It is often said that it is not the end goal that counts, but the journey towards it. Unfortunately, this is not true for most business scenarios and certainly not for automated processes.

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A good comparison to the journey towards intelligent automation is the following. Imagine yourself traveling on a daily basis to your work, going from your home to a destination 60 kilometers away. Your journey starts with a 15 minutes' walk, followed by a one-hour trip by train and then a 10 minutes trip by bus. Sometimes there is a problem with the train, resulting in long delays before reaching your goal.

Now you decide to buy a car, so you don't need to walk to the train station anymore and will save 10 minutes on every single trip. Of course, this will save 20 minutes a day, but is it worth the investment? Does it make sense to only optimize the first and least error-prone part of the journey? Does it help you in reaching your goal?

The Biggest Pitfall in Implementing Intelligent Automation

For most of us, the situation described in the scenario above makes no sense. Nevertheless, this is common practice for a lot of organizations when they start their journey towards Intelligent Automation. At one of my clients, for instance, we are looking at pro-active monitoring of systems and taking actions to keep these systems up to date. In a recent conversation, this client asked me to configure a robotic process to gather and analyze information from various systems and present it in one overview.

Of course, it is perfectly feasible to configure a Robotic Process Automation (RPA) platform to do just that, but like using the car to go to the train station, it only optimizes the first step in the process. To fully utilize intelligent automation - or even RPA- we need to look beyond the first station and aim for the end goal. In this case that would mean planning automatic follow-up actions based upon the outcome of the system analysis.

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"To fully utilize intelligent automation, we need to look beyond the first station and aim for the end goal"

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Ask Yourself: What Do I Want to Achieve? 

When I am asked to speak about robotics, or perform an assessment, my first question after selecting a process is always: "What do you want to achieve at the end of the process?" In such a case I often get answers like:

  • I want to copy data from source system A to destination system B;
  • I like to have a robot that generates reports every hour;
  • I like to automatically send out invoices to customers.

It is very hard to get to the actual end goal, which for instance could be:

  • All invoices are paid by my customers, in time and with the proper references;
  • Service interruptions are instantly resolved and the underlying problem is corrected in the related systems;
  • Customer questions, complaints and disputes are handled quickly, efficiently and in a proper way.

It's the end goal you need to keep in mind when you start designing robotic solutions. Unlike traditional workflow solutions, Intelligent Automation requires enterprises to think in terms of what requirements need to be met to reach an end goal. These requirements should include a definition of the data, decisions, choices and calculations needed to reach the end goal. For a robot it is not the activities that count, it is only the end result.

Prepare for What's Coming: 30 Billion Smart Things 

By 2020, Gartner predicts that our planet will be home to 30 billion 'things' (refrigerators, coffeemakers, lighting) with embedded intelligence combined with nearly 8 billion smart devices.

That means that by 2020, there will be a ratio of approximately six intelligent devices for every human on the planet 1. To be able to provide people with context-dependent services that interact with these smart devices, systems need to be able to respond to changing circumstances. A flow-driven approach for robotics - where robots perform linear processes step by step - won't work in such a case because the context in which people operate is constantly changing. Instead enterprises need to move towards intelligent automation systems that respond like a navigation system, goal-driven and instantly responding to changing circumstances. Now is the time to make this move towards systems that know and act.

Steps to Intelligent Automation: Analyze - Reason - Act - Learn

Let's go back to the situation at my client, where there was a need for pro-active monitoring of systems in order to take actions and keep systems up to date. As a solution we introduced an Intelligent Automation Platform that combines the proven effectivity of flow-driven robotic solutions with a goal-driven orchestration layer and artificial intelligence. This orchestration layer can handle complex decisions, classifications and calculations and is able to trigger both manual and robotic processes. The addition of artificial intelligence makes it possible to create virtual assistants that interact with customers and enables cognitive - self-learning - robotic systems.

The result is a flexible and future-proof solution that enabled us to start small - with processes that were easy to automate - and expand gradually towards the more complex processes. For that particular client, the introduction of the Intelligent Automation Platform now allows them to aim for full automation of their Operations processes, starting small with an intelligent update process for their monitored systems.

1 Digital Humanism Makes People Better, Not Technology Better - Patrick Meehan, Brian Prentice, Gartner 2015

What are your philosophies on intelligent automation and how to get there? Get involved and leave a comment. Wonder what your journey towards intelligent automation will look like? Or what the impact of it could be on your organization? Start your journey now and get in touch. Interested in the field and would you like to discover your career opportunities? Contact our recruiter.

Also have a look at the other articles I wrote in the series on contextual intelligence, intelligent automation, and robotics: 

Kees van Mansom

Lead Automation – Accenture Operations

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