As Artificial Intelligence (AI) becomes increasingly accessible and easier for businesses to implement, so does Intelligent Automation. When implemented well, this technology can open up a world of new possibilities for your organization by minimizing errors, increasing productivity, and enabling better decision-making, all while reducing the amount of time your team spends on repetitive tasks.
Despite its tangible benefits, implementing Intelligent Automation is often easier said than done — and as with any digital transformation process, you’re likely to encounter roadblocks along the way. But by being aware of these challenges, planning to address them, and working with the right partners, you can overcome them and reap the benefits of a well-executed implementation.
Whether you’re implementing Intelligent Automation for the first time or looking to optimize your existing strategy, this blog post will explore some of the most common challenges you may face and how to overcome them.
What can be accomplished with Intelligent Process Automation?
Let’s start by taking a closer look at the purpose of Intelligent Process Automation (IPA) and how it’s different from rules-based automation as a foundation.
IPA is the application of AI, Machine Learning, and Optical Character Recognition (OCR) to Robotic Process Automation (RPA). IPA is often used to:
- Reduce errors
- Complete repetitive tasks
- Overcome bottlenecks
- Simplify complex business processes
- Enhance customer experiences
While traditional rules-based automation is limited to executing predefined rules, IPA leverages AI and Machine Learning to make decisions, enabling it to adapt and continuously improve over time. This makes it significantly more flexible than traditional automation.
Common Intelligent Automation challenges and how to solve them
While the appeal of Intelligent Automation is readily apparent, implementing it successfully can be easier said than done for a number of reasons:
1. Resistance to change
Adopting any new technology comes with human challenges, including organizational resistance to change. Your staff may not fully understand the benefits of the new technology, be hesitant to adjust the workflows they’re used to, or feel that their job is at risk of replacement by automation.
Without collaboration from everyone involved, your organization won’t be able to use Intelligent Automation to its fullest potential.
The solution: Prioritize communication in your implementation strategy. Secure buy-in from all relevant parties and stakeholders, keeping lines of communication open.
“Leveraging Ashling Partners’ change management approach, we can ensure understanding of resistance and headwinds in the face of change. We employ a methodology that ensures understanding the criteria of success, executive and stakeholder participation, change readiness and communication.”
James Wilson, Head of Advisory at Ashling Partners
Create a training program that teaches team members not just the basics, but also more advanced uses of automation tools. Highlight how automation can streamline their workflows and automate tasks that apply to their specific roles.
Particularly with AI-powered technology, it’s also vital to reassure employees that they’re not being replaced and clearly communicate how they’ll benefit from the new system.
2. Data privacy concerns
As AI technology becomes more integrated into everyday life, more people are raising questions about how their data is being used and protected. At the same time, data breaches are on the rise — they increased by 72% between 2021 and 2023.
This means that more than ever, data privacy is a critical issue for maintaining consumer trust.
Failure to manage data properly can lead to privacy violations and data breaches, fines, and legal action.
The solution: When implementing Intelligent Automation solutions that utilize AI, keep data privacy considerations front and center. Adopt practices that are compliant with data protection regulations (e.g. GDPR), transparent in how data is used, and secure against potential threats.
“Bots don’t do anything with PII that can’t be controlled. If you need to redact information or validate private data a bot can do that securely and hand off a document scrubbed from private information to a person to finish a process which can dramatically reduce risk of handling customer information.”
James Wilson, Head of Advisory at Ashling Partners
3. Integration issues
Integrating Intelligent Automation into your existing IT infrastructure requires careful thought and planning, especially if your tech stack is particularly complex. Not every solution will come with off-the-shelf integrations for your existing platforms, so building custom integrations may be required — and the more platforms you use, the more integrations you’ll need.
Additionally, consider how the quality of your organization’s data will impact the AI learning process. Data that is low-quality and/or siloed in different systems will compound your integration challenges and ultimately impact the outputs of your Intelligent Automation system.
