As we brace ourselves for yet another deluge of dodgy automation and AI predictions for 2018, where people just make stuff up and hope we don’t remember them in a few months, we thought we’d break the mold and actually release some real numbers based on real adoption trends and real expenditure date on software and services.
We also had the audacity to define the market so this might actually make some sense.
As we revealed earlier this year, despite all the ridiculous hype, the global market for RPA Software and Services will pass $400 million in 2017 and is expected to grow to $1.2 billion by 2021 at a compound annual growth rate of 36%. The direct services market includes implementation and consulting services focused on building RPA capabilities within an organization. It does not include wider operational services like BPO, which may include RPA becoming increasingly embedded in its delivery.
RPA Definition: RPA describes a software development toolkit that allows non-engineers to quickly create software robots (known commonly as “bots”) to automate rules-driven business processes. At the core, an RPA system imitates human interventions that interact with internal IT systems. It is a non-invasive application that requires minimum integration with the existing IT setup; delivering productivity by replacing human effort to complete the task. Any company which has labor-intensive processes, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation. Similarly, RPA offers enough advantage to companies which operate with very few people or shortage of labor. Both situations offer a welcome opportunity to save on cost as well as streamline the resource allocation by deploying automation.
Intelligent Process Automation
RPA is only 10% of the true picture when it comes to total spending by enterprises on automating their processes. The internal training and development, pilot projects and trial implementations, is so much larger than simply software licences and third-party professional services to work the software effectively. We term this broader automation market, beyond RPA as “Intelligent Process Automation”. This market will surpass $6bn this year and more than double over the next four years.
And the most talked about area is Artificial Intelligence (AI), which is already emerging as a billion dollar market for enterprise operations, and could almost treble in spend in four years.
AI Definition: AI refers to the simulation of human thought processes across enterprise operations, where the system makes autonomous decisions, using high-level policies, constantly monitoring and optimizing its performance and automatically adapting itself to changing conditions and evolving business rules and dynamics. It involves self-learning systems that use data mining, pattern recognition, machine learning. virtual agents, computer vision and natural language processing to mimic the way the human brain works, without continuous manual intervention.
The Bottom-Line: Automation and AI have a significant part to play in engineering a touchless and intelligent OneOffice
However which way we spin “digital”, the name of the game is about enterprises responding to customer needs as and when they occur, and these customers are increasingly wanting to interact with companies without physical interaction. This means manual interventions must be eliminated, data sets converged and process chains broadened and digitized to cater for the customer. This means entire supply chains need to be designed to meet these outcomes and engage with all the stakeholders to service customers seamlessly and effectively. There is no silver bullet to achieve this, but there is emerging technology available to design processes faster, cheaper and smarter with desired outcomes in mind. The concept was pretty much the same with business process reengineering two+ decades ago, but the difference today is we have the tech available to do the real data engineering that is necessary.
In short, every siloed dataset restricts the analytical insight that makes process owners strategic contributors to the business. You can’t create value – or transform a business operation – without converged, real-time data. Digitally-driven organizations must create a Digital Underbelly to support the front office by automating manual processes, digitizing manual documents to create converged datasets, and embracing the cloud in a way that enables genuine scalability and security for a digital organization. Organizations simply cannot be effective with a digital strategy without automating processes intelligently – forget all the hype around robotics and jobs going away, this is about making processes run digitally so smart organizations can grow their digital businesses and create new work and opportunities. This is where RPA adds most value today… however, as more processes become digitized, the more value we can glean from cognitive applications that feed off data patterns to help orchestrate more intelligent, broader process chains that link the front to the back office. In our view, as these solutions mature, we’ll see a real convergence of analytics, RPA and cognitive solutions as intelligent data orchestration becomes the true lifeblood – and currency – for organizations.
Source: Horses for Sources
This article explores the present business climate, identifies four main emerging trends, and reviews additional future tendencies that might impact M&A transactions in 2024. Speaking with experts at Deloitte, they share some insight into the current trends in this space and how this all aligns with corporate sustainability investments and objectives.
The business touts great drive towards a more environmentally friendly and socially acceptable supply chain with a focus on packaging, emissions reduction, electrification, and inclusivity. This relies on the support of its Hellenic Bottling Company (Coca-Cola HBC), which—based in Steinhausen, Switzerland—produces a sales volume in the billions.
Wildly inefficient—that too often describes the state of our global supply chain. With 90 percent of worldwide trade relying on shipping and $13 trillion spent on logistics annually, the industry is a behemoth. Yet, it lacks data-based decision support and information sharing.