing, predictive application program interfaces and speech and image recognition – offers potential beyond the comparatively “basic” insight potential of big data.
Machine intelligence also means that anything with a decision tree involved can, and will be, automated. In other words, flesh can be replaced with silicon.
Brexit may have been partly driven by economic inequality and resentment but the recession it will spawn will speed up the adoption of technologies that can accelerate unemployment.
The threat to jobs is widespread, and not just to lower-paid areas traditionally impacted by technology changes. Previously “safe" professions such as investing or law are open for transformation, as exemplified by the 160,000 parking tickets in New York and London overturned by a simple chatbot lawyer called DoNotPay.
And if only a small fraction of marketers are trusted by their chief executives to drive growth, then marketing and advertising is ripe for machine disruption.
Dr Stephen Thaler claims his Imagination Engines AI research will lead to the creation of “creativity machines” within five years. We have already seen the PR stunt of “hiring” an AI creative director, while machine learning is also used to extract every cent of programmatic value from media buys.
It is hard to imagine wider society’s heart bleeding for advertising, but take another example – what about the 3.5 million ordinary families supported by truckers in the US?
While the battle around self-driving cars is currently being fought in luxury cars, the real challenge is for the billion tons of goods hauled by road every day. Tragically, we have seen the first death associated with Tesla’s autopilot. Tesla has responded by claiming: “Of the one million auto deaths per year worldwide, approximately half-a-million people would have been saved if the Tesla autopilot was universally available.”
The blame for the accident is still uncertain and public policy organisation the Rand Corporation has pointed out serious flaws in Tesla’s calculations. But in the correct domains and circumstances, machines are better than humans.
It offers the potential to not only discern the “truth” behind data and behaviours at a scale, speed and accuracy that would be impossible to achieve without it but also reveal the contradictions, inversions, oddities and coincidences that point the way to innovation.
The benefits of machine intelligence in healthcare go beyond retrospective analysis and predictive models to influence diagnostic decision-making. IBM is currently partnering the Memorial Sloan Kettering Cancer Center to enable patient-specific diagnostic test and treatment recommendations for cancer.
Many of IBM Watson’s features from its famous Jeopardy! victory are also relevant to the healthcare domain, including its ability to incorporate huge volumes of unstructured text data (patients’ electronic health records, medical literature), respond to natural-language queries, provide probabilistic reasoning to assist evidence-based decisions and improve its performance through learning from user interaction.
But what happens to the people left behind or deliberately discarded in this tornado of progress?
It is easy to think in dystopian terms. It is simpler to describe the loss of existing, familiar jobs than to imagine industries and functions yet to be created. The challenge is: what will these jobs be and how long will the lag be between “creative destruction” and creation of new industry?
If you work in technology, you also work in ethics.
We have a responsibility. We are not politicians; we need a plan. Technology and data must be harnessed to build something better from the debris of yesterday’s shattered dream and prevent people from being buried under it.
Sustainable customer value(s) can be created through pairing machine intelligence with human-centred innovation. We can be mindful that our work ladders up to the Sustainable Development Goals and social balance, economic prosperity and a healthy environment.
Technology can connect positively to the people excluded from the metro-elite and break out of our industry bubble that lacks diversity in age, sex, race or class. Machine intelligence can be the impetus in sustainable innovation rather than divergence and human obsolescence.
Ultimately, whatever we do for people, brands and businesses, we need to make it count.