AI in Customer Success: 5 Key Guidelines
First and foremost, it’s important to understand that AI (generative or otherwise) is not magic, it is not a panacea, a cure all to fix every ill, and it won’t take anyone’s job wholesale. AI does not create a system; it helps scale existing systems and processes. Therefore, the idea is to integrate AI from end to end throughout your support experience to enhance and empower internally and externally. Every component needs to reinforce every other component. When you start approaching these systems and thinking about them, you should approach them from the frame of reference that you’re building a system of which each component helps the other components as well.
It’s important to evaluate whether AI is the right fit for every step. For deflection, ensure that the tool you’re using for your chatbot or for your QA is connected to each other so that they feed info back to each other and the system continues to self-improve. When it comes to self-serve, AI may not be necessary or required at all. If you’re going to be using AI to improve AI, every component needs to reinforce every other component. Every role in your support team plays a part in the organization and understands how AI can be leveraged to fulfill their function.
Investing in knowledge management is also essential. Every source of data, documentation, and resource that you have for training your agents and your team and outlining your processes are very useful because as these models get better, these are the same inputs that are going to be provided to them. The idea is to lay down the groundwork and foundation so that when it is time for you to automate or as you look to integrating AI more into your operation, it fits in perfectly in your organization, and you’re in a good position to take advantage of it.
We know AI will completely transform the support industry, and with change comes hard questions about shifting roles, structures, and priorities of our teams. Here are five things that should be top of mind for all support leaders who want to scale or maintain exceptional customer support amidst the advancements in AI.
5 Key Guidelines to Success with AI in Customer Support
To be successful with AI in customer support, there are several key steps to consider.
1. Audit and Consolidate Your Tools
Tool consolidation is a no-brainer for reducing cost and context-switching, but it will be absolutely essential to operate effectively with AI. Making sure your systems and data can connect and talk to one another will increase the quality of work that AI can augment and produce. The first step is to audit your tech stack. You probably don’t need Salesforce AND ServiceNow, Notion AND Guru, and a knowledge base. Centralizing your knowledge and ensuring connectivity across tools will give AI the broadest context. Make sure you have as few apps as possible and a help desk that connects to the relevant areas of your business. Utilizing API access across tools that don’t directly integrate is your best bet to success in achieveing the results your team needs.
2. Transform Your Team
Let’s be 100% clear: AI will change the way your team works, but it won’t take away your team. Your team and their knowledge are your strongest asset in the support world, and you need to make sure they feel supported and valued. Be transparent with your team about how AI will impact their jobs, and provide ample training and resources to help them adjust. Reevaluate your hiring practices and determine what skills will be necessary moving forward. For example, hiring for empathy is critical to build a more cohesive team as AI takes on more of the repetitive tasks.
There’s no denying that some support roles will be automated, but leaders will also have the opportunity to build out new career paths within their teams. For successful implementation of AI, there will need to be roles that focus on content creation and training to drive bot intelligence. A new part of the support teams role will be determining when and to what extent to trust AI tools and the creation of robust QA processes. Other roles might become more technical and include data analysis or automation management.
3. Focus AI on the Transactional Issues
AI tools will free up time for support workers to focus on more complex problems. These can range in example like high-touch billing changes for high-value customers, debugging integration errors with engineering, or locating issues in custom integrated inventory management systems, rather than simple tasks like finding order numbers or resetting a password. Roles will shift and support agents will focus on primarily complex tasks, less on the transactional; a shift opposite of what we currently see today. Since transactional support will be commoditized with AI, your support team will now be differentiated by your teams’ abilities to resolve more intricate and complicated inquiries, and with more time to provide more white-glove service offerings.
As a leader, your focus will also need to shift away from optimizing transactional workflows within the team to enabling their capabilities for swift resolution of the complex. Here are some questions to start thinking through with your leadership team:
- How can I unlock the cross-organizational collaboration and coordination that is necessary to resolve complex customer issues?
- How can I escalate customer inquiries with the right context?
- How will I think about data integration across organizational teams and processes?
It’s time to focus on what AI can’t replace, not on what it can.
4. Measure the Value
Of course AI can help you meet your customer service goals, but it’s important to be realistic about what it can and cannot do. Set clear expectations for your team and your customers about what AI can handle, and what will still require human intervention. Be transparent about your use of AI and communicate how it’s being used to enhance the customer experience. Few things can irritate customers more than being offered chat, only to find out it is a mindless bot behind the scenes, and even worse when the bot tries to pretend it is human. Just be upfront and transparent here and you’ll find far greater benefit. Measuring this impact through chat script sentiment analysis can be one way to determine the value.
But, to truly understand the impact of AI on your customer support, you need to measure its effectiveness. To do this, define clear metrics and KPIs to measure the impact of AI and track these metrics over time. These metrics will be different for every company and implementation at the granular scale, but things like successful solution delivery rates can be a good start for measuring value. Measuring AI like you do your team is one great way to move forward quickly. Using the same KPIs for your AI implementations to which you currently hold your agents will help you determine where AI is most effective, and where it needs improvement.
5. Never Stop Learning
Lastly, AI is a rapidly evolving field and it’s important to stay up to date on the latest developments and advancements. Attend conferences, read white-papers and blogs, and stay engaged with the AI community to stay ahead of the curve. Play with the tools, test pilots, and stay close to the work. This will mindset help you anticipate changes and adjust your strategy accordingly and not only stay relevant in the age of generative AI, but on the forefront of innovation in the Customer Success industry.
Remember, while the models now are pretty good, but they will continue to improve at an accelerated rate. You’ll need to position yourself, your organization, and your practices to be primed for adoption and adjustments as those capabilities improve in order to capitalize on it all. AI can be an incredibly useful tool for customer support when used correctly and in the right context. It’s important to evaluate whether AI is the right fit for every step and ensure your product and operations are standardized in order to really see the benefits.
AI will completely transform the support industry. By following these five guidelines, you’ll be well on your way to scaling or maintaining exceptional customer support amidst the advancements in AI and handling those hard questions about shifting roles, structures, and priorities of our teams.