This Approach Will Make You Selective When Choosing Decisions For Automation

Choosing Decisions For Automation

When choosing decisions for automation you need to be mindful. You should ideally take into consideration an approach governed by multiple experts in an organization before reaching a decision point.

The hardest aspect of choosing decisions for automation is to choose which process to start with. Companies need to think whether they want to automate the complex processes or the simple processes first.

This is because their decision on process automation can have a huge impact on company’s operations, in terms of solving time or cost constraints.

Choosing decisions for automation depends a lot on the time

With time, simple cases become more manageable. The pipeline of work required to deliver context data incurs high costs and considerable complexity.

For instance, it is relatively easy to control the automatic car headlights depending on the ambient light. But speed, clarity of air, surrounding and approaching traffic, the curvature of the turns and other context are taken into account by today’s advanced vehicles.

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An application planner should examine multiple factors before choosing a decision, including the cost of operating the tools versus the cost of tools required to derive the contextual insight.

Since the demand for rapid and intelligent decisions is increasing and the cost of deriving insight is declining, more decisions will qualify for contextual automation over time.

A workflow plan for selective use of external context data

Companies can take into consideration several steps while planning for the selective use of external context data in the automation of application decisions.

Choosing Decisions For Automation

  1. Consideration of business decisions that they want to automate and supply with context data. While doing so, they can analyze the expected business value to be gained from contextualizing and automating the decision. Moreover, assessment of the expenses involved in automating and not automating the decision should also be taken into consideration. Companies should evaluate the worst-case scenarios and recovery options in case the automated decisions give incorrect results.
  1. Organizations should discuss about the context data that would materially refine the business outcome of the automated decision.
  1. Enterprises should not only find out the location of the desired context data but should also find out whether it exists in digital form or not.
  1. Companies should take note of the tools and resources that are required to retrieve the desired context data.
  1. Corporation should consider the fact whether they have the required tools and resources to critically analyze the retrieved context data and derive actionable insights out of it.

Also Read: Knowledge Discovery Process and Machine Learning’s Impact

  1. Firms should think whether they have the required tools and know how to introduce the derived context data into their decision-making process.
  1. Companies should be ready to assess the quality of the results and detect any error that may creep in.
  1. Organizations should be prepared to deal with any unwanted outcomes and be in a position to mitigate them.
  1. They must have know-how of how to collect and apply the quality evaluations to upgrade the future decision-making process. Besides, they may also want to make sure whether the tools and resources that are required are available with the vendors or service providers.

While choosing decisions for automation, an organization must remember that it may be expensive to obtain external context data, even when it is available in digital form. Application design changes may be required for injecting the context data into application decision logic.

However, improvements in context awareness, such as these, can have a major impact on business outcomes. The ideal approach is for enterprises to look for the “low hanging fruit” initially and expect to get more with time.

(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors. All the contents in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)

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About The Author:

Naveen is currently the CEO at Allerin Tech Pvt Ltd. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. Naveen is a keynote speaker and thought leader in the area of IoT solutions, Machine learning and Block Chain Technology.

Specialties: Solution Design and consultancy , Data Science, Machine Learning, Deep Learning Enterprise Application Planning, Cost Optimization and Block Chain