The 3 Most Common Mistakes Leaders Make with Agentic AI

Mistake #1: Cutting Headcount Too Soon

There’s a seductive logic to automating a process and then reducing staff. 

You built an agentic AI system that underwrites policies or generates memos. Why keep the analyst who used to do it?

According to Andrew, many companies do not, in fact, see a good reason to do so.

“Companies often implement some AI automation and immediately reduce headcount,” Andrew explains. However, according to him, these companies are missing the point.

That might sound like something you'd hear in a town hall. But there's deep operational wisdom behind it.

When companies cut staff prematurely, they strip their AI systems of the human knowledge needed to improve. These agents, no matter how sophisticated, still rely on human input to navigate edge cases, validate exceptions, and adjust thresholds. And in early deployments, that guidance is critical.

Real-world AI systems don’t work like the flashy demos. Despite this, as Paul Yacoubian notes, many companies are rushing to replace workers with agentic AI due to, likely, “a mixture of FOMO, perverse shareholder approval for adoption of the latest AI fashion, and overoptimism from management.“

The problem is, without the right feedback loop, even the best agents degrade into compliance risks or workflow blockers.

That’s why our agentic AI platform, AgentFlow, is designed around human/AI orchestration, not just execution.

Confidence thresholds and audit logs give teams the tools to intervene when agents get uncertain. Every action is logged, scored, and traceable. And when agents exceed confidence thresholds, they can still loop in a reviewer for final sign-off.

Long-term AI ROI doesn’t come from cutting. It comes from compounding: automating what can be automated, so people can focus on what shouldn’t be.

Mistake #2: Defaulting to Generic Tools Instead of Domain‑Specific Agents

The second mistake is quieter but equally costly.

“Most companies don’t vet their options well,” says Ishita. “They’re already in the Microsoft or Google ecosystem, so they just go with the tools that are available there.”

That’s fine if you're auto-sorting emails. But when you’re underwriting a policy or approving a loan, generic agents start to break.

Why? Because they weren’t built for this work.

“Horizontal platforms are great for horizontal tasks,” Ishita continues. “But when you’re deep in the weeds of finance or insurance workflows, they just don’t hold up.”

There’s research to back this up. BloombergGPT, a finance-specific model trained on 363 billion tokens, dramatically outperformed general-purpose LLMs on financial reasoning tasks. In other words, domain matters. Deeply.

Or, in more general terms (no pun intended), vertical fine-tuning outperforms generalized AI.

So, how do we, as an agentic AI company, overcome this challenge? 

Our solution is threefold.

Firstly, we built AI agents specifically for each stage of financial and insurance operations. They may not work for any and all workflows across industries, but they can handle financial and insurance workflows exquisitely. Each agent specializes in one core stage, allowing it to go deeper into that workflow’s logic, compliance constraints, and domain context—rather than stretching to solve too many problems at once.

Secondly, we’ve architected AgentFlow with security, trust, and governance at its core. For instance, every agent runs in your private infrastructure, every action is logged and traceable, and every decision is backed by confidence scores, audit trails, and human override paths. This is the foundation that makes agentic AI safe to deploy in high-stakes workflows.

Finally, each agent can also be configured to better align with your unique workflows, internal knowledge and guidelines, and business needs. Document AI can be trained to understand your schema. Decision AI is trained on your policies, ensuring every decision aligns with internal guidelines. Conversational AI supports frontline teams by pulling insights from your knowledge base, not an unvetted third-party source.

Vertical AI solutions understand the work as it actually happens inside a firm. That’s what makes them safe, scalable, and suitable for production.

Mistake #3: Skipping Governance in the Rush to Deploy

The final and most dangerous mistake? Ignoring oversight.

As Andrew puts it: “Stable, long-term ROI comes from integrating AI into workflows, not chasing short-term automation wins.” And integration doesn’t just mean plugging in an API. It means building governance from day one.

For a glimpse into the stakes, consider Wired’s recent article exploring the legal gray area surrounding AI agent failures, notably titled “Who’s to Blame When AI Agents Screw Up?”. 

While there is no straightforward answer to this just yet, this much is true: when agents misfire in regulated environments, companies shouldn’t count on pleading ignorance. Who’s to say a regulator—or a court—will see an “AI mistake” as anything less than a corporate failure of oversight? 

That’s why companies must be proactive, not reactive, about governance. 

The first priority is ensuring your systems are explainable. If they’re not, they’re not defensible either. And if they’re not defensible, they won’t survive internal audits, let alone regulators.

That’s why AgentFlow is architected with explainability in every layer. Every decision can be traced back to its input, confidence score, and output hash. Teams can visualize how agents reached their conclusions, and override them when needed.

This kind of governance is the foundation of enterprise trust. And for organizations that answer to regulators, boards, and customers, that trust is non-negotiable.

What Smart Leaders Are Doing Instead

We’re seeing a growing split in the market. Some leaders chase fast ROI through layoffs and lightweight tools. Others take a different approach.

They:

• Start with one high-leverage workflow, like loan processing or claims adjudication.
• Keep their experts in the loop to guide the AI.
• Choose vertical AI platforms with built-in compliance tooling.
• Deploy slowly but scalably, with governance and traceability from day one.

The results speak for themselves. 

Multimodal clients see AI agents performing high-value work within 90 days, without sacrificing oversight. They ship real work, in production, audited and trusted.

That’s what happens when you build the system right from the start.

The Bottom Line

Agentic AI isn’t a silver bullet. But deployed correctly, it’s the most transformative tool available to regulated industries today.

Avoiding these three mistakes—rushing to cut, defaulting to generic tools, skipping governance—can be the difference between a one-quarter experiment and a five-year advantage.

So, now you know what to avoid. And what about what to aim for? 

Well, building a system that regulators, engineers, and employees will still trust a year from now sounds like a good initial goal.