Executive summary that answers the board’s first question
Leadership cut Average Handle Time by 30 percent in a complex, multi-channel contact center while holding Net Promoter Score flat. The program focused on process re-engineering, guided workflows, and after-call work automation. Average Handle Time, or AHT, measures the total duration of a customer interaction including talk time, hold time, and after-call work.¹ Net Promoter Score, or NPS, measures customer loyalty using a single recommend question within the Net Promoter System.² The team treated AHT as an outcome of design, not a target for agents. This mindset change protected loyalty while unlocking productivity.
What is Average Handle Time and why does it pull the wrong behaviors?
Operations define Average Handle Time as the average total time an agent spends handling a customer interaction from start to finish, inclusive of on-hold and after-call work.¹ When organizations chase AHT with frontline incentives, agents feel pressure to speed the conversation rather than solve the problem. That pressure often triggers repeat contact and failure demand, which increases total cost to serve and can erode customer loyalty. Treating AHT as an outcome of better design aligns service quality, employee experience, and efficiency. Clear definitions matter. NPS captures the share of Promoters minus Detractors based on the likelihood to recommend question.² In practice, the Net Promoter System uses closed-loop learning to convert feedback into improvements.³
Where did we start and what made the problem worth solving?
The contact center handled voice, chat, and email across ten product lines with inconsistent workflows and knowledge scattered across several tools. Mean AHT was 8 minutes for voice and 11 minutes for chat, with wide variance by reason code. The team avoided blunt AHT targets and instead mapped end-to-end journeys to see where time accumulated. Industry guidance stresses that AHT begins when contact starts and ends after the agent completes all work tied to the interaction, which reinforces the importance of after-call work design.¹ A credible benchmark program must track both efficiency and loyalty in parallel to avoid false savings. Leading CX teams institutionalize NPS as a management system, not a vanity score.²
How did the team diagnose the drivers of AHT?
Analysts combined interaction analytics with time-in-state data to pinpoint steps that added time without adding value. The heaviest contributors were knowledge retrieval, authentication handoffs, and manual summarization during after-call work. External research and vendor case studies consistently show that automating after-call summaries can reclaim dozens of seconds per interaction while improving accuracy.⁴ In this environment, knowledge friction alone consumed 60 to 90 seconds on complex calls. The team labeled each time driver as either structural (workflow or system), behavioral (capability or habits), or demand-mix (what customers contact about) to focus re-engineering on root causes rather than coaching agents to talk faster.
What design changes delivered the step change in AHT?
Designers built a guided workflow that front-loads authentication, automates context capture, and surfaces the exact knowledge article by reason, product, and customer profile. Industry definitions helped scope the work by confirming that both hold time and after-call work are part of the AHT equation.¹ The team also simplified reason codes to make routing smarter and resolution steps clearer. For after-call work, a summarization service pre-populated the case record with structured fields for outcome, next step, and disposition. External evidence shows that automating call summaries can save nearly a minute per interaction and improve data quality.⁴ In parallel, knowledge managers re-wrote top articles into task-based patterns and removed duplications. Public best-practice guides support the link between streamlined workflows, better training, and lower AHT.⁵
How did we protect NPS while driving speed?
Leaders positioned AHT reduction as the by-product of better problem solving, not the primary goal. The quality framework weighted first contact resolution and empathy. Supervisors used call calibration to ensure the guided workflow never cut short discovery. Industry advice warns that AHT varies by complexity and should not be forced to a single target across all demand types.¹ Calibrating by reason code allowed complex cases to breathe while simpler contacts flowed quickly. The Net Promoter System’s closed-loop practice was applied at the micro level. Teams reviewed detractor verbatims weekly and fed fixes into the backlog.³ This cadence ensured that any signal of effort or rushed handling triggered content or workflow changes rather than frontline pressure.
What results did the operation achieve and how were they validated?
The program reduced AHT by 30 percent across priority queues within twelve weeks and held NPS flat within normal weekly variance. The improvement came from three levers. First, guided workflows shaved navigation and hold time. Second, automated summaries cut after-call work by 40 to 60 seconds per interaction, consistent with published case evidence.⁴ Third, knowledge refactoring removed duplicate and outdated content, which aligns with independent guidance that better knowledge and training reduce handle time.⁵ The team confirmed no loss in quality using calibrated QA and Promoter ratio tracking. Bain’s Net Promoter methodology supports pairing operational fixes with closed-loop customer feedback to protect loyalty.²
What is different about this approach compared with traditional AHT programs?
Traditional programs often set blanket AHT targets and push agents to comply. Modern programs treat AHT as a composite signal of workflow design, knowledge quality, and after-call friction. Reputable sources define the AHT formula consistently, which prevents local variations from masking root causes.¹,⁶ By focusing on the system rather than the person, the operation lowered variance between agents, similar to outcomes reported in Lean Six Sigma case studies where standard work reduces cycle time.⁷ A compliance-only approach may move the average while hiding repeat contact. A design-first approach lowers AHT and repeat contact together, which is the combination that creates durable impact.
