You’ve decided to move from sampling 2% of calls to scoring 100% with AI. The vendor demos looked great. Leadership is excited. The business case is penciled out.
Now you have to actually make it work.
Most AI quality management deployments underdeliver. Not because the technology doesn’t work — it does. They underdeliver because organizations skip steps, underestimate change management, or expect magic instead of doing the groundwork.
Here’s what I’ve learned from watching these deployments succeed and fail.
Before You Start: The Prerequisites Nobody Talks About
Your current QA scorecard is probably a mess.
Before you automate quality scoring, you need to know what you’re scoring. Most organizations have QA forms that have evolved over the years — questions added after incidents, criteria that made sense in 2019, weighted scores whose logic nobody remembers.
AI will score exactly what you specify. If your scorecard is bloated, contradictory, or disconnected from what matters, you’ll get consistent scores that don’t mean anything.
Fix your scorecard first. Ruthlessly cut anything that doesn’t directly connect to customer outcomes or compliance requirements. If you can’t explain why a criterion exists, delete it.
Your supervisors need to be ready to act on data.
AI QM will surface more issues than ever. But if your supervisors lack time, skills, or a process to act, you’ll have a dashboard full of unaddressed problems.
Successful organizations redesign supervisor workflows before deployment: less random call listening, more targeted coaching based on AI insights.
If your supervisors are already at 110% capacity, adding more data won’t help. You need to take something off their plate first.
You need executive sponsorship that survives the first bad month.
AI QM will find things you didn’t know about. Some of those things will be uncomfortable. An agent who’s been rated “exceeds expectations” for years might suddenly show patterns of skipping verification steps. A team that looked strong might have systematic issues nobody caught with 2% sampling.
When this happens — and it will happen — someone will want to blame the AI. “These scores don’t match our experience.” “The AI doesn’t understand context.” “We should go back to how we did it before.”
You need an executive sponsor who understands that finding problems is the point, not a bug. Without that air cover, the project dies the first time the data gets uncomfortable.
The First 90 Days: What Actually Matters
Days 1-30: Calibration, not scale.
Don’t try to score everything immediately. Start with a subset of interactions — maybe one queue, one team, one interaction type. Run AI scoring in parallel with your existing QA process.
Compare results. Where does the AI agree with human scorers? Where does it differ? When it differs, who’s right?
This calibration phase is where you tune the AI to your actual standards, catch edge cases, and build confidence that the scores mean something. Skip this, and you’ll spend months explaining why the numbers don’t make sense.
Days 30-60: Supervisor enablement.
Your supervisors need to learn a new way of working. Instead of listening to calls and scoring them, they’re reviewing AI-generated analysis and deciding where to focus.
Some supervisors will welcome AI’s pattern detection. Others will distrust it. Encourage doubters to document specific misses so their input can improve the system and help them become co-creators, not just critics.
Invest in training. Not just “here’s how to use the dashboard” — actual coaching on how to interpret AI insights, how to prioritize, how to have conversations with agents about AI-generated feedback.
The supervisors who don’t adapt will undermine the whole project. Address this early.
Days 60-90: Agent communication.
Agents will inevitably hear about AI scoring informally unless you communicate with them directly. When left to rumors, misconceptions grow: “They’re using AI to fire people.” “Every word is being analyzed.” “Robots are monitoring us.”
Get ahead of this. Explain what AI QM actually does (scores interactions against the same criteria humans use, just consistently and at scale). Explain what it doesn’t do (make employment decisions, replace human reasoning, spy on conversations).
Most importantly, explain what’s in it for them: fairer evaluations not based on random sampling luck, targeted coaching on things that actually matter, and less time spent on QA reviews that feel arbitrary.
Agents who understand the system will work with it. Agents who fear it will game it.
The Pitfalls That Kill Projects
Pitfall #1: Treating AI scores as verdicts instead of signals.
AI QM is very good at pattern detection. It’s not error-free. There will be edge cases, unusual interactions, and contexts that the AI misses.
Organizations that treat every AI score as the absolute truth create resentment. “The AI said I failed this call, but the customer was already angry when they called in.” If agents can’t dispute or add context, they’ll disengage.
Build in a feedback loop. Let agents flag scores they disagree with. Have humans review disputed scores. Use disagreements to improve the model. This isn’t undermining the AI — it’s making it better.
Pitfall #2: Measuring everything, acting on nothing.
AI QM can generate an overwhelming amount of data. Average scores by agent, by team, by queue, by hour. Trending topics, sentiment changes, compliance flags, and coaching opportunities.
If you try to act on all of it, you’ll act on none of it. Pick 3-5 metrics that matter most. Focus there. Ignore the rest until you’ve moved the needle on what matters.
I’ve seen organizations build 40-metric dashboards that nobody looks at after the first month. Start with less.
Pitfall #3: Expecting AI to fix process problems.
If your IVR routes calls to the wrong teams, AI QM can tell you that agents are handling out-of-scope calls. It won’t fix the IVR.
If your knowledge base is outdated, AI QM will tell you that agents are giving inconsistent information. It won’t update the knowledge base.
If your staffing is inadequate, AI QM will tell you that agents are rushing calls. It won’t hire more agents.
AI QM is a diagnostic tool, not a treatment. It finds problems. You still have to fix them.
Pitfall #4: Going dark on the vendor after deployment.
The first version of your AI QM deployment won’t be perfect. Criteria will need tuning. Edge cases will emerge. New interaction types will require new scoring logic.
Organizations that treat deployment as a finish line plateau quickly. Those that maintain ongoing vendor relationships — frequent check-ins, feedback, continuous improvement — get better results.
Budget for this. Build it into the contract. The first deployment is the starting point, not the finish line.
How to Know It’s Working
Leading indicators (first 90 days):
- Supervisors are using the dashboard daily, not ignoring it
- Agents are asking questions about their scores (engagement, not fear)
- You’re finding issues you didn’t know existed
- Calibration scores between AI and human reviewers are converging
Lagging indicators (6-12 months):
- Customer satisfaction scores are improving
- First-call resolution is increasing
- Compliance incidents are decreasing
- Agent attrition is stable or improving (not fleeing the surveillance state)
- Supervisors have more time for coaching, less for call monitoring
If you’re seeing the leading indicators, the lagging indicators will follow. If you’re not seeing leading indicators by day 90, something is wrong with the deployment — not with the concept.
The Bottom Line
AI quality management works. Organizations that deploy it well see real improvements in quality, compliance, and agent performance. The technology is mature enough to trust.
But the technology is maybe 30% of the project. The other 70% is change management, process design, calibration, and leadership commitment.
Don’t underestimate the 70%.
Want to see what 100% interaction scoring looks like? Explore AI Quality Management →
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