Business intelligence (BI) is entering a new phase. Organizations want insights that move at the pace of the business, yet many still rely on workflows shaped years ago: dashboards for every new question, exports when dashboards fall short, and manual stitching of data spread across tools, clouds, and spreadsheets. The result is an environment full of reports that rarely match how people actually work.
AI agents are emerging as a new analytical interface that reshapes how people reach and interpret information. These agents expand access to data, streamline workflows, and reduce friction in the path to insight. They support analysts and business users by bringing structure, context, and reasoning directly into the flow of work. The shift is already underway, and it’s changing expectations for what modern BI can deliver.
A New Understanding of Dashboard Sprawl
In many organizations, the number of dashboards continues to grow each year. Some environments contain thousands; large enterprises often reach tens of thousands. Yet when teams begin rationalizing what truly matters, a consistent pattern appears: More than 80% of dashboards no longer need to be rebuilt or migrated because they are no longer used.
The challenge is not the dashboard itself. It’s the volume, the fragmentation, and the difficulty of knowing which version reflects current definitions. Dashboards are also inherently brittle: changing markets, new data sources, and additional fields can quickly render them obsolete, driving further sprawl as new versions are created. When users cannot get exactly what they need, they often revert to manual processes — exporting data into Excel or desktop tools, reshaping it for the specific view they require. Over time, this creates multiple interpretations of the same metric.
This drift is easy to overlook but has significant consequences. Even with shared data sources, minor variations in filters or definitions can lead to results that diverge. The phenomenon resembles “human hallucination”: different people working from the same information yet arriving at outcomes that no longer align. The need for a more adaptive, less brittle environment is clear.
A More Flexible Interface for Data
A conversational interface for analytics changes the experience of exploring and interpreting data. Instead of navigating a series of dashboards or relying on ad hoc exports, users can work directly with the information they need through guided, flexible interactions.
This style of workspace brings together several capabilities:
- Access to structured and unstructured data in one environment
- Tools that allow agents to run queries, summarize content, generate charts, or use approved models
- Transparent reasoning and traceable steps
- The ability to refine questions and follow threads of analysis without starting over
The interaction becomes a dialogue rather than a search process. Users ask a question, review the answer, inspect the underlying data, and continue refining their exploration. When they need visualizations, the system can generate them. When they need narrative summaries, those appear as well. Just as importantly, this isn’t only a chat interface on top of data. Users can still work directly with the underlying tables and logic, with the agent accelerating the analysis while they retain full control over the final steps.
In this environment, dashboards still play an important role, but they act as one type of artifact rather than the center of the workflow. The focus shifts to understanding, with the format adapting to the question.
What AI Agents Make Possible
AI agents expand the reach and depth of analysis by coordinating several actions behind the scenes:
- Searching across approved structured and unstructured sources
- Working with curated knowledge banks created by analysts
- Executing reusable tools, such as SQL templates, forecasting models, extraction logic, or API-based functions
- Returning answers with citations, reasoning steps, and full lineage
This creates an experience similar to working with a highly capable analytical partner. The agent handles orchestration while the user guides the intent and examines the results. For example, when an analyst asks for drivers of profitability, an agent can identify relevant financial data, interpret supplemental documents, and deliver a concise explanation with the option to view each source. Every action — every query, search, extraction, or transformation — is visible in the trace.
Agents can also carry out operational tasks. They may run a forecasting model, populate fields in another system, or generate a draft presentation. As more tools become available, the range of agent capabilities continues to expand. The value grows further when analysts can design their own agents. By defining tools, knowledge banks, and instructions, analysts shape how the agent performs specific types of analysis, embedding domain knowledge into a repeatable system.
Speed Through Integration
Answering a business question often requires connecting information scattered across databases, cloud services, legacy systems, and documents. Analysts spend significant time locating data, validating definitions, and preparing it for analysis. AI agents benefit from this integration as well, not only through unified data access, but also through the ability to draw on the analytics, models, and reusable logic organizations have already built. Instead of reconciling definitions or recreating work across systems, agents can rely on established assets to deliver consistent, reliable outputs.
Centralizing access reshapes this workflow. When analysts and agents can reach the same data and the same analytical building blocks through consistent patterns, discovery accelerates and ambiguity decreases. Shared catalogs, semantic search, governed datasets, and reusable models make it easier to rely on established definitions and avoid duplicating effort.
Once the right data is identified, preparation moves faster through a blend of visual tools, governed transformations, and optional large language model assistance. Users remain fully in control while gaining support for routine steps. Model development benefits as well: training, testing, and evaluating predictive workflows occur in the same environment as preparation and analysis, reducing technical overhead. Deployment becomes a structured step instead of a bottleneck, creating shorter cycles and more opportunities to refine insight in real time.
The Central Role of Trust
Trust determines whether analytical insight becomes action. When numbers diverge or lineage is unclear, confidence erodes quickly. This challenge has existed long before AI. Slight differences in definitions or filters already create drift, and agents reveal these inconsistencies more visibly.
A modern analytical environment strengthens trust through transparency. Each answer shows its source data, the tools used, and the sequence of steps. Shared definitions reduce interpretation gaps. Guardrails ensure safe access and consistent handling of sensitive information. Monitoring reveals how agents behave across interactions. Evaluation workflows help analysts refine outputs, and versioning places analytical assets within a governed lifecycle that scales. These capabilities create the clarity needed for broader use of AI across the organization.
How This Strengthens Business Impact
When transparency is built into the system, the benefits extend across teams. Analysts no longer rebuild logic for each request, which shortens turnaround and reduces duplicate work. Business users can explore data more confidently because explanations and sources are visible. Work that previously depended on manual preparation or extended communication becomes easier to repeat, evaluate, and improve.
This leads to faster analysis, clearer interpretation of documents and unstructured material, and smoother workflows where agents support tasks such as running models or producing summaries. The value increases as analysts and business users rely on the same governed foundation.
A New Role for Analysts
As agents take on coordination and pattern-based tasks, analysts shape the structure and quality of insights across the organization. Their work focuses on:
- Designing knowledge banks
- Creating and refining the tools agents use
- Reviewing traces to enhance reasoning
- Embedding domain expertise into instructions and workflows
- Guiding standards for how data is defined and applied
This elevates the impact of the analyst. Expertise becomes part of a system that applies it consistently across more questions and users.
Building the Foundations for Agent-Driven BI
Organizations of any size can move toward this model. Three elements form the foundation:
- Unified access to data, regardless of where it lives
- A shared workspace where analysts, business users, and agents operate from the same logic
- Governance for both human and agent activity, ensuring lineage, access rules, and consistency
While these capabilities strengthen each other, they do not need to be perfected before teams begin working with agents. Unified data access, shared workflows, and governance often mature in parallel with agent development. Many organizations start with partial components in place, consolidating the most critical data first, defining early governance patterns, and expanding shared workspaces over time.