November 23, 2021

You Wouldn't Have AI without Humans – A Chat with our Founder

In our last discussion with Paul Blase, Speciate AI’s founder, we learned about the powerful capabilities brought forth by algorithms that access billions of unstructured data points, while also understanding the value unstructured data has in discovering novel competitive intelligence insights. With the amount of unstructured data growing daily, so too does the value of artificial intelligence. That’s why our market monitoring solution is driven by AI – it helps to provide focus in a world full of noise, and gives our analysts – and our customers – the best look into highly relevant data.

While artificial intelligence powers our service, it’s the expert engineers, data scientists, and developers that employ these powerful algorithms to pinpoint the information, refine the algorithms, and present the data. Speciate’s firm belief is: AI will transform the world. However, we acknowledge, respect, and appreciate the human intelligence behind that is really driving the value we provide to clients.

In this discussion, we turn our focus to the human intelligence behind Speciate AI. We talk about our data and analytics team, the complex nature of competitive intelligence projects, and the role of “the analyst” today. Find details on these topics and more in the Q&A below.

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‍Paul, let’s talk about human intelligence today. What are the core components of Speciate’s data and analytics team? Do you employ dozens of analysts who work tirelessly to sift through the data you collect?

Paul: No. There are two main components to our data and analytics team: our AI algorithms and our data scientists. Instead of hiring numerous analysts and eating up our resources, we use our AI algorithms to sift through data faster than any group of analysts could. The speed of our data collection helps to find early trends and novel patterns that lead to insights. It’s a huge productivity boost for us, which really allows one business analyst to complete the work of 10

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Our data scientists, on the other hand, continually review and refine the algorithms we use to process and find insights in the data. So it becomes less about how many people we have on our team, and more so about our masterful combination of artificial and human intelligence. It’s like the adage says “work smarter, not harder.”
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How do you think analysts and their respective teams are navigating competitive intelligence projects given new and existing resources, vendors, and trends?

Paul: Every analyst and competitive or market intelligence team knows that there are mountains of market data available to them. They know that there are tons of data sources and digital channels where intelligence lives, but they have no idea that it can be efficiently tracked and mined. Basically, these teams don’t know what they don’t know, especially when it comes to applying AI to generate novel competitive intelligence. This spawns an uncertainty that could transform into fear of missing out on something important. Our own analysts know this feeling, yet we are able to parse through noises in data and really hone in on specific topical areas to complete our analyses.

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Of course, there’s always the challenge of getting the right information. When we present Speciate to competitive intelligence teams, most will conceptually understand the potential but remain in disbelief until the “proof” is provided. I think the crowded market of similar-but-slightly-different resources and vendors has given analysts a jaded perspective. Given the generic tracking of brands and competitors that other solutions and vendors provide, or the time-intensive nature of acclimating to unnecessarily complex systems, it makes sense that analysts have become jaded.

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That’s why when analysts see how Speciate distills billions of market data points into charts they can navigate that provide actionable metrics and insights… they simply get it.

What have you noticed about analyst roles in the competitive intelligence space?

Paul: Companies approach competitive intelligence in multiple different ways. Many Fortune 500 businesses have full-time teams focused on market research (which also includes understanding customers) and competitive intelligence, but they are still largely viewed as a cost center and understaffed relative to the number of requests they receive and what their analysts have the capacity to do. These same teams are also asked to monitor which start-ups – those that may be future competitors – are doing in the market, and evaluating whether Buy vs. Build vs. Partner strategies make sense.

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Conversely, for many mid-tier companies competitive intelligence isn’t important until it is. They usually don’t have dedicated teams, and if they do it’s a part time job for one analyst. In that case they get sporadic requests and can only react with the information sources they have available instead of being proactive.

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That’s why Speciate’s analysts are able to achieve so much more than many competitive intelligence teams out there: our AI drives the data collection, which allows for refined, focused attention to be paid on hidden insights and novel patterns.

What is the main driver that motivates you to keep building Speciate into a leader in competitive intelligence? Has this motive changed since you founded the company?

Paul: When I started the company, I was interested in providing world-class market analysis to companies, showing them the potential to find untapped value in unstructured data. This is still of utmost importance to me: that timely, relevant competitive intelligence is delivered so that analysts can rest easy, so they don’t get surprised and to eliminate their fear of missing out.

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However, I see more potential for a data & analytics platform that is the go-to-source for sourcing, managing and using data for complex market analysis and other analyses that require executives, business analysts and data scientists to collaborate in order to get the best insights.

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