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Sanjay Chalke, Global Head of Automation, AI and Analytics for Sogeti, shares his top five predictions for Intelligent Automation in 2020.
While we continue to see a focus on the challenges predicted last year around unstructured data and building automation centers of excellence, our predictions this year look at the opportunities presented by Intelligent Automation.
1. End of the paper-driven enterprise
Will 2020 spell the end of paper-driven processes in large enterprises? This is certainly a possibility. It will result from the advent of cloud-native, web-based robotic process automation (RPA) platforms and rapidly maturing machine learning technologies that understand the written word, can extract relevant information from digital documents with human-like reasoning and integrate any resulting actions within an RPA platform. We will see a further evolution in easy-to-deploy-and-teach AI models that will increase adaptability across sectors so that human workers invest less effort on data extraction and processing.
2. Role of RPA continues to expand
I expect RPA to play a pivotal role in global data privacy and governance initiatives moving forwards. The 2020s are shaping up to be the decade defined by big data—with the launch of 5G and the explosion of connected devices. In this new era, we’ll see even more pressure on companies to be fully transparent about the information they collect and how it’s used, with legislation like GDPR and the upcoming California Consumer Privacy Act (CCPA). That’s not all. In a world of zero budget marketing and increasing pressure on the CMO to deliver more with less, RPA has already begun to prove its value in expediting repetitive and mundane data entry tasks, with some sectors ahead of the game, such as financial services. Early adopters in the marketing world have already found multiple use cases, most notably with the use of digital workers showing great promise to help free up marketing teams.
3. Intelligent automation to replace rules-based automation entirely
While many RPA platforms now offer artificial intelligence (AI) capabilities, RPA and AI are currently used as two separate entities—one is rules-based and the other is adaptive and predictive. In the coming year, RPA and process analytics will become entirely infused with AI and machine learning (ML), accelerating process mining and discovery.
I also expect to see the high effort around AI and ML shifting from research into engineering, whereby we will see an increased focus on managing the AI/ML lifecycle in production. There will be increased investment in data preparation and monitoring AI/ML pipelines. This should help to address many of the issues causing data science projects to fail by driving improvement in data quality and relieving IT from the pressures of preparing data.
4. Chatbots get smarter
Voice recognition technology continues to improve in accuracy, with advanced services being added all the time. With voice experiences becoming mainstream, new chatbot technology will be voice-enabled like Alexa. So, in 2020, instead of typing your question in the chatbot, you will talk to these bots to get your query resolved. It is clear that AI-driven chatbots are becoming smarter and smarter, being able to analyze past and current data to predict or anticipate what the customer may need and lead them to the right solution.
5. Automating for the enterprise
The intelligent automation market will see a shift from point solutions to more comprehensive offerings-led implementations. These will address integration challenges and enable the best-in-class features demanded by the modern enterprises. Service providers will own complete enterprise digital transformation programs with defined KPIs. This will see them deciding on which business processes to automate, the tools required, and the positioning of technical teams for the complete project life cycle (including the production environment maintenance)—all fused with service offerings from the RPA-as-a-service stack. This approach will save time and effort, reducing the required ‘handshakes’ between teams and the need to transfer tribal knowledge of the overall automated process landscape.
Great read, Sanjay and totally in agreement with your views and observation.