Are Businesses Ready to Embrace Artificial Intelligence?

When we see artificial intelligence’s (AIs) on screen or read about them in books, they tend to fall into two broad categories: mad, bad and dangerous to know, or inherently good, a credit to its human creators.

In the world of fiction, most AIs – robots or computer minds – are either exploring the human condition or as Elon Musk, Professor Stephen Hawking and many other smart people warn, artificial intelligence will be responsible for the downfall of our civilisation.

In an open letter to Artificial Intelligence, Hawking, Musk and 150 experts said the fear is that “we could one day lose control of AI systems via the rise of superintelligences that do not act in accordance with human wishes – and that such powerful systems would threaten humanity.”

However, in other works of fiction, such as the Culture series by Ian M. Banks, we see super intelligent AIs with many, if not all, of the nuances and character flaws and quirks of humans. In works of fiction, AIs can destroy whole worlds, but more often than not, their power is used wisely, on a far more local, human scale. At this stage in humanity’s development, the more realistic concern should be around finding practical applications for AI in the private and public sector that creates professional and personal benefits for humans, so that we can start turning science fiction into science fact.

AI Investments and Potential

According to a McKinsey Global Institute AI discussion paper (June 2017), global tech giants are some of the main investors in AI technology. Google, Baidu and others have invested between “$20 billion to $30 billion on AI in 2016, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions.”

Angel, seed, government grants, venture capital (VC) and private equity (PE) money flowing into AI “grew rapidly [in 2016], albeit from a small base, to a combined total of $6 billion to $9 billion.”

Right now, while tech giants and investors have the funds, that is a lot of money pouring into a relatively unproven technology. A lot of the focus, which underpins future AI developments is around machine learning, “as an enabling technology.” Adoption outside of the tech sector is in the early stages, normally an experimental program or something at startup or departmental scale. If we were to compare this to social networks or Software as a Service (SaaS), we are still in the early adopter phase.

Many can see the potential. As McKinsey points out, “Artificial intelligence is poised to unleash the next wave of digital disruption, and companies should prepare for it now.” The challenge is making that potential tangible, something that can be attached to a sensible business case to implement larger scale, scalable projects that generate real-world results.

What are the challenges preventing wider adoption?

Autonomous robots can cut the grass. AI machines, such as the famous Google AlphaGo, can beat human champions playing Go and other games requiring logic and statistics. In these scenarios, we can show and teach and guide, making it easy for an AI to adapt to our world.

In scenarios where there are fixed rules or fixed parameters, machines can learn, adapt and implement. Business isn’t like that. The rules can change from one day to the next. We are, all of us, relying on human needs and motives that are also changeable. Business, in other words, is messy. As the author and Guardian columnist, Tom Chatfield points out, “When the arena is something as messy, unrepeatable and ill-defined as actuality, the business of adaptation and translation is a great deal more difficult.”

Artificial Intelligence does hold an enormous amount of promise. With the help of AI, we could find solutions to some of the most serious challenges facing humanity. But to get to that point, we need AI that can be deployed in messy, real-world, human scenarios, such as businesses and organisations with vast amounts of data.

We need professionals skilled in interfacing with the data, with people and with machine learning tools that can make discoveries far quicker and more efficiently than humans. Once we get to that point, the future promise of humanity and machines will be tangible and visible.