Intelligent-Automation-Ecosystems are intended to act as a bridge between all parts of an organisation’s business and its technology stack.

The ecologist Arthur Tansley defines an ecosystem1 as “A community of living organisms in conjunction with the non-living components of their environment, interacting as a system.” A ‘technology ecosystem’ imitates nature’s ecosystem by creating interconnected and interdependent systems required to establish innovative services. Technology ecosystems enable small technology firms to partner with large established technology vendors and bring innovation to the marketplace together. An Intelligent-Automation-Ecosystem is where a set of Robotics Process Automation (RPA) and Artificial Intelligence (AI) systems come together harmoniously to create intelligent, cognitive, end-to-end automation and help in creating enterprise-level transformation strategies/roadmaps.

The term Intelligent-Automation-Ecosystem is ubiquitous in the automation domain. Intelligent-Automation-Ecosystems are intended to act as a bridge between all parts of an organisation’s business and its technology stack. Organisations that benefit most take the time to design a Target Operating Model for their business and then select the appropriate mix of new and existing technology solutions to enable this model to be realised.

So, what does a unique ecosystem look like and how are IA (Intelligent Automation) technologies, as enablers, working together to ultimately drive results for the business? First and foremost, an organisation-wide initiative for implementing IA solutions must always aim to deliver across the following 4 key areas:

 

 

To be successful, it is important to look at the big-picture, evaluate the ASIS business operating model, and leverage the right set of IA levers, tools and methodologies to establish an “Intelligent-Automation-Ecosystem”. As a result, the TOBE (Target Picture) will help answer the challenges faced by organisations and help in building forthcoming plans. At Client Solutions, we enable the set of key levers for establishing the ecosystem as illustrated below:

 

 

 

1.Business Process Reengineering & Management 
Business Process Reengineering / Management (BPM/BPR) is the method of streamlining processes by challenging each step of the ASIS process. The BPR (Business Process Reengineering) and BPM (Business Process Management) initiatives are strategic and aim to deep dive into all the areas of business process. BPM and BPR offer a holistic approach to optimise and automate real-world business operations and functions. The set of BPM/ BPR tools and methods act as predecessors for the successful ‘IA implementation.’ Through process reengineering, organisations can rethink and take the opportunity to redesign operations, tasks, actions, customer interaction, internal communication, etc.

Recommended IA lever: Sig Sigma, Lean, DMAIC, Kaizen, etc.

 

2.Robotics Process Automation
RPA platforms such as UiPath and Blue Prism have added significant value to automation initiatives to date. RPA enabled BOTs are focused on desktop automation and emulate user performed actions in existing systems. Lately, Process Mining and Workflow Automation have appeared as complementary technologies to further extend automation capabilities. Process mining tools capture digital actions from multiple systems and apply analytics to discover, monitor and improve real processes. Workflow Automation tools help organisations to automate the allocation of tasks, documents, and information across work activities following defined business rules that improve and enhance the core-offering of RPA.

Recommended IA lever: UiPath, Blue Prism, Automation Anywhere, Nice, Pega, etc.

 

3.Decision Modelling & Predictive Analytics
Decision Modelling focuses on identifying important data, streamlining data preparation and exploration. Predictive Analytics supplies the actionable insights that help to solve or supervise issues. Both ‘Decision Modelling,’ and ‘Predictive Analytics’ workstreams are inspired by data science. The relationship of automation and data science is perfectly unbiased and complementary to each other. The bots and automation engines enable better business decisions with the bots gathering, cleaning and collating data for use in rule-based automation engines. On the other hand, Predictive Analytics uses data, statistical algorithms, and Machine Learning (ML) to identify possible future outcomes by analysing historical data. Combining ML with Robotics is a perfect example of an IA solution. This combination is a state-of-the art solution to visualise, simulate, and autonomously identify improvement opportunities in organisations.

Recommended IA lever: SAP Predictive Analytics, IBM Watson, Oracle Crystal ball, etc.

 

 

4.Digital Assistant, Virtual Agent
Digital Assistant is a digital software that helps in performing daily activities such as scheduling an appointment, making calls, typing messages, extracting emails, etc. Virtual Agents emulate humans by helping individuals via textual or auditory means. Virtual agents are usually known as chatbots. These are AI-powered, automated programs used by organisations to enrich the customer service experience. In the last few years, there has been an explosion in the adoption of virtual agents (Chatbots) due to their ability to deliver instantaneous and accurate responses to users. In addition, they have eliminated many menial and repetitive tasks, thus freeing up resources to work in more challenging and interesting tasks. Finally, organisations that invest in Digital Assistants typically yield a high and rapid return on investment.

Recommended IA lever: Oracle intelligent bots, service bots, kore, Kofax, Kissflow etc.

 

5.Cognitive, NLP, Semantics
Natural Language Processing (NLP) helps systems and robots to understand, interpret, and manipulate human language. Cognitive systems are designed to understand human thoughts and replicate and mimic human thought processes. Semantics analysis helps to process information like a human does and to understand its real meaning. All 3 sets of technologies aim to understand human interaction and behaviour. The growing trend in IA is towards cognitive automation which takes things a step further by enabling organisations to automate processes that include unstructured data sources, such as scanned documents, emails, letters, and voice recordings by leverage technologies such as Optical Character Recognition (OCR), Text Analytics, etc.

Recommended IA lever: Microsoft Cognitive Toolkit, BMC Helix, IBM Watson, etc.

 

6.Artificial Intelligence
Artificial intelligence (AI) attempts to simulate human intelligence. AI involves using systems and tools to perform tasks that traditionally require human thinking and intelligence. AI may use all or a combination of the levers identified thus far to reproduce human-like-intelligence. AI enables the computer program to learn, reason and be inspired by the intelligence of humans. Artificial Intelligence has enormous potential advantages. AI applications and systems are intended to work alongside existing systems within the organisations to add precision, accuracy, and speed.

Recommended IA lever: Tensorflow, IBM Watson, Microsoft Azure ML, etc.

 

We are combining IA technologies to gain maximum advantage

Intelligent Automation is an ocean with a variety of technologies and platforms. IA tends to benefit business in two ways 

  1. Helping consumers (end-customers) enjoy the best possible experiences and hassle-free service.
  2. Empowering businesses from within by freeing up the workforce and its staff to concentrate on business critical areas and functions.  

With the correct set of IA technologies and platforms, this dual approach helps build enterprise-level transformation strategies and roadmaps.  

We are more than happy to discuss enabling the Intelligent Automation journey for your business and helping you build the right strategy for your Enterprise-level transformation. Get in touch with our in-house Intelligent Automation team at enquiries@clientsolutions.com  

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1: The Context of Ecosystem Theory: https://www.jstor.org/stable/3658685?seq=1