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Intelligent automation has made organizations “scary smart,” but the true value has been unlocked in the way that they offer simplicity. Automation has been instrumental in amplifying the impact of business efforts.
Companies that embraced intelligent automation have been able to do more with less, become more agile and adaptable to change and hold their ground in challenging recessionary times — thus rapidly changing customer preferences and intensified talent wars. In fact, for 95% of enterprises, intelligent automation is a critical component of their digital transformation strategy.
However, many companies that have adopted automation have not yet unlocked their full potential. While many U.S. headquartered Fortune 250 companies have started their automation journeys, few have a truly mature automation practice.
The main obstacles — a shortage of automation-proficient talent, an undefined automation mandate and a lack of accountability — become more prominent and impede a company’s growth as automation initiatives scale. When implemented correctly, automation programs can improve experiences and decision-making and enable more fulfilling work for employees.
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Going beyond the low-hanging fruits of automation
Most companies begin by using manual methods to determine what processes to automate, which can be done by leveraging crowdsourcing techniques like workshops, hackathons, botathons and ideation tools. Companies then start their automation journey with basic use cases like employee onboarding, payroll management, account creation and account reconciliation in HR, finance and other business functions.
These are easy first steps to implement. But manual methods of discovering processes have their limitations. Workshops and hackathons, for example, can take weeks from ideation to execution, and these techniques are not very scalable in large organizations with multiple offices or business functions. This is where process intelligence tools come in.
Process intelligence: The key to scaling up automation initiatives
Process intelligence is a data-driven approach that helps identify processes and areas that can be automated to get high-yield results. It also eliminates bottlenecks by understanding process data across the company and provides clear visibility of the organization’s current state. It also provides the following benefits:
Provides insights into previously overlooked business aspects
Process intelligence generates a ready pipeline of use cases for automation implementation in key business areas. For example, in customer service, these systems can help employees achieve target KPIs such as reduced wait times or improved accuracy of information provided by analyzing data on customer interactions.
Delivers improved experience
As enterprises prioritize customer, partner and employee experience, process intelligence can help experts narrow their efforts to areas with the highest impact on stakeholder experience. Using automation, more than 81% of business leaders reported enhanced customer experience through faster responses, and nearly 78% saw a reduction in the number of processes associated with queries.
Makes enterprises more agile
Automation allows companies to design and build customized workflows for different customers, in turn addressing macroeconomic challenges to keep pace with the changes in the business environment. For instance, for a loan application at a bank, process intelligence can help customize workflows for different customer profiles like new customers, existing customers and high net-worth individuals (HNIs).
Enables data-driven decision-making
Automation assesses processes and information in real time, helping companies measure the effectiveness of their automation initiatives. By analyzing data on process performance before and after implementation, organizations can determine the impact of automation through key metrics such as cost, efficiency and customer satisfaction, which helps correct their course of action.
Leveraging process intelligence to drive automation
Companies across all industries are turning to process intelligence to successfully scale their automation efforts. In fact, 20% of Fortune 250 companies that prioritized process intelligence are already seeing it pay off.
However, Process Intelligence tools are not perfect solutions. While they can leverage years of data and capture user interactions, the insights from these tools lack business context and background and shell out biased results. To augment these insights effectively, businesses need to take a 3-phased hybrid process discovery approach.
Phase 1: Discovery
The first step is to leverage either process mining or task mining tools to capture the operational process details to provide a preliminary analysis.
Process mining tools help sift through years of enterprise data and event logs stored in legacy applications such as ERP and CRM systems, which can effectively evaluate and visualize steps involved in business processes.
Task Mining, on the other hand, uses computer vision and machine learning (ML) algorithms to capture and analyze user interaction data focusing on the keystroke level details — what the steps are and how they need to be executed.
If an organization is primarily concerned with improving overall business processes, process mining would be the right starting point to gain insights into the flow of activity across departments and systems and identify bottlenecks. However, if the objective is to enhance employee productivity and experience, they should opt for task mining instead.
In some cases, organizations may choose to pursue both process mining and task mining simultaneously to gain a more comprehensive understanding of the organization’s business processes — the decision is based on the organization’s specific goals, challenges and priorities.
Enterprises can then evaluate partner platforms based on their technological capabilities, ability to integrate with the existing enterprise tech stack and platform focus of their respective industry. After a thorough evaluation and Proof of Concept (POC) of shortlisted partners, the platform best suited for their needs can be onboarded for process intelligence. Enterprises can then align a team of business analysts trained and certified for using the platform to carry out the discovery.
Both technologies provide substantial details about the process, including various visualizations and analytics to generate insights. But they fall short when it comes to providing business context — the purpose of each step, the outcome it needs to drive and so on.
Phase 2: Prioritization
Each company should then identify its priorities and goals for automation, whether it’s reducing operational costs, freeing up employee time for high-value work, improving process efficiency, eliminating errors or enhancing stakeholder experience. This can be achieved through workshops or ideathons with business leaders and process owners and will lead to more comprehensive insights to eventually build a framework for prioritization for automating processes.
An outside-in analysis of industry trends, peer benchmarking and best practices can also help process owners refine their prioritization framework for automation of processes. Various external factors such as an ongoing recession, supply chain disruptions or talent crisis may also force enterprises to rethink their priorities and tweak their approach accordingly.
For example, during the onset of the COVID-19 pandemic, enterprises across sectors prioritized reducing their operational costs and improving process efficiencies to deal with the uncertainty. As the environment evolved and the world witnessed ‘The Great Resignation’ wave, enhancing employee experience became the leading enterprise priority to retain top talent.
Phase 3: Roadmap finalization
Once the processes have been identified and prioritized for automation, IT teams, process owners and business heads need to come together to build a roadmap. This includes understanding the technologies to be leveraged for automation: RPA, API, and low-code app development.
No singular approach can solve the enterprises’ needs in isolation. The 3-phased hybrid process discovery approach will be key, as it is scalable, delivers timebound results and adds business context to the insights generated, making it more strategic, sustainable and impactful.
Currently, automation adoption in most organizations rests within the operations teams, which leverage such tools to optimize processes — and hence the workloads are concentrated with them. As tools become simpler to adopt and implement, and organizations become more adept at using them, process intelligence and process discovery will be democratized. Teams across functions will be able to leverage process intelligence to enhance internal processes independently, with reduced reliance on operations teams. This will build self-reliance, resilience and autonomy within an organization to compete in a dynamic business environment.
Nischay Mittal is partner and global head of automation/AI at Zinnov.
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