Frequently Asked Questions & insights to help

plan your RPA journey

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Standing for ‘Robotic Process Automation’ is software tools which use workflow, coding and scripts to build automations which operate and orchestrate existing enterprise application processes and tasks via the user interface in the same or a similar way that a person does. RPA is typically intended to automate manual, repetitive and rule-bound tasks with structured data and predictable or deterministic outcomes, with an effectively more mimicry approach to automation of tasks.

Standing for ‘Cognitive Automation’ are algorithmic-based automations which attempt to mimic higher human functions, however, still require a human designer and cannot be considered Artificial Intelligence in the true sense. CA is intended to automate inference-based processes with (machine learning) algorithms and much less structured data to produce probabilistic outcomes. These automation tools can however be used to recognise patterns, read complex language and sentiment, apply learnt knowledge, pose questions and solve problems. CA is more often than not augmenting human work and improving robotic automation to perform more reliably across a broader set of challenges.

RPA software doesn’t require the replacement of any systems nor integration in the traditional sense with the various applications that it interacts with. This enables business operations to be automated in an agile and cost-effective manner through rapid automation of manual, rules-based, repetitive processes, reducing manual effort and improving productivity. The “robots” can therefore augment human workforce by automating robotic activities, freeing them up for more human, value-adding activity.

RPA can have a surprisingly low entry cost, however, like any technology solutions and tools, must be chosen and applied correctly together with a consideration of the timelines and resources necessary. Producing a business case with expected returns on investment is one such way to evaluate this, but it’s important to also recognise the potential qualitative opportunity value.

Generally speaking, any task or activity where a person interacts with a system, application or computer screen to manage, enter, edit or access data, is potentially suitable for RPA.

As a general rule, activities which meet the following criteria should be sought out as a priority:

  • Highly manual and repetitive tasks
  • Processes with standardised, predictable and readable electronic data inputs
  • Activities which follow rule-based logical steps and low variation or exceptions
  • Processes with high transactional volumes
  • Activities which are manual and time-consuming for higher-value resources

Some suitable examples include accessing and logging in to systems, accessing and updating folders and files, running macros, executing APIs, extracting, downloading or uploading content/data, sending emails, performing calculations and interacting with other software tools such as OCR and BPM.

Attended automation involves or relies on a human-in-the-loop who interacts with the robot via inputs, outputs or triggers, possibly due to the level of judgement or decision-making required to complete the process.

Unattended automation effectively uses the same technology but doesn’t require a human-in-the-loop to be operated and is therefore generally capable of “lights out” operation.

Ultimately this depends on the scope and complexity of the activities to be automated, but on average between 6 and 8 weeks, including the time to understand and document, build, test and deploy.

Another major benefit of robots is that they are task agnostic and can therefore perform any number of varied tasks, switching from one to another as required.

Ideally, automation candidates will deliver a triple-win, with benefit for shareholders, customers and employees alike. However, underpinning a good RPA business case will be a clearly stated “why” or business objectives, some good examples of which include:

  • Hours Back to the business
  • Cost Saving, avoidance or reduction
  • Regulatory Compliance or Control
  • Customer Experience
  • Employee Experience
  • Business Agility & Decision-Making
  • Business Optimisation
  • Business Continuity & Resilience

The business case is a quantified statement to express the total expected costs and benefits to achieve these stated goals using RPA, with some essential financial metrics to track performance.

Starting an RPA programme needn’t be difficult, in fact can actually be quite simple. The best place to begin is with an RPA Immersion Workshop – gather a selection of individuals who represent key parts of the business who’ll likely be involved in the programme, for instance: finance, HR, IT, business operations, etc. An Immersion Workshop will introduce RPA to the group, explain what it is and isn’t, explore the art of the possible and then review potential automation candidates and build a pipeline. A number of activities can then run in parallel to ensure you build maximum momentum and start recognising value, such as process assessment and infrastructure set-up.

A clear RPA Roadmap is essential for gaining programme scalability and will include an established Governance Framework to ensure a smooth and rapid yet controlled trajectory.

Read our blogs for more information.

An RPA journey involves a rolling programme of work driven by operational teams within IT managed environments to deliver value through ongoing process automations and therefore requires a robust, structured framework to define acceptable parameters and business practices to facilitate this.

The Governance Framework therefore defines how and by who RPA solutions will be developed, deployed and maintained, how IT infrastructure and systems are controlled and how operational assessments are performed.

Key components of a good Governance Framework will cover all aspects of how to manage process selection and assessment, solution delivery, operational maintenance and environmental control, all wrapped within a clear responsibility matrix.

Running an RPA programme can be simple but not necessarily easy and will require a number of specific skills and capabilities over time. The right mix of skills at the right time will ensure maximum momentum and scalability, safely and efficiently. Primary competencies for an RPA programme include:

  • Business Leadership
  • Programme / Project Management
  • IT Infrastructure
  • Process Analysis
  • Operational Teams
  • RPA Developers
  • RPA Operations
  • Change Management

RPA software are generally available (depending on the platform/vendor) as either on premise or cloud hosted and can be set up and operated in a variety of different ways, according to requirements and preferences.

Yes, some level of ongoing maintenance and support will be required for an RPA programme, covering two key aspects: First, the maintenance upgrades and support of the RPA software products themselves; Second, support for the automated process solutions which have been deployed to cover changes required due to the systems or applications and processes.

Lawrence & Wedlock offers simple support services to cover all automations deployed.

There a several providers of RPA and intelligent automation software products offering a wide variety of software license solutions. “Our Partners” page has a current list of those which Lawrence & Wedlock is currently affiliated with and a broader list of other suppliers can be found here. Be sure to properly evaluate software vendors and their tools based on your needs.

Natural Language Processing or NLP, is a branch of Artificial Intelligence which involves using machine learning algorithms, data science and linguistics. Its purpose is to help computers to understand, interpret, analyse and process human language interactions, so that we can effectively communicate with computers, for example, Chatbots and Conversation Assistants.

Natural Language Generation or NLG, is a branch of Artificial Intelligence, that involves machine learning algorithms for a computer programme to actually produce language that can be clearly understood and comprehended by humans.

Machine Learning or ML, is a branch of Artificial Intelligence, that involves algorithms so that computer programmes can actually “learn” how to respond and act based on interactions with data. The “learning” process can be both supervised (human taught and corrected) and unsupervised (self-taught) to improve automatically through the algorithms experience with the data.