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Artificial Intelligence and Robotic Process Automation: a Match Made in Heaven

With 60% of global data and analytics decision makers saying that their company is sitting on 100 or more terabytes of unstructured data, there is no surprise that organizations are looking for proven solutions to accelerate the use of unstructured information to unleash the full potential of intelligent RPA.

It analyzes, categorizes and extracts relevant information trapped in unstructured data (such as text fields of various business documents, purchase orders, invoices, emails, survey reports, forms, etc.) to organize it into clean files for RPA.

The integration of Expert System AI capabilities into Blue Prism RPA platform delivers improved labor efficiency and productivity while accomplishing higher levels of accuracy in unstructured data access, extending business automation to new strategic areas by automating tasks that once were reserved only for humans.

Artificial Intelligence in 2020: Urgency and Pragmatism

In the dawn of the computing, computer science era in the 1940s and '50s, before we even had proper hardware, some computer scientists thought that we would solve the general AI problem by the 1970s.

“We look forward and then there was what was called an AI winter, which was a period of disillusionment when people realized that practically speaking, certain problems could not be overcome.

“But in the last three or four years, we've entered this new phase of AI development where not only have hardware and software become more capable to move to the cloud that you mentioned and other factors making us feel like AI could be done effectively.

However, when we come right down to it, we need AI to be in service of something, hopefully it's in service of improved efficiency or customer obsession or operational effectiveness.

If you look at what Alibaba has done in the retail sector, or Amazon in terms of personalization and choice and predictive analytics around what people want to buy.

“So our data also shows that about 53% of data and analytics decision makers say that their company is in the implementing phase or they've already implemented some AI.

“We have a lack of governance, most companies don't know from ethics to explainability to exactly who to participate in the process of overseeing governance.

“So what we're finding is a distribution, but many companies do turn to external experts, companies that are able to offer data hygiene, data engineering services that are offering a variety of different kinds of analytical techniques.

“Finally, there's also what you could think of as everyday or embedded AI, which is to say, ‘I am already using software and the vendor of that software has decided to add AI features to make my experience better.’

So there's a large span of different ways to do this from systems integrators, who are gonna do big projects, to existing software providers to building off of a platform to maybe a little bit of internal work.”

I think that some of the moonshot projects in a year in which we don't necessarily expect recession, but reasonably slow economic growth, companies are turning pragmatic.

And what makes an RPA bot –which is effectively relatively dumb and deterministic –what makes it effective is adding layers of artificial intelligence, whether that is machine learning to capture successes and failures, a better decision engine that takes advantage of those successes and failures, and something like maybe a natural language understanding.

Four Forrester forecasts for 2020 - AI advances, but chatbots still fail the Turing Test

After all the hype, 2020 will be the year that automation and Artificial Intelligence (AI) technology starts moving out of experimentation mode and into more serious levels of adoption, believes Forrester Research.

Here are four of the top forecasts from Laura Koetzle, the company’s vice president, group director and head of research for Europe: The robotics process automation (RPA) services market has grown over the last few years, mainly because organisations have focused on tackling simple challenges and undertaken projects focused on ‘low-hanging fruit’.

Some groups of citizens will start reacting to rising levels of mass data collection by both private and public sector bodies by using anti-surveillance technology to protect themselves against what they see as an invasion of their privacy.

Dubbed Adversarial Fashion, it was designed by hacker and fashion designer Kate Rose in order to confuse automatic number plate recognition systems - and is something that Koetzle describes as both an “information security menace and a fashion opportunity”.

Deep Fakes - or videos in which an individual’s face has been either swapped or digitally altered using AI software – are likely to become more widespread and will be used to cause PR problems for brands, for example, by damaging the reputation of celebrities fronting their businesses.

Although the technology to create deep fakes “only escaped the academic labs a couple of years ago,”says Koetzle, it is already possible to subscribe to ctrl shift face, a website that creates them for between $1 and $10 per month, and more such sites are expected to follow.

But a key problem with the whole deep fake phenomenon is that not only is it embarrassing for the celebrity or company concerned, but it also “degrades trust” among the public and makes it harder for legitimate companies to be seen and heard.

In other words, like all technology, it will have positive and negative repercussions depending who is using it and in what way – only by the nature of the beast, the repercussions this time could prove to be an order of magnitude higher than anything witnessed to date.

Artificial Intelligence everywhere

PwC was recently cited as a Strong Performer in The Forrester Wave™: AI Consultancies in Asia Pacific, Q3 2019 report.[1] In recognising PwC, the Forrester research says, “PwC marries AI skills with industry expertise and leverages tax and audit skills.

PwC helps clients realise value from AI investments along two distinct paths — automation and analytics — and improve their productivity, profit, and experience.” What this simply means is PwC is helping our clients realise significant business value by transforming their analytics capabilities using a collaborative ‘design-to-value’ approach.

For example, we developed a machine learning algorithm-based methodology for one of our healthcare insurer clients to segregate claims which were at a high risk of containing errors in terms of billing or filing.

A separate PwC study found that the region is at the forefront of AI with 15% of surveyed companies having already implemented AI compared to only 9% globally.[2] This willingness to embrace AI is not surprising.

With this in mind, PwC has developed a Responsible AI Toolkit[4], a suite of customisable frameworks, tools and processes to help business leaders assess and develop AI across their organisation in an ethical manner.

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