RPA and intelligent automation challenges: 5 ways to overcome them

Aug 22, 2019 by Maggie Orion


Change is never easy. There are general challenges associated with adopting new technology and specific challenges that come when adopting RPA and intelligent automation. Let’s discuss what challenges you can expect and opportunities to overcome them.

Challenge 1: Lack of strategy

It’s difficult to develop a strategy for adopting any new technology – and the newer it is, the harder it can be to plan for it as there are just so many unknowns. You’re not alone. Most companies are making important decisions with a variety of unknowns. For the best results, pull together something that can serve as a starting point.  A less-than-perfect strategy is fine (actually, it’s better than fine) because analysis paralysis at this stage is deadly in a situation where first automator advantage will determine long term winners and losers. 

It’s critical to know that when it comes to intelligent process automation, there are certain strategic considerations to face early on, namely:

  • Schedule of adoption: How fast will you move compared to your competitors considering that whoever gets enhanced productivity through automation first has a competitive edge (e.g. “first automator advantage”)? 
  • Scope of adoption: Companies must decide whether to start small with pilot projects or develop an enterprise-wide adoption plan to apply RPA and intelligent automation in all departments. The latter has a track record of failure as companies find themselves mired in complexity and uninformed enterprise-scale decisions. Most companies find success with proof points in a pilot, iterating as they go with agile development and applying what they learn to successive enterprise-wide rollouts. 
  • Governance: Determining a governing structure early won’t immediately solve for the unknowns, but it can ensure success by determining a decision-maker and process for solving future unknowns. Ideally, bureaucratic barriers to decision making should be low and a single decision-maker empowered with the highest authority should make quick and important decisions to keep projects moving. 

Building a team to inform decision making and carry out execution is also critical and a center of excellence (CoE) for process automation has proven to be very successful, but companies without a CoE can begin by bringing together an ad hoc group through the first proof of concept project.  Who needs to be involved? A very important question – a mix of technical and business experts are needed. Business process managers should be considered for inclusion and ground-level end users – experts in the real day-to-day processes – should be consulted along the way. Depending on your industry, compliance and legal expertise may be required as well. It’s best to bring legal in early rather than try to explain software robots to a group of lawyers right before deployment! 

This group will also evaluate success measures and provide recommendations for expanding intelligent automation solutions over time. 

Each challenge holds opportunity. Best practices suggest starting small, starting early, and staying agile with a clear decision-maker and potentially a center of excellence to serve as an advisory group. Smaller scope lighthouse projects generate both knowledge and enthusiasm for further expansion.

2. ROI analysis

Running the numbers is more complicated than it may initially seem. This can lead to unexpected analysis paralysis as analysts try to perfect the ROI model. Companies run into challenges predicting and clarifying costs as different vendors charge in dramatically different ways: some for the amount of information processed by automations, some for the automation itself, some for the software robot that performs the automation, others for the platform that manages the robots. And, of course, all of this is differentiated by subscription versus one-time upfront costs and where it’s hosted. 

In terms of calculating ROI based on costs saved versus revenue generated, running the numbers on ROI based solely on the number of full-time employees that might be replaced is short-sighted and generally overlooks some of the more powerful benefits of RPA and intelligent automation. For example, customers receiving more personalized and efficient service will lead to higher net promoter scores and increased purchasing, but how will you put that in your calculator? Different use cases impact the calculations as well as the type of automation technology being applied. RPA tends to see a shorter time to ROI, but intelligent automation has more enduring savings over time. 

Opportunity: Set realistic expectations by expanding your analysis beyond reducing headcount and accept that initial ROI models are simply best guesses. Don’t waste time trying to predict every number with 100% accuracy. You will be much better served by calculating ROI after your first proof of concept use case and then continuing to measure ROI at regular intervals over time to decide whether to scale intelligent automation up or down or apply it in a different direction. 

