Buyers have an increasingly vast choice of robotic process automation (RPA) vendor technologies all making big claims. Although having a choice is usually considered a positive, once you look beyond the hype there remain significant differences in technical advancement, capabilities, and delivery approach of these technologies. In fact, it’s now easier than ever to choose an RPA branded capability that won’t successfully operate and scale in large, demanding, enterprise environments - where performance, security, flexibility, resilience, usability, connectivity, and governance are absolute necessities.
There’s another notable requirement too. RPA’s real value can only be truly realized when it enables a business or IT leaders to deliver smarter and faster ways of working; across front-to-back office environments. This means selecting only the most advanced RPA technology running a digital workforce that’s continually enriched with AI and other cognitive capabilities to deliver ‘intelligent automation’. It also means enabling users to more easily apply these capabilities at greater speed and scale too.
Here are seven key characteristics of ‘enterprise-grade’ intelligent automation that are proven to consistently deliver on promises.
1. High performance and resilience
To ensure the most successful enterprise-level process automation are achieved, digital workers must be able to work continually without fail, across the widest range of operating environments. This means they have to automatically make adjustments according to any interface change, different screens, layouts or fonts, application versions, system settings, permissions, and language. The most advanced digital workers possess this capability because they’re continually being augmented with AI surface automation using machine vision, so they can read, understand and interact with any virtual application or user interface that exists within any IT environment.
Be aware that record-and-deploy robots still struggle to adjust to any unplanned changes across an ever-changing digital environment, which really limits work performance, productivity, and resilience. Record-and-deploy robots also sit and wait for target systems when they could be proactively working.
2. Increased scope and scale of automation
Digital workers already deliver joined up, data-driven, process automation across enterprise environments of disjointed, difficult to modify legacy systems and manual workflows. However, to address any type of business problem means automating increasingly complex work processes that don’t rely on simple rules and structured data alone. We’re talking about data that’s in a semi and unstructured form; such as free form text, chatbot interactions, emails, and many other interfaces.
Look for digital workers that can extract, understand and process this data. Therefore, seek out digital workers augmented with technologies such as OCR, machine learning and natural language processing, which used in tandem is key to unlocking all structured semi-structured and unstructured data, which then unlocks the potential for more complex automation. For example, these digital workers can manage email triage simply by reading through emails using natural language understanding technology to analyze the content and context of a message against a set number of scenarios. Digital workers can then either respond or pass to a person for further action.
3. Optimised workflows - smarter and faster at scale
To achieve sustained success with intelligent automation involves the continual discovery, analysis, and optimization of those process automation opportunities most strategically aligned to swiftly achieving the highest business value. Another challenge is designing these processes as the best possible workflows to be automated by digital workers, at the lowest cost. To support enterprises, at any stage of their automation journey, look for RPA vendors that offer tools that help streamline, govern and scale, end-to-end automation – while reducing the typical planning and process design time too.
4. Intelligent orchestration of digital workers at scale
Many enterprises discover that smarter management of all automation operations is required, especially when they’re delivering higher volumes of work automation - at an ‘industrial’ scale. The key enabler is central intelligence productivity platforms that provide the orchestration and management of all the entire automation lifecycle. We’re talking about intelligent orchestration that enables the seamless interoperability of human and digital workers in a hybrid context, to ensure that all operate at peak efficiency - and which also optimizes productivity and generates the fastest ROI.
To further reduce the human management burden of intelligent automation at scale, more advancements are enabling digital workers to self-manage their ever-changing work priorities. People will still be directly involved in controlling a digital workforce’s priorities and validating exception handling, but AI-powered orchestration agents will manage the detailed daily allocation of specific work demands and scheduling.
5. Lowest coding effort
Intelligent automation – especially at enterprise scale, should enable business users to swiftly respond to market demands, so they don’t want to waste time and effort building digital workers. It’s far better to swiftly deliver automated work using an intuitive operating system to train and manage them. We’re talking about simply drawing work process flowcharts that orchestrate an underlying ‘process definition’ language that both digital workers and humans understand - which also removes the need for coding and any associated risks too.
Any vendor that requires programming expertise to automate each process will actually create code-heavy deployments, frequently also entailing extensive debugging effort and high overheads. Also, due to a growing scarcity of coding skills, these automation projects will have to get in the IT queue, which is contrary to intelligent automation’s ‘operational agility’ promise. The IT community’s proper role in intelligent automation is to uphold the necessary governance, security and compliance requirements – and not be burdened with ever more technical debt.
6. Scalability and collaboration
Intelligent automation-driven transformation at scale is only ever achieved through centralized effort, so insist on collaboration capabilities. The best way to scale and compound even more value from intelligent automation is having the freedom to employ robots wherever they’re needed to deliver automated work collaboratively, so it’s shared and multiplied across the businesses. Ask vendors to demonstrate how users can not only centrally design, draw and ‘publish’ new ways of automated working, but share, improve and re-use these automated work assets - anytime, anywhere.
Unfortunately, when any automation technology is distributed across desktops and used in individual contexts - it may help that individual, but it won’t help the whole organization transform work. This ‘siloed’ approach naturally limits any scaling potential and is obviously not effective in the current climate of constrained and remote workers.
7. Security and auditability
Within any enterprise environment, all intelligent automation activities must be performed most securely, compliantly and transparently or it becomes shadow IT. You’ll clearly need a vendor with an operating system that generates a centralized irrefutable audit trail of all process automation, providing granular detail of all robot actions and all training history too. Even better is enabling users to create automated processes, which they publish as a document that ‘is’ the actual process. Change the document and the process is instantly changed, and it’s all securely managed within the central system. This best protects the business from rogue employees and rogue robots.
Most desktop automation technologies present drawbacks because a robot and a human share a login, so no one knows who’s responsible for the process, and this creates a security and audit hole. Another challenge is when a single human user is given autonomy over each process recording this obscures a robot’s transparency and hides process steps. Duplicate this over time and it becomes a potential security threat as there’s almost zero clarity for compliance and governance purposes. Also, any inevitable coding introduces shadow IT – with unaudited process models that represent “back doors”, security flaws and audit failures.
The big challenge for organizations this year will be how to grow in a climate of global uncertainty, constant change and ever-evolving stakeholder demands - with increasingly constrained and disconnected resources. The bigger challenge is choosing the wrong intelligent automation technology can limit any digital transformation potential and actually introduce the risk of digital chaos. A better way for intelligent automation is to introduce business and IT collaboration, smartly, securely and at scale. In fact, by employing this approach, more than 2000 of the world’s largest organizations are achieving major productivity increases via improved ways of working, so they stay agile, safe and ahead.
Peter Walker, CTO EMEA, Blue Prism