AI and machine learning are now moving beyond hype into practical applications, and CIOs are starting to invest in major deployments of the technology.
According to Gartner’s 2018 CIO Agenda Survey, only 4% of CIOs have already implemented AI in their organisation but almost half (46 percent) have developed plans to deploy it.
CIOs in the UK are among those leading the way. We look at the use cases they’ve found for AI and machine learning, from customer service to medical diagnosis.
Amnesty International is using AI to identify violence and abuse against women on social media platforms and track how the organisation is represented in the media.
“There are many media monitoring services available, and they are great at tracking sentiment and reporting how much is being written about an organisation,” Amnesty International CIO John Gillespie told CIO UK.
“This is sufficient for a company that is sending out a handful of press releases each month, but when you are issuing four or five a day and you want to know the impact of each one individually, you need something more sophisticated.”
To develop the tools, the human rights NGO turned to ASI Data, a London-based startup with the mission statement of “AI for everyone”. One of the ways it pursues this is through its ‘Data Science Fellowship’, a six-week programme for PhD graduates and software engineers that helps them apply data science to real-world problems.
The fellows developed mathematical models that evaluated where stories in the press related to Amnesty’s recent releases.
“The success of the fellowship project far exceeded our expectations,” said Gillespie. “The opportunity for Amnesty to experiment with data science in a low cost, low-risk way was perfect for us.”
Any organisation that needs to analyse a vast volume of data could save much time and money by deploying AI. The Serious Fraud Office is already reaping the benefits, thanks to an AI robot called RAVN that helps share the burden of reviewing more than 100 million documents annually in its investigations into major cases of fraud and corruption.
“In a large [case] such as Rolls-Royce, which resulted in a £671m settlement, we had 70 investigators working to review over 30 million documents,” Ben Denison, the Serious Fraud Office’s CTO, told CIO UK.
“It’s just not possible to manually review that amount of data, so we worked with our technology partners to develop an AI robot to assist with that. We were able to prove that this approach is both more accurate and much more efficient than human review alone – in some instances at one-fifth of the cost.”
Virtual assistants have been a popular early use case for AI. Financial services companies such as Capital One Europe have been exploring how they could improve better customer service.
“I’ve seen some early prototypes in our North American labs of virtual agents – be that chatbots, be that the recently announced integration into Amazon’s Alexa product – and I think we’ll see a lot more of virtual agents in the financial services industry and other industries,” Rob Harding, Capital One Europe’s Chief Operations and Technology Officer, .
“I think it’s a good example of helping customers interact with financial services companies with a lot less friction.
“We could use machine learning where we currently have manual intervention in aspects of our workflows, and we could even get to the stage where we use a lot of machine learning in our underwriting algorithms.”
Allied Irish Bank has already found a wide range of uses for data science, from a scanning system that digitises the information in customer documentation to finding errors in tax deductions on mortgages that can arise due to regular changes in the rules.
“It’s usually something we figure out and fix, but it annoys customers and it annoys the regulator and it costs us effort and time,” said the bank’s CIO Tim Hynes at the AI Congress London.
“We applied artificial intelligence looking backwards, and we discovered that if we had an AI watching what was going on and dealing with this for us, it would have identified over 90% of the errors that had slipped through the net.”
He believes that the true potential of AI will be unleashed through the combination of quantum computing and 5G.
“In the future, with quantum computing on the back-end and faster communication, the processing doesn’t actually have to be on the robot, so you start getting even more intelligent or clever activities and uses for the robots,” he added.
“As you look at this stuff, think about where we’ve come from, understand the pace of technology, understand it’s going to keep happening and ground it in practical use with a view to the future.”
AI could threaten jobs in all manner of sectors, and journalism is sadly no exception. Supporters of the technology often emphasise that it will augment rather than replace human workers, as the Financial Times Chief Product and Information Officer Cait O’Riodan .
“AI is coming on in leaps and bounds. Things that were ropey not that long ago are getting good very quickly. Speech-to-text recognition is really phenomenal now; there’s really good text-to-speech that we are experimenting with at the FT,” she said.
“All of this with AI is not aimed at replacing journalism but augmenting it, how can we use those things to make sure our journalists are concentrating on the high-value content that’s going to really drive engagement while removing some of the repetitive steps they may have concentrated on in the past.”
Working with startups can add expertise in AI to make deployments successful in large enterprises, as professional services and real estate investment management JLL found through a partnership with ‘proptech’ startup Leverton.
JLL turned to the Berlin-based startup to increase automation of its lease management operations and digitise key processes. The startup used machine learning and deep learning to optimise the review of lease documents by identifying, extracting and managing key terms such as rental values, dates and figures contained in JLL’s contracts.
“Our work with Leverton on machine learning technology implementation across our lease administration business is transforming the way we do things,” Chris Zissis, JLL’s EMEA CIO, .
“Lease contracts can comprise between three and 15 documents, hence, digitising the process significantly reduces the time spent reading and reviewing each document. It also allows the lease administrator to spend time applying subject matter expertise in recognising patterns, anomalies and opportunities.”
Healthcare organisations have provided some key proving grounds for AI. Data analysis can provide personalised treatment for patients and scan test results to discover early signs of diseases. Rachel Dunscombe, Salford Royal Group’s Director of Digital, she has high hopes for the technology.
“For me, the coming year is all about the big data side of it. And that’s going to be the new frontier along with artificial intelligence, machine learning – which allows us to automate more of the diagnostic process,” said Dunscombe.
“Those are challenges we’ve started work on and in the next year we’re going to see those actually bringing benefits and operationalised into care settings. It’s going to take time before we are doing this en masse; these are very early days – but it is real now.”
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