3. Lack of Talent

Lack of talent is not a challenge specific to the RPA and intelligent automation market. When adopting new technology, finding the people with matching skills and knowledge to support your business is critical. This includes the technical skill, understanding of your business processes, and change management expertise to guide your company to the future. Bringing on or upskilling talent to develop automations is usually top of mind, but assigning people to ongoing maintenance, support and troubleshooting is also important. 

Opportunity: Depending on the talent and hiring capabilities of your company, consider a product without a steep learning curve. Many environments offered by vendors do not require coding. In the case of intelligent automation that means democratizing machine learning and AI and creating AI models practical for your business. Upskilling, contracting, partnering, in-house and external training support should all be considered. Also important to consider is early recruitment of RPA and AI experts in both technical and business concerns to appropriately build out your team.

4. Vendor and use case selection 

As they say, the devil is always in the details. Selecting your vendor, choosing your use case and deciding which data is most critical for developing organizational capacity can quickly become overwhelming.

Choosing the right vendor or vendors to begin your process automation journey can be a challenge for two reasons: they’re all promising the same thing and much of the technical nuts and bolts that different platforms offer can be obscure for business decision-makers. Companies may also choose to work with a consulting partner or several vendors at once to compare best fit. 

In the past, a tremendous challenge in implementing process automation was dealing with unstructured data. Many processes incorporate some level of semi-structured or unstructured data, and this data simply wasn’t accessible for process automation. With RPA and intelligent automation, this barrier is effectively removed as AI capabilities unlock access to unstructured data. 

The truth remains however that RPA cannot solve every inefficient process in your organization, and even with added AI, there are processes that may not need automation – some processes may need to be revised or dismantled altogether. Deciding how best to deploy intelligent automation and which processes will see the most return is challenging and critical to success. Again, start small, target the “lowest hanging fruit” in your processes, and over time build a library of processes to automate.

After selecting and executing initial use cases, setting up maintenance and oversight is also important. You may have already considered that if automations are programmed incorrectly, then they become efficient at performing processes but incorrectly, which can pose many risks for your company. Prevent this by building responsibilities for maintaining, reviewing and updating your automated processes.

Opportunity: Determine needed and nice-to-have features such as machine learning, back-end security, cloud deployment, and a pay-as-you-go model for easily increasing and decreasing your automations. Host a use case discovery workshop with your vendor or partner, purchase a proof of concept with single or multiple vendors and evaluate the application of RPA and intelligent automation. 

5. Change management, culture readiness

The final challenge companies face when adopting process automation technology should be addressed from the beginning to ensure sustained success. Let me say it plainly: people are afraid of robots and AI. People are afraid of losing their jobs. And people are afraid of change. 

Each company has a unique culture determining the company’s resistance or acceptance of change. While some embrace new technologies enthusiastically, others can be very resistant. Without a tailored change management plan and people-centered approach to intelligent process automation, bringing on this type of technology could cause resistance, non-compliance and upheaval. 

Successful organizational change nearly always requires leadership sponsorship and high-quality communication regarding the purpose and outcomes of adopting intelligent process automation and should be started early. 

Opportunity: Consider the impact process automation will have on people early and the messaging around it. High-level leadership sponsorship and ground-level contributor buy-in will ensure success. Host a use case discovery workshop and ask your employees what their pain points are. Start there and the benefits of intelligent automation will be obvious to everyone. 

Consider a platform with a user-friendly interface that will integrate with your employees’ current working habits in a helpful, non-threatening way. 

Plan to educate management in RPA and intelligent automation to effectively usher in change or partner with change management specialists. Down the line, you may need to collaborate with HR to revise job duties to accurately reflect a shift from mundane tasks to higher-value job activities – keep HR in the loop on intelligent automation developments. 

One often overlooked aspect of technology initiatives is making sure to explain and highlight the benefits of intelligent process automation for everyone in accessible and understandable terms rather than technical jargon. You’re already on the right path to overcoming the challenges for RPA and intelligent automation – research fundamentals, clarify benefits and minimize the myths surrounding process automation.