Embedded Files
...and why improvement is needed.
The following are 2 examples of what you can find in the first generation of business AI. Scroll to the bottom to understand what their limitations are, and what opportunities exist to move these concepts to the next level.
Example 1: demo where a chatbot appears inside our favorite CRM or ERP system. It allows a user to ask natural language questions against their system data.
Example 2: demo where a user can upload a pdf or image and have AI pull key information from that pdf and return it into a data record in a source system.
Opportunities for Improvement
While AI applications like data querying and PDF extraction offer an unprecedented view into CRM and ERP systems, critical limitations remain when aligning AI to strategic business goals:
Manual effort is still required to run prompts and upload files, constraining scalability and usage frequency.
Prompt expertise varies, leading to inconsistent results and limiting the reliability of insights across the organization.
Data capture is inconsistent, reducing reporting accuracy and undermining the value of AI-driven insights.
Historical context is often lost, impairing quality control and the ability to audit or refine AI outputs over time.
Workflow automation is fragmented, as AI actions depend on individual user behavior rather than being embedded into standardized business processes.
To fully realize the potential of AI in the enterprise, organizations must address these challenges by moving beyond isolated use cases toward integrated, automated, and governed AI-driven workflows.
Click here to understand how to overcome these challenges using the Industrial AI platform
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