Towards reaching a 95 % CSAT score at Autodesk, we mapped every category of customer contact and asked these questions:“ How can we reduce customer friction?" and " Where can automation deliver value while maximizing CX?”
For many high-volume, low-complexity contacts, such as order status, account updates, and basic troubleshooting, AI can deliver faster, better experiences than routing customers to queues and waiting for humans.
But for anything involving escalating frustration, account risk, or complex multi-system issues, the human touch is critical.
The deployment question should be:“ Where does AI create a genuinely better experience and where does it create friction that we are willing to accept because it reduces cost?” That second category should be small and it should be called out in your planning process.
Klarna’ s own CEO eventually put it plainly: the key distinction in customer satisfaction lies in the type of task. Basic tasks are often handled more efficiently by AI. Complex problems still require human interaction( CX Today).
That insight should have been the starting point, not the lesson learned 18 months and a public reversal later.
STEP 2: SEGMENT YOUR INTER- ACTION PORTFOLIO BEFORE WRITING A SINGLE USE CASE.
Before deploying any AI agent, you need a clear map( see FIGURE) of customer interactions. Segment them across two dimensions: interaction complexity and emotional intensity.
• Low complexity, low emotion. These are your best AI candidates: password resets, order status, balance inquiries, appointment scheduling, basic policy lookups. The customer wants speed and accuracy, not empathy. AI can outperform humans here when implemented well.
• Low complexity, high emotion. Proceed with care. A billing dispute is technically simple, but the customer calling about it may be stressed or at risk of churn. AI can start the interaction, but the escalation path to a human should be frictionless and swift.
Klarna’ s AI chatbot took up to 20 seconds to answer simple FAQs, not to mention the runarounds afterwards. That latency alone destroyed the experience.
• High complexity, low emotion. AI can assist but should not lead. Agents with AI co-pilot tools, such as real-time knowledge surfacing, case summarization, or next-best-action prompts, perform measurably better here than either AI alone or unaided humans.
• High complexity, high emotion. This is a human-first zone. Customers in financial distress, experiencing product failures with downstream consequences, or navigating multi-channel escalations need a skilled, empathetic agent who can reason through a tricky situation.
No current AI agent does this well, and attempting to automate these interactions is where CX is severely compromised, leading to customer churn. This segmentation is not a onetime exercise. As AI capabilities evolve and your interaction mix shifts, revisit and revise it.
STEP 3: DEFINE WHAT“ PRODUCTION-READY” MEANS BEFORE PILOTING.
The gap between a successful pilot and a failed production rollout is usually a measurement problem. Pilots optimize for what’ s easy to track: containment, handle time, deflection. Production is judged on what actually matters: customer satisfaction, repeat contacts, retention, lifetime value.
That misalignment is fixable, but only if you define production success before you begin the pilot. This requires early alignment across Finance, CX, and Operations on the metrics that will govern scale decisions.
FIGURE
20 CONTACT CENTER PIPELINE
Four thresholds should determine whether an AI agent is ready for production.