Business Intelligence's Inflection Point.

AI is everywhere, embedded into dashboards, layered onto reporting tools, and marketed as a shortcut to faster insights. But for many teams, the reality feels familiar: faster time to the same charts, more query sprawl, and an overwhelming amount of human-based interpretation.

The problem in BI isn't AI adoption.

It's AI without governance and innovation.

Governance: The Table Stakes of AI for BI

Before organizations can build AI-native systems, they need governance. This is foundational, not optional.

That reality became clear in 2025 as early text-to-SQL tools built in 2024 failed to gain traction. Pure text-to-SQL products promised natural-language access to data because AI was able to ingest a ddl, the database's catalog, and generate SQL automatically that could be validated and run. Despite the ability for these to functional work, they did not actually conform to how data operates in an organization. They struggled where it mattered most: with the definitions teams had already invested heavily aligning, and operationalizing. What is the source of truth for a given concept, what are the canonical joins, are there filters and conditions needed to align the data to the business' expectations? Those are where data teams spent the most time and the reason and have been a critical pillar to making data work for organizations. The metrics that appear in executive reporting always give the same number. Always (unless we agreed on a new reality and sent out a memo that our view of the world has changes ...).

So while text-to-SQL tools could generate syntactically correct SQL, they could not reliably guarantee semantic correctness. "Revenue" did not always mean the same thing. Business rules were inconsistently applied. Results often diverged from what stakeholders expected or recognized as correct.

As AI systems have improved in recall and instruction following, text-to-SQL is becoming more capable but is still not a full governance solution. For metrics that drive real business decisions, probabilistic interpretation is not sufficient.

This is the gap a semantic layer fills.

A semantic layer provides the governance that text-to-SQL alone lacks:

1. Metrics have a single, consistent definition
2. Business logic is enforced by default
3. Analysts are not required to validate every result
4. Reporting remains consistent over time

Without this foundation, AI systems produce variability. Variability undermines trust. And when trust erodes, teams spend more time reconciling numbers than acting on them.

Text-to-SQL still has a role. It is valuable for exploration and ad-hoc analysis, and we use it at Push for exactly that purpose. But for metrics that require cross-functional alignment and long-term consistency, governance is non-negotiable. A semantic layer ensures that when someone asks about revenue, churn, or any other core metric, the answer is consistent and defensible.

Governance is table stakes. It is what makes AI usable in practice.

But it is only the starting point. Governance establishes the foundation; it is not, by itself, the innovation.

The False Promise of "AI-Powered BI"

Most AI-enabled BI tools today optimize for speed: text-to-SQL, auto-charting, natural language queries. These are useful improvements, but they don't fundamentally change how decisions get made.

The fundamental problem? AI has been bolted onto existing solutions rather than rethinking what BI should be.

How AI Gets Bolted On As a Feature, Not a Platform

Most "AI-powered" BI tools fall into one of three categories:

1. AI within the data warehouse: Built-in natural language querying that generates SQL, but still outputs tables and requires manual interpretation
2. Add-ons to existing tools: AI features layered onto traditional BI platforms, automating chart creation but not changing the underlying model
3. SQL query generators: Tools that convert natural language to SQL queries, which still produce tables that inevitably lead to yet another chart

In each case, the workflow remains the same: question → query → table → chart → interpretation. AI speeds up the query generation, but teams still end up building more charts, creating more dashboards, and spending more time interpreting results.

The Primitive Problem

The issue isn't the AI technology itself. It's that no one has rethought the primitives of BI.

Traditional BI is built on primitives designed for human interpretation:

1. Tables of data
2. Charts and visualizations
3. Dashboards for monitoring
4. Reports for sharing

AI that generates these faster doesn't change what they are. It just creates more of them, faster.

BI has been the interface to data. While selling the Modern Data Stack™, the sales process required an ETL to load data into a Data Warehouse and then a BI tool to visualize and create meaning from the data the company now centralized. This allowed teams to get insights across the business, own the logic and fulfill on many meaningful improvements.

But what would it look like if we instead had AI as the tool? Visualizing data so a human can read it is

With a shared system of metadata, metrics, and business context, we could do so much.

