Businessman using digital tablet with virtual property documents and checklist icons. Concept of real estate technology, online mortgage, smart contract, and house investment.
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On September 1, three mortgage brokers stood on a stage at Detroit’s historic Fillmore Detroit and pitched working technology prototypes to the company that engineered them. I watched from the mezzanine, seated in one of the theater’s original seats and a part of a building that opened in 1925 as the State Theatre, that survived Detroit’s decades of ebb of decline and flow of reinvention, and was ultimately restored again as the Fillmore.
The theater was built by C. Howard Crane during Detroit’s great movie-palace era, when going to the movies was itself an experience. The physical setting had an early twentieth century charm but the energy was decidedly twenty-first century and much more than atmospheric. Now, nearly a century later, the same room was being used for a different kind of experience: mortgage brokers showing a technology company what they believed their industry needed next.
That occasion was “The Big Pitch”, a contest Rocket Pro launched in June with an unusual premise for enterprise AI innovation: rather than telling brokers what it had built for them, the company asked brokers what needed to be built. Then came the twist. After months of framing the competition around a single $100,000 winner chosen by broker vote, Rocket announced it would build all three tools, awarding $100,000 to Seth Hasan of West Capital Lending for Quick Counter, $60,000 to Andrew Haff of Barren Hill Mortgage for Right Track, and $40,000 to George Chevalier of Clearview Lending Solutions for Signal.
The 95% Problem
The announcement matters beyond mortgages because it lands in the middle of a defining tension in enterprise AI: enormous investment in technology has yet to translate consistently into measurable business results.
A widely cited report from MIT’s NANDA initiative, The GenAI Divide, found that roughly 95% of the enterprise AI initiatives it studied delivered no measurable profit-and-loss impact. The report examined 300 AI deployments, interviewed executives and surveyed employees. Its diagnosis pointed less to model quality than to the way that companies select problems to solve and how they integrate AI into actual workflows.
Lead author Aditya Challapally told Fortune that the small minority of winners succeed because they “pick one pain point, execute well, and partner smartly” with the people who actually use the tools. That pattern actually predates generative AI.
The Pattern Predates AI
In 2018, Maersk and IBM launched TradeLens, a blockchain platform designed to digitize global trade. The technology (or, solution) was built first and offered to an industry afterward. Maersk shut it down in 2022, acknowledging that while the platform itself was viable, it hadn’t achieved the industrywide collaboration necessary for commercial viability.
Walmart Canada approached blockchain from the opposite direction, starting with a specific operational problem: tracing food through a fragmented supply chain. In an initial pilot with IBM, the time required to trace an item from store to farm fell from seven days to 2.2 seconds. Walmart subsequently required leafy-green suppliers to implement blockchain-based, farm-to-table traceability, explicitly connecting the technology to faster food-safety investigations and more effective recalls.
The relevance is hardly theoretical. FDA investigations this year have repeatedly relied on traceback to identify likely sources of contamination, while the agency has been working with industry to improve the speed and accuracy of food traceability. Same broad technology category. Same era. Different starting point. And the distinction was not simply what the technology could do. It was what someone needed it to do. Fast forward to September 1st: that is the premise behind “The Big Pitch”.
Problems First, Prototypes Second
Rocket Pro received more than 350 submissions, open to any licensed wholesale professional rather than only Rocket partners, and asked participants to submit problems, not finished solutions. According to Rocket Pro Chief Revenue Officer Austin Niemiec, AI emerged as the dominant theme in the submissions. The three finalists then worked with Rocket’s engineering team to develop prototypes of their ideas. The winner was determined through combined online and live voting, with one vote per licensed professional.
Each tool addresses a friction its creator encounters firsthand:
Chevalier’s Signal tackles the pipeline problem: systems can flag outstanding loan conditions without telling a loan officer what to do next. His concept uses AI to surface issues earlier, propose specific fixes, and draft the customer and agent communications needed to resolve them, functioning as a digital expert alongside less experienced loan officers.
