Four Mistakes to Avoid When Implementing AI
AI holds immense promise for businesses, offering the potential to streamline operations, enhance customer experiences, and boost efficiency. However, the journey to successfully implementing it can be fraught with pitfalls. Here are four critical mistakes that companies make when implementing AI and how to steer clear of them.
Failing to Optimize Their Existing Tech Stack first
Imagine trying to build a futuristic robot with old parts — it’s bound to collapse. The same principle applies here. Failing to optimize your existing technology infrastructure before introducing AI can lead to inefficient processes and unreliable outcomes.
If your knowledge base is outdated, your AI may provide incorrect information, eroding customer trust and satisfaction. Similarly, if your data is disorganized, AI may struggle to deliver accurate insights.
Take the time to streamline and clean up your existing tech stack. Ensure your data is accurate, organized, and up-to-date before integrating. A well-optimized system provides the solid foundation your AI needs to flourish.
Failing to Automate Their Existing Tech Stack first
Before investing in advanced solutions, leverage the free automation tools available within your existing systems. Neglecting this step could mean paying for AI capabilities that you could have achieved at no extra cost.
Here’s an example. At one client, team leads were manually closing SPAM tickets, amounting to 12% of total tickets. We were able to automate this using basic capabilities in Zendesk. Implementing a sophisticated AI for this task would incur additional expenses when basic platform automation could suffice.
Identify and automate manual processes within your existing tech stack wherever possible. Save your investments for tasks that truly require advanced capabilities. This ensures cost-effectiveness and efficient use of resources.
Failing to Define a “Resolution”
One of the most common pricing models for AI services is based on resolutions or usage fees. However, the definition of a “resolution” can be a gray area. Misjudging what constitutes a resolution can lead to unexpected costs and inefficiencies.
For example: A fashion retail company implemented an AI IVR system with the expectation that it would resolve 30% of incoming tickets. However, most of these resolutions were only partial, requiring agent intervention.
But they had already reduced staffing by 30% because of the efficiencies they were expected to achieve! So when customers were transferred to agents, they got long wait times. The result: increased operational costs and customer dissatisfaction.
You should clearly define what qualifies as a resolution and negotiate with your provider to ensure you are not billed for partial resolutions. A precise understanding of what you’re paying for will help you manage costs effectively.
Failing to Update the entire Customer Journey to Incorporate the AI Bot
Introducing an AI bot into your workflow is not just a matter of plugging it in and hoping for the best. It necessitates a thorough review and update of your entire customer journey to ensure seamless integration.
In one instance, an e-commerce company integrated a bot into its “save a sale” strategy but failed to modify their offers accordingly. As a result, customers received the same offer from both the bot and the agent, leading to redundancy and customer frustration.
Reevaluate and adjust your workflows, processes, and offers to incorporate the bot seamlessly. Ensure that AI plays a meaningful role in your customer interactions, complementing your existing strategies.
While implementing AI can bring numerous benefits to your organization, it’s crucial to avoid these four common mistakes. Optimize your existing tech stack, automate where possible, define resolutions clearly, and update your entire customer journey. By taking these steps, you will maximize the potential of AI while minimizing potential pitfalls, ensuring a smoother and more cost-effective implementation.
