From Overwhelmed to On-Board: The Five Traps That Stall Enterprise AI

AI Adoption · Enterprise Perspectives · Insurance

What one insurance executive’s conversation reveals about how most large organizations think about AI and what world-class teams know about getting unstuck.

Last week, Ramp put everyone on notice: 99.5% AI adoption, 1,500 internal apps, six weeks. A blueprint built for a hypergrowth fintech.

That same day, I sat down with a claims leader at a regulated insurance carrier working through her AI adoption strategy. Their approaches were night and day, even though their goals were nearly identical.

Most enterprises do not have the culture or the appetite to copy Ramp’s playbook. But they can absolutely steal the lessons.

Lauren is not lagging her industry. She has taken almost every step a consultant would tell her to take.

In a recent conversation, she walked me through her company’s AI plans with remarkable clarity: she set up a governance committee, outlined a change management plan, reviewed her organization’s data readiness, and mapped out the vendor landscape. By almost any measure, she leads the industry average. And yet, when she described her plan, I could hear the limiting belief that holds so many careful organizations back: the belief that everything has to be right before her organization could take action.

Her situation is not unusual, it’s representative. Her experience maps almost perfectly onto a decision pattern that plays out in board rooms and IT departments across financial services, insurance, healthcare, and every other heavily regulated industry. Recognizing that pattern, and knowing what actually breaks it, ranks among the most valuable moves any leader can make right now.

Part One: The Five Traps That Stall Careful Organizations

Lauren Carlson, an operations leader at Meridian Specialty Insurance Group, is a fictitious character representing real stakeholders whose identities will remain anonymous for the purposes of this article. Lauren described her company’s current reality in careful, measured terms. Read charitably, she is doing exactly the right work. Read urgently, a gap separates where she stands from where she needs to stand. Both readings hold. Below are the five traps most regulated enterprises fall into, and the one Lauren is sitting inside right now.

Trap 1: The “Learning Phase”

Her words: “We’re really in this learning phase, just trying to understand all the things.” Few leaders admit as much out loud, and saying so takes courage. Her team runs book studies, hosts AI literacy sessions, and builds awareness across the company. The danger arrives when the learning phase hardens into a permanent holding pattern. Reading about AI without deploying any is the organizational equivalent of reading about swimming. Helpful, perhaps, but avoidant.

Trap 2: Building the Governance Layer First

She is forming an AI governance group with the CEO, legal, compliance, infrastructure, and business representatives to identify potential risks and outline plans for mitigating them in future projects. Governance committees like hers reflect genuinely good practice. The real risk comes from treating governance as a prerequisite rather than a companion to action. Governance that arrives before any pilots hardens into a veto machine. Governance that runs alongside pilots becomes a learning accelerant.

Trap 3: The Change Management Fear

Lauren voiced the fear every thoughtful leader voices: “We don’t want our first AI story to be ‘we replaced three people,’ and now nobody in the org wants to talk to us about AI.” The risk is real and deserves real attention. But that same fear can slow an organization until it trails less-careful competitors and the gap then becomes its own threat to the workforce.

Trap 4: The Vendor Paralysis Loop

She is talking to vendors, attending conferences, collecting ideas in a “backlog,” and waiting for the business to surface use cases. She is staying careful not to overlap with what her existing platform vendors already build. The due diligence is smart. But due diligence can harden into an endless loop where every new vendor conversation surfaces new information and restarts the evaluation clock.

Trap 5: The Quiet Team Problem

Lauren made the single most revealing observation in our whole conversation: her claims team stays “quiet” compared to underwriting. They do not surface their pain points loudly. “They’re just out there trying to survive,” she said. Quiet claims teams show up everywhere. The groups carrying the most manual, repetitive, documentation-heavy work rarely advocate for themselves and will wait the longest before asking for help.

“The claims team is the quietest. They’re just out there trying to survive.”

Lauren Carlson (name anonymized), Meridian Specialty Insurance Group

Together, these five traps describe a thoughtful, risk-aware organization doing careful work- an organization that may get lapped by less-careful peers who simply started faster.

Part Two: What the Fastest-Moving Companies Have Learned

While Meridian sits in Q2 planning mode, other companies have already compressed years of AI adoption into months. Ramp, the corporate spend management company, recently described an adoption journey that moved from “everyone debating the strategy” to near-total internal adoption in a matter of weeks. Their experience reads like a field guide for what works, translated here into terms that fit organizations like Lauren’s.

Lesson 1: Stop Waiting for the Full Plan

Ramp’s most counterintuitive lesson: they did not have a plan. They had a culture that valued speed and a leadership team willing to back bets without waiting for certainty. They began with the obvious moves: leadership naming AI usage as an expectation, an internal communication channel where people can share how they’re using AI, and an all-hands time to celebrate early wins-and grew from there.

For Lauren, the committee and the Q2 planning framework add real value, but neither should hold up the first three pilots. The governance group should learn by observing real work in progress, not by drafting a theoretical framework in a vacuum.

