Copilot Cuts Average Handling Time By 12 Percent, Claims Microsoft

Microsoft reveals the impact of its Copilot on service and sales teams


Published: November 15, 2023

James Stephen

Copilot has slashed the average handling time by 12 percent, according to new Microsoft research.

Average handling time is a contact center metric that measures how long the average contact center interaction lasts. By reducing the length of customer contacts, agents are free to take more calls, resulting in lower labor costs.

Over the years, that has often caused contact centers to pressure agents into reducing their handle times – sometimes harming agent satisfaction and first contact resolution (FCR) rates.

Yet, agent-assist tools – like Copilot – offer contact centers a way for contact centers to lower AHT without compromising the agent or customer experience outcomes. Instead, it enhances them.

Indeed, that is what Microsoft’s research suggests, which the vendor has published after launching a combination of surveys and experiments via the Copilot for Microsoft 365 Early Access Program to measure changes to role-specific pain points and opportunities, productivity, and knowledge work.

Microsoft infers the transformational power of Copilot from the results of its Work Trend Index report: “New data shows the productivity gains are real. Already, Copilot makes people more productive and creative and saves time.

“Early users don’t want to go back to working without it: 77 percent said they don’t want to give it up. And this is just the beginning.

“As the use of generative AI at work spreads, the real opportunity is not only to transform personal productivity but to lift the capability of the entire organization.

Copilot sets a new baseline—one where every employee gains the skills to write, design, code, analyse data, and more. And it amplifies expertise, taking work from good to exceptional.

As Microsoft stated above, 77 percent of employees recorded that they did not want to give up Copilot, and 77 percent said they would even sacrifice a free lunch every week in return for their continued access to Copilot.

Role-Specific Pain Points and Opportunities

Microsoft investigated the pain points and opportunities that Copilot has proven most effective in assisting with, focusing on contact center agents alongside sales personnel.

Starting with Copilot for Service, not only did it demonstrate a 12 percent reduction in AHT, but it also found that ten percent of cases that usually require colleague collaboration were independently resolved with the help of the virtual assistant.

Meanwhile, 70 and 68 percent of agents cited intelligently routing issues to appropriate agents and detecting trends across agent-customer interactions – respectively – as areas where generative AI could have a significant positive impact on their jobs.

In regards to Copilot for Sales, Microsoft found that, on average, users were saving 90 minutes per week.

Moreover, 83 percent said Copilot for Sales was making them more productive, and 79 percent found that it reduced their admin work.

Finally, with Copilot, 75 percent of salespeople reported spotting new sales opportunities, and 74 percent unlocked more unified marketing and sales data.

Microsoft’s Advice to Copilot Users

Microsoft has concluded that Copilot has demonstrated “real and significant” productivity gains, but companies must be prepared to change their ways to get the most out of Copilot, which involves viewing it as a capable assistant instead of simply a search engine.

It advises organizations to build a daily routine with Copilot to enjoy the most productivity gains. You should also “think like a manager”, Microsoft says, and learn when to delegate tasks to AI and when to apply human intelligence.

Finally, Microsoft recommends making the most of the time savings that result from using Copilot. For example, contact center managers could spend more time coaching their teams, and salespeople could dedicate extra time to building relationships with customers and closing deals.



Artificial IntelligenceGenerative AIWorkforce Optimization

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