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Making Sure that you’re up to the Speed of Automation

Ashok Pandey

By: Ashok Pandey, Founder & CEO at Innovsol

The momentum of automation, intelligent automation at that is way beyond any prediction that experts could have done. As technology is evolving, the way intelligent automation is implemented is also changing shapes and features. However, implementing Intelligent Automation is not easy neither the success is guaranteed. It is easy to do a Proof of Concept or a pilot project, but it becomes increasingly difficult when organization to scale it up. As famously conjectured by Bill Gates, co-founder of Microsoft, “Automation applied to an inefficient operation will magnify the inefficiency.”

Not only will the organizations need to check out what kind of automation is required for turning their business into more efficient one, they also need to be aware of core challenges while implementing Intelligent Automation. The enterprise leaders want investment in automation to actually deliver on their potential to drive digital transformation, agility, efficiency, and revenue. As the leaders are entrusted with the work of ensuring that automation investments fully deliver, here are the few stumbling blocks that need to be taken care of, to make automation successful.

  • Limited scope: Opportunities of automation in an organization identified are sometimes too small and does not create enough impact on business. While efficiency is surely affected, the immediate business results may influence the stakeholders and make them less convinced about investment. Going forward, either the project is aborted or held back over favorable decisions for another transformation tools. Thus Identifying and prioritizing the biggest opportunities automation can deliver to your organization is one of the key first step towards future efficiency.
  • Lack of assessment: Lack of comprehensive assessment of Intelligent Automation opportunities is a roadblock for success of project. Normally companies rush to implement the IA, without doing the necessary due diligence and prioritization.
  • Silo Implementation: Organizations are just focused on one single technology rather than making it end to end solution. Organization needs to form a group from across different the business and bring different level of skills, such as developers, Business Analyst, data engineers, and others to implement end to end automation instead of just process task. Due to silo implementation, organization are not able to scale as well and currently connecting islands of automation within the organization is a big task in itself.
  • Lack of communication: Any change is very difficult for human because they want to live in their comfort zone. People think that Intelligent Automation will eat their jobs and this creates the fear among the people. For IA to succeed, people must be educated in terms of benefits and opportunities for them. Building out automation skills and capabilities across the enterprise is a step that needs to precede any effort to start implementing IA.
  • Data Issues: Data is the key to implement IA. The most common challenges are the difficulty to create value out of unstructured data, and lack of consistency in structured data. Organization also face challenges due to data present silos in nature. The outdates, inaccurate data will provide an unreliable insight which can have a severe impact in the long run.
  • Cost: IA projects are human resource consuming. Building Robot using RPA tool or low code platform is manual click by click process. Building chatbot or a machine learning program involves collecting a certain amount of data to train, test and validate the algorithm. As a result, the implementation cost can be high due to human resources.
  • Lack of Talent: IA requires diverse set of skills ranging from Business Analyst, Data Scientist, Data Engineer, Developers, and system architects. These talents are difficult to find and recruit and retain. Building up the right pool of manpower and in similar breath bringing in the efficiency required to deal with data, machines and changing technology needs a retrained human resource perspective.

Thus, it is not only automation that will bring in desired result of efficiency but a coordinated change within the organization is the much required support necessary to build the automated platform. Choosing an automation platform that provides enterprise-grade scale, governance, and security along with right inputs of human resource, cost to return calculation, tenacity over gestation period and proper end to end advisory to conceptualize and implement the same are a few tick marks required to be successful in embracing IA.

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