5 challenges Intelligent Automation CoEs must fix to scale business process automation

Updated: Apr 17


Intelligent automation is now a critical part of the business transformation agenda. A Deloitte survey revealed that 70%+ organizations have embarked on intelligent automation. The survey covered organizations with a range of automated processes. It revealed only 13% had scaled beyond 50 bots. The number of bots may not reflect the number of processes automated or the value, but it is a good indicator of the adoption of robotic process automation. Clearly, scaling is a challenge.


Software robots or digital workers are augmenting human workforce. Organizations embarking on digitization and automations are achieving significant operational and financial outcomes. Yet scaling business process automation is proving to be a challenge.


Intelligent Automation CoEs have been driving process automation across organizations. Scaling automation initiatives globally. We take a look at the top 5 challenges faced by Intelligent Automation COEs to scale business process automation

Challenges faced by Intelligent Automation CoEs to scale business process automation -

(1) Scaling process automation initiatives

Here are the key impediments to scaling –

  • Process Fragmentation – Business processes differ from one business unit to next within an organization.

  • Legacy IT landscape – A legacy IT landscape that doesn’t offer APIs to integrate, doesn't allow resilient automation

  • Lack of business ownership – If your business teams aren’t championing automation & digitization, it aint happening.

  • Dedicated Funding & Budget for automation – Business teams need dedicated budget to drive automation and digitization

The first two impact the ability to productize at scale and achieve faster GTM at lower cost. The last two make the COE always vying for funds and clamouring for business attention, killing both pace and passion of the CoE.

(2) Creating an idea pipeline

Most CoEs have processes in place to support ideas that feed into the automation pipeline. Many approaches to ideation have evolved. Analysts work with business teams to ideate. Business teams submit ideas themselves. Continuous improvement process specialists work on spotting automation opportunities. Botathons and ideathons are run to build that pipeline.


Regardless, most COEs report lack of a continuous and active idea pipeline as a big challenge. Generally there are 3 reasons for this –


  • Not enough inspiration –Most CoEs have success stories but not enough story tellers! Story tellers are business evangelists that can influence.

  • Expectation gaps between management and operations - Ideas from the leadership may be very different from those from operations teams

  • Lack of understanding/Averse to change - Business teams simply don’t understand automation very well and can’t visualize how it can change their operations. Or, they see automation as a risk.

Process mining tools are meant to help spot opportunities to automate. But, may get limited by data availability, process fragmentation and technicalities.


(3) Generating a strong ROI for the automation program


To generate a strong ROI, high impact ideas need to be identified. Or ideas need to be executed fairly quickly and cheaply. Most CoE portfolios have few blockbuster ideas and the rest generate low ROI. Key challenges faced on

the low ROI ideas are –

  • Growing an idea - Functional & Ideation experts can help build on an idea and expand but most COEs lack roles to facilitate this

  • Productizing the idea - Inability to productize the idea across a function/geography/business due to reasons mentioned in #1 above


(4) Transitioning from Robotic Process Automation to Intelligent Automation

Organizations have realized that moving from robotic (aka rule based) to intelligent (aka data & decision driven using AI) is a difficult task. The key reasons are –

  • Data strategy and implementation is still evolving, making data availability and quality of data a challenge

  • Generating high volume of training data for AI models is a challenge. User bandwidth and expertise to annotate data remains limited.

  • Results and models improve over time, but organizational processes have long relied on 100% predictability. So, stakeholders find it hard to accept results over a longer period of time.

  • Identifying use cases, defining value & ROI for AI use cases are almost exact opposite of RPA.


(5) Driving Change and Enabling AIY


Driving an automation first mindset and enabling AIY (Automate It Yourself) faces many issues in organizations today -

  • Choosing the right tools to enable citizens to experiment and automate their own work

  • Consistent Training plan that balances hands-on experience and classroom training

  • Simple model for IT to support citizens when AIY gets into issues

  • Consistent communication strategy to share use cases, benefits and success stories


In Summary, automation CoEs have their hands-full in 2022. Building a consistent idea pipeline. Scaling automation initiatives. Generating strong ROI for the program. Moving the organization from RPA to AI initiatives. And driving AIY for citizens.

At Senzcraft we are helping organizations navigate through many of these issues. Drawing on years of experience in trenches across industries and functions. Get in touch!


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