AI agents in customer support are no longer experimental. They’re operational. We are seeing them in our everyday interactions across chat, email and voice. Teams are moving fast to automate conversations, improve resolution speed and free up human agents for higher-value work.
The opportunity is real. So is the pressure to move quickly.
And yet, many deployments don’t deliver the impact teams expect. Not because the technology isn’t capable — but because implementation is treated like a feature launch instead of an operational shift.
I have been on the frontlines for AI deployments from startups to Fortune 500s and when leaders start to question why their AI implementations aren’t what they expected, there are a few friction points that are often the culprits. Here are five common AI deployment pitfalls — and practical ways to navigate them
1. Starting with AI instead of the problem
It’s natural for momentum to build around “adopting AI” without a clear charter of how.
The teams that see meaningful return on investment (ROI) don’t start with coverage goals. They start with a sharp problem statement and a measurable outcome. That might mean improving resolution rate for password resets in chat, stabilizing customer satisfaction (CSAT) during peak season or deflecting a defined set of billing intents in voice.
Vague goals like “increase AI coverage” or “improve CSAT” sound reasonable. In practice, they often compete with each other. Expanding automation without giving your AI the same system access and action authority as a human agent invites friction — not efficiency.
When the objective is concrete, trade-offs become easier to manage. Coverage expansion, for instance, may not make sense if the AI doesn’t yet have the data access or action authority required to resolve issues end-to-end.
Before vendor evaluations or prompt design, align on a single, measurable definition of success. That clarity will guide every downstream decision.
2. Applying traditional procurement to a fast-moving category
AI technology evolves quickly. Procurement models designed for slower-moving software categories were not built for this pace.
I’ve seen eight-month RFP cycles where, by the time a decision was ready to be made, the market had shifted or turned completely. The feature gaps that drove the original comparison no longer existed. Vendors had shipped major releases. Capabilities had been commoditized. In some cases, teams were effectively evaluating a different product category than the one they started with.
Lengthy RFP processes and rigid feature scorecards tend to optimize for a static snapshot in time. But AI is not static. What matters just as much as current capability is velocity: how fast the platform improves, how transparently the roadmap is shared and how tightly the vendor partners with your team.
Another pattern I’ve seen: organizations evaluate chat automation, voice automation and agent-assist tooling in isolation. It feels methodical. In reality, it often creates siloed data, duplicated effort and fragmented learning across channels that should be informing one another.
Stronger outcomes tend to come from a different approach:
- Run short, outcome-based pilots under production-like conditions.
- Evaluate speed of iteration, not just feature depth.
- Prioritize unified data and shared insights across channels.
- Assess the vendor’s ability to co-build, not just configure.
This isn’t about rushing decisions. It’s about aligning procurement rigor with the actual pace of innovation.
3. Overlooking knowledge readiness
Let’s address the question that makes many teams nervous:
“Do we need to overhaul our knowledge base before we deploy AI?”
The honest answer is no, but you do need a plan.
Strong knowledge foundations matter. AI performance is directly tied to the quality of the information it draws from. If your documentation is outdated, scattered across disconnected systems, image-based or largely tribal knowledge living in your agents’ heads, AI will surface those weaknesses quickly. Automation doesn’t fix knowledge gaps. And it will expose them.
At the same time, waiting for a perfectly structured, fully audited knowledge base before launching is often unrealistic and in many cases, unnecessary.
The goal is not perfection, rather intentionality.
The teams that move effectively do three things:
- Start small. Choose a narrow, high-volume use case where documentation is already relatively strong.
- Create a knowledge update plan. Define who owns content hygiene, how often it’s reviewed and how gaps uncovered by AI interactions get fed back into documentation.
- Know where your gaps are. You don’t need to fix everything immediately, but you should be clear-eyed about what’s incomplete, outdated or undocumented.
AI can actually accelerate knowledge maturity. Early deployments often reveal missing edge cases, unclear instructions or conflicting documentation. That feedback is valuable — if you’re prepared to act on it.
In other words, don’t let imperfect knowledge stop you from starting. But don’t treat knowledge as an afterthought either. Treat it as a living system that improves alongside your AI.
4. Transferring ownership after the contract is signed
Purchasing the platform often feels like the major milestone. In reality, it is the starting line.
I’ve seen strong implementations lose momentum not because the technology failed, but because ownership was never fully defined. Engineering assumes support will manage workflows. Support assumes content owns knowledge updates. Operations assumes someone else is tracking performance. Everyone is involved, but no one is clearly accountable.
AI systems do not improve on autopilot. They require active management. Knowledge must be maintained. Performance metrics must be monitored. Workflows must be adjusted as edge cases surface. Escalation logic needs refinement. Without clear authority to make those changes, iteration slows and results plateau.
Before launch, leadership should be explicit about roles and responsibilities. Who owns knowledge quality? Who monitors containment rates and customer satisfaction? Who has decision-making authority to adjust workflows? How are insights shared across teams? When those answers are documented and socialized early, deployments move faster and compound over time.
The difference between a pilot that stalls and a program that scales is rarely technical. It is operational clarity.
5. Waiting for perfect coverage
It’s tempting to delay launch until coverage feels comprehensive. In practice, incremental rollout tends to produce faster learning and stronger outcomes.
The teams that achieve ROI in months don’t wait for full coverage. They launch one high-volume workflow with clear boundaries. They limit scope — by geography, queue or hours. They instrument feedback loops early. Then they expand, use case by use case.
Early versions will surface edge cases and gaps. That’s expected. Real customer interactions provide insights no sandbox can fully replicate.
Celebrate early wins. Share metrics widely. Let real customer interactions shape iteration. A sandbox teaches you something. Production teaches you everything.
The common thread
None of these failure points are fundamentally about model quality. They’re operational.
CX leaders who treat AI as a shiny technology layer will keep revisiting the same problems. The ones who treat it as a new operations layer — anchored to outcomes, disciplined in evaluation, intentional about inputs, clear on ownership and committed to iteration — are the ones turning pilots into scalable programs.
AI agents are powerful tools. With the right operational foundation, they become something more: a scalable layer of support that grows stronger over time
