25 November 2020

AI and 2020 Vision

“Not only will they possess their own computer, capable of individual programming; they will be linked to the World Data Network. Thus everyone will be able to enjoy the equivalent of an Einstein in their own homes.”

In many ways we are still pursuing the Artificial Intelligence dream of Henry Swinton, a character in Brian Aldiss’s brilliant 1969 short story ‘Supertoys Last All Summer Long’, where ‘supertoys’ are synthetic life forms designed to help humanity.

From Chatbots to Self-Driving Cars to Patient Care, companies continue to invest in Artificial Intelligence (AI) and Machine Learning to build systems that can simulate human intelligence, but also that can evolve and enhance their capability over time.

But two recent studies into how we are using AI in business in 2020 reveal that real progress is being made.

McKinsey’s Global Survey ‘The State of AI in 2020’ reveals that 50% of companies report adoption of AI in at least one business function.

And the Gartner Hype Cycle for Artificial Intelligence reports that despite the global impact of COVID-19, nearly half of AI investments were unchanged since the pandemic began and 30% of organizations were planning to increase investments.

McKinsey’s survey also shows that companies are now using AI as a tool for generating value with some respondents attributing 20% or more of their organisations’ earnings (pre-EBIT) to AI.  However, where AI has been most widely adopted – Service Operations, Product or Service Development plus Marketing and Sales – these functions remained largely unchanged from the 2019 survey.  It also highlighted that companies need to do more to manage the risks associated with AI beyond cybersecurity, such as in equity and fairness, national security and physical safety.

Nonetheless, Gartner believes that AI is ‘rolling off the peak of inflated expectations’ in its Hype Cycle analysis and that two megatrends ‘Democratization of Artificial Intelligence’ and ‘Industrialization of AI platforms’ dominate the landscape.  Indeed, they are both key to its continued success as they ensure that AI is no longer the ‘exclusive subject matter of experts’ and that its industrialization should allow greater reusability, scalability and safety.

My own experience of helping to build a machine learning predictive model that identified key sales and marketing targets, plus working within a digital infrastructure company whose platform was host to a range of AI led initiatives, makes me agree with that hypothesis.

Managing the continued development of AI, alongside a true understanding of its risks, continues to offer the potential to improve business and address real world challenges. It may even take us beyond the dreams of science fiction into reality.