Automation and artificial intelligence (AI) are important, interrelated tools that help organizations streamline their processes and add intelligence to their workflows.
They allow businesses to reach organizational goals by automating business processes, whereby they can increase efficiency and adapt to new business procedures.
Through analyzing G2 data, product releases, and hot funding rounds over the past month, we’ll get an idea of just how hot this space is and better understand the importance of the technology.
How G2 Defines Process Automation and AI Software
Process automation software, such as robotic process automation (RPA) software, allows users to automate routine tasks within software applications normally performed by a company’s employees. These products are used to save time and eliminate the need for human employees to conduct time-consuming, repetitive, and tedious tasks. Examples include automation of:
- Invoice processing
- Accounts payable
- Employee onboarding
Artificial intelligence software provides developers with tools to build intelligent applications, whether that be adding machine learning or speech recognition to a solution, or creating an entirely new application from scratch with the help of an AI platform. Examples include:
When AI and automation collide
AI and automation are separate category groups on G2. The former is focused on tools which allow companies to incorporate AI technologies like deep learning into their products and the latter on tools, such as robotic process automation (RPA) software, process mining software, and business process management (BPM) software, which help to streamline company processes, workflows, and operations. However, that is not to say that there is an impermeable wall dividing these categories with a digital bouncer disallowing any software from incorporating aspects of both. Au contraire.
G2 has seen an increasing number of software companies who are bridging the gap by creating powerful automation tools which incorporate AI, allowing the user to focus less on figuring out the ins-and-outs of what to automate and more on higher level, creative work. We have seen this trend of incorporating AI into automation tools in two main ways:
- Intelligent process mining in which AI assists in determining the best processes to automate, based on factors such as cost and resources
- Intelligent automation of tasks in which AI assists in making the automation more effective and efficient, including image recognition and natural language processing (NLP)
Through the following funding rounds and product releases, we can see how these trends are coming to fruition.
Tonkean raises $24 million to automate enterprise workflows
Tonkean uses AI to autonomously coordinate, execute, and manage business workflows across data and people. Through a combination of smart RPA bots and a no-code development platform (a combination called digital process automation), users are able to automate the delivery of critical business data with their AI-powered bot. The bot proactively seeks and acquires input from teams when important data changes, empowering managers to take immediate action. In a press release announcing the funding round, Tonkean also announced that they have built NLP capabilities in their platform, which give the bots the ability to understand text and context.On G2, users left positive reviews about the tool, they only wish it was more visual:
"I don't ever have to remember to check in with my team to find out where they are on their assignments. It's also great for pulling data from my other business apps into one interface.It could use a bit more visualization. I'd like to be able to see trends that way."
Blue Prism raises over $120 million to bolster its robotic process automation suite
Blue Prism, a provider of a robust RPA platform for a number of Fortune 500 and public sector companies, has intelligence as a pillar of their software.
“In this environment, our [RPA solution is] arguably more important than ever in driving organizational adaptation and resilience, and our role as a strategic technology partner to our customers in many ways becomes more vital.”
Through computer vision or image recognition, sentiment analysis, and translation capabilities, Blue Prism has built intelligence into the platform to enable a more streamlined and simple process.
The purpose of these capabilities is to make the process easier for the end user and more efficient. Indeed, users on G2 consistently rate Blue Prism highly for ease of use, making it in the top 9 easiest to use Robotic Process Automation (RPA) software products. With at least 60 players in the RPA category on G2, it may be overwhelming to choose the right tool for a given use case. The G2 Grid for Robotic Process Automation (RPA) can help businesses in this regard with insight into the experiences of other similar businesses.
Automation Hero announces Hero_Sonar for intelligent process mining
Before one can begin the process of automation, some sort of process mining must take place. There are a number of different ways to conduct this task, but the most common one is quite rudimentary. In this process, colleagues huddle around a whiteboard and write down the best processes to automate. There may also be a voting process in which the ideas are whittled down. Tech-savvy companies are now bringing this whiteboarding to the digital screen of a computer.
However, some companies are looking to tie up the power of AI to this process. To do so, they are injecting process mining software into the AI engine, giving organizations deeper insight into their processes and the impact that automation would have on them.
Automation Hero, an AI-powered RPA solution), has introduced an intelligent process mining tool which can help companies discover processes ripe for improvement. For example, Automation Hero can help hospitals see what is happening, find what is working, and more importantly, turn those decisions into an AI model for automation (the company is also currently offering its platform free of charge to the healthcare community).
FortressIQ raises $30 million to help bring AI to process mining
Another company looking to bring AI to the automation process is FortressIQ, which is using deep neural networks to scalably acquire training data by observing the world around us. According to the company’s press release, the platform combines computer vision, natural language processing, machine learning, and artificial intelligence to capture all process steps across any system with zero integrations, APIs, or application logs.
According to FortressIQ Founder and CEO Pankaj Chowdhry:
“We’re building this kind of cool computer vision to help with process discovery, mostly in the automation space to help you automate processes.”
They announced $30 million in series B funding led by M12, Microsoft’s venture fund, and Tiger Global Management. The Microsoft investment is connected to their strategic partnership with the company which was announced in November. In addition, they announced a new integration with Microsoft Power Automate in April 2020 which allows businesses to build an end-to-end solution for intelligent automation.
UiPath introduces their end-to-end automation platform
On May 12, UiPath (Leader on the G2 Grid for RPA) announced the release of their hyperautomation platform, which they first announced in October. As we saw with Automation Hero and FortressIQ, UiPath has incorporated Process Mining and Task Mining into their broader RPA suite (fueled by their 2019 acquisitions of ProcessGold and StepShot) to help customers scientifically discover opportunities. Based off G2 reviews, a common reason for user’s positive sentiment toward UiPath is their ability to handle process mining.
By incorporating AI into every part of the platform, UiPath is looking to be a go-to source for automation from beginning to end. The UiPath AI products provide greater capability for robots to automate more use cases including dealing with unstructured data, recognizing dynamic interfaces, and complex decision making (e.g., predict loan defaults, interpret complex documents, classify emails with natural language processing).
These products include:
- UiPath Document Understanding
- UiPath AI Computer Vision
- UiPath Conversational Understanding
In addition to enabling these skills, UiPath allows customers to bring their own models or ones provided by their large ecosystem. Finally, AI Fabric enables data scientists to overcome the barriers of deploying models into automation and retraining. One particularly interesting new feature of the platform is Model Retraining, which helps to bridge the gap between AI and automation and helps in the process of training machine learning models.
Talking ‘bout consolidation in AI & automation
It’s all about the platform plays. Where there once were multifarious, disparate, and distinct machine learning algorithms, we expect to see more data science and machine learning platforms and AI and machine learning operationalization software, which do much of the heavy lifting for the user, putting everything under the same roof.
"Data science is undergoing its own transformation, from a vast collection of disparate but interdependent tools to an integrated platform."
Tom Pringle, Head of Market Research at G2
In the same vein, disparate tools for process automation, such as process mining and RPA software may be gradually replaced by broader, multi-function intelligent automation platforms. The various stages of the automation journey (from process mining to automating tasks) is increasingly becoming platformized, allowing the user to access all the capabilities they need using one multi-purpose software.
That being said, we don’t expect machine learning software or process mining software to go away any time soon, as there will still be cases in which these standalone solutions work better than the platform components.