AI-First: Building the AI-Native Layer for the Data Warehouse

AI-first BI isn't about creating a human-only interface through charts. It's about building systems that are fundamentally AI-native, giving AI the tools it needs to work effectively within an organization as an intelligent partner.

Traditional BI tools are built on primitives designed for humans: tables, charts, dashboards. AI-first BI rethinks these primitives entirely, building systems optimized for AI reasoning rather than human interpretation.

What Makes a System AI-Native?

AI-native systems are built with capabilities that only make sense in an AI context:

1. Context Management: Systems that access, maintain and update context across conversations, people and time. These systems remember what was discussed, what decisions were made, and what questions were asked. Context ties to the relevant business objects and period in time so that it is accessed only when relevant. It persists and evolves with your business, so the AI understands not just the current question but the history behind it.‍
2. Knowledge Graphs: Structured representations that connect metrics, entities, people, projects, decisions and any other arbitrary data. Instead of isolated tables, these graphs show relationships. Combined with the related concept of ontologies,it gives a more natural interface for AI to use. The AI can reason about how a product change affected customer satisfaction, or how a decision impacted multiple metrics, because it understands the connections.‍
3. Agent Tools: AI agents use tools, make decisions, and take actions. Tool design impacts how the agent operates, giving it thoughtful ways it can query databases, pull in context from other systems, synthesize information, and present insights. Agents reason through problems step by step, using tools as needed.‍
4. MCPs and Integrations: Model Context Protocol and other integrations that connect AI systems to your entire tech stack. The AI can pull context from your CRM, documentation, communication tools, and other systems. When you ask about customer churn, it can reference support tickets, sales notes, and product changes, not just the numbers.‍
5. Extensible Architecture: Systems designed to incorporate new AI capabilities as they emerge. As new protocols, frameworks, and reasoning capabilities develop, AI-native systems can integrate them because they're built on flexible primitives, not rigid features.

These aren't features added to existing BI tools. They're fundamental primitives that require rethinking how BI systems work from the ground up.

Where Push Fits

Push is built as an AI-native system for your data from the ground up.

Push generates a semantic layer from existing context: the queries, charts, and documents teams already trust. This provides the governance foundation. It is updated as users interact with the platform, maintaining a robust understanding

On top of that foundation, Push builds AI-native capabilities:

1. Context management: Maintains understanding across conversations, remembering information relevant to data systems, modeling, analysis, discussions and decisions
2. Knowledge graphs: Connects metrics, entities, and decisions to enable relationship-based reasoning
3. Agent tools: AI agents that can use tools, reason through problems, and take actions
4. MCPs and integrations: Connects to your CRM, documentation, and other systems to pull in context
5. Extensible architecture: Designed to incorporate new AI capabilities as protocols and frameworks evolve

Push connects directly to your data warehouse and uses your existing definitions and business logic. No re-modeling, no duplication. The AI Agent applies reasoning on top of governed metrics, ensuring analysis stays consistent and auditable.

Push is built on AI-native primitives, not bolted-on features. As new AI capabilities emerge, new protocols, new agent frameworks, new reasoning capabilities, Push can incorporate them because the architecture is designed for evolution.

This is what AI-first means: systems designed for AI from the start, not retrofitted for it.

The Path Forward

For data teams considering AI-native BI, the path is clear:

1. Establish governance: Build or leverage a semantic layer that defines metrics, dimensions, and business rules. This is table stakes.
2. Use AI-native systems: Look for platforms built on AI-native primitives, context management, knowledge graphs, agent tools, and extensible architectures.
3. Plan for evolution: Choose systems designed to incorporate new AI capabilities as they emerge, not ones that bolt on features after the fact.

Governance makes AI usable. But AI-native systems unlock capabilities that go beyond faster queries and charts. They're built to evolve, adapt, and incorporate new innovations as the industry develops.

The next six months will bring new AI capabilities, new protocols, frameworks, and reasoning approaches. Systems built on AI-native primitives can incorporate these as they emerge. Systems with bolted-on AI will struggle to keep up.

Ready to see how AI-native BI works?

Push builds governance-first foundations and AI-native systems on top. Request a demo to see how AI-native BI can transform your operations.