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<\/p>\n\n\t<\/p>\n
\n\tAs <\/span>we\u2019ve recently written<\/a>, IT – and cybersecurity in particular – has a serious shortage of skilled personnel. Jobseeker interest supplies less than 65% of employer demand in most countries. In the UK, 44% of firms<\/a> are not where they want to be in terms of IT infrastructure and staffing; 150,000 positions are currently unfilled.<\/p>\n \n\tAs artificial intelligence (AI) becomes more accessible, efficient and sophisticated, we find ourselves wondering – can it fill some of those empty vacancies, and, importantly, should it?<\/p>\n \n\tIn many cases, AI can do boring jobs <\/span>better than humans. Over 90% of cybersecurity incidents<\/a> are ultimately down to human error – users clicking bad links, opening bad attachments, failing to change or secure passwords and so on. AI can help the average user spot phishing websites<\/a>, malware attachments and spam, reducing the number of incidents at the greatest point of failure.<\/p>\n \n\tIt can also help cyber security professionals. The average company handles <\/span>200,000 cybersecurity events per day<\/a>. Most are insignificant; some are significant threats demanding human intervention and web trawling to find appropriate hands-on removal strategies. Deep learning computers can screen and filter these threats, as well as carrying out the research more rapidly than human users. <\/p>\n \n\tAI is already transforming businesses outside the realms of sec<\/span>urity, too. In the finance<\/a> sector, extra raw computing power can detect bad transactions, offer personalised loan deals based on harvested data, and carry out the formulaic, repetitive work of data processing involved in satisfying compliance requirements.<\/p>\n \n\tGeneral Electric – a 120-year-old business not immediately associated with tech agility – has transformed itself by <\/span>collecting and analysing big data<\/a>. Everything from inspecting bills and logistics to improve bargaining with suppliers to \u2018digital twin\u2019 modelling of equipment to predictively diagnose faults is now carried out by AI. <\/p>\n \n\tThis grunt work <\/span>could be done by people – but at a tremendous cost in man-hours and with a far greater margin of error. Research by Accenture indicates that productivity could increase by up to 40%<\/a> if AI were fully harnessed for such data-handling tasks.<\/p>\n \n\t\u201cRobots will take all our jobs\u201d – that\u2019s the short version.<\/p>\n \n\tBill Gates<\/a><\/span> predicts that by the mid-2020s, labour demand for low-skill jobs will be so low that businesses will need tax incentives to hire humans at all.<\/p>\n \n\tGates is concerned about minimum-wage jobs, but more skilled roles are also set to change. It\u2019s a question of finding meaningful work for employees to do while they\u2019re on the clock. If businesses can automate formulaic, repetitive data handling and research tasks, the people who currently do that work will have time on their hands – and when there\u2019s spare time, personnel cuts are likely.<\/p>\n \n\tAn Oxford University study from 2013 identifies over <\/span>700 jobs which could be automated<\/a> so extensively that half the workforce currently performing them would be redundant. Loan officers, receptionists, legal assistants and sales personnel are all facing greater than 90% odds of replacement<\/a> by bots. <\/p>\n \n\tCustomer-facing roles are particularly vulnerable<\/a><\/span>. Facebook is pitching chatbots to automate customer service conversations via its Messenger app. McDonalds is negotiating an AI ordering service<\/a> that will run through chat app Kik Messenger – and when it becomes apparent that chatbots are notably cheaper than self-service kiosks, supermarkets may follow suit.<\/p>\n\n\tGlass half full<\/h2>\n
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\n\tGlass half empty<\/h2>\n
\n\t“Half the workforce currently performing them would be redundant.”<\/strong><\/h4>\n
\n\tGlass is not full, let\u2019s fill it<\/h2>\n