When an AI agent is set up well, it can change the pace of work in ways that feel immediate. A task that once shuffled between teams for most of the day might be wrapped up before lunch. People have more room to think through problems, make better decisions, and pick up ideas that have been sitting untouched for months.

Yet many organizations never reach that point. A recent McKinsey study, cited by The New York Times, found that nearly 80% of companies using AI report no significant bottom-line impact, and 42% abandoned pilot projects in 2024. AI spending is still projected to reach $62 billion this year, even as many companies grow more cautious after the initial wave of excitement. History shows that this pause often precedes major breakthroughs, as it did with personal computers and the internet.

Inside a large organization, making that shift from vision to reality is rarely simple. Plans twist and stall. Progress can even loop back on itself before launch day arrives. Along the way, familiar issues tend to appear. Sometimes no one is linking the technical build to business goals. Leaders may want proof the investment is worth it. And in many cases, teams hesitate to change the way they have always worked.

These roadblocks are not unique to one company. In our work, we see the same set of objections surface again and again. Addressing them early can be the difference between a stalled pilot and an AI agent that delivers lasting value.

1. We Do Not Have the Right People to Make It Work

The first big hurdle is not the AI agent itself. More often it is the lack of someone who can connect the technology to the organization’s needs. We have seen strong pilots lose momentum simply because no one could translate business requirements into technical steps.

Placing the right person in a forward-facing AI operations role can prevent that. It might be a new hire, a retrained team member, or a trusted partner. What matters most is their ability to lead deployment, guide training, and refine the system so it performs well over time.

2. Our Data Is Not Ready

If the data going into the system is poor, the results coming out will be too. In many projects we have joined, the technology itself was fine, but the data was out of date or missing key details.

A data health check before rollout is worth the time. Look at accuracy, completeness, and accessibility. Ensure security measures and governance are in place.

This is also where Squid’s semantic layer helps. It serves as a business translation layer that makes messy, inconsistent data understandable and usable. With this in place, organizations can move forward even if their data is not perfect, instead of feeling stuck. When the data foundation is strong and made usable, deployment becomes much smoother.

3. We Cannot Prove the ROI

A working system is not enough. Leaders want clear evidence that the investment is paying off. Without a baseline, it is hard to tell whether the AI agent is improving performance.

Before launch, agree on the measures that matter most. They could include faster turnaround on service requests, higher output, or fewer hours spent on repetitive work. Capture those numbers before the system goes live so you can benchmark the AI agent’s results against current performance. This comparison makes it clear whether the technology is delivering meaningful gains and where adjustments may be needed.

4. Our Teams Will Resist the Change

Even a useful tool can struggle if people feel it is being imposed on them. Change management works best when it is built on trust and involvement.

Begin with a small pilot in one area of the business. Invite the people who will be using the system to help shape it. Listen to their input and make adjustments. Sharing early wins can build interest and encourage adoption across other teams.

Squid AI embeds into existing systems and workflows, so disruption stays low. Teams do not need to overhaul how they work, which reduces resistance and accelerates ROI. They see the new capability as a natural extension of tools they already know.

5. We Cannot Risk Compliance or Security Issues

In regulated industries, questions about security, compliance, and ethics come up early and often. Without clear answers, projects can slow down or stall completely.

The solution is not to restrict the system so heavily that it loses value. Instead, put safeguards in place that control risk while keeping the system effective. Role-based permissions, audit logs, and explainable decision-making help build long-term trust and confidence.

Turning Objections Into Momentum

Every AI agent project will meet challenges along the way. The difference between one that stops and one that succeeds is in how those challenges are handled.

With skilled people, reliable data, clear performance measures, thoughtful change management, and trusted safeguards, the very obstacles that could have slowed progress can become the reason the system works and keeps working. The real opportunity lies not in rushing to deploy but in building the capacity to make AI agents thrive for the long term.

At Squid AI, we work with teams to address these challenges, helping them move from pilot to production with AI agents that deliver measurable impact.

Overcoming the Top 5 AI Agent Rollout Challenges