TechBytes with Oded Karev, VP, Head of Advanced Process Automation, NICE

Oded Karev
VP, Head of Advanced Process Automation, NICE

Customer experience is now a key factor in any omnichannel engagement. The rise of Artificial Intelligence and Machine Learning has also led to the growth and proliferation of virtual assistants or chatbots. Brands are now leveraging chatbots for a variety of purposes, especially to provide customers with a seamless experience.  We spoke to Oded Karev, Head of Advanced  Process Automation at NICE, to understand the nuances of this trend.

Tell us about your role at NICE and the team/technology you handle.

I lead the advanced process automation line of business within NICE which covers the full spectrum of robotics solutions. Our Robotic Automation line of business extends globally and we have a large team of seasoned professionals. From a product perspective, we work with talented and experienced teams of R&D professionals and developers, as well as product managers. Our sales and marketing functions are comprised of technical and field specialists with experience in robotic automation technology. We have a significant network of global partners, whom we collaborate with, in addition to fully-fledged customer success and professional services teams.

The technology that we work with is extremely dynamic and spans robotic process automation, desktop automation technology and desktop analytics. The ability to integrate any of our advanced process automation solutions with other NICE portfolio solutions and cognitive assets positions us as a leader in innovation.

Do Virtual Assistants for Enterprise signify the highest maturity of automation for businesses?

Virtual assistant technology is a vital component in the digital workforce and ranks high in terms of automation maturity. We are seeing more of this type of technology emerge in the market due to the need to offer employees both front and back office assistance. This dual assistance enables employees to work and process complex tasks more efficiently; inevitably improving the customer experience.

Virtual assistant technology is essentially powered by cognitive tools, which provide a course of learning, enabling the virtual assistant bots to become more intelligent by closely resembling human behavior over time.

From our experience, robust virtual assistants should not just facilitate interactive conversations with customers but should also have enough functionality to automate tasks, assist with compliance adherence, even boost sales.

How could enterprises with small number of employees benefit from Virtual Assistants?

The expansion and increasing availability of cloud offerings has made virtual assistant technology more accessible and affordable to small business entities.

There is typically more pressure placed on employees within smaller enterprises, due to limited resources and the need for most individuals to take on multiple roles. The adoption of virtual assistant technology is perfectly positioned to elevate the stress and strain resulting from multi-tasking, all the while reducing operating costs.

Which market and geographies are you targeting with NEVA? How are you competing against the established enterprise intelligent assistant-makers?

NEVA is targeting a global market and will be available to enterprises worldwide. We observe that other virtual assistant technologies still have limited capabilities and as such, we see that our competitive edge in the market stems from our intelligent Desktop Automation technology (the underlying technology powering NEVA).

NICE desktop robots have built-in intelligence to navigate the complexities and dynamism of the desktop environment. This enables them to integrate with any enterprise application in order to automate both front and back office tasks. Furthermore, the technology provides real-time process guidance to employees by way of interactive screens. The desktop robots are continuously monitoring employee behavior (mouse clicks and keyboard strokes) and will be automatically triggered to offer real-time guidance to employees at the perfect moment when they need it. Some examples of real-time process guidance include:

Prompting an employee, in real-time, to read a disclaimer script to a customer as a means of ensuring compliance adherence.

Presenting a sales script to an employee, again, at the most opportune moment within a customer interaction to harness an opportunity to upsell or cross-sell.

Presenting a summary of important information from a single interactive screen, saving the employee the time it would have otherwise taken him/her to access multiple applications.

What role will Chatbots for Marketing and Sales have in 2018?

We see great value in integrating chatbot solutions with Robotic Process Automation (RPA) technology, as this combination extends the intelligence and capabilities of the chatbot to execute both front and back office tasks. With this in mind, intelligent chatbot and RPA integrations have the capabilities to support the following marketing and sales tasks:

Source and validate data for competitive analyses

Present real-time sales scripts to employees at an optimal moment within a customer interaction. Should a sale be closed, the RPA robots will update the back-end applications and automatically generate a personalized e-mail to the customer.

What technologies in AI/machine learning are you deploying to make bot adoption agile?

Leveraging Natural Language Processing (NLP) to allow our bots to ‘read’ and interpret rich and descriptive unstructured data, such as an invoice or a customer query and convert it into a structured format which can then be utilized by a robotic workforce to execute various tasks and processes.

Our newly released Automation Finder solution utilizes machine learning to detect new opportunities for business process automation. The machine learning engine observes human input and over a period of time learns to recognize activity patterns which can best benefit from process automation.

In the event of an error or a process irregularity, a human may need to intervene by exercising judgment and authorizing certain data. An ML engine observes the human response to process exceptions or irregularities and over a period of time the system will learn how to automate similar exceptions without human intervention.

Thanks for chatting with us, Oded.
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