What does “AI readiness” actually mean? Every business leader will define this term differently. Some may refer to “GPU capacity,” while others may define it as “clean data” or “MLOps.”

These definitions are not wrong; they just describe the conditions for AI readiness, not the cause of failure. Effectively, they only describe the required conditions for an AI-ready enterprise. This is why 85% of enterprises with AI initiatives fail to meet business expectations.

The only necessary condition that AI adopters fail to mention is “context infrastructure.” This defines the persistent, machine-queryable representation of enterprise data, including the business logic, KPI relationships, data lineage, and governance. Without context infrastructure, any AI deployment is an expensive initiative running on data it cannot actually interpret. 

The 2024 Gartner Data and Analytics Summit has identified “knowledge-infused AI” as one of the top emerging patterns that can provide a competitive advantage. In this final blog, we’ll examine its potential advantage for early adopters.

Escaping the stack trap

Over the last 5 years, enterprise data and AI stacks have become more complex and fragmented. To address specific business problems, enterprises are deploying a variety of tools for data catalogs, observability, data governance, data quality, and much more. The issue is that each of these tools operates in isolation.

The stack trap refers to adding more tools without any shared context layer. For instance, the data catalog can identify where data exists, while semantic layers can define metrics. However, what’s lacking is a common representation of data, which causes conflicts whenever there’s any change in the business logic.

The 2023 Accenture report on technology vision found that enterprises with over 20 data and analytics tools in their stack have lower AI deployment success than those with less than 12 tools. The right solution is not to reduce the number of tools, but to create a shared context layer that can connect the tools.

Next, let’s understand how Onix’s semantic twin can serve as a shared context layer to reduce fragmentation.

How Onix’s semantic twin works in an existing architecture

As part of Onix’s Wingspan 2.0,  Eagle builds the Semantic Twin to operate within any existing architecture, not replace it. This means it can operate on top of any infrastructure, comprising:

• Data warehouses
• Cloud data platforms
• Data pipelines
• Execution logs

What’s more, it doesn’t require any data movement or schema changes and can “go live” in 4-6 weeks, as the required inputs already exist. Here’s how Onix’s semantic twin connects to existing tools:

1. Data catalogs
   The semantic twin can extend document catalogs with more meaning. By ingesting existing catalog metadata, Eagle can accelerate the construction of the semantic twin and continuously update catalog records for new lineage paths and relationships.
2. Semantic layers
   Designed for business intelligence (BI), semantic layers define the metric logic for real-time query translation.  The Semantic Twin supplies the governed business definitions that BI tools rely on, so metric logic stays consistent regardless of which query tool or dashboard sits on top. 
3. Data governance
   Onix’s semantic twin can inform governance platforms for improved policy enforcement. The policies can reference governed definitions from the semantic twin, instead of local glossaries. The Pelican tool can continuously monitor data quality using the semantic twin’s business context layer for any technical anomalies or violations.
4. MLOps platforms
   The semantic twin also provides the business context that makes machine learning models more trustworthy in the production phase. The Kingfisher tool can leverage the twins’ grounded business logic to generate appropriate training data.

Introducing the enterprise intelligence fabric

Onix’s Wingspan 2.0 is not a conventional AI platform or a standalone migration tool. It is the intelligence layer that makes both permanent. Rather, it’s best described as an integrated, agent-powered system that works on shared intelligence. This is essentially the enterprise intelligence fabric, which integrates and contextualizes the current infrastructure.

Here’s what defines the enterprise intelligence fabric:

• Provides the shared context in the existing enterprise data environment.
• Operates continuously, so that every migration, every deployment, and every system changes compounds into the Semantic Twin rather than disappearing after project close. 
• Delivers a collection of AI agents that coordinate reading from and writing to the semantic twin, including:
  • Eagle for building and maintaining the intelligence fabric.
  • Raven and Condor to weave the migration threads.
  • Pelican to monitor the integrity of the fabric.
  • Kingfisher to generate context-aware training data.
  • Phoenix to extract real-time insights from the fabric for business users.
  • Eagle FinOps to optimize the cost aspect of the fabric.

How can enterprises get started with the enterprise intelligence fabric? Here are some practical steps:

1. Start with the semantic twin.
   Assess your current infrastructure using Eagle’s discovery phase, which typically takes around 4-6 weeks. The semantic twin provides an accurate “picture” of your data environment, including what you have, your dependencies, quality issues, and migration complexity levels.
2. Identify the domain with the highest debt.
   Onix’s semantic twin can expose the longest-running analytical workload or critical operational pipelines. This step identifies the domain where the Semantic Twin delivers the fastest cost reduction and the clearest early ROI. 
3. Activate the outcomes in parallel.
   Blog 2 highlighted the 3 outcomes – Data platform modernization,  Autonomous operations, and  Connected intelligence that Wingspan  can execute in parallel. For the best results, activate all 3 pillars in concurrent mode, instead of sequentially. The compound value depends on parallel execution against the same semantic twin.
4. Measure the right metrics and KPIs.
   Finally, measure the semantic twin-powered transformation with relevant metrics & KPIs, including:
   • Discovery time per initiative
   • AI model production survival rate
   • Data quality incident rate
   • Compliance reporting effort

Conclusion

This 3-part blog series started with the pressing question about enterprise memory. How can accumulated knowledge from every migration and AI deployment be retained even after project closure?

Onix’s semantic twin is the solution. Organizations that build a foundation of enterprise intelligence modernize faster and have a clear advantage over other methods. As intelligence compounds, so do the dividends.

Want to turn data debt into intelligence dividends? Contact us now.