The solution: When exploring options for Intelligent Automation, think of it as part of your organization’s holistic technology strategy. Consider which systems will require custom integrations, which come with out-of-the-box integrations, and which integration approach aligns best with your needs, timeline, and budget. Wherever possible, determine how your organization will address any issues around low-quality or siloed data before implementation.
“IA capabilities can fit across the entire tech stack and leverage a combination of integration techniques including User Interface, API or direct application integrations to achieve the need of the solution at hand. These broad capabilities leave the design to the framework of the organization and what solution fits best for which situation.
A common need for AI builds is a basic human-in-the-loop integration before green-lighting data from an LLM being used in a source system. This can be easily solutioned via sharepoint lists, low code apps or a fully customized app.”
James Wilson, Head of Advisory at Ashling Partners
4. Redundancy considerations
From cyber incidents to natural disasters, disruptions to critical IT infrastructure can occur at any time. What happens if your organization’s systems go down temporarily and Intelligent Automation can’t be used? How will your workflows be impacted, and how can you minimize disruption?
Proactively securing your operations, including those powered by Intelligent Automation, is vital for ensuring business continuity.
The solution: To minimize downtime, consider redundancy — both power and network — for your Intelligent Automation systems. Using cloud-based disaster recovery plans and wireless backups can make your automation much more resilient. Conduct tests often to ensure your backups work as expected in different scenarios.
“Ensuring IA platforms are part of the organization’s disaster recovery policy is a critical step in ensuring automation up time. Having a cut over plan and procedure protects the company from unforeseen interruptions of automated processes.
It is also critical to ensure all automation projects are subject to the same source control and deployment standard as any traditional IT projects so they can be easily recovered in the event of disruption.”
James Wilson, Head of Advisory at Ashling Partners
5. Unrealistic expectations
There’s a misconception that automation, particularly AI-driven technology, is a catch-all solution for every problem. While automation can deliver tremendous benefits, it’s just one ingredient in the recipe for efficiency — and without extensive testing, validation, and ongoing improvement, it can stagnate and slow productivity just like any other technology.
The solution:
“It’s important to both understand business outcomes and automation capabilities. Molding a solution (and sometimes the process) into something that leverages the capabilities at bear and the outcomes desired is a critical step in achieving success. Ashling employs both continuous process improvement mindset and automation capabilities based solutioning and marries the approaches for the ultimate solution. Often times you cannot automate a broken process, or capabilities of one automation tool is not enough to get the full value. That is why it’s imperative to ensure both approaches are included.”
James Wilson, Head of Advisory at Ashling Partners
Don’t lose sight of your goals. As you create and implement your Intelligent Automation strategy, it’s crucial to avoid “silver bullet” thinking and unrealistic expectations. Instead, you should establish clear, achievable goals around what Intelligent Automation can do for your organization and communicate them organization-wide (not just to leadership).
Additionally, keep in mind that leveraging Intelligent Automation to its fullest potential requires ongoing learning and development. The best results won’t happen overnight — they’ll happen as your team gains mastery of the tools and the AI becomes more accurate over time.
Facing automation challenges head-on
Intelligent Automation can deliver significant benefits, like higher accuracy, increased productivity, and better decision-making.
However, the implementation process may surface challenges like resistance to change, data privacy issues, integration problems, and unrealistic expectations. By recognizing these challenges — and strategizing to overcome them — you can use Intelligent Automation to its full potential.
As you navigate strategy and implementation, having the right partner can make all the difference. Ashling Partners is here to guide you on your Intelligent Automation journey.
With expertise spanning Robotic Process Automation (RPA), Intelligent Process Automation (IPA), Process Mining, Document Understanding, Artificial Intelligence (AI), and Machine Learning (ML), we craft strategies that are uniquely tailored to your needs — and are designed to effectively address the above challenges, delivering tangible benefits for your organization.
