Top 10 Tips on starting with Intelligent Automation

Barbara Hodge, Editor
Posted: 12/14/2017

As with any change management, preparedness and planning are key. Here are 10 tips to get off on the right footing: 

  1. Start by talking to someone who has actually had the benefits delivered. Don't just rely on a vendor's promises. Get proof.
  2. Consider the total cost to build, maintain, and even grow your robotic aspirations. The total cost of ownership is far more than the original cost of the bot. Something that seems cheap in the early stages may not end up cheap in the long run if it requires lengthy and complex workarounds.
  3. Do not assume you have to automate the “as is” process. Take a critical review and consider how robotic automation may consume data differently to humans. Many manual steps can be eliminated completely and don't even require automation.
  4. Before you build up a large center of specialist experts to run a robotic initiative, consider how many people you actually need. There are three basic models for an Intelligent Automation COE: Inside, Hybrid and Outside. Decide what your company wants before you begin.
  5. Consider the scalability of your automation even if in the short term you have no greater plans. It's important to be aware of the limitations of scaling within a siloed environment. Desktop practices cannot be scaled.
  6. Consider how the planned automation strategy allows you to exploit innovations from your incumbent ERP vendors. If you are locked into one version of your ERP, because you are locked into one version of the user interface and your robotic IP is tied up in the relevant keystrokes, mouse moves, etc. generated from the current version, you are effectively locking in obsolescence. Consider the implications of updating your ERP. For example, most of SAPs productivity improvement is in the user interface. This means you may find yourself in a bind as desktop innovations are integrated into the underlying systems.
  7. An end-to-end automated solution connects the process to the technical functionality you need beneath it. The separation of business process and rules from the technical approach allows you to execute journals in Oracle or SAP upgrades if your process does not change, because you are just updating the parameter that tells you where to source the data. You cannot change the underlying standardized APIs because everything else would break.
  8. Every time you have a maintenance upgrade, automation maintenance must be considered. Without good planning and “unitization” of automation, updating configuration can be cumbersome. 
  9. Machine-to-machine options are generally more stable than the user interface
  10. Start small, and grow your capabilities over time.

And, in the season of Christmas giving, let's add four more!

  1. Look at automation for things other than cost, such as CX, quality, consistency, speed.
  2. Consider establishing a pilot program that draws on the strength of an experienced partner. Professional services firms are acquiring experience with technology providers, and the specialized skill set needed to make RPA processes work in tax, accounting, administration and other specialized workflows.
  3. Involve your team. Employees hear about the threat of robotics replacing white-collar workers but the real-world experience has been that robotics allow workers to focus on more valuable tasks and help employers build higher-functioning teams.
  4. Make RPA a business-led initiative, not an IT-led initiative. Bots can be configured within the teams, close to the business processes and expertise required to make them work.

 

If you have your sights set on the top end of the continuum, then it’s important to build the right team around you. In order to set yourself up for the best outcome, it's advisable to establish working teams that combine multiple skill sets, including data architects, who can build the required structure; BPM experts, who understand the principle of automation and process management; data engineers, who manage the transfer of data; data scientists, who understand the learning implied in ML or NLP; data visualization experts, who are able to tell the story; and those with the consulting skills to understand the business, know what the issue is, and how to solve it with technology.

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Read the full report: Global Intelligent Automation Market Report (H1 2017)

Barbara Hodge, Editor
Posted: 12/14/2017