The contact center of the future isn’t so far away these days. Seemingly overnight, artificial intelligence for CX went from cutting-edge concept to must-have inclusion in every new platform or tech tool. But even as the industry buzzes about AI, plenty of contact centers encounter internal or operational challenges on the way to adopt this next-gen technology.
If you’re having trouble getting the budget from stakeholders, approval from IT, or buy-in from agents or customers, fear not. These smart strategies for breaking through the barriers to AI and automation adoption can help ensure your contact center doesn’t end up stuck in the past.
How to Plan for Technology when Plans Keep Changing
Greek philosopher Heraclitus once said, “There is nothing permanent except change.” Since he lived around 500 BC, he probably wasn’t talking about contact center operations, but he may as well have been. If you’ve ever made a plan to implement new technology in your CX program, you know that one of two things often happens between signing on the dotted line and going live with your agents. Either the technology has already advanced beyond what your program just implemented, or your organization’s internal processes have changed and different pain points have emerged — and sometimes it’s both.
Fear of investing in a tech tool that’s redundant as soon as it’s launched is enough to keep many risk-averse stakeholders from getting past the research phase. So, how do you overcome the apprehension of introducing AI and automation to your contact center?
If this is your company’s situation, you would do best with a standalone product that layers over your existing tech stack and doesn’t require a total overhaul or replatforming. This nimble, low-lift approach removes the pressure from resource-strapped IT departments to deal with complicated integrations and ground-up solutions. A low-configuration tool like Laivly’s Sidd Spark, for instance, adds transcription, agent reminders, and workflow automation to your voice channels and is typically ready to deliver tangible results in a matter of days to weeks, not months to years.
Another great strategy for getting ahead of ever-changing systems and processes is to start small and look for places where AI and automation could make a noticeable difference right now, even if it doesn’t seem like a high wow-factor use case. Rather than trying to solve every problem at once — inevitably adding time, money, and complexity to the solution — target small, incremental improvements and watch the impact snowball quickly.
Build Your Business Case Roadmap to Deliver Value Early and Often
Those small, incremental improvements do more than protect your investment against unforeseen changes to systems or processes. They also help you front-load value for your chosen AI and automation solution, which in turn encourages buy-in for your business case. You can sell your organization on the end goal without making them wait so long for ROI.
Picture this: You find a platform that promises huge savings on every KPI in your contact center. It checks every box on your wishlist — but it also comes with a very large price tag, a complete overhaul to your tech stack, and a minimum of 12-18 months before it’s in front of your agents. To secure the budget, you need to convince stakeholders that they just need to trust you (and the tech vendor!) that it will all be worth it in a few years.
Now imagine how much easier it would be to present stakeholders with a clear roadmap that slowly and iteratively gets your contact center where you want it to go, using a solution that layers over your existing tech. Ideally, your roadmap would start by addressing a small task that happens on every call and can be implemented quickly. For example, transcription and call summaries could shave precious seconds off after-call work and allow agents to focus more fully on the customer — and could be live on the floor in a matter of weeks. Then, once that use case is in place, you might add on agent prompts and guidance to increase policy adherence, then generative email responses, and so on, until you reach your desired level of automation and AI integration.
This approach minimizes risk by delivering results earlier in the process and allows for adjustments along the way, since making improvements to one KPI might uncover challenges that can be addressed somewhere else. It also allows you the freedom to backtrack if a use case isn’t working out as planned, without feeling like you have to force something because of how much time and money have already been spent.
Want Agents to Use a Tool? Make Sure They See the Benefits.
The success of any new solution introduced in the contact center largely depends on agent adoption. Resistance to change — whether a new policy, a new process, or a new tech tool — is common, and not just in contact centers but society at large. For every voice celebrating the breakthroughs in artificial intelligence, there’s one lamenting how AI will take all our jobs. How, then, do you get agents to see a new AI and automation tool as an asset rather than a nuisance or, worse, a threat? You need to help them see how it benefits them.
Choose a small but high-frequency use case to introduce the tech to the contact center floor.
This could be as simple as automating customer lookup or form-field fills, but it should get agents comfortable with using the tool and seeing it in action. While it might be tempting to look for the uses that deliver the most impressive numbers and assume those will generate more enthusiasm, consider how often those particular workflows come up. Say one use case saves 10 minutes but only comes up in 15% of calls. Agents may not remember how to use the tool effectively by the time the situation arises or may forget about it altogether. Meanwhile, another use case saves 20 seconds on 95% of calls. This higher-frequency case is more valuable in the long run, since it gives agents the opportunity to become familiar with the tech and build new habits.
Address agent pain points.
Automating after-call work and case notes is often a great place to start, since they’re incredibly tedious yet vital for so many reasons. When agents can simply edit a call summary or refer back to a transcript, they save time and cognitive effort. It also helps agents understand a customer’s history better on future calls, since case notes will be more accurate and consistent. These things can go a long way to making an agent’s job easier or more enjoyable.
Get feedback from the agents.
Give them a voice in where and how the tech is applied, and you might be surprised at the smart and insightful suggestions you get. And speaking of insight, use the analytics from the AI solution itself to discover where agents are using it, where they aren’t using, when coaching and training might be needed, and which workflows are and are not good candidates for automation.
What Customer Experiences Are Only Available with Technology?
You have buy-in from stakeholders and agents are using the tool, but how do you ensure customers will be okay dealing with AI and automation? Generally speaking, customers won’t mind whether they’re being helped by a human or technology, as long as their issue is addressed quickly, efficiently, and to their satisfaction. Chatbots, IVR, and other such solutions may have shown promise or even delivered time or money savings for businesses, but historically, this technology causes more friction and frustration for the customer.
When considering an AI and automation solution, consider the entire customer experience. Sometimes a customer just wants to know the status of their order or needs to update their shipping address for a recurring delivery — something quick that could easily be automated. But then there are times when a customer has a more complex issue and having a conversation with someone would be the most straightforward way to reach a resolution. Those are the cases when you might want to look for ways to integrate technology behind the scenes, on the agents’ side, to facilitate a faster, smoother interaction while preserving that human touch the customer wants.
Safeguard Customer Data and Keep InfoSec Happy
Contact centers deal with sensitive customer data. There’s no way around that. And customers expect and trust that a business will not do anything to put that information at risk in any way. It’s not surprising, then, that PII security and data privacy are a common roadblock for CX leaders looking to implement AI and automation. One way to get a stamp of approval from your organization’s information security team? Look for solutions that don’t require backend access or API integration. When the technology layers over your existing tools, as the Sidd platform does, it requires the same access or credentials as an agent. There’s no privileged API access necessary — which minimizes the potential for security breaches.
When choosing your AI and automation partner, don’t be shy about asking questions. A reputable and trustworthy technology vendor should be able to answer any security concerns you or your infosec team might have.
Some of the security-related questions you might want to ask potential vendors include:
- What security measures do they have in place?
- How do they handle data, and which data?
- When and how do they sanitize data?
- Do they scrub data at the source to limit the collection of data, as Laivly does?
- If the solution includes generative AI or a large language model (LLM), how much control does the vendor have over prompts and input?
- Is the LLM proprietary to the vendor or is it a publicly available model like GPT Turbo?
Broadly speaking, the more control you and your vendor have over the data that comes into contact with the tool, the lower the risk of data exposure.
Adopting AI and automation in your contact center doesn’t have to be an overwhelming process filled with barriers. Let the Laivly team show you how simple it can be. Book a discovery call today.