The term ‘artificial intelligence’ (AI) is increasingly
attached to emerging technology. While newspaper headlines focus on
developments like driverless cars and robot assistants, the real revolution for
businesses is currently happening in the context of process automation.
The kind of AI used in process automation may not be smart
enough to pass the Turing test, but it does involve increasingly more complex
processing algorithms, iterative learning and sophisticated data abstraction,
replacing decision-making and administrative skills that have, until now,
necessarily been performed by people.
Are we living in the
era of AI revolution?
The boundaries of the technologies that are collectively
referred to as AI are being challenged every day in labs and start-ups around
the world, covering everything from machine learning, natural language
processing, and image recognition, to any manner of applications that were once
the exclusive remit of human intelligence.
While many have heralded the advent of a new machine age,
this is, at the moment, mostly still hyperbole. It is true that
machine-to-human interactions have increased in our daily lives in situations
as varied as ID verification, responding to customer queries and automated
investment advice (robo-advice), but this technology isn’t yet at a level where
it is going to replace the more detailed and analytical work that, say, a
skilled financial adviser may engage in when it comes to complex portfolios.
The quiet revolution
There is, however, a quiet revolution happening with this
nascent technology that many will be unaware of, and it is having a very human
impact. It is taking place in the back offices of large corporations, where
sophisticated software using various strands of the AI technologies or
‘robotics’ (as the industry has labelled it) is being used to automate a lot of
the simpler and more straightforward processes that have historically been
provided by humans.
At the moment, it is industries such as financial services
that are embracing the technology with open arms. Certainly most of the
business process outsourcing deals we have advised on in the last 18 months in
this industry have included a significant element of robotics, resulting in a
large component of re-engineering of the business processes involved to include
them and to streamline what remains.
For once, it seems to be the insurance industry rather than
the banks leading the charge and, if you consider the sorts of business
processes involved in that industry, you can see why. Robotics are perfectly
suited to a situation where the back offices have to administer millions of
policies, contracts and claims, and make decisions using processes with defined
parameters; basically the AI ‘sweet spot’. Perhaps one of the most visible of
examples is Lemonade, an American insurance company whose USP is its focus on
AI. Lemonade reported back in January, that it had determined and settled a
claim submitted by mobile phone within three seconds using AI tools –
considerably faster than the usual 30-45 day process that many insurance
companies would normally have to go through.
Over the past decade, corporations have tended to cut the
cost of repetitive business processes by offshoring them to a country with
lower labour costs or by outsourcing to a specialist company which has
economies of scale. They will now be considering increasing their investment in
technology to cut out the labour cost altogether for certain functions.
Replacing basic human tasks with technology is a centuries-old concept, but
businesses are now seeing the value of machines that can be trained to
understand how to make decisions and automate processes to a previously
unimagined extent.
Process automation is increasingly being used to analyse
policies and claims, detect fraud, and for marketing. Even in its current early
guise, it has demonstrably improved efficiency and minimised the risk of human
error, all the while providing a compelling business case with its cost
advantages.
Challenges
A lot of this technology, especially tools like chatbots
which can only operate based on what they have been trained to do, is heavily
reliant on the data that is given to it. We saw evidence of this last year when
Microsoft’s chatbot Tay was trained by users to spout offensive content almost
as soon as it was launched!
The trick for customer-facing tools appears to be to figure
out what specific need you are trying to fulfil, and then to make it clear to
customers what the tool can and cannot do. It is vital to ensure that you know why
you are using AI for a particular process, and to understand how ‘intelligent’
your AI tool actually is. The tool needs to be able to identify the limits of
its own capability and call on human intervention when needed and there must be
an absolute assurance of the privacy, accuracy and completeness of the data
being processed by the tools and of the output that the tool delivers.
The continuing need for process assurance and the importance
of contextualisation help explain why most current deployments of AI technology
in business processes have been part of a wider structured transformation. The
smart algorithms behind AI products must be integrated with legacy systems,
rest on reliable datasets, and support the end-to-end processes that humans
must still do in a way that is efficient and risk free. In short, you cannot
just upgrade a particular function in isolation – you need to ensure that all
other systems and people who interact with that function are going to work
seamlessly with the upgraded AI technology, so that both the AI function and
your legacy systems are running on the correct data and are communicating
effectively with each other.
What next?
So a few final thoughts on AI trends in 2018:
- start-ups will continue to lead the way in their
adoption of AI; - larger businesses in the insurance and fintech
sectors will invest more in the use of AI in their processes but will do so
coupled with larger scale process re-engineering; and - the use of AI will become more visible to
customers, as they start to benefit from the speed of back-office AI tools, as
well as interact with customer-facing AI.
This quiet revolution has already started and will only grow
during 2018. When you strip away the industry speak of ‘leaning processes’ and ‘optimisation’
what is essentially happening is that AI technology is indeed replacing humans
in certain areas – perhaps the hype is not so overstated after all.
Martin Cotterill is a partner in the IT, Telecoms & Competition
group at Taylor Wessing LLP.
Thomas John is a senior associate in the IP/IT group at Taylor Wessing
LLP.
This article first appeared on Taylor Wessing’s tech and media law
microsite, Download.
