Having a vision for the contact center of 2030 gives service leaders something to work towards.
With that vision, they can drive enthusiasm and form a strategy to execute.
The trouble is, with all the latest advancements in technology – alongside the typical day-to-day contact center firefighting – it’s tricky to plan ahead.
As such, here are some predictions that may help contact centers get ahead of the curve.
It Will Leverage AI Agents to Automate Even More Complex Customer Queries
Gartner predicts that AI agents will autonomously resolve four in every five common customer service queries by 2029.
“Common” is an important qualifier here, but even so, contact centers won't get close to that number with the more dialog-tree-based self-service bots largely in use today.
However, agentic AI leverages knowledge and data sources to complete tasks autonomously. That introduces a new paradigm for customer contact automation.
Not only that, but AI (or virtual) agents can also reason and work with other AI agents across integrated systems to automate multi-step flows.
As such, contact centers will soon be able to automate more complex customer queries as they orchestrate AI agents to resolve various common customer contacts.
Noting this likely shift, Buster Hansen, Director of Solutions Engineering at Enghouse Interactive, stated:
“Right now, organizations are tackling the low-hanging fruit, but in five years, AI-driven self-service will become significantly more profitable and impactful.”
Indeed, Gartner also predicted that AI agents will enable a 30 percent reduction in operational costs over the next four years.
Nevertheless, Hansen stresses the need for a safe approach to future agentic AI by first uncovering common customer contact reasons and then targeting high-volume, low-complexity queries.
From there, the contact center can optimize the knowledge and data sources the AI agent can access, test, and deploy with guardrails to only answer targeted queries.
Such a modular strategy will build confidence and allow the contact center to scale.
It Will Utilize Workforce Engagement Management (WEM) Data In New Ways
Contact centers are evolving their data strategies to deliver a “single pane of glass” customer view and power AI innovations.
However, alongside customer data, service operations also hold a wealth of employee data that often fails to deliver its potential.
Now, with more contact centers investing in cloud-based quality assurance (QA) and workforce management (WFM) systems, that data pile is swelling.
Thankfully, “AI can turn massive amounts of employee data into actionable insight, making processes smoother and more efficient,” said Hansen.
For example, consider how AI may utilize those insights to power the routing engines of tomorrow’s contact centers.
The engine may leverage WFM data to spot when an agent's shift is about to end and then route them a query that's likely to be simple so they can leave on time.
Alternatively, it may harness QA data to isolate the queries an agent excels in answering and then send lots of those contacts their way to drive up customer satisfaction.
Yet, that’s just routing. Consider how AI could dynamically adjust agent shifts based on sentiment or auto-draft a personalized coaching plan.
In five years’ time, these possibilities will become realities, with AI agents working within WEM systems to bring them to life.
It Will Be Pre-Emptive, Not Just Proactive
Alongside reactive customer service, AI agents can monitor signals from products, networks, and smart devices to spot problems in real time. They may then proactively - and autonomously - resolve customer issues.
“For example, a grocery store could have sensors on refrigeration units that detect malfunctions,” noted Hansen. “Instead of waiting for someone to notice the issue, an AI virtual agent could trigger an outbound notification—via call, email, SMS, or web chat—alerting staff to check the problem and guiding them on possible solutions.
“The IVA could also, where necessary, offer the option to transfer them to a live agent.”
“The same applies to network outages, where automated messages could inform customers before they even reach out.”
While these are significant opportunities to improve experiences, there’s a step beyond proactive customer service: pre-emptive customer service.
Pre-emptive customer service spots issues before they happen and triggers a preventative action to avoid hampering the experience.
Here’s a good example, again involving an AI agent. Consider a previous contact center conversation where the customer says: “I start work at midday” or “I pick up the kids from 3-4pm.”
