Q&A With Scott Opitz, CMO, ABBYY

After a 20-year tenure with the company, ABBYY’s Chief Marketing Officer Jupp Stoepetie retired in February, and the firm announced the appointment of Scott Opitz as his successor. We recently asked Opitz a few questions about his new role.

What is your goal as CMO of ABBYY, and why did you decide to accept this position?

It’s interesting to see that the trajectory of my career has aligned with the way organizations are needing to solve critical challenges within their business processes. I have founded several companies and when I look back at the roadmap of technologies and solutions developed, they focused on helping companies understand and improve their business operations. Since joining ABBYY with the TimelinePI acquisition, I had a chance to more deeply understand the power of ABBYY’s products and its unique position in the market.  My goal is to ensure global organizations know they have a strategic partner who can help them gain digital intelligence from the vast amount of data that is consumed and generated by their core systems and how their processes perform under different business conditions.

What do you think are the biggest challenges facing companies like ABBYY that are working to help organizations achieve digital transformation?

We’re advocating companies take a different approach to their digital transformation, and that can be challenging for people used to doing the same thing. ABBYY is educating the market as to how digital intelligence can be applied to their various digital transformation projects. For instance, organizations focused on implementing automation projects using RPA often lack the visibility of the full end-to-end process and therefore frequently fail to consider the human workflow steps in the process and the documents on which they work. By understanding the full process, they can make better decisions on what to automate, how to measure it and how to monitor the entire process in production.

We’re hearing a lot about AI in the enterprise. What does that really look like?

We have finally hit the point where organizations have seen through the hype and are now focused on use cases where AI is able to deliver real results. Like most successful AI examples, the best successes come from well-defined use cases, such as invoice processing and customer onboarding.  ABBYY’s content intelligence solutions are great examples as they use AI to provide powerful document and unstructured data analysis without the need for human intervention. We also are applying AI to new mechanisms for forecasting process outcomes and recommended process optimizations.

RPA has had a lot of hype over the past three to five years. Do you think it’s hit a tipping point?

RPA has been very successful in getting organizations to more aggressively think about automation opportunities. Until now though, most of the use cases have been focused on automating desktop activity which might be better described as sophisticated macros to assist human users. For RPA to truly hit an inflection point, it will require organizations to apply a combination of best-of-breed tools for not only the automation but perhaps even more important the analysis, planning and monitoring of the overall processes of which the automation is part.

There have been notable movements with process mining vendors. What impact does process mining have within business processes, is it really needed and what more needs to be done?

While process mining has been a great catalyst to get people thinking about how to better understand their processes, it is too limited. What is needed is a broader process intelligence capability that is capable of understanding any type of process (regardless of complexity) and is capable of both in-depth analysis of process histories and real-time monitoring and prediction of future process states. When all of these capabilities are brought to bear, it becomes an invaluable part of any digital transformation initiative.

We see a lot of emphasis on KPIs and ROI. How should organizations measure the performance of digital transformation and RPA projects?

Organizations need to evolve from trying to measure isolated automation projects to understanding the overall impact of automation on the entire process lifecycle including the interplay between different process types.

What is the biggest mistake you’ve seen organizations make when deploying digital transformation projects?

The most common and often the most costly mistake results from organizations failing to do the analysis and planning, which is critical to the long-term financial and technical success of these project. Rarely does the enthusiasm to just jump in and figure it out on the fly ever prove to be a recipe for success.