Start with a Problem: How Fast-Growing Startups Can Leverage Machine Learning – A Conversation with Andrew Filev

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Start with a Problem: How Fast-Growing Startups Can Leverage Machine Learning - A Conversation with Andrew Filev 3

Episode SummaryLearning about the research behind machine learning is always fun, but so is learning about the real-world applications. In today’s episode, we’re joined by the CEO and Founder of Wrike, Andrew Filev. Filev speaks about where Wrike is currently applying machine learning and AI in their fast-growing, data-driven company. He shares his insights as to why he thinks marketing might be the most ripe for disruption by AI, and also discusses how most companies in any industry can prepare to take advantage of machine learning.

Expertise: Software engineering

Recognition in BriefAndrew Filev founded his first software consulting company at age 17, and has 10+ years of experience in software engineering, product management and marketing. Andrew completed his M.S. in Computer Science at Saint Petersburg State University in Russia. He founded Wrike in 2006 to focus solely on building a new class of business software. He serves as CEO of Wrike and remains the primary visionary behind the product and company. He is active in the Silicon Valley start-up world and enjoys being an advisor to young entrepreneurs.

Current Affiliations: Founder & CEO of Wrike


Interview Highlights:

(1:24) Where does machine learning (ML) sort of have a boots-on-the-ground presence within your company?

(2:47) How does lead scoring sort of tie into (ML) with you, and what else is going on in marketing with Wrike?

(10:36) It seems like volume of data is closely tied to ROI…it seems part of (the reason) marketing is where you’d point the effort (in using ML) is because of the total tie to revenue…

(16:17) Where do execs like yourself have to go, given how new this field is, in order to do this kind of research, to figure out ‘where do I want to allocate company money to receive a return on AI?’

(19:42) Is there anywhere that information is being pooled or pulled together (best practices)…or is it more do events, talk to your VC (venture capitalist), see what they might know?


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