How to Combine AI and Blockchain

Artificial intelligence and blockchain have had a banner year. New, successful use cases abound in a variety of industries, with investors seeking out new opportunities for both technologies amidst growing mainstream recognition. Ian Foley, a serial entrepreneur and partner at SaaS-focused Xenon Ventures, sees massive potential in not only AI and blockchain’s individual functions, but their ability to work for each other’s benefit.

In an interview with James Bourne, editor-in-chief of TechForge Media, Foley calls AI and blockchain “the next evolution around enterprise software.” That evolution comes in part from their complementary characteristics. Foley sees blockchain’s immutable ledger as a solution for AI’s current transparency problem.

Currently, businesses can set parameters for AI to perform a specific function (like approve someone for a loan); the AI will crunch data and spit out a result. But how and why, exactly, that result was achieved is often obscured, leaving it open to questions of bias, impropriety, and more. Foley believes blockchain’s “clear audit trail” can pull back the curtain on AI decision-making. Because blockchain logs every step of a process without the ability for alteration, it can make the so-called “explainability problem” (in Foley’s words) more explainable.

Additionally, blockchain offers a way to clean up redundancies in a data set. “The advantage of blockchain is that if people have their own unique identity, you’re going to have a far cleaner data set than the mismatch where people would now…infer, or have to use a workaround to understand, what [specific data represents],” explains Foley.

Whether the two technologies can work together is a moot point if there are no actionable use cases with tangible benefits to sell a product to businesses. In Foley’s experience, vertically-based AI provides an answer. As leader of AI-powered customer acquisition platform acuteIQ, he learned to take “a very large data set of 21 million US small businesses with over 500 million data points” and leverage it to create insights in a specific space – banking. Banks could reach out directly to small businesses and, using AI-parsed data, offer services specific to each use case. Findings and information from further experiences were reintegrated into the database to derive additional insight as “the systems learned from each other.”

That feedback loop “[taught] our database how to be better,” says Foley. “By making the product vertically relevant, it helped tremendously to quickly achieve product-market fit…it’s a combination of vertically-based product with vertically-based AI that proves to be a winning combination.”

Banking is hardly the only field that could benefit from vertically-based AI – any business possessing reams of customer data has the potential to “identify and mine a connection” and derive real value. “If you can reduce costs by 20 percent, [and] you don’t need to make significant changes with existing technology, [and] you have five other customers that have done it beforehand, and you can get them to buy without saying, ‘Actually we’re using AI, or blockchain’, I think that is the ultimate marker,” says Foley. With AI and blockchain complementing each other, that marker is closer than ever.

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