AI and Automation
Make smarter business decisions faster with WFM data and AI

Make smarter business decisions faster with WFM data and AI

Whitney Rose
Content Marketing
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Navigating the labyrinth of AI options for your customer support operations is no small feat. Between the pressure to incorporate AI, the overwhelming array of choices, and the slew of exaggerated claims, finding the right AI for your business can feel like an uphill battle.

The secret to setting yourself up for success? It’s all about finding an AI tool that connects to your workforce management (WFM) data. Let’s be clear: we’re not talking about AI-powered WFM — a term often used but rarely lived up to. What we’re discussing is WFM-powered AI.

With AI tackling repetitive customer interactions via automation workflows, it’s essentially functioning like an additional support agent. Therefore, it’s crucial to have a holistic view of your team’s performance, including both human agents and AI. This is where the magic happens with the connection between your WFM and AI systems. This powerful duo provides a comprehensive view of all your support resources, enabling you to make informed decisions that drive real results.

In this article, we’ll explore the essential data you’ll want to connect from your WFM to your AI, ensuring you have the insights needed to optimize your support operations.

How’s CSAT going?

Keeping customer satisfaction (CSAT) high is probably a top priority for your team, and that data should already be feeding into your WFM system. You’ll track it holistically across all interactions, segment it by team or queue, and managers will drill down to the agent level to see how each team member is tracking against goals. But here's the kicker: you’ll want to be sure you’re tracking how AI is impacting CSAT too.

Are AI-assisted interactions improving CSAT? Are fully automated interactions leading to high CSAT? These are critical questions to ask.

By connecting your WFM and AI systems, you can gain insights into how each support channel is performing and make data-driven decisions to optimize both human and AI agent interactions. This comprehensive view enables you to identify areas where AI excels and where it might need a human touch, ensuring that you maintain high levels of customer satisfaction across the board.

QA for humans and AI

A support function is only as good as the quality of support it provides, and your quality data should be integrated with your WFM system. You have specific requirements for your human agents, and you hold them to these standards through regular quality assurance (QA) checks. This QA data helps track performance over time and identifies areas for improvement. The same approach should be applied to your AI.

Sample the AI’s interactions, grade its performance, and make necessary adjustments to improve quality. If a human agent provides inaccurate or off-brand responses, you might invest in additional training. The same goes for AI: if its answers are incorrect, update the knowledge base; if its tone deviates from your company’s voice, tweak the AI settings.

By maintaining a rigorous QA process for both human and AI agents, you ensure that the quality of your customer support remains consistently high, leveraging the strengths of both to deliver exceptional service.

Right person, right time, right answer

At the core of WFM data is the ability to schedule and forecast staffing needs effectively. Introducing AI into your operations will inevitably impact how you staff your team. To optimize this process, it’s crucial to track AI's performance over time: Are queries being handled faster? What tasks are being automated? Is ramp time for new agents quicker? Each of these factors can significantly influence your staffing strategy.

By connecting WFM data to your AI tool, you gain the ability to quickly connect the dots between AI performance and its impact on staffing. This allows for a more agile approach to WFM, enabling you to adjust staffing levels dynamically based on real-time data.

Whether it’s optimizing shift patterns, allocating resources more efficiently, or enhancing the overall responsiveness of your support team, having all your data in one platform makes it much easier to make informed, strategic decisions that drive better outcomes.