AI is becoming increasingly central to the corporate agenda, with PwC predicting it could add $15.7 trillion to the global economy by 2030. This “Artificial Intelligence for Business” special report, originally published in The Times, UK, examines the many areas that can benefit from the use of Everyday AI, including a feature that discusses how WorkFusion self-service automation powers the digital workforce in RPA 2.0.
The technical roadblock for the first generation of RPA is unstructured data, such as invoices, email messages and web pages. Rules-based RPA 1.0 cannot process variable unstructured data, which comprises more than 90 per cent of the data in a typical business. This has left data-intensive industries like banking, insurance and healthcare unfulfilled by RPA 1.0.
Building artificial intelligence (AI) into RPA enables businesses to tackle high-volume unstructured work as well as automate simple rule-driven tasks. By extending the automation to unstructured work, we can double the rate of automation in business processes and therefore the benefits and value it creates.
Automation not only gives people the opportunity to reduce reliance on manual labour and through that reduce costs and increase accuracy, but it also offers them an opportunity to deal with complexities such as new regulation. If they’re a leaner, more agile operation, they can adapt to change with agility and create better digital experiences for their customers.
Working side by side with software robots offers an opportunity for businesses, and the digital workforce they create, to transition towards data-driven, technology-first operations. This transition has already been evident in the marketing industry, which has shifted drastically from primarily direct mail and advertising to an extremely data-intensive digital discipline with real-time ad bidding and personalised segmentation.
Marketers have retooled their teams to enable this transition over the last decade and the same is now occurring in operations teams with AI-driven automation technology, also known as RPA 2.0. As business operations become increasingly automated and data driven, the teams behind them can stop managing bots and processing unstructured data, and focus more on making decisions through analytics generated by data from bots, people and processes.
Our company was the first to create free online automation training through its portal, Automation Academy, which guides users through the automation upskilling journey. It offers introductory courses they can go through at their own pace to learn the essentials of automation, all the way up to advanced courses for engineers of previous generations to become machine-learning engineers and practise AI.
Historical challenges have prevented companies from getting the benefits of AI-driven automation. AI has up until recently required data scientists to cleanse data, select and train machine-learning models, and tune models to ensure accuracy. The skillsets to do this kind of work are in high demand and generally found only in big-name technology companies.
However, as the AI landscape evolves, new solutions are emerging that can help all companies enjoy the benefits of automating business processes, rather than just innovation leaders like Google and Facebook, which invest billions in AI talent and their own custom, proprietary and full vertically integrated systems, such as TensorFlow.
WorkFusion’s flagship product, Smart Process Automation (SPA), makes all the automation techniques available in one seamlessly integrated software, which doesn’t require advanced skills to operate. It can automate both simple rule-based steps and subjective decisions through built-in machine-learning, all without the need for any data scientists or tools integration.
Machine-learning can be a difficult and cumbersome technology to deal with, often taking weeks to build a single model and then months to insert it into production systems so the business can actually start using it. With WorkFusion’s patented Process AutoML™, data generated during business-as-usual work trains machine-learning models on-premise in a customer’s own environment, on the hardware they already have and with very low data intensity, typically hundreds of examples rather than the hundreds of thousands required by cloud-based AI providers.
We also drive the benefits of having it all in one platform by providing our users with analytics. By having a single pane of glass across both their manual workers and software robots they can get diagnostic information on what has happened and predictive information on what will happen, such as forecasting process accuracy or capacity needs. This allows us to drive greater benefits for customers relative to what is possible by approaching innovation through best-of-breed integration.
Our view is that AI is one of the key contributors to making automation more self-service. We’re very much focused on creating a tool that reduces reliance on manual labour in business operations without running an IT project or hiring new people. Businesses want AI packaged in a way they can predictably manage, understand how it performs and explain the decisions that it makes, all at enterprise scale.
We are still in the very early days of this progression and a lot more will come. There is a study from McKinsey that says 40 per cent of work can be automated with technology today. Our mission is to package technology to actually accomplish that, but more importantly to push the capabilities of AI so we can train algorithms with even less data, do it even faster and solve a greater set of business problems.
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