The growing trend of integrating artificial intelligence into business processes not only augments any work involving data-driven decision-making, but also changes the way knowledge work gets done in many organizations.
In the words of Peter Drucker, who first coined the term ‘knowledge workers’ back in 1959, the greatest challenge is to “increase the productivity of knowledge work and knowledge workers.” Given how the office environment is changing, automation can significantly increase the overall scale of a department or organization by eliminating routine tasks and freeing up employees to handle exceptions or perform creative work. It empowers knowledge workers to focus on higher value scenarios and help minimize human error.
Two important trends are having a significant impact on knowledge work. One is mobile, which is moving many business processes towards a self-serve model. The other is automation, and the use of AI, when it comes to basic decision-making.
How Mobile and RPA transform knowledge work
The exponential growth in the use of mobile devices has introduced a major paradigm shift in the workplace. Many tasks that the knowledge worker used to own and drive are now being driven and owned by the end user initiating the mobile engagement.
This has had a profound impact on knowledge workers. Their responsibilities have transformed from driving an interaction or task, to supporting an interaction or task and ensuring any breaks in a process are mitigated as quickly as possible. This means they are now more responsive and driven to ensure that exceptions are dealt with effectively.
Like mobile, automation – or more specifically, robotic process automation (RPA) – is having a profound impact on knowledge work. The elements of RPA software are not new, but the compound effect we are witnessing now is unprecedented.
Organizations are using RPA to automate reoccurring, support-driven tasks, often referred to as ‘swivel chair’ activities. These are activities where the knowledge worker interacts with various systems and data streams to complete a task. RPA does an excellent job automating these tasks, reducing manual operations costs by 25 to 40% or more, and the tools are typically easy enough to use – allowing almost anyone to build a ‘robot’.
Furthermore, AI adds significant value to both RPA and mobile. In relation to RPA, sophisticated machine learning algorithms can watch and learn how knowledge workers react and take action in given circumstances and predict future outcomes or recommended courses of action.
What the future holds for AI in business
Despite the hype and general exaggeration around AI, organizations should expect tangible benefits from artificial intelligence applications in the coming year. Near and dear to our hearts is the need for organizations to simplify and automate the transformation of unstructured content into structured content, so they can take action and make better decisions quicker.
AI is also advancing in the area of extracting semantic insights from content. A good example is extracting numbers from invoices, various parties and terms mentioned in a lease contract, or information contained within a passport. Recent advancements in content processing will result in significant material gains and the transformation of the content intelligence market. This trend will have a long-lasting impact, along with the advancement of RPA: the automation market is expected to grow by $1.2 billion by 2021 at a compound annual growth rate of 36%.
In order to generate value from knowledge work, organizations must establish a clear, coherent link between AI and business value. The automation of business processes promises huge productivity gains and workforce changes, with benefits accruing only to forward-thinking organizations. AI is maturing at a rapid pace relative to determining the type of content you are processing, be it forms, invoices, contracts, general office documents or photos.
This is the abridged version of the article: New Content Intelligence Solutions Link AI To Business Value - written by Ciarán Daly. Please click here to read the full version in AI Business: https://aibusiness.com/document.asp?doc_id=760573&site=aibusiness
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