Rising interaction volumes, tighter budgets, and higher customer expectations makes the playing field increasingly difficult for contact centers to remain competitive. The good news is AI is proving to be the rare lever that improves both efficiency and outcomes when implemented correctly by experts in the field.
Below is a practical, research-backed tour of where the productivity lift actually comes from and how it’s measured.
1) Automating the “Front Door” Without Trapping Customers
Modern self-service (voice and chat) resolves routine intents, order status, password resets, appointment changes, and more before they hit the queue. McKinsey reports that digital self-care can already direct roughly 30–50% of contact center volume to AI self-serve tools. The win isn’t just fewer calls; it’s faster resolutions and less context-switching for agents. Industry surveys show organizations are prioritizing AI to absorb simple work and protect human time for complex issues, a theme that tracks with broad AI adoption trends among U.S. workers and consumers. For example – our client Best Buy saw an 87% accuracy in understanding customer intent leveraging the NLU bot within four months of deployment. This expectedly resulted in a 61% containment rate.
The key is design: make automation the fastest path to a fix, and make escalation effortless. When self-service and human support complement one another, contact centers reduce abandonments and shorten queues.
2) Forecasting, Scheduling, and Intraday Agility
Volume volatility creates costly over- and understaffing. AI-enhanced forecasting improves accuracy by factoring seasonality, product releases, marketing campaigns, and even external signals. Combined with intraday automation (smart breaks, micro-training, and dynamic re-queues), you keep service levels steady while using fewer labor hours. Broad workplace research reinforces that value shows up when leaders pair new tools with clear operating changes and guardrails, not just a software switch.
We have a great video here showcasing the AI-powered Analytics and Forecasting capabilities in Genesys Cloud CX.
3) Intent-Aware, Skill-Based Routing
Misroutes, transfers, and “tell me again” loops burn minutes. AI improves routing by classifying intent from language (voice or text) and matching to the best-fit resource in real time. Academic work shows machine learning driven triage in live-chat settings improves service levels and lowers labor cost versus common manual approaches, because requests start in the right place more often.
Think of this as precision logistics for conversations: better first placement reduces average speed of answer (ASA), average handle time (AHT), and prevents second-touch contacts. Over time, the model learns from outcomes (e.g., which agents resolve which intents fastest), compounding the effect. Reviews of machine learning enabled routing in broader networks echo these gains, highlighting the value of data-driven policies versus static rules.
4) Real-time Agent Assist
One of the strongest pieces of AI-driven productivity comes from a large-scale study of 5,000+ customer support agents using a generative AI assistant. Issues resolved per hour rose ~14–15% on average, with the biggest boosts for less-tenured agents, and spillover benefits to customer satisfaction and retention.
In practice, “Agent Assist” listens for intent and sentiment, surfaces relevant knowledge, proposes next-best actions, and drafts notes or summaries, effectively shrinking after-call work and accelerating time-to-resolution. Macroeconomic analyses suggest these kinds of activity-level gains add up to meaningful productivity growth at the organization level when paired with good change management.
5) Post-Contact Automation That Actually Sticks
Even small reductions in wrap-up time compound across thousands of interactions. AI can auto-summarize calls, draft dispositions, and update CRM fields, freeing minutes that agents can spend on the next customer rather than manual work. This is also where quality improves: consistent, structured notes make it easier to find and fix recurring issues, smooth handoffs, and feed coaching.
In the image below, you can see how the agent facing AI solution in Genesys (Copilot) auto summarizes the interaction and adds accurate notes. A wrap up code is also automatically selected.
6) Quality Management & Insights at Scale
AI speeds up quality assurance by turning every conversation into structured, searchable data and then doing the first pass of review automatically. Speech recognition and natural-language processing transcribe calls and chats, tag intents and sentiment, detect holds/silences/overlap, and spot compliance phrases or missing disclosures. Instead of manually sampling a tiny fraction of interactions, QA can monitor nearly 100% with real-time alerts for high-risk or low-quality moments. Generative tools then draft call summaries and auto-score against your scorecards (greeting, verification, empathy, resolution, wrap-up), so human analysts spend their time validating exceptions and coaching, not hunting for needles in haystacks.
That automation translates directly into contact-center efficiency. Faster, data-rich feedback loops help supervisors coach the right behaviors sooner, which improves first-contact resolution and reduces repeat contacts.
The Takeaway
AI increases contact center productivity by removing friction at every step of the journey. It deflects routine intents through well designed self service, routes complex issues to the best fit resource, assists agents in real-time, automates the tedious wrap up, expands QA from sampling to complete coverage, and optimizes staffing to match demand. The payoff shows up in faster resolutions (lower AHT), fewer repeat contacts (higher FCR), steadier queues (better ASA), and more consistent quality without sacrificing customer or agent experience.
To turn those gains into durable results, start focused and measurable. Identify your top ten to twenty intents, pick two or three high impact use cases, connect the right data and knowledge sources, and pilot with clear baselines for containment, AHT, FCR, ACW, and CSAT. Keep humans in the loop while models learn, then scale what works with good governance and regular coaching. Treat AI as a workflow upgrade, and you will see efficiency and experience improvements compound over time.
References
2024 July Market Study: AI-Powered Contact Center, 2024, https://www.customercontactweekdigital.com/ai-for-cx/whitepapers/2024-july-market-study-ai-powered-contact-center
34% of U.S. adults have used ChatGPT, about double the share in 2023, 2025, https://www.pewresearch.org/short-reads/2025/06/25/34-of-us-adults-have-used-chatgpt-about-double-the-share-in-2023/
Genesys Cloud Agent Copilot Deep Dive, 2025, https://www.genesys.com/en-gb/blog/post/genesys-cloud-agent-copilot-deep-dive