Companies deploy workflow automation to improve the quality and cost of the customer experience. Automation can help organizations achieve their goals through several means: robotic process automation (RPA) to track competitors’ websites and prices in real time, or marketing automation to tailor emails, offers and outreach to leads, or predictive routing that uses artificial intelligence (AI) to automatically route inbound callers to the right sales representative.

By automation, we refer to the implementation of tools and technology to make processes run with minimal human assistance. Automation can vary in complexity, from the automation of workflows, to the use of bots for performing rules-based tasks, to the deployment of AI, such as machine learning and natural-language processing, for cognitive processing.

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Customer Experience Tools and Trends

Our insights share how the right CX tools make customers’ lives richer and more fulfilling and strengthen a company’s economics by holding down costs and securing new revenue streams.

RPA, for instance, is a faster solution with a higher return on investment than some technology solutions. For all the automation tools, companies often reap a triple play of benefits: a better customer experience, lower costs and better control of the operating environment. This is particularly true in times of recession or downturn. During the Covid-19 pandemic, companies have faced significant pressure on high-touch processes, such as customer service and financial transactions, leading most to accelerate automation initiatives to cut costs and achieve better business and process outcomes.

How companies use workflow automation tools

• Sales teams use RPA to estimate and report key metrics based on existing data. This allows automatic comparison of data points from different sources and points in time, pattern identification and recommendations, at reduced error rates.
• Improving customer episodes by seamlessly executing different service episodes or components of service delivery such as order entry, billing and dispatch.
• Task orchestration through business rules using automated handoffs across departments, alerts upon specific triggers and real-time visibility on choke points throughout a workflow. 
• Automated onboarding for customers using virtual assistants such as natural-language processing and optical character recognition. A contract-intelligence chatbot can analyze legal documents and extract important data points or clauses.
• Automated loan processing, including credit risk assessment, decision making and approval, using RPA, machine learning and predictive analytics. Companies also use machine learning to review payments and reduce false positives in sanction alerts.

Key considerations

• Focus on the broader objective. Ground automation in an ambitious vision to digitally simplify and enable business processes, rather than finding lots of small tasks to automate.
• Adopt the customer lens. Define your customer episodes and understand the pain points for customers and employees. Prioritize episodes for automation based on value created and ease of implementation.
• Take a clean-sheet approach. Adopt a zero-based approach to reach transformative levels of improvements, as opposed to the traditional approach of focusing on what to remove.
• Embed tools in day-to-day operations. Start change management from the start. Involve business stakeholders and IT when the journey begins.
• Align service delivery and talent. Defining key changes in service delivery and talent management may be required to deliver on the redesigned process or customer episode.
• Test for results. Test and learn by focusing pilots on big process pain points for the organization and proving value.
• Maintain the basics. Fix and simplify processes, data and systems. Automation does not eliminate the need to address the root causes of performance.
• Ensure ownership. Make someone accountable for owning the end-to-end process, and get all the necessary stakeholders to buy in. Handing it over to a small team for experimentation will never lead to a large-scale effort.