Equipment finance leaders under pressure to show AI progress often fall into the same trap: They buy the technology before they agree on the problem. Kin Analytics and TomorrowZone formed a strategic alliance, announced Sept. 21, to close that gap by helping lenders identify where AI can create meaningful business value, build the organizational readiness to act and translate the right opportunities into measurable outcomes and ROI.
The partnership brings together two firms built on the same conviction: Technology creates value when organizations are clear on the problem they are solving, the outcome they want and what it will take to get there. TomorrowZone contributes a clarity-first approach that defines the desired outcome, identifies the real problems worth solving and builds readiness before a company commits to a path. Kin contributes deep credit-process expertise through a risk-first approach designed to improve the quality of the risk decision, not simply to process deals faster.
“AI creates value when it’s applied to the right problem, not simply because the technology is available,” Patricio Pazmiño, chief product officer at Kin Analytics, says.
The Cost of Not Knowing
For Deborah Reuben, CLFP, founder and CEO of TomorrowZone, the stakes are easy to state. “The most expensive sentence in transformation is ‘we didn’t know what we didn’t know,'” she says.
She describes an equipment finance company that paid for the same seven-figure implementation twice. The first time, the team did thorough due diligence and picked a strong software vendor, but the project failed anyway. Leaders hadn’t aligned on the current state or what the business needed, and they expected the vendor to provide those answers. “In my experience, it’s how good technology fails,” Reuben says.
Under board pressure, the company tried again, this time bringing in TomorrowZone to align the leadership team on the current state, goals and customer needs before anyone re-evaluated software. It chose the same vendor, and the second implementation went live successfully. “Clarity and readiness are the difference,” Reuben says. “With upfront clarity and team readiness the first time, they would have paid once.”
What Ready Looks Like
Reuben says readiness is not a deliverable or a mindset. “Readiness is a set of conditions a team establishes and then sustains,” she says. TomorrowZone’s assessment covers 10 questions across five dimensions, and a team that reaches a threshold score avoids the expense of a false start. The starting point is simple: Ask each team member to describe the current state in their own words, ask cross-functional leaders what problem they are actually solving, and listen for where the answers diverge.
That matters because enthusiasm can masquerade as preparation. “Eager looks a lot like ready,” Reuben says. She recalls one company that was setting kickoff dates with its vendor when its technology leader brought in TomorrowZone first. The assessment revealed gaps the team didn’t know it had, most of them unrelated to technology.
Readiness also changes over time. “Think of it like credit exposure. You never look at that number just once,” Reuben says. “As deals pay down and new ones book, you recalculate. Readiness works the same way.”
Pressure to move fast is real, she acknowledges, but “urgency is not the same as readiness.” She asks leadership teams what progress means in their context and how they will measure it. “When AI progress gets measured in logins, tokens used, or headcount cut, budgets and customer experience take the hit.” The pause can be brief. One company closed its readiness gaps in days and went on to a successful implementation. “Pausing for clarity feels like slowing down,” Reuben says. “It actually accelerates your ability to succeed.”
Risk-First Is Not Slow
Pazmiño pushes back on the idea that a risk-first approach costs speed. “Risk-first doesn’t mean slowing the process down. It means judging every part of the process by one question: does this help us make a smarter credit decision?” he says.
The pattern he sees most is a lender that wants faster approvals and instinctively automates the workflow. “But if the underlying information is inconsistent, you’ve only automated the guesswork,” Pazmiño says. “Every exception still comes back to a person, and the speed never shows up in the numbers.”
Kin’s forward-deployment model puts its engineers and credit risk specialists inside the client’s operations from day one. “Week 1 looks a lot like week 1 at a new job,” Pazmiño says, with the team gaining system access, reading policies and procedures and walking through the credit process from intake to funding. What gets built first follows from what they learn. “We build towards real problems by deeply understanding them first,” he says.
On performance, Pazmiño says custom models trained on a lender’s own portfolio history outperform existing processes from day one by standard statistical measures. Business impact is another matter. “A model nobody uses outperforms nothing,” he says. Adoption depends on the lender’s readiness, including leadership alignment, buy-in from users and a shared definition of success, and on the governance layer that keeps a live model’s performance visible. “When underwriters help shape a model, they trust its output,” he says.
Kin measures the payoff in four ways: an increase in approval rate at a constant loss rate, a decrease in loss rate at a constant approval rate, the auto-decision rate and time to decision.
The misconception he corrects most often in early client conversations is that technology is the hard part. “It isn’t,” Pazmiño says. “AI has made building technology fast and cheap. The harder part is understanding the credit process: what a sound decision looks like, which signals deserve attention, and how underwriting teams actually make those calls.” Generic AI tools, he adds, “break the second the real business shows up.”
Where the Two Approaches Meet
The partnership changes where Kin’s engagements begin. “I love TomorrowZone’s principle: clarity before technology,” Pazmiño says. Kin now arrives at problems that are already defined, priorities that are already ranked and a leadership team that agrees on what it is solving for. “We don’t skip anything, but time to value gets accelerated,” he says. “We can develop faster, and most importantly, adoption happens sooner.”
The match was evident early, Reuben says. The two firms connected at the ELFA Innovation event in Nashville in March, and she describes the mindset alignment as genuine. She points to a moment on TomorrowZone’s podcast, when Pazmiño was asked what a client needs to bring for a technology implementation to succeed. His answer: “A clear problem tied to a KPI. Not a wish list.”
“TomorrowZone is technology and vendor agnostic, Reuben says, and chooses partners who share its clarity-first conviction.” Our clients work with many technology providers, and I want readers to understand that our readiness work is independent of any one vendor.
TomorrowZone sets the conditions before any technology work begins, Reuben says, and Kin’s embedded engineers and credit specialists turn that readiness into progress. “Together, we measure success in business outcomes,” she says.
Rita Garwood is editor in chief of Monitor.
