Brenda's AI Blunder: The Stagnation Station – Why Continuous Measurement Fuels AI Success

Brenda, the CEO of Pivotal Solutions, was feeling a sense of accomplishment. They had successfully implemented an AI-powered system to streamline their customer support, carefully defining objectives, preparing data, engaging their team, setting realistic ROI expectations, and even navigating ethical considerations. The initial rollout was smooth, and they saw a noticeable improvement in response times. "Fantastic!" Brenda thought. "Another AI win in the books!" 

With the system up and running, the team moved on to other priorities. The AI continued to process queries, but no one was actively monitoring its performance beyond the initial metrics. They didn't track how its accuracy evolved, whether new types of customer questions were emerging that it couldn't handle, or if there were opportunities to refine its responses. Feedback from the support team, initially enthusiastic, dwindled, as they felt their insights weren't being acted upon. Over time, the AI's effectiveness plateaued, and in some cases, even began to degrade as new scenarios arose that it wasn't trained for. Brenda realized, with a growing unease, that they had built a great system but had left it to stagnate. They had neglected the crucial practice of continuous measurement and iteration. 

This is the pitfall of Not Measuring and Iterating. Many businesses, once an AI solution is deployed, treat it as a "set it and forget it" technology. They fail to continuously monitor its performance, gather user feedback, and make ongoing adjustments or retraining. AI models are not static; they operate in dynamic environments. Without continuous measurement and iteration, their effectiveness can decline, opportunities for improvement are missed, and the initial investment may not deliver sustained value. 

Why is this such a common trap for small and medium businesses? It's often due to: 

• Deployment Fatigue: The exhaustion after a successful implementation, leading to a desire to move on to the next project. 

• Lack of Dedicated Resources: Not allocating ongoing time or personnel for monitoring, analysis, and refinement of AI systems. 

• Misconception of "Finished Product": Believing that once an AI is deployed, it's a complete solution that doesn't require further attention. 

• Difficulty in Measuring: Not having 

•  The right tools or processes to effectively track AI performance and impact. 

Brenda's "aha!" moment came when she saw a dip in customer satisfaction scores related to support interactions, despite the AI being "active." She realized that AI, much like a skilled employee, needs ongoing feedback and development to stay at its best. She understood that deployment is just the beginning of the AI journey, not the end. 

My advice to you is this: Treat your AI systems as living entities that require continuous care and feeding. Establish robust monitoring frameworks, actively solicit feedback, and commit to iterative improvements to ensure your AI delivers sustained and evolving value. This embodies the principle of strategy over technology, emphasizing that the long-term strategic value of AI is unlocked through a disciplined approach to measurement and refinement. How can you use AI to continuously improve your operations and adapt to changing needs? Your AI's longevity and impact depend on your commitment to iteration. 

To ensure your AI efforts continue to deliver value and adapt over time, consider these practical steps: 

• DO: Establish Clear Performance Metrics (KPIs): Beyond initial success, define what ongoing optimal performance looks like for your AI and how you will track it regularly. 

• DON'T: Assume AI Will Self-Optimize: While some AI learns, it still requires human oversight and strategic direction for significant improvements. 

• DO: Implement Feedback Loops: Create easy ways for employees who interact with the AI (e.g., customer support agents, sales teams) to provide feedback on its accuracy and usefulness. 

• DON'T: Neglect Data Drift: Be aware that the real-world data your AI processes might change over time, potentially impacting its performance. Regularly review and update training data if necessary. 

• DO: Schedule Regular Reviews: Set aside dedicated time to review AI performance data, analyze feedback, and plan for model retraining or system adjustments. 

• DO: Allocate Ongoing Resources: Budget for the continuous monitoring, maintenance, and enhancement of your AI systems, just as you would for any critical business software. 

• DO: Seek Expert Guidance: Partner with advisors who specialize in practical AI implementation for SMBs. They can help you set up effective monitoring, analytics, and iteration processes. 

Next time, we'll explore another common pitfall Brenda faced: "Viewing AI as a Replacement, Not an Enhancement" – and why fostering a collaborative human-AI environment is key to unlocking true potential. Stay tuned! 

If your business is looking to ensure its AI investments deliver sustained value through continuous measurement and iteration, reach out to Origamic Solutions. We specialize in helping businesses like yours pinpoint practical opportunities and achieve real, measurable results with AI. Learn more about our approach to Practical AI here: https://origamicsolutions.com/practicalai