The AI Readiness Toolkit for Contact Center QA 

AI can help QA teams cover more interactions, spot issues sooner, and coach with clearer evidence instead of guesswork. 

The challenge is that AI only delivers those gains when the foundations are in place. Data, process, alignment, and trust all matter. Without that readiness, it is easy to spend time on pilots that stall or initiatives that do not scale. 

Here you’ll find two practical resources to help you assess readiness, identify what could slow you down, and take the right next step. 

Why “AI readiness” matters before you invest 

Many QA and CX leaders are being asked to “do something with AI” before there is a shared definition of what ready looks like. 

That is where projects wobble. Teams end up debating the basics: Do we trust the inputs? Do we have consistency? Can we align stakeholders? Can we scale without creating risk or rework? 

The AI Readiness Assessment helps you get a clear view of where you stand, so you can move forward with fewer assumptions. 

Two resources to help you move forward 

If you are wondering where to start, start small and get specific. 

Start with the AI Readiness Assessment 

A quick assessment built for QA and CX leaders who want a clear, practical starting point. 

What you will get: 

  • A readiness result you can share internally 

  • The most likely blockers for your situation 

  • Clear direction on what to focus on next 

Take the AI Readiness Assessment

Then use the AI Readiness Handbook as your action guide 

A guide you can use as a reference, a planning tool, or a shared framework across teams. 

Inside, you will find: 

  • A simple readiness model to align on 

  • Common blockers with practical actions 

  • A short roadmap, plus tools and templates to support execution 

Download the AI Readiness Handbook

The cost of waiting (or guessing) 

The gap is already visible. 44% of contact centers say automated and real-time QA is the number one change they want, yet 40% are not using AI in QA at all. 

Even when AI is on the roadmap, the same blockers show up again and again: 

  • Budget and ROI (49%) 
  • Lack of expertise (40%) 
  • Integration difficulty (33%) 

The longer those issues stay unresolved, the longer QA stays constrained by manual effort and slow change. 

Choose your next step 

If you want AI to work in QA, clarity beats confidence. 

Start by understanding your readiness. Then focus on the few moves that unlock progress. 

Take the AI Readiness Assessment

Prefer in-person discussion and peer learning? 

The Future of QA: Clarity at Scale with AI Excellence 

A small, in-person breakfast briefing for QA and CX leaders to compare notes on scaling AI, building trust, and moving from limited coverage to meaningful insight.