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16 Potential Barriers to The Successful Implementation Of Intelligent Automation

by Ant Sh
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16 Potential Barriers to The Successful Implementation Of Intelligent Automation

From the challenge of architecting end-to-end solutions to getting team members on board with new ways of doing things, a business can face many obstacles on its Intelligent Automation journey. Here is a list of common barriers to the implementation of intelligent automation and how they can be overcome. The insights were shared by 16 members of Forbes Technology Council.

  1. Neglecting Change Management

Change management can be unintentionally minimized during new technology implementation. Dedicating time up front to your change management strategy helps avoid barriers to adoption. Gain top-down leadership support, overcommunicate and realign people and skills to new processes. Share the value teams can expect from embedding intelligent automation to recognize maximum efficiency gains. – Raj Indupuri, eClinical Solutions

  1. Not Focusing On Discovery And Reusability

A common barrier to the implementation of intelligent automation is not focusing on the discovery and reusability aspects of automation. It’s easy to just identify ad hoc automation candidates, but building out a framework of user journeys as part of your automation strategy can help ensure permeation across business domains. – Jeremy Sindall, digitalML

  1. Insufficient Data

Data, data and more data: The biggest challenge to implementing intelligent automation is gaining access to an adequate amount of high-quality data. AI models function best when they are trained on precise data, and the accuracy and size of the data set are critical. This often requires the reorganization of current processes while still ensuring the privacy and security of end users. – Geetika Tandon, Deloitte Consulting LLP

  1. Inaccurate Data

A common barrier to the implementation of intelligent automation toward driving better business decisions is the inaccuracy of the underlying data that feeds into these automation systems. For example, if an intelligent system needs to change your marketing expenses on the fly, but the underlying data for making this decision is incorrect due to broken processes, then the outcome will be incorrect too. – Preeti Shrimal, Chaos Genius

  1. An Underskilled Workforce

The skills and knowledge of the workforce involved are the most common barriers to ensuring that the right processes are being automated to reduce the burden and make processes effective. Efforts to reskill the existing workforce in digital and automation technologies should be at the forefront of any future-of-work discussions. – Rashmi Rao, Philips

  1. Not Employing A ‘Human In The Loop’ Strategy

Robust intelligent automation and the addressing of edge cases are only possible with a “human in the loop” strategy. While automation takes on the bulk of the work, exceptions are handled by humans, which allows the overall process to work seamlessly. These human-handled exceptions become the basis of a virtuous feedback loop that teaches the overall process to become more and more intelligent. – Bryton Shang, Aquabyte

  1. The Difficulty Of Capturing Baseline Knowledge Around SOPs

Typically, standard operating procedures within businesses are legacy, are tied to the special knowledge of older workers, or are idiosyncratic to the way one worker “wants to do something.” But that just isn’t standardization. Hence, a common barrier is capturing, auditing and qualifying that baseline knowledge, because it goes hand-in-hand with the process of adoption of automation too. – David Francis, Virtual Method

  1. Siloed And Legacy Processes And Systems

One common barrier to successful implementation and value realization from intelligent automation is having siloed and legacy processes, systems and talent. Consider three core areas of intelligent automation in an enterprise: business operations, IT operations and business analytics. The best way to overcome this is to have an experience-led, domain-oriented and data-driven automation strategy with milestones. – Rajat Sharma, Zensar Technologies

  1. Focusing On The Latest Technology Instead Of Business Outcomes

One of the barriers to the successful implementation of intelligent automation is focusing on getting the newest technology in place, rather than the business outcome that is desired as a result of implementing intelligent automation. As an example, improving the throughput of a specific process or improving end-user satisfaction should be the first focus, followed by alignment to an intelligent automation tech solution. – Dutt Kalluri, Celsior Technologies

  1. High Or Prohibitive Costs

Companies face a cost barrier when implementing AI automation, including investment in technology, personnel, and infrastructure. There is a risk of data privacy concerns, software compatibility issues and inaccurate insights with AI-generated data. To overcome these challenges, it’s important to assess company needs, create a detailed plan, identify risks, ensure employee awareness and monitor system performance. – Mahanth Mallikarjuna, Mergen IT LLC

  1. The Lack Of Ready-Made Tool Kits

Intelligent automation combines automation and AI. There’s a lot more work to do to provide tool kits, including verticalized templates, workflows, and implementation APIs. Implementing one is more “build your own” than “buy your own,” and I think that’s the biggest barrier. Overcome that by demanding free implementation help from the vendor. Goodness knows those companies are getting paid well enough. – Rhonda Dibachi, HeyScottie.com

  1. Employees’ Fear Of Change

One common barrier to the implementation of intelligent automation is the fear of change and uncertainty among employees, stemming from resistance to using the new technology or concerns about job security. To overcome this, companies can provide support to help employees adapt to the technology or involve them in the decision-making process so that they feel invested in the company’s success. – David Bitton, DoorLoop

  1. Analysis Paralysis

The expansiveness of intelligent automation can be an obstacle. Some see the myriad of potential use cases for which it can be applied, especially in the healthcare field, which drives anxiety and apprehension about how to operationalize. Overcome this by implementing it in phases. Apply the tech to one or two simple use cases, iterate, and then build out the complexity and application from there. – Isabelle Meyer Stapf, Artera (formerly WELL Health)

  1. Adding Too Much Complexity Too Quickly

Automation journeys can get unnecessarily complex with the introduction of many bots. It defeats the purpose of automation and creates challenges with downstream implications. It’s best to start by automating smaller, easier processes and going on from there. That will help you to fail fast and learn—then move forward guided by those lessons, so you know what to improve. – Siby Vadakekkara, Marlabs LLC

  1. The Lack Of Documentation Of The End-To-End Workflow

Companies minimize the value of maintaining end-to-end global processes until faced with optimized and scalable automation. Information can be fragmented when it spreads across organizations due to the ways one department interacts with another. This can reduce the speed of executing timelines, so it’s important to have an up-to-date, documented workflow of the way your ecosystem works. – Bob Dechant, ibex

  1. Not Having An Automation Manager

Many companies think of intelligent automation as a technical process and fail to consider how it fits into business goals, the customer experience and the overall organization. Appointing an automation manager can help organizations connect the technical and business aspects of automation and make sure it is done in a way that helps advance overall business goals. – Eli Israelov, CommBox

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