16 Potential Roadblocks to Successfully Implementing Intelligent Automation

16 Potential Roadblocks to Successfully Implementing Intelligent Automation

16 Potential Roadblocks to Successfully Implementing Intelligent Automation

Using automation to simplify and speed up business processes is not new; for example, robotics has long been used in the manufacturing industry. By adding technologies such as artificial intelligence and machine learning to the mix, intelligent automation potentially has a much higher and far-reaching impact. Leveraging intelligent automation can help businesses make better decisions, create more efficient processes, and free up staff members for more impactful work, if implemented successfully.

From the challenge of architecting end-to-end solutions to onboarding team members with new ways of doing things, a business can encounter many obstacles on its intelligent automation journey. Below, 16 members of the Forbes Technology Council share some common barriers to implementing intelligent automation and how they can be overcome.

1. Neglecting change management

Change management can be unintentionally minimized when implementing new technology. Spending time upfront on your change management strategy helps avoid barriers to adoption. Gain executive support, overcommunicate, and realign people and skills to new processes. Share the value teams can expect from integrating intelligent automation to recognize maximum efficiencies. – Raj Indupuri, eClinical Solutions

2. Not focusing on discovery and reusability

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

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3. Insufficient data

Data, data, and more data: The biggest challenge in implementing intelligent automation is accessing the right amount of high-quality data. AI models work best when trained on accurate data, and the accuracy and size of the dataset are critical. This often requires re-engineering current processes while ensuring end-user privacy and security. – Geetika Tandon, Deloitte Consulting LLP

4. Inaccurate Data

The inaccuracy of the underlying data that powers these automation systems is a common barrier to implementing intelligent automation to make better business decisions. For example, if an intelligent system needs to change your marketing spend on the fly, but the underlying data to make that decision is incorrect due to interrupted processes, the result will also be incorrect. – Preeti Shrimal, Genius of Chaos

5. An underqualified workforce

The skills and knowledge of the workforce involved are the most common barriers to ensuring the right processes are automated to reduce load and make processes efficient. Efforts to re-skill the existing workforce in digital and automation technologies should be at the forefront of any discussions about the future of work. -Rashmi Rao, Philips

6. Not using a “human-in-the-loop” strategy

Robust intelligent automation and addressing edge cases is only possible with a “human in the loop” strategy. While automation takes care of most of the work, exceptions are handled by humans, allowing 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 increasingly intelligent. – Bryton Shang, Aquabyte

7. The difficulty of capturing basic knowledge around SOPs

Typically, standard operating procedures within companies are inherited, tied to the special knowledge of older workers, or are idiosyncratic to how a worker “wants to do something.” But that’s just not standardization. Therefore, a common hurdle is capturing, auditing, and qualifying this foundational knowledge, as it also goes hand-in-hand with the automation adoption process. – David Francis, virtual method

8. Siled and Legacy Processes and Systems

One of the common barriers to successfully implementing and leveraging intelligent automation is the silos and legacy of processes, systems, and talent. Consider three key areas of intelligent automation in a business: business operations, IT operations, and business analytics. The best way to overcome this is to have an experience-based, domain-driven, and data-driven automation strategy with milestones. – Rajat Sharma, Zensar Technologies

9. Focus on the latest technology rather than business results

One of the barriers to successfully implementing intelligent automation is focusing on getting the latest technology in place rather than the desired business outcome as a result of implementing automation. smart. For example, improving the throughput of a specific process or improving end-user satisfaction should be the first priority, followed by alignment with an intelligent automation technology solution. – Dutt Kalluri, Celsior Technologies

10. High or prohibitive costs

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

11. Lack of ready-to-use toolkits

Intelligent automation combines automation and AI. There’s still a lot of work to do to deliver toolkits, including verticalized models, workflows, and implementation APIs. Implementing one is more “build your own” than “buy your own,” and I think that’s the biggest hurdle. Overcome this by requesting free implementation assistance from the vendor. God knows these companies are paid pretty well. – Rhonda Dibachi, Top10Scottie.com

12. Employee fear of change

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

13. Analysis Paralysis

The scope of intelligent automation can be a barrier. Some see the myriad of potential use cases it can be applied to, especially in healthcare, leading to anxiety and apprehension about how to operationalize it. Overcome this by implementing it in phases. Apply the technology to one or two simple use cases, iterate, and then expand the complexity and application from there. – Isabelle Meyer Stapf, Artera (formerly WELL Health)

14. Add too much complexity too quickly

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

15. Lack of end-to-end workflow documentation

Businesses downplay the value of maintaining end-to-end global processes until they are faced with optimized, scalable automation. Information can be fragmented as it propagates through organizations due to how one service interacts with another. This can reduce the speed of timeline execution, so it’s important to have a documented and up-to-date workflow of how your ecosystem works. – Bob Dechant, ibex

16. Not having an automation manager

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

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