How to Overcome the Challenges Associated with Intelligent Automation

by April 5, 2020

As per UiPath, 2014 was the moment when robotic process automation began to be a noteworthy contender to business process outsourcing. A while later, it took just two additional years until it began to be institutionalized by business organizations. Where are we today? We are at a point where both adoption and scaling have progressed colossally, and RPA has arrived at new degrees of development, turning into an unquestionable requirement for organizations resolved to seek after a real competitive advantage.

To put it in an unexpected way, numerous companies have learned at this point productive approaches to beat the challenges of RPA implementation and deployment, and automation has evolved into a core technology. In spite of the fact that this can give useful assistance to white-collar workers who perform business processes, it’s something a long way from a human commitment in the workplace, representing a deterministic type of business process management.

In the past few years, we’ve seen the headway of rising innovations, for example, artificial intelligence (AI) and analytics. With deep learning, you can train a neural system to mirror the human cerebrum and with machine learning, you can analyze huge amounts of data to discover patterns that illuminate great decisions.

The solid progressions of these advances are inescapable and organizations need to harness them to improve their operations. These large shifts and developments are driving the digital “workforce” transformation. So as to receive the best reward, organizations must be aware of and plan for this change. However, likewise with any good thing, additionally there are challenges and purposes of disappointment involved.

 

Inability to automate end-to-end processes

For the more complex processes, RPA tools might be deficient for straightforwardly automating all the process steps. “Divide and conquer” is the prescribed approach to this. Upgrade these sophisticated tasks, break them into less complex parts, and start automation here. Also, try to use the joint work of RPA and other digital technologies like machine learning or optical character recognition. Remember, however, the additional costs required by this, so don’t go for end-to-end intelligent automation when cost-efficiency becomes questionable.

 

Security by design in IA

With any technology, security totally should be a primary need. For IA the greatest security issue regularly emerges at where human and machine connect. For instance, human error during an automated financial reporting process can bring about losing-man weeks, not days and a deferral on the reporting of the group’s finances. Other security issues that should be considered incorporate rebel get to; data loss; hacking; privilege abuse; vulnerabilities and malware, which all show the centrality of security to IA deployments.

However, similar to well backed up data, everything can’t be lost! Those hoping to deploy IA should notice security conventions like encrypting information and different layers of verification, alongside decreasing access rights and requiring human approval on specific procedures.

 

Selection of Appropriate Process

Not all procedures are appropriate for automation. You should identify processes with clear processing instructions (format driven), in view of institutionalized and prescient principles. Procedures that require a high level of manual info, structured and repetitive input involve exercises that are increasingly susceptible to human error; this is the reason they are additionally good candidates for automation.

Another plan to be considered in the selection procedure is that the more steady a business task, the more smooth and viable (and along these lines cost-effective) its automated version. Relatedly, processes with measurable savings will make it simpler to assess realistically the impact of RPA on your company.

 

The need of Appropriate Skills

Soft skills are a significant establishment to build upon. For instance, we should consider problem-solving: It’s true that IA can tackle a few issues that people can’t. In any case, when issues aren’t completely characterized, people can utilize their reasoning aptitudes to make sense of a solution that machines wouldn’t have the option to discover.

Besides, the ability to team up and conceptualize to make new thoughts is a component that can boost business processes and optimize performances and it has nothing to do with robots. These are human abilities and represent the additional worth that automation essentially can’t offer. Therefore, organizations should concentrate throughout the following decade on the way that, in spite of the fact that machines will bit by bit become all the more powerful, people will really be even more important since innovation will go about as an integrator and not as a substitution for abilities required.

The jobs of the future will require a reskilling of the human workforce. The response to digital disruption lies in our ability to apply humankind to the new challenges that emerge. IA and AI have just been shown to have significant and expansive advantages and, while it is troublesome if not impossible to foresee with complete certainty where the innovation will go next, what is increasingly sure is that its utilization will just multiply.