Lesson 2: Think in Levels, Not Light Switches

Ramp’s most useful framework treats AI adoption as a learning curve with distinct levels, not a binary switch from “not using AI” to “using AI.” Most of Lauren’s employees sit at Level 0 or Level 1. They know AI exists, they have used ChatGPT, they may experiment with some claude projects or skills (if they have access at all). That baseline is fine. The goal is not to vault them to expert status overnight.

The breakthrough arrives at Level 2: the moment someone uses AI to automate a piece of their actual job. Not a demo, not a training session, but a real workflow that saves real time. Ramp calls that first real result the “aha moment”, where a skeptic becomes an advocate. Every step before that moment compounds into setup. Every step after compounds into gains.

For Lauren’s claims team that “just survives life,” the first question is not “what is our AI strategy for claims?” The first question is “what would save a claims adjuster two hours next Tuesday?” Find that task, automate the task with a simple tool, and let the results do the selling.

Lesson 3: Job Shadow the Quiet Teams

Lauren mentioned job shadowing the claims team almost as an aside, a thought she was holding “in the back of her head.” That instinct points exactly the right direction, and it should move to the top of her priority list.

The organizations that found the biggest AI wins did not find them through a formal use case submission process or a business case template. They found the wins by watching people work. Ramp’s biggest efficiency gains came from operators, risk analysts, and finance staff who spotted their own pain and prototyped their own fixes-once they had a tool that made building easy enough to try.

The workers comp claims team at Meridian Specialty Insurance Group is almost certainly drowning in documents (police reports, medical records from treating physicians, IME packets, first reports of injury) arriving in no particular order, demanding manual review, and then keystroke-by-keystroke entry into claims systems. Document AI creates immediate, measurable, undeniable value on exactly that kind of work. But nobody on the claims team will add that workflow to a use case backlog. Someone has to go watch the work happen in person.

“The biggest surprise wasn’t who built the most. It was how many people had been waiting for permission to build at all.”

Ramp Engineering Leadership

Lesson 4: Don’t Let Change Management Become a Veto

Lauren’s concern about change management carries real weight. The fear that “AI replaced three people and now no one will talk to us” describes a real organizational dynamic that has broken adoption efforts at many companies. But the fix is not delaying AI work until you hold a fully-formed change management plan. The fix is picking first use cases that make work easier, not smaller, and staying radically transparent about that intent.

Ramp found that the fastest adoption arrived not when leadership handed down AI as a mandate, but when employees received AI as a personal superpower. When a risk analyst automated 16 hours a month of manual modeling, nobody in the company heard “management is cutting jobs.” Everyone heard “what can I build?” The competition to build became the engine for adoption instead of the fear of replacement.

The framing matters enormously. Meridian should ask: which three people are drowning right now, and how do we hand them AI as a life preserver? Not: where can we reduce headcount?

Lesson 5: Remove Every Barrier Between People and Their First Result

Technical decisions matter more here than they appear to. Ramp found that despite high AI tool adoption, most employees stayed stuck because the tools cost too much to set up. Terminal windows, software installations, IT tickets, API configurations. Each barrier formed a wall between the employee and their “aha moment.” Ramp solved the friction problem by building a tool that required exactly one login and immediately connected to everything the employee needed.

Lauren mentioned that Meridian runs as a Microsoft shop with Copilot already deployed. That footprint gives her a significant advantage. Copilot, properly configured and wired into the right data, can serve as that low-friction entry point. The question is not whether to use Copilot. The question is whether Copilot actually connects to the data and workflows that matter to each team, rather than sitting as a generic chat interface that impresses in demos and gets ignored in practice.

She also flagged concerns about employees “dropping documents into Copilot and saying, summarize this” without knowing what to do next. That gap looks less like a security problem than a training problem, and the training is not a class. The training is a workflow. Show someone exactly how to take a piece of work they do every day, run the document through the tool, and immediately apply the output. Run that exercise once with a real task, and the training finishes itself.

Part Three: What Lauren Should Do in the Next 90 Days

None of this asks Lauren to abandon her careful, thoughtful approach. The governance committee is good. Her data strategy work from last year is an asset. The data exists, the data is reasonably clean, and that often proves to be the hardest part. Her change management thinking matters. These foundations belong in the plan, not in the way.

None of them should become a reason to wait.

In the next 90 days, three moves would accelerate everything else.

The Permission Problem

The hardest message to deliver to a careful, responsible leader like Lauren reads like this: the risk of moving too slowly now roughly matches the risk of moving too fast. Moving slowly feels safe. Moving fast feels reckless.

The feeling misleads. The organizations that get this right in the next two years will hold structural advantages very hard to close later. The ones still forming governance committees in 2027 will face a different kind of crisis, one where their best examiners have already walked out the door for a carrier that handed them better tools.

Lauren already knows what she needs to know. She needs permission to start. The good news is that only she can grant it.