Avinash Misra, CEO & Co-Founder of Skan.AI – Interview Series

Avinash Misra is the CEO and co-founder of Scan. Avinash is a lifelong entrepreneur with a proven track record of taking businesses from seed to liquidity. He has built successful businesses in the enterprise digital transformation space and his latest venture was acquired by Genpact (NYSE: G). Avinash’s insight for Skan took seed in large-scale Business Process Transformation projects he led over the last decade.

Your previous company Endeavor Software Technologies was eventually acquired by Genpact. What was this company like and what were some of the key lessons you learned?

This company was a front office digital transformation specialist. That is, it specialized in the construction and deployment of specific technologies such as computer vision, chatbots/natural language processing (NLP), and enterprise mobile applications to improve and transform customer business processes.

We learned two main lessons. First, when technology is applied just for the sake of it, it creates both technical and process debt. Second, the most value is derived when technology specifically engages the end user with empathy and a design mindset.

Could you share the genesis story behind Skan?

“Automation begins when automation fails.” In one sentence, this was our beginning. When we built RPA bots for complex business processes, we repeatedly noticed that once a bot was deployed, it failed quickly because it did not take into account all the nuances, permutations and exceptions of that business process. Each time a robot failed, it became one more missing permutation of work. It was an endless cycle of deployment and failures.

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So, why don’t we know all the nuances of business processes?

We don’t know all the nuances of business processes because all process discovery is done by human business analysts who ask the process agents to describe work. Humans are spectacularly unreliable in describing things that have a sense of familiarity or familiarity and routine. These are often things they can do well, but can never describe with the required precision. That’s why we built Skan to observe real work and understand that work and the processes, rather than interviewing and documenting people.

Skan is partly a process discovery platform. Could you define what procedural discovery is for our readers?

Process discovery is a broad term that refers to the act of discovering or learning how processes work at a functional or structural level. This is especially difficult with processes that involve human-system interactions with hundreds or thousands of workers, dozens of software applications, and complex workflows. A great example is the ad management process.

Today, Skan is actually more than a process discovery platform. Skan generates a deep understanding of work (process discovery) and provides advanced analytics to help process owners and transformation leaders measure, analyze and improve KPIs that drive business results such as the customer experience, revenue and cost. We call this broader capability: process Intelligence or the systematic collection of data and the end-to-end process and application of that knowledge to control business results or to learn, understand and make decisions.

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According to a study conducted by Ernst & Young, 30% to 50% of automation projects fail. Why do you believe this is so high?

Based on working with our clients, we find that one of the key obstacles to automation success is a lack of visibility into current state of KPIs throughout the lifecycle of automation projects.

For example, to qualify an automation project, we need to base the current state KPIs and build a business case. In the experimental phase, we need to identify technology patterns and define target (future) KPIs based on current state KPIs. During the design, development, testing and operation phase, we must align with the root cause of the problem to solve.

Finally, in the validation phase, where we measure investment payback and profits, we need traceability to the future KPIs. So, we see that throughout this entire life cycle, transparency and traceability to current state KPIs and root causes is required. And yet, according to Forrester Research (2021), only 16% of organizations say they have complete visibility into how processes work. It’s no wonder automation projects struggle to deliver value.

Can you explain what procedures Skan takes to protect the privacy of people who are being monitored and sensitive business data?

It is important to note that we do not control people. We observe only specific elements of work (not the entire screen). These elements are specific job applications that are predefined.

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That said, for any applications observed, all sensitive work data is redacted. We also have the ability to anonymize the link between the person who did the work and the process. The names of individuals working in the process may also be anonymized.

Could you discuss how Skan uses machine learning and specifically deep learning?

Skan includes several AI and machine learning algorithms to handle various problems such as anonymizing sensitive information (both text and image data)abstracting low-level events to business activities, inferring process graphs, and discovering process variations.

What are some examples of actionable insights that were gained from this process?

Skan helps process owners and transformational leaders measure, analyze and improve KPIs that drive business results. Some examples of insights are:


  • First cost of production
  • Resource (manpower) utilization
  • NPS improvement


  • Automatic discovery
  • First pass rate
  • Process compliance
  • Skill (workforce) planning
  • Reduced process variability

What is your vision for the future of process intelligence?

Our vision for the future of process intelligence is to transform the way people work so they can improve productivity and reach their full potential.

Today, the global pyramid of work has a broad base of non-value-added tasks and a very narrow peak of value-added tasks. Our vision is for process discovery to invert this pyramid.

Thanks for the great interview, readers who want to learn more, visit Skan.


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