The common sales pitch of AI is the potential to automate tasks, and let humans do what they do best: being inefficient.
Everyone has heard of AI, deep learning and machine learning in some way, shape or form. Businesses are racing to jump on this bullet train and investors are hungry for the latest bit of AI technology – as seen by the massive $15.2bn of Venture Capital going into AI startups in 2017 according to CB Insights.
Don’t let the copycats fool you!
VC’s and Financial Services have seen their fair share of chatbots and ‘Inspirobots’ in recent years. The latter is little more than pseudo-randomly generated terms, a far cry from the need specified in the Turing test, developed all the way back in 1950 – still not passed to this day.
Sue Xu, Managing Partner at Amino Capital said that “In Silicon Valley, it is common for venture capitalists to encounter fake AI startups” and this is no surprise. AI is powerful, but so is marketing. The cynics among us might say that some AI startups are ready to bluff investors on their supposed AI expertise in a manner that is only comparable to what is going on in the blockchain world. Despite this you can only hide among the noise and bluff an investor for so long – eventually, there will come a point where you cannot continue to hide your ROI among buzzwords and sub-par technology.
Do companies know whether their AI investments are going to the right places?
Are investors simply investing in instructing automated machines and missing AI’s real capability to learn, improvise and evolve like human beings? According to a McKinsey report, investment in AI has grown three-fold since 2013, with tech giants alone investing a massive $30bn last year.
According to Statista, 84% of enterprises believe investing in AI will lead to greater competitive advantages. However – a massive 41% of firms say they are uncertain about the benefits, so clearly there is a huge lack of understanding of how exactly this competitive advantage is actually meant to arise.
Can AI solve our process inefficiencies?
Unsurprisingly, the industries leading the AI race are High Tech, Telecoms and Financial Services. Their common feature: willingness to invest in the technology to tackle internal process inefficiencies and gain a competitive edge. While the early adopters take the stage with their driverless vehicles, robotics and fraud detections, where could we see AI next?
Healthcare, for example, remains rife with process inefficiencies and prime ground for neural networks. Yet, relative to Financial Services and Retail, Healthcare has a low AI adoption rate and maturity. We have seen the wearables and nurse bots, but there are powerful applications of AI operationally and medically such as in Radiology for visual pattern recognition. With sufficient investment, could AI in Healthcare reach beyond the current hype and improve key processes?
Estimates state that AI investment could reach $46bn by 2020 and you would be foolish to ignore AI’s potential to revolutionalise certain processes. However, how and when we will actually start seeing significant results are questions few can answer.