Oleg Rogynskyy, CEO at People.Ai, speaks to Digital Bulletin about AI’s role in boosting productivity in the workplace, and how machine learning can help employees effectively analyse data.
Could you briefly talk us through your career journey?
I actually got my degree in Business Administration, Political Science and International Relations. After graduating, my first job was in sales for a company that pioneered in Artificial Intelligence (AI) and Natural Language Processing (NLP) for enterprise back in 2006, called Nstein Technologies. This is where I fostered an interest in AI, NLP and machine learning and from there I moved to Lexalytics, where I saw the need for democratised, cloud-based analytics. I left Lexalytics and started Semantria which was later acquired by Lexalytics, before starting People.ai in 2016.
How did you come to found People.ai and what were your reasons for believing in your proposition?
Since starting out at Nstein, I have always been interested in how data-science, AI, NLP and machine learning can be combined and leveraged to boost human productivity and, having started my career in sales, I was aware of the pain points CRM can cause. I saw the need for reliable technology to help make the most of all of the data go-to-market teams collect and that’s why I started People.ai.
Could you provide an overview of People.ai’s product and services, and why you believe it stands out in the marketplace?
We have three main products: The Revenue Intelligence System, Campaign360 and The Wire. These are all tailored to suit the needs of go-to-market teams. The Revenue Intelligence System automates the capture of all contact and customer activity data and automatically updates CRM to provide actionable intelligence across CRM to sales and marketing teams. Campaign360 gives marketing teams full visibility into the impact of their campaigns on pipeline, while The Wire gives sales reps personalised, actionable insights from their CRM to data to help them prioritise their day. Our platform is the only platform of its kind that automates data capture, provides real-time insights and augments the sales person with the next best course of action.
Which industries are proving the most fertile for People.ai’s product and why?
Our platform is designed for sales and marketing teams; two departments that are ubiquitous across the enterprise. Our platform is most effective when used by enterprise businesses with large go-to-market teams that are able to collect large amounts of data. Sales and marketing are two very person-centric industries, yet both salespeople and marketers get bogged down with data-heavy administrative tasks. Our platform automates both sales and marketing team’s data capture and augments by using this data to provide intelligent insights to drive new revenue opportunities.
When and why did you first become interested in machine learning?
I first became interested in machine learning when I started at Nstein. I saw the impact NLP could have on everyday processes, and I was fascinated by how much time it could save sales and marketing professionals when applied to areas of their work that are neglected – such as creating actionable insights with the help of massive amounts of data. Without machine learning, these pools of data are too vast to be analysed by humans. Machine learning offers accessibility to overcome this challenge.
Of the many new technologies changing businesses, how vital will machine learning become in a data-centric enterprise?
A data-centric enterprise often possesses large amounts of inaccessible data – which is caused by the complexity of the data landscape. Machine learning, and namely People.ai products do, is able to turn big data into useful data. Any data-informed enterprise can be overwhelmed by the large amounts of information that they deal with on a daily basis. Machine learning helps to contextualise this massive pool of data to create actionable insights.
What are the key advantages that machine learning can deliver for organisations?
There are many benefits organisations can gain from implementing machine learning. Most notably, machine learning does not require a clearly defined task as it works intuitively, unlike other solutions. This means that a greater number of tasks can be completed digitally, leaving humans to spend their time on tasks that require strategy or creativity.
What are the main barriers to machine learning adoption in enterprise?
Businesses often struggle with cultural resistance from teams who believe implementing a solution like that will make their roles obsolete. In reality, the opposite is true. Adoption of machine learning can lead to better use of their time, focusing on more meaningful tasks instead of data-centric tasks. Another barrier is the skills gap in IT. Some companies believe that to implement and maintain the right solution, they first need to hire IT personnel fit for the task at hand. However, channel partners are well-equipped to provide this service and often come with a wealth of experience, having worked with other companies to implement the right solutions.
What roles do humans have in a future where machine learning and AI become more prominent?
Human roles in the workplace are evolving rapidly. The adoption of AI and machine learning mean that humans can now spend less time on tasks that are data-intense, repetitive, time-consuming and quite frankly mundane. Instead they can focus their efforts on the more creative and strategic tasks that add more value. A study by the World Economic Forum reported that 65% of children who entered primary school in 2017 will eventually have jobs that do not exist yet. I find this very intriguing and I’m confident that with the dawn of AI and machine-made decisions in the workplace, job roles will evolve to tap into creativity, interpersonal skills and other traits that are exclusive to humans.
How is People.ai deploying machine learning techniques to improve its product and services?
People.ai uses machine learning to enhance the sales ecosystem. Our offering helps organisations automate manual data entry and augment by understanding and recommending areas of improvement, such as deciding on who the highest value sales lead to pursue might be.The system works by ingesting data such as email, calendar, phone, WebEx, conference tools and other data sources and identifying common threads, then recommending the best next action of what it deems to be the best way to close a deal. Not only is the tech able to see what a salesperson did correctly, but it also learns to continue building a smarter system.
What upcoming innovations (around machine learning or anything else) is People.ai focusing on?
We’re focused on supercharging your sales, marketing and customer success teams by automating the processes that shouldn’t be taking valuable time, and then augmenting them with best next actions based on historical data to ensure all of your go-to-market teams are maximising their impact.
The Wire is one of our latest innovations. It provides personalised, actionable insights, creating a real-time “to do list” that’s easy-to-consume and provides the best next steps for your customer-facing teams (sales, marketing, customer success and others). It represents a deep understanding of your professional world and how your prospects, customers, and internal teams are engaging to drive revenue.
What are the emerging trends that might impact upon People.ai and your industry?
One trend that we have already seen drastically impact the enterprise technology industry as a whole already and that will continue to do so is the ‘consumerisation’ of enterprise technology. In consumer tech, we have seen companies such as Uber be built on three pillars. Firstly, they collect activity data of their users. Secondly, they combine all this data into one big pool, before lastly, they analyse trends in this data to predict the next best course of actions for users to take. Our platform has been built on similar pillars and as consumer brands such as Uber develop new and innovative ways to leverage their data, enterprise tech companies will follow suit.
