TECH TRENDS: More on the relentless progress of Digital Transformation….
Updated: Apr 29, 2022
Last month we discussed how major retailers Selfridges, M&S, Salesforce, New Look and TfL were using innovative AI technologies to streamline and improve their business processes, but finding that traditional organisational ‘silos’ and ‘corporate treacle’ often hindered this progress.
This article discusses some of the emerging technologies that ‘Brands’ need to adapt to using; innovating to ensure they retain customers and avoid the risk of disappearing completely. The article last month also coincided with us recently being asked by one of our major customers to comment on the emerging trends in IT, including AI, Decision Intelligence, RPA and Hyperautomation. Consequently, this months article firstly discusses these technologies in general, and concludes with our own views.
The first wave of automation, Robotic Process Automation (RPA), was disruptive; automating rules-based admin tasks, increasing efficiency and waking businesses up to the art of the possible. Whilst the global RPA market is still expected to reach $7.64 billion by 2028 (Source: Fortune Business Insights), RPA promised much and certainly delivered some benefits but left many disappointed. It solved only a small piece of the jigsaw, connecting to legacy systems but neglecting the importance of data and documents to those automated processes.
Almost overnight, digital initiatives around the world increased in response to the global pandemic to facilitate remote working, deal with key worker shortages and support the eCommerce boom. According to McKinsey, this accelerated digitisation of internal business operations by three to four years. (https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever).
Automation tools, advancements in Artificial Intelligence (AI) and the reduced cost of computing proved digital transformation to be a cost-effective alternative to outsourcing and offshoring jobs. Since then, AI has become mainstream and proven its worth with an ability to adapt as conditions change and to automatically extract, classify, validate, and make decisions based on unstructured or conversational data.
Gartner defines Hyperautomation as a business-driven, disciplined approach that organisations use to rapidly identify, vet and automate as many business and IT processes as possible (https://www.gartner.com/en/information-technology/glossary/hyperautomation). Intelligent Automation (IA) achieves this by combining AI-powered RPA, Intelligent Document Processing (IDP), and Chatbot functions, preferably built in-house, to work together seamlessly.
Given that markets constantly evolve and are not static, any automation a business employs must be able to adapt and grow. IA doesn’t just increase efficiency and productivity; it also gives businesses past and predictive data to diversify product offerings according to changing market and consumer needs. Tech giants like Netflix and Amazon bring a new threat to all sectors should they decide to expand using their vast historic pools of consumer data as they already know your customers better than you do! But IA can change this for businesses.
Defined by consultancy firm, EY, Intelligent Automation is ‘When robotic, intelligent and autonomous systems are integrated, the result is intelligent automation, widening the scope of potential tasks and processes that can be automated.’ (https://www.ey.com/en_uk/intelligent-automation). Also termed Hyperautomation, this new generation of RPA uses Natural Language Processing (NLP), Optical Character Recognition (OCR) and other deep learning algorithms to automate operations, AI recognition, reading and writing data.
IA constantly adapts to its environment, learning how to do its tasks more efficiently and is a strategic game-changer for organisations. When combined with inputs from people, learning accuracy increases and provides for simple, efficient task processing. IA also enhances the breadth of processes that can be automated. At an enterprise level, document processing can be automated, including recognition, interpretation, classification, verification as well as other business related functions. These changes can be transformational for document-heavy industries such as Health, Insurance, Law and Retail.
So what of our own views regarding the various ‘labels’ in this area? These could be summarised as follows:
Decision Intelligence - Not new but will continue with the addition of AI. Trending = Low but Steady.
Hyperautomation – Not new but more tools are now available. Trending = Middle Tier & Continuing.
AI Engineering - A ‘natural’, continuing trend of the application of AI. Trending = Middle Tier & Growing.
Autonomic Systems – Adds an ‘environment’ to AI. Trending = Middle Tier & Steady.
Generative AI – A newer, emerging trend using existing content to create new; using AI. Trending = Low but likely to grow!
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