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


Avinash Misra is the CEO and co-founder of skin. Avinash is a lifelong entrepreneur with a proven track record of taking businesses from seed to liquidity. He has built successful businesses in digital business transformation and his latest business was acquired by Genpact (NYSE:G). Avinash’s insight for Skan is rooted in large-scale business process transformation projects he has led over the past decade.

Your former company Endeavor Software Technologies was finally acquired by Genpact. What was this company and what were the main lessons you learned from it?

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

We have learned two essential lessons. First, when technology is applied only for itself, it creates both technical and process debt. Second, the greatest value is achieved when the technology specifically approaches the end user with empathy and a design mindset.

Could you share the genesis story behind Skan?

“Automation starts when automation fails.” In one sentence, that was our start. When we created RPA bots for complex business processes, we repeatedly noticed that once a bot was deployed, it quickly failed because it didn’t take into account all the nuances, permutations and exceptions of that process. job. Each time a bot failed, it became another missing job permutation. It was an endless cycle of deployments and failures.

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 process agents to describe the work. Humans are spectacularly unreliable at describing things that have a sense of familiarity or habit and routine. These are often things they can do well, but can never describe with the necessary precision. Therefore, we built Skan to observe actual work and understand that work and processes, rather than interviewing and documenting humans.

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

Process discovery is a general term that refers to the act of discovering or learning how processes work at an operational 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 good example is the complaints management process.

Today, Skan is actually more than a process discovery platform. Skan generates deep insight into the work (process discovery) and provides advanced analytics to help process owners and transformation managers measure, analyze and improve KPIs that drive business results such as customer experience, revenues and costs. 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.

According to a study by Ernst & Young, 30-50% of automation projects fail. Why do you think it’s so high?

Working with our customers, we find that one of the biggest barriers to automation success is the lack of visibility into the current state of KPIs throughout the automation project lifecycle.

For example, in order to qualify an automation project, we need to base the current state of KPIs and build a business case. In the experimentation phase, we need to identify the technological models and define the target (future) KPIs based on the current state KPIs. During the design, development, testing and operationalization phase, we must align with the root cause of the problem to be solved.

Finally, in the validation phase where we measure ROI and benefit realization, we need traceability to future KPIs. Thus, we see that throughout this life cycle, transparency and traceability of KPIs and root causes of the current state are necessary. Yet, according to Forrester Research (2021), only 16% of organizations report having complete visibility into how processes are working. It’s no wonder automation projects struggle to deliver value.

Can you explain the procedures followed by Skan to protect the privacy of monitored individuals and sensitive business data?

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

That said, for all observed applications, all sensitive working 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 people working in the process can also be anonymized.

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

Skan integrates several AI and machine learning algorithms to solve various problems such as anonymizing sensitive information (text and image data)by extracting low-level events from business activities, inferring process graphs and uncovering process variations.

What are some examples of actionable insights that came out of this process?

Skan helps process owners and transformation leaders measure, analyze and improve the KPIs that drive business results. Here are some examples of insights:


  • Production unit cost
  • Use of resources (staff)
  • NPS improvement


  • Discovery of automation
  • First success rate
  • Process compliance
  • Capacity planning (staffing)
  • 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 their productivity and reach their full potential.

Today, the global work pyramid has a broad base of non-value-added tasks and a very narrow top of value-added tasks. Our vision is that process discovery inverts this pyramid.

Thanks for this great interview, readers who want to know more should visit Skan.


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