Why Chat-Based AI Fails to Address Critical Queries?
Complex Comparisons Across Time
It cannot compare data from multiple businesses or systems, especially for long-term analysis.
Insights Beyond the Surface
Chat-based AI focuses on extracting what’s in the data but lacks the ability to deliver deeper, actionable insights about the data itself.
Handling Complex Processes
It struggles to execute multi-step workflows (e.g., processes with 20+ steps), limiting its ability to support comprehensive tasks.
Long-Document Analysis
Analyzing large documents that exceed its context window is challenging, leading to incomplete or inaccurate insights.
Scalability for Large Datasets
The system is ill-equipped to analyze extensive datasets, such as 10,000 documents, in a cohesive manner.
Lack of Continuous Learning
It doesn’t improve or adapt based on feedback or usage patterns, resulting in static performance over time.
Inability to Provide Contextual Recommendations
Chat-based AI often lacks the capability to connect disparate data points and generate personalized or context-specific recommendations for complex scenarios.
Limited Integration with External Systems
These systems frequently fail to seamlessly integrate with multiple platforms or data sources, restricting their ability to work in a broader ecosystem.
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