Beyond just knowing the difference between AI and automation, success in equipment finance depends on laying a rigorous foundation of defined outcomes and mapped processes before the first tool is ever deployed.
In the first part of our series on AI and automation, we made the case for knowing the difference between the two and why treating them interchangeably is how you end up with a 95% failure rate. But knowing the difference only gets you so far, the real work is figuring out what to do first. Getting the order right is where most companies fumble.
Some changes are foundational, if you get them wrong everything built on top is shaky. Others are experimental, designed to be piloted, measured, and adjusted. Knowing which is which matters more than most people realize.
Here’s where things stand across the industry, 79% of organizations now say they’re using generative AI, yet fewer than 10% report scaling AI agents in any function. Gartner projects more than 40% of agentic AI projects will be canceled by 2027, cleared from the roadmap because value was never defined and controls were never put in place. Turns out, buying a Ferrari doesn’t do much for you if the road is still full of potholes.
Start with the outcome, not the tool
Before workflow. Before automation. Before AI, leaders need to define what success looks like. What measurable metric should improve? By how much? Over what timeframe? And who owns the result?
In equipment finance, that might mean reducing application-to-decision time from a day to a few minutes. It might mean increasing deals per underwriter while keeping credit standards consistent. Or getting to a place where a deal doesn’t flame out because someone forgot to chase down a document three days ago. Specific, measurable targets are the only way to know whether a technology investment actually worked or whether it just looked good in the vendor demo.
McKinsey’s State of AI data shows only about one-third of organizations are successfully scaling AI, and the ones succeeding are those tying AI to specific financial and operational KPIs rather than broad innovation mandates. Only 5.5% of companies attribute more than 5% of EBIT to AI, and those companies almost always have explicit, P&L tied targets with named business owners for each use case.
Without a defined outcome, there’s no way to measure progress, and no way to justify the next investment when someone asks what the last one delivered.
Garbage in, garbage out (but faster)
Once you know where you’re going, process is how you get there reliably. Technology can’t fix a broken process. It can only move things through it faster, and if you’ve ever watched a bad process pick up speed, you know that’s not a good thing. Before any workflow or automation goes in, the process itself needs to answer some basic questions. What steps are consistent? What does “complete” mean? What qualifies as an exception? Where does accountability live?
In equipment finance, the cracks tend to show up in the same places every time. Who decides what counts as a complete package? What makes something a credit exception versus a judgment call? How much does a stip get interpreted versus enforced? When these questions don’t have clear answers, everyone just does it their own way. For a while that works. The team is small, people know each other, and the inconsistencies get smoothed over by institutional knowledge. Add technology and those same inconsistencies become the reason your pilot stalls.
Before you touch any technology, get the process out of people’s heads and onto paper. Start by mapping it out. Tools like Lucidchart or Miro work well, but a good old-fashioned whiteboard and some honest conversation will also do the job. The medium matters less than your team documenting every step, decision point, and data source from application to funding.
Bain’s 2025 data strategy research documented a client that recovered approximately $10 million in value and achieved 25% efficiency gains after fixing process definitions and data stewardship across 20+ use cases, before broadly scaling AI. Bain’s research is a good reminder that the unglamorous process work is often where the real money is, and that technology performs better when it’s not doing the heavy lifting alone.
When the process is unclear, everything downstream pays for it. Automation just moves the problems through faster, and AI builds on definitions nobody agreed on in the first place.
40% of AI projects will be canceled by 2027. Workflow is usually why.
Workflow means different things to different people, so let’s be specific. In this context, it’s the set of rules that govern how work moves across your organization. Who gets what, when, and under what conditions. Things like approval routing, document gating, threshold triggers, and compliance checkpoints. People often think of these as software configurations, but they’re really just decisions about how your business is supposed to run, written down and enforced consistently (for once).
Unlike an AI tool you can pilot and quietly shelve if it underperforms, workflow redesign is harder to walk back. It touches how people operate day to day, who’s accountable for what, how decisions get escalated, and how work moves between teams – all habits that took years to form. Getting it wrong creates a people problem as much as a tech problem.
Gartner projects more than 40% of agentic AI projects will be canceled by 2027, not because the technology failed, but because the value was never clearly defined and the operational controls were never put in place. Microsoft’s own internal AI transformation work points to the same conclusion: simplify processes first, then apply AI, otherwise you risk automating waste and inefficiency directly into your operations.
When exceptions regularly happen outside the formal workflow, any technology layered on top is working with incomplete information. Think of it less as a feature rollout and more as deciding, finally and officially, how your business works.
So you’ve done the hard part. Here’s where it gets fun.
Once you’ve worked through the outcomes, processes, and workflow – AI becomes something you can experiment with and expect results from. There are plenty of places to start for equipment finance lenders, but these two are worth looking at first.
- Document processing
Pick one document type, tax returns are a good starting point and use AI to classify and pull data from just that one before expanding further. Measure how often it gets it right compared to what your team catches on manual review. If accuracy is high, expand to the next document type. If it’s inconsistent, the culprit is usually training data quality or intake variability, both of which are fixable before you scale. Make sure you build in a way for your team to flag errors as they find them. AI models learn from corrections, and without that feedback, you end up with a tool that keeps making the same mistakes with total confidence.
- Credit memo summarization
Pilot AI to draft the narrative section of a credit memo from structured deal data, then have your underwriters tell you honestly whether it’s any good over 20-30 deals. Track time saved, note where it falls flat, and fix it before you roll it out to everyone. It works well as a starting point because the deal structure, approval criteria, and memo format are already nailed down. The AI isn’t guessing, and neither are you when you’re evaluating it.
Final takeaway
McKinsey puts the share of companies that consider themselves mature in AI integration at roughly 1%. Yes, one percent. The difference between those companies and everyone else running AI pilots that quietly disappear from the roadmap comes down to the foundation that was laid before the tools ever went in.
Next up, what automation and AI actually look like inside equipment finance workflows
Want to see how this works in practice? Northteq’s aurôra platform is built with the mindset of automation first, AI where it matters.