Which controls locked in the gains and kept NPS stable?
Leaders embedded three controls. A daily control chart monitored AHT by reason code and channel to detect drift. A weekly closed-loop routine reviewed Detractor feedback and agent comments, in line with Net Promoter System practice.³ A monthly knowledge governance forum retired stale content and prioritized new articles based on top contact drivers. Industry resources emphasize that AHT benchmarks vary by industry and complexity, which supports continuous calibration rather than a single universal goal.⁶ Coaches also used side-by-side reviews to reinforce discovery and advocacy so that faster never meant shallower. The control plan ensured speed gains came from design, not corner cutting.
What evidence convinces executives that the change will last?
Executives want evidence, not slogans. The program showed that AHT fell because the work changed, not because agents talked faster. System logs showed fewer application hops. After-call timestamps confirmed reduced wrap time that aligns with external case study ranges for summarization automation.⁴ Coaching forms captured improved adherence to a defined resolution path, a hallmark of process control in call centers and a recurring theme in operational case studies.⁷ Public definitions of AHT and NPS grounded the metrics in accepted practice, which increases trust in the measurement system.¹,² These anchors make the results auditable and repeatable across brands and geographies.
How can leaders replicate the outcome in their own environment?
Leaders can replicate the outcome by following a simple sequence. Define AHT precisely and align on scope, including hold and after-call work.¹ Segment demand and set glidepaths by reason rather than mandating one number. Build guided workflows that reduce navigation time and surface the exact knowledge needed at the moment of truth, in line with best-practice guidance.⁵ Automate after-call summaries to recover time and improve data quality, which evidence shows is achievable at scale.⁴ Run Net Promoter closed loops to protect loyalty and direct fixes where effort persists.³ Treat AHT as the by-product of thoughtful design. When the system works, the seconds follow.
FAQ
What is Average Handle Time and how is it calculated in contact centers?
Average Handle Time measures the total duration of a customer interaction, including talk time, hold time, and after-call work, divided by the number of interactions.¹
Why can Average Handle Time targets harm customer experience?
Blanket AHT targets can push agents to shorten conversations rather than solve problems. Treating AHT as a design outcome avoids rushed handling and protects loyalty measured by NPS.²
Which interventions most reliably reduce AHT without hurting NPS?
Guided workflows, high-quality knowledge, and automated after-call summaries remove non-value time while preserving discovery and empathy. Evidence shows after-call summarization can save close to a minute per interaction.⁴
How does the Net Promoter System help manage trade-offs between speed and quality?
The Net Promoter System uses closed-loop feedback to convert customer comments into fixes, which helps maintain or improve loyalty while efficiency programs proceed.³
Which benchmarks should Customer Science clients use when setting AHT glidepaths?
Use precise AHT definitions that include hold and after-call work and calibrate by reason code and channel since benchmarks vary by industry and complexity.¹,⁶
Who should own knowledge governance to keep AHT low over time?
A cross-functional forum that includes operations, knowledge management, and quality should retire stale content and prioritize new articles based on top contact drivers. Best-practice guides link strong knowledge to lower handle time.⁵
Which proof points show automation does not degrade service quality?
System telemetry that shows fewer application hops, reduced wrap time from automated summaries, and stable NPS under a closed-loop process offers credible evidence of sustainable improvement.³,⁴
Sources
“What Is Contact Center Average Handle Time (AHT),” NICE, 2025, Glossary. https://www.nice.com/glossary/what-is-contact-center-average-handle-time-aht
“Net Promoter Score System,” Bain & Company, 2025, Consulting Services. https://www.bain.com/consulting-services/customer-strategy-and-marketing/net-promoter-score-system/
“Measuring Your Net Promoter Score,” Bain & Company, 2025, Net Promoter System. https://www.netpromotersystem.com/about/measuring-your-net-promoter-score/
“Leading Telecommunications Company Slashes After-Call Work and Average Handle Time,” Uniphore, 2024, Case Study. https://www.uniphore.com/resources/case-studies/leading-telecommunications-company-slashes-after-call-work-and-average-handle-time-to-streamline-cx-operations/
“Average Handle Time: Formula and Tips for Improvement,” Zendesk, 2025, Blog. https://www.zendesk.com/blog/average-handle-time/
“How to Calculate Average Handling Time (AHT),” Call Centre Helper, Jonty Pearce, reviewed by Hannah Swankie, 2017, updated 2025, Guide. https://www.callcentrehelper.com/how-to-measure-average-handling-time-52403.htm
“Improving Time of Average Call Handling Via Lean Six Sigma,” Juran, 2025, Case Study. https://www.juran.com/results/case-studies/average-call-handle-time/