What Microsoft’s Softomotive Acquisition Means for Robotics Process Automation

The Robotics Process Automation (RPA) industry recently grabbed headlines when Microsoft announced its acquisition of Softomotive, an up-and-coming RPA startup, in May 2020. Based in the United Kingdom, Softomotive raised US$25 million in 2018 and has since expanded its footprint to different parts of the Asia-Pacific region and the United States. This acquisition enables Microsoft to incorporate Softomotive’s enterprise automation tool together with the existing Microsoft Power Automate solution. Prior to the acquisition, Microsoft’s automation features were mainly exclusive to its Microsoft 365 for Business ecosystem. The acquisition of Softomotive will allow Microsoft to expand and provide vendor-agnostic automation tools.

The acquisition is generally viewed as a good catch given the current hype in valuation among RPA startups. For instance, two leading RPA firms, UiPath and Automation Anywhere, have raised US$1 billion and US$840 million from the private equity market and are being valued at US$7 billion and US$6.8 billion, respectively. Meanwhile, U.K.-based Blue Prism, another leading RPA vendor, has struggled to turn a profit since its Initial Public Offering (IPO) in 2016. With traditional Information Technology (IT) giants like SAP also offering RPA solutions in their software suites, it is challenging for newcomers to survive in this space.

One way to address this challenge is to be laser-focused and niche. At the end of the day, the role of RPA is to help companies that want specific tasks to be automated without the need to hire and deploy a team of IT engineers. This requires strong domain expertise and therefore opens new opportunities for companies with vertical specialization and a strong understanding of industry-specific work procedures. Over the years, several companies have launched vertical-focused RPA solutions:

• Tupl: Based in Seattle, Washington, the startup is founded by veterans in the telecommunication sector. Focusing solely on telecommunication, Tupl’s value proposition is to offer smart workflow management for telco engineers by minimizing their complex repetitive business work, thereby allowing these engineers to focus on solving more mission-critical tasks. By leveraging both conventional Artificial Intelligence (AI) and Machine Learning (ML) models, Tupl targets customer care, fault troubleshooting, and alarm management in both network operation centers and cell sites.
• Datamatics: Based in Mumbai, India, Datamatics started as an IT Services and business process outsourcing company. Now, the company has significantly grown in terms of innovation capabilities and started offering RPA solutions to its clients under its Digital Solutions in 2015. Focusing on the banking and finance industry, Datamatics has automated over 800 processes with the largest Indian bank. Datamatics’ TruBot offers several exciting and unique features, including a universal recorder, an intelligent Optical Character Recognition (OCR) platform, a dynamic bot station selector, proactive health monitoring, and a detailed designer log. Datamatics also offers a future upgrade to AI- and ML-based solutions.
• RPA Labs: Based in San Jose, California, RPA Labs provides a full-service solution in robotic process automation for companies in logistics and supply chain. RPA Labs offers three types of automation: process or documentation, email, and conversation. In order to provide quick Time-to-Market (TTM) and Return on Investment (ROI), the startup integrates a Logistics Language Library in its technology, with pre-trained bots that are plug and play in nature. RPA Labs also offers conversational AI and OCR platforms for ease of use.

In recent years, more and more attention has been given to the implementation of AI in the enterprise space. To many enterprises, the level of automation and augmentation offered by RPA may be more than sufficient for now. Meanwhile, for RPA vendors to be more competitive, they will need to future-proof their solutions by demonstrating a migration roadmap to full-scale ML solutions in the near future for clients who need more than RPA.

Going niche also allows RPA vendors to narrowly focus on solving industry-specific challenges. A lot of current business administrative efforts go into compliance with various standards and regulations, such as audit report generation, background checks, and business continuity requirements in banking, which require a full understanding of the current legislative and regulatory landscapes. The added benefit of going niche is the ability to provide industry-specific jargon and lexicons, whereas chatbots and conversational AI programs will need to master all of these terminologies to search for, and relay, accurate information.

Given the current development—the acquisition of Softomotive—ABI Research expects more consolidation in the RPA market. As discussed in our Cloud-Based AI in a Post COVID-19 World Whitepaper, COVID-19 is a golden opportunity for cloud AI vendors. As enterprises embrace the new normal, they should consider more automation of the existing workload, and RPA is the right toolkit to do so. It will be interesting to see whether more cloud players will follow in Microsoft’s footsteps or move to develop their own internal solutions.