The increasing popularity and uptake of Artificial Intelligence (AI) is giving rise to concerns about its risks, explainability, and fairness in the decisions that it makes. One big area of concern is bias in the algorithms that are used in AI for decision making. Another risk is the probabilistic approach to handling decisions and the potential for unpredictable outcomes based on AI self-learning. These concerns are justified, given the implicit ethical and business risks, for example, impact on people’s lives and livelihood, or bad business decisions based on AI recommendations that were founded on partial data.
The good news is that the software industry is starting to address these concerns. For example, last year, vendors including Google, IBM, and Microsoft announced tools (either released or in development) for detecting bias in AI, and recently, there were more announcements.
Last year IBM brought out:
Last month, IBM further augmented its offerings with the release of AI Explainability 360, an open source toolkit of algorithms to support the understanding and explainability of machine learning models. It is a companion to the other toolkits.
Cognitive Scale recently unveiled the beta of Cortex Certifai, software that automatically detects and scores vulnerabilities in black box AI models without having access to the internals of the model. Certifai is a Kubernetes application and runs as a native cloud service on Amazon, Azure, Google, and Redhat clouds. Cognitive Scale also unveiled the AI Trust Index. Developed in collaboration with AI Global, it will provide composite risk scores for automated black-box decision making models. This is an interesting development that could grow to become a badge of honour for AI software, and a differentiator for those with the most trusted rating.
While these announcements and those made last year are good news, there are aspects of AI training that will be difficult to address because bias is all around us in real life. For example, public data would show AI that there are many more male CEOs and board members than female ones, leading it to possibly conclude that male candidates are more suitable for shortlisting for a non-executive director vacancy than women. Or public data could lead AI to increase mortgage or auto loan risk factors for individuals living in a particular zip code or postcode to unreasonably high levels.
It is the encoding and application of these kinds of biases automatically at scale that is worrying. Regulations in some countries address some of the issues, but not all countries do. Besides, the potential for new threats and risks is high.
There is still a lot more for us to understand when it comes to making AI fair and explainable. This is a complex and growing field. As demand for AI grows, we will see more demand for solutions to check AI as well.
Companies widely recognize the potential power of artificial intelligence (AI). They instinctively understand that it feels like we’re on the cusp of something that will change our lives and our businesses in a profound way. Yet, many struggle with where to apply it. Executives can’t shake the feeling that they should have use cases for AI and use it productively today, even recognizing that AI is not mature yet and will be far more powerful tomorrow and in the future. If you’re looking for how and where your company should use AI, let me give you a perspective on a great application of AI today: your digital platforms.
In a previous blog post, we explored the evolution of enterprise IT infrastructures from a cost-center positioning to one that enables digital transformation through a concept known as aware automation — a combination of intelligent automation and cognitive/Artificial Intelligence (AI)-driven automation. In this post, we’ll explore some potential use cases and best practices for aware automation within the enterprise.
As a reader of this blog, you likely know that we’ve been researching and analyzing the RPA market in-depth for more than five years and have conducted multiple RPA technology vendor PEAK Matrix® evaluations in the same time frame.
Starting in 2015, Blue Prism earned a Leader’s spot in our assessment because of its extensive features and strong market presence. Thoughtonomy made it into our Leader’s group starting in 2016 for its Software-as-a-Service (SaaS) offering, and for combining RPA and AI for unstructured data processing.
Because it is a public company, Blue Prism’s strong growth over the years is a matter of public record. Thoughtonomy has also grown strongly, gaining around 77 direct clients and another 200 indirect through its service provider partners.
Against that backdrop, we believe that Blue Prism’s announcement earlier this week that it is acquiring Thoughtonomy for a total consideration of £80 million is a positive move for three reasons.
First, Blue Prism gains several hundred mid-sized direct clients in an instant. Second, and more importantly, its ability to deliver intelligent automation through a SaaS delivery model gives it the opportunity to much more easily sell into the mid-market. Third, this is a strategic move by Blue Prism. Right now, the adoption of RPA on the cloud is in the early stages. At the same time, many AI solutions are offered on the cloud to enable access to computing power on demand, and many work with RPA in combination when needed. Having both RPA and AI on the cloud could help companies realize the full potential of intelligent automation and achieve higher scalability. Blue Prism is becoming cloud-ready with this acquisition.
But there is more.
Thoughtonomy was set up in 2013 to provide a cloud-based intelligent automation platform. At its core, it is a cloud version of Blue Prism’s RPA, combined with other capabilities that Thoughtonomy has developed over the years, including:
In addition, Thoughtonomy will help enhance Blue Prism’s presence in some verticals, such as healthcare and government & public sector, where it currently has limited market share.
With Blue Prism at the heart of Thoughtonomy’s SaaS platform, the job of integrating the two product sets should be relatively straightforward.
All in all, we believe in this case that 1+1 does add up to more than 2. Is it a 3? Maybe not, but it is a solid 2.5.
Blue Prism’s model includes a minimum licensing requirement that can make it expensive for smaller companies to get started with its RPA offering. Thoughtonomy was absorbing these requirements. Blue Prism will no doubt clarify how it will handle licensing for its SaaS offering.
The addition of Thoughtonomy’s human-in-the-loop interface will help boost Blue Prism’s attended automation value proposition. But if it intends to target this segment – which primarily consists of front-office and contact center use cases where thousands of robots might be required – it will need to adjust its pricing to reflect large orders. Additionally, it will need to deliver more desktop-based features in order to outshine established attended automation vendors such as NICE and Pega. As this doesn’t appear to be a high-priority segment for Blue Prism, we may not see those additional features in the near future.
With this move into SaaS, Blue Prism has captured a competitive edge. We expect other companies will quickly follow suit. Several RPA vendors are cash-rich thanks to recent private equity investments, as well as good organic growth, and they may well have their eyes trained on potential acquisitions. Other RPA technology vendors and other companies that provide complementary technologies, like chatbots, could well be either acquirers or acquisition targets. AI-based automation vendors, e.g., those with NLP or intelligent virtual agents, could make acquisitions of their own to complement their products. And we wouldn’t be surprised to see large software vendors acquiring RPA vendors, just like SAP did last year with its acquisition of Contextor, an RPA vendor that we positioned as an Aspirant in our 2018 RPA Technology Vendor PEAK Matrix® Assessment several months before SAP made its move.
This is just the beginning of the consolidation phase of this expanding market, and we have no doubt there is more to come.
Everest Group will be publishing its 2019 RPA Technology Vendor PEAK Matrix® Assessment in the next few weeks. In the meantime, please check out our recent service optimization technology-focused publications, including Intelligent Document Processing (IDP) Annual Report 2019 – Let AI Do the Reading.
FDM Group and BCSWomen are hosting this evening event that will address the responsibilities employers have to ensure teams and technologies reflect the society in which we live. Join to hear about the impact that the AI diversity crisis is having on the sector and hear from influential leaders to find out how to enhance diversity across tech teams.
Sarah Burnett, Executive Vice President and Distinguished Analyst, Everest Group, will serve as one of the distinguished panelists.
17:30 – 19:30 BST, Wednesday, June 12, 2019
Sarah Burnett, Executive Vice President and Distinguished Analyst