Are Your AI Agents Training For Gold—Or Settling For Bronze?

Forbes

a day ago

George Stelling, Quadrillion Partners and Lunation's CEO—transforming businesses with AI innovation and digital capabilities.
Companies rushing into AI-driven solutions often face a common pitfall: deploying AI agents that initially impress but quickly plateau. Gartner reports (via VentureBeat) that 85% of AI projects fail without proper follow-through, while a study published in Nature notes that 91% of machine learning models degrade if left unattended. The message is clear: Without ongoing improvements, your AI agents risk falling behind, ultimately failing to deliver sustainable business value.
The solution? Embracing an AI flywheel agent—a semi-autonomous AI agent specifically designed to provide real-time metrics on data quality, usage patterns, costs, execution cycle times and other critical KPIs. This agent continuously analyzes these metrics and proactively recommends improvements to your core workflow agents, creating a self-sustaining cycle of enhanced AI performance.
It should also capture customer satisfaction data to inform continuous improvements. Its "flywheel" is a continuous loop of recommendations across multiple dimensions that matter to both business users and AI engineers.
To practically achieve continuous improvement, adopt the straightforward yet powerful approach called LIFT:
• Learn: Continuously gather and analyze data from each interaction with each agent.
• Iterate: Regularly refine processes and adapt to evolving user needs.
• Feedback: Consistently incorporate real-time user insights.
• Transform: Convert insights into measurable business outcomes.
Together, these steps "LIFT" your AI agents from average performance to peak effectiveness, ensuring they're constantly training for gold rather than settling for bronze.
An AI flywheel agent is a specialized and focused AI agent designed to measure, monitor and enhance the performance of all of your core AI agents. It actively tracks key metrics, including data quality, user adoption, operational costs, workflow efficiency and execution cycle times. Beyond simple reporting, the flywheel agent proactively analyzes data and recommends actionable enhancements.
The concept closely aligns with NVIDIA's idea of a data flywheel, which emphasizes continuous data enrichment and iterative learning. Similarly, an AI flywheel agent creates a reinforcing loop that yields exponential performance gains over time.
The LIFT approach complements this flywheel by structuring continuous improvements into clear, actionable phases:
• Data Quality And Usage Insights (Learn): Capturing and analyzing data from user interactions to continuously refine predictive accuracy
• User Interface Enhancements (Iterate And Feedback): Systematically incorporating user feedback to enhance adoption and satisfaction
• Workflow Efficiency (Iterate And Transform): Identifying bottlenecks to streamline processes and reduce cycle times
• Business Outcome Maximization (Transform): Aligning recommendations directly with strategic business goals for optimal ROI
Deploying static AI agents yields immediate results, but rarely sustainable success. Real-world conditions, data environments and user expectations constantly evolve, rapidly rendering inflexible systems obsolete. In contrast, AI flywheel agents utilizing the LIFT approach stay adaptive, responsive and aligned with evolving business contexts.
Integrating analytics platforms such as Tableau, Power BI or Qlik into your AI workflows enables immediate performance tracking. AI flywheel agents leverage these tools to provide continuous visibility into critical operational metrics, allowing rapid issue identification and proactive improvement.
Poor usability consistently undermines AI initiatives. Flywheel agents prioritize integrating continuous user feedback, iteratively refining user interfaces to boost adoption rates. Higher adoption generates richer interaction data, further fueling the continuous improvement cycle.
Extended cycle times reduce organizational responsiveness. Flywheel agents continuously analyze processes, identifying and eliminating inefficiencies, significantly reducing delays. This ensures rapid realization of AI-driven value and boosts organizational agility.
High-quality data underpins AI effectiveness. Flywheel agents continually refine data accuracy and relevance, resulting in more accurate predictions, actionable recommendations and better strategic decisions.
To successfully adopt an AI flywheel agent with LIFT, follow these practical steps:
1. Clearly define success metrics. Establish upfront KPIs (e.g., customer satisfaction, data quality, user adoption, cost savings, cycle time improvements and business performance against goals) that indicate the success of AI agents.
2. Embed analytics heavily. Integrate analytics tools from the beginning, providing immediate insights into agent performance and areas for improvement.
3. Establish automated feedback loops. Automate user feedback collection and systematically incorporate it into iterative improvements.
4. Enable autonomous recommendations. Allow your flywheel agent to analyze metrics proactively and suggest targeted improvements independently, weekly or monthly, for consideration by both your business and IT teams.
Deploying AI isn't the finish line—it's the starting point. Gartner's statistic that 85% of AI projects falter without continued improvement, paired with the finding in Nature that 91% of ML models degrade without monitoring, underscores the urgency of sustained optimization. Static, neglected AI systems quickly lose their competitive edge, wasting valuable resources and opportunities.
By implementing an AI flywheel agent using the LIFT approach, you ensure that your AI investment remains agile, responsive and continuously valuable. Instead of settling for bronze, your AI solutions will be perpetually training for gold, consistently delivering robust, lasting, competitive advantages and business outcomes.
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