Etheus means lower costs, more productivity, more engagement and career satisfaction.
We have described our below on how the Etheus model differentiates from the current state.
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The OS mainly supports I/O devices and has both desktop and web users. The OS has no knowledge of work roles or processes.
AIs and team resources work towards building a single enterprise app.
Each department works as its own silo, with others not having much information about it.
Siloes are reduced and removed
Management knows little how IT and Software development process works
AI Agents are available to help understand and create what if scenarions
AI and Analytics are mainly left to IT
Every role is involved in creating analytics, trainings LLMs and Agents
It ends when the stakeholders sign off.
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Mainly focused on pro-code development, some low code with minimal costing analysis.
95% No Code Development fully costed.
Apps are independent units without any ontology definition. Users are trained in apps by other users, training videos, and wikis.
Apps and the processes they encumber are fully defined in a Transportable App Ontology Model (TAOM). AI agents can train users on apps and make changes to the app’s ontology.
Current apps have minimal information for guiding AI agents on how to operate them.
A roadmap is fully provided to AI agents with Transportable App Ontology Models.
Mainly focused on pro-code development with minimal costing analysis.
95% No Code Development fully costed.
Very manual, across multiple documents.
Focus is on top problem definition, solutions, and sub-solutions.
Multiple apps are integrated via APIs, data buses, or other methods to build an overall enterprise solution.
AIs and team resources work towards building a single enterprise app.
Mainly by user seats.
Licensed by role automation and improvement results.
Multiple apps are integrated via APIs, data buses, or other methods to build an overall enterprise solution.
AIs and team resources work towards building a single enterprise app.
Work roles are a mystery.
AIs and team resources work towards building a single enterprise app.
Apps and processes are loosely documented.
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Only one vendor contributes to the improvement of an enterprise app such as an ERP.
Multiple vendors contribute.
Multiple apps are integrated via APIs, data buses, or other methods to build an overall enterprise solution.
AIs and team resources work towards building a single enterprise app.
Multiple apps are integrated via APIs, data buses, or other methods to build an overall enterprise solution.
AIs and team resources work towards building a single enterprise app.
Multiple apps are integrated via APIs, data buses, or other methods to build an overall enterprise solution.
AIs and team resources work towards building a single enterprise app.
Enterprise adjusts calls API from other apps to use their logic.
Vendors provide ready-made Transportable App Logic Models, which are merged into a CEA.
IT builds app internally or with a vendor.
IT and the business are one.
Re-done when new apps are implemented.
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Developed by vendors and internal IT department.
95% developed by AI agents guided mainly by business.
Data Warehouses and Lakes are built from multiple systems.
The EEROM itself is a heavily deep knowledge data warehouse which AIs have been trained on.
User roles are not familiar with data schemas or how AI works.
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How data works and how it’s integrated is loosely documented.
The Enterprise Schema is fully documented.
Every role's AI potential is maximized, AIs are integrated into each work role.
Every app has its own security model.
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Multiple UIs are used for different apps and systems.
An overall enterprise UI is chosen or multiple variations are made available to the app.
Multiple apps are integrated via APIs, data buses, or other methods to build an overall enterprise solution.
AIs and team resources work towards building a single enterprise app.
Chat bots are used as query tools from one vendor.
Every role's AI potential is maximized, AIs are integrated into each work role.
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