Haff’s Right Track addresses the research burden of difficult files, where brokers can spend hours navigating complex product guidelines. His tool points toward a viable path for the borrower rather than simply identifying another obstacle.
Hasan’s Quick Counter, the winning entry, addresses speed in competitive deals. A broker can import a rival loan estimate and generate a competitive counteroffer in minutes, while the client is still on the phone.
Augmentation, Not Replacement
Across all three, the common thread is augmentation; that is, how AI-enabled solutions strengthen the professional already responsible for the client relationship.
That approach contrasts with years of predictions that AI (like blockchain technology) would completely upend and disintermediate trust-based professions. It also reflects a more practical enterprise use case: putting AI into the hands of practitioners who understand the workflow, the customer and the problem.
Rocket described the philosophy in the partner promises unveiled at the event as “human expertise, with the speed of AI.”
Where Web3 Meets Legacy Industries
The same logic extends beyond AI.
For years, Web3 was discussed primarily as a technology category: blockchain, digital assets, smart contracts and decentralized networks. The conversation often began with what the technology could do and worked backward toward potential applications.
Legacy industries offer a different starting point. They have customers buying homes, purchasing food, moving money and making decisions in systems built over decades. They also have accumulated friction that technology companies cannot always see from the outside.
Consider property records. Georgia’s National Agency of Public Registry offers online registration of immovable-property rights through a smart-contract service. The significance is not merely that a government record can be maintained digitally. The service is designed to move a traditionally paper- and intermediary-heavy transaction into a remote, digitally executed process.
Or consider diamonds. De Beers’ Tracr uses blockchain to trace natural diamonds from source through the value chain. The company has moved that infrastructure into a consumer proposition through its ORIGIN program, providing provenance information intended to give buyers greater assurance about where a diamond came from and how it moved through the supply chain.
The technology is not the product. Trust is.
Financial services are moving along the same path. J.P. Morgan’s Kinexys platform uses blockchain infrastructure for payments and digital assets; the bank says the platform has processed more than $1.5 trillion in notional value and more than $2 billion in average daily transaction volume. The SEC now formally recognizes tokenized securities as securities whose ownership records are maintained in whole or in part on crypto networks.
The point is not that blockchain replaces banking. It is that banking can use blockchain to change how money and assets move.
That is a distinction with a difference.
A faster mortgage process is useful. A mortgage process that gives a borrower greater visibility into where a deal stands, identifies problems earlier and gives the loan officer better tools to solve them is more effective. A blockchain food ledger may improve supply-chain efficiency. The ability to trace a potentially contaminated product in seconds rather than days can change a public-health outcome. A digital property record may reduce administrative friction. A remote transaction can change who is able to participate in the market and how easily. This is where legacy industries become important to the next phase of Web3.
The mortgage broker knows where a loan breaks. The title professional knows where property records break. The retailer knows where its supply chain breaks. The banker knows where money gets trapped in infrastructure built for another era. Those professionals don’t need to begin with AI, blockchain or digital assets. They can begin with the problem.
The Next Problem Worth Solving
And the problems those practitioners identify could become more consequential. For example, deed fraud sits at the intersection of real estate, financial services, government records and emerging technology. Forged documents can enter public records because recording is administrative rather than investigative. Generative AI adds another dimension, making sophisticated forgeries easier to produce even as governments explore AI tools to detect them.
Nearly a decade ago, the Cook County Recorder of Deeds demonstrated that property transfers could be recorded on a blockchain. The technology held. Broader legal and institutional adoption did not follow. Perhaps, that is the larger lesson from The Big Pitch. The most consequential technology adoption in legacy industries may not begin with a technology roadmap. It may begin with the person closest to the customer identifying what does not work.
AI, blockchain, tokenization and digital assets become relevant when they change what that customer or consumer can accomplish. Not solely how efficiently a company performs the same old task. And for legacy industries, that is the opportunity: not to bolt tomorrow’s technologies onto yesterday’s processes, but to use them to produce better outcomes for the people those industries ultimately serve. Start with the problem. Put the practitioner closest to it in the room.
Then ask what technology makes possible.
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