A potent combination of robotic process automation (RPA) and artificial intelligence (AI), intelligent automation (IA) solutions enable the end-to-end automation of complex business processes. While IA has the potential to deliver transformational results, it also has an alarming high failure rate. In fact, according to our very own research, about half of all IA implementations fail to deliver anticipated business results. 

Though IA and RPA implementations can fail for numerous reasons, many of these pitfalls can be avoided if confronted ahead of implementation during the strategic planning phase. With this in mind, we’ve created a list of 4 questions you need to ask when formulating your IA strategy to ensure success.

IA Vision: What does intelligent automation success look like?

IA has many benefits. Increased efficiency, enhanced compliance, elevated customer experience and improved data quality are just a handful of potential ways IA can add value to your organization. But don’t stop at vague gains: be specific. 

Determining how exactly you want IA to benefit your organization as well as how you will measure them is paramount to building a high impact IA strategy. Afterall, you can’t improve what you can’t measure. 

READ NEXT: Establishing a Vision for Intelligent Automation Success: 6 Key Considerations

Process and optimization: are your processes ready to be automated? 

We at IAN hear it all the time, “My team automated the wrong process and it brought our entire initiative down.” Selecting the right process to automate requires a delicate balancing act. You must choose a process that is simple enough to easily automate but high value enough so that it delivers tangible business results. 

One potential solution to this problem is automated process discovery. Such tools monitor business users as they perform normal activities, collecting and aggregating relevant data as they go. By pulling together process data from across multiple (i.e. ERPs, CRMs, event logs, web applications, etc.) organizations can more effectively map out and visualize processes as well as pinpoint the ones best suited for automation. 

It should also be noted that trying to automated “broken” or highly ineffective processes will only doom you to failure. In other words, before you automate, you must optimize. 

Process mining is emerging as the go-to method for preparing processes to be automated. Both an analytical method and category of software solutions, the goal of process mining is to transform data pulled from multiple transactional systems into a digital twin. Engineers can then visualize, map out and simulate re-engineered processes. 

People: Who are your stakeholders and what do they want?

It’s a simple fact, the more people that use and benefit from IA, the greater ROI it will deliver. Without substantial stakeholder buy-in, scaling IA will be impossible. 

Your stakeholders will come from across your entire organization: the business, IT and the c-suite. Engaging them early on and getting their input in the initial stages of planning will go a long way in ensuring adoption and buy-in down the line. 

IT Readiness: How does your IA strategy align with digital transformation strategy?

Though IA technologies vary in terms of sophistication and compute needs, outdated and overly complex legacy systems can completely derail IA implementations right off the bat. Before you devote any resources to your IA project, have an honest conversation with your IT team about whether or not your technology ecosystem can handle large-scale programs of work. 

Second to that, successful IA implementation does not happen in vacuum. It happens in conjunction with larger digital transformations. You may think that you’re only automating one or two processes, but these processes are and the platforms they run on are interconnected with larger enterprise systems. 

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