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Without exploring AI, companies can quickly fallbehind. But how can AI be used to push the limits of how businesses can develop and grow?
In an exclusive interview with DesignRush, Signal AI’s founder David Benigson explores the growing popularity of AI development in risk assessment, the irreplaceable “human aspect” of decision-making, and how Signal AI went from working in a garage space to working with Fortune 500 companies.
Who Is David Benigson?
David is the founder and CEO of Signal AI. He has been recognized for his innovative approach to AI and has been nominated as a Forbes 30 under 30, DataIQ100 “Most influential people in data,” and “PR Week Dashboard 25 most influential people in communications technology.”
AI is expected to see an annual growth rate of 37.3% from 2023 to 2030. How does Signal AI position itself during these constant changes in the AI industry?
David: Iteration and innovation are core to our DNA at Signal AI and we will continue to invest in technology to push the envelope of what’s possible in enterprise AI. Our platform leverages a unique mix of discriminative and generative AI that will be an important differentiator in the months and years to come, especially as concerns continue to mount over AI accuracy and generative hallucinations. Our investments in research and our partnerships with leading academic institutions aid us in this journey, helping us drive innovation and stay well ahead of the curve.
How does Signal AI utilize AI in risk assessment and decision augmentation? Where does the “human aspect” of business decision-making play into this process?
Our AI model is trained on more than 4 billion documents to automatically identify content that mentions an organization, topic, person, or a range of other “entities.” Since we’re able to do this dynamically and without the need for static Boolean queries, we’re able to surface critical information in real time that enables organizations to audit their supply chains and spot risks before they snowball into reputational liabilities.
Think of it as an early warning system that casts a very wide, but very fine net. The human aspect is inextricable to the process because these systems need to be directed. Our platform won’t look for risks unless you ask it to. And ultimately, whatever output Signal generates still needs to be actioned by a human.
What have you found leaders prioritize when making decisions? How does this change when they work with Signal AI?
From our experience, business leaders prioritize accuracy, timeliness, and relevance of data when making decisions. The most common feedback we hear from customers switching from a legacy solution is that they’re astounded at the speed at which our platform ingests new content and the accuracy with which it surfaces relevant data and insights.
Can you tell us a bit about how Signal AI was founded and the way that you fought toward success when artificial intelligence was just beginning to take off?
We founded Signal AI 10 years ago in an auto repair shop (still technically a garage!) with a mission to transform how business leaders make decisions and create a better way for brands to track their media mentions.
At the time, AI was still very much in its infancy. There was a budding community of machine learning researchers in London and many of our early hires, including our co-founder, were sourced from this group. We continue to maintain strong ties with the academic world and frequently present work at AI conferences and international research workshops.
What are the most popular solutions clients request from Signal AI’s offer, specifically the world’s largest 500 companies that you work with?
Our core web app supports the day-to-day PR and comms work of more than 40% of the Fortune 500, making it our most popular offering. The same data that powers the web app also underpins our curated insights reports, our API dashboards, and a host of other AI-powered solutions designed for decision augmentation. As organizations increasingly lean into more strategic applications of AI, we expect to see substantial growth across our tailored offerings.
How do your solutions harness AI technology to make sense of the overwhelming amount of available external intelligence?
This is exactly the problem Signal AI was founded to address. Ten years ago, we saw there was already too much data and content out there for companies to track using conventional methods and legacy tools. Today, the volume of content is exponentially greater, and the advent of generative AI only promises to exacerbate that predicament.
Our solutions tackle this problem in three ways:
- We curate high-quality data from our premium content partnerships, ensuring that all data on our platform comes from validated sources
- We leverage the power of our purpose-built AI to extract and enrich insights at scale
- We deliver robust infrastructure and tailored functionality that empowers customers to streamline real-world tasks like reporting and analysis.
Your launch of the External Intelligence Graph is a groundbreaking way for organizations to have a comprehensive view of their external world based on real-time data and content. Why is the External Intelligence Graph a key part of Signal AI’s strategy and how can it help companies grow?
The External Intelligence Graph is our proprietary AI model that underpins everything we do at SignalAI. It allows organizations to understand their external environment based on real-time data and content from validated sources. The Graph is what enables us to uncover patterns, trends, and insights that would otherwise be obscured by the sheer volume of data. In turn, these insights can guide companies in their growth strategies, risk assessment, and decision-making processes.
How do you see AI evolving in the segment of risk assessment through the next decade?
As any risk specialist or crisis comms practitioner can tell you, the name of the game is speed and accuracy. The quicker a company can identify risk and intervene with a sound response grounded in validated data, the likelier they are to stave off reputational damage. As AI continues to grow smarter and more reliable, we will naturally see a rising adoption of AI solutions for risk assessment.
