Gadi Shamia is the CEO and cofounder of Replicant.
How can automation be leveraged to make contact centers more efficient? If you asked this five years ago, you’d find recommendations like “point callers to the right channel,” “highlight your self-support options” or “assist agents to make them more productive.” In other words, deflect.
To customer service leaders, these tactics are common knowledge, but so is the reality that these tactics often do not truly move the efficiency needle.
By no fault of their own, contact centers have been limited in how much they can accomplish with automation. The constraints of interactive voice response (IVR) and chatbots meant that service leaders had no choice but to use tactics like deflection to combat agent shortages, rising costs and increased customer demand.
Fortunately, the GenAI boom has made human-to-machine interactions mainstream while increasing the types of calls that can be automated. Now, automation is finally having the kind of impact on contact centers that ATMs once had on banking: end-to-end assistance for common customer requests.
As a result, service leaders no longer have to focus their efforts on squeezing more productivity out of agents, designing complicated routing trees or redirecting callers to channels other than the one they’ve already chosen. Instead, enterprises can use AI to fully resolve common customer issues without agent involvement, all while improving customer experience and delivering cost savings.
Resolving Customer Issues Gets To The Root Of CX Challenges
When automation is measured by deflection or containment success, it’s not uncommon for contact centers to see only marginal improvements toward these pain points. But when automation resolves customer requests at scale, substantial improvements can be made in cost savings, customer satisfaction and their overall preparedness for unpredictable call spikes.
A high-resolution rate means that customer requests are being fully completed by automation and agents are receiving a significantly lower call volume for requests they previously spent time on. With AI, the flows that can be automated go far beyond simple routing and authentication tasks to include complex requests like payments, appointment scheduling and even roadside assistance.
A high-resolution rate implies that callers are getting an experience where they don’t feel the need to escalate to an agent. This gives callers natural conversations, the ability to correct themselves, low latency and more—all with no wait times, annoying phone trees or small talk.
When automation resolves requests end to end, the cost to complete each call is around 50% lower than agents or business process outsourcing with untold additional value coming from a better agent experience that features fewer mundane calls and more engaging interactions.
A resolution-based strategy means service leaders can focus less on finding day-to-day fixes for unpredictability and more on making every customer interaction better.
Pitfalls To Avoid When Pursuing A High-Resolution Rate
While a fail-fast strategy may work in other AI applications, it can negatively impact resolution rate and brand trustworthiness in customer service.
In February 2024, for example, Canada’s Civil Resolution Tribunal ruled that Air Canada must fulfill a reimbursement to a customer erroneously promised a refund by the airline’s AI chatbot. The airline argued that it can’t be held responsible for any incorrect information that AI provides on its own. However, the Tribunal determined that it’s incumbent upon companies “to take reasonable care to ensure their representations are accurate and not misleading.”
Organizations that rush to resolve without first implementing the proper guardrails put their business at risk. Conversely, a ‘wait-and-see’ approach pushes your brand’s innovation horizon out while allowing competitors to gain an edge.
A balanced AI strategy takes into account the difficulty of designing an automation solution that can safely resolve calls at a high clip. This includes requisite features like a multi-model architecture to leverage the best available LLMs, a high capacity for flexibility to incorporate your business logic, and an enterprise level of scalability and security.
While solutions like contact center as a service (CCaaS) add-ons may allow you to configure AI on your own, it’s important to note that they are limited in their conversation accuracy and flexibility. It’s not unlikely that organizations may face limitations when attempting to resolve complex use cases and may receive a lower level of ongoing support and expertise.
Meanwhile, organizations that attempt to build a solution on their own may underestimate the complexity of the project. If this is a consideration, it’s important to carefully analyze the resources of your organization to ensure this is a realistic pursuit. This way, you can better avoid cost overruns and resource-heavy maintenance responsibilities.
As more organizations partner with solution providers, ask prospective partners how they plan to mitigate risk in your strategy without delaying your timeline. Furthermore, be sure to discuss the customization potential of pre-built solutions for your business.
The Contact Center Of The Future
Gartner projects that 80% of organizations will apply generative AI to improve CX by 2025, resulting in a 20%-30% reduction in agents and the creation of new jobs to enable generative AI. I also expect that a reduction in tier-one agents will lead to the creation of an “expert class” of tier-two agents who will specialize in an industry or company and consider their job as a career, not a gig.
For service leaders, implementing a resolution-based automation solution in 2024 begins with connecting your contact center’s strategy with your enterprise’s strategy. Performing a call assessment is a great place to start.
A call assessment is an impactful exercise that analyzes the contact center’s data to show where your agents currently spend the most time. It uses AI to identify high-volume call flows that can be automated with a high-resolution rate as well as the savings to expect by doing so based on your current costs. More importantly, a call assessment makes it easy to tie the impact of resolution rate directly to business goals like net hiring savings, average hold times, CSAT and your ability to meet SLAs during call spikes.
Regardless of the approach, refocusing your automation strategy from one based on deflection to one focused on resolution can help lead to efficiency that benefits everyone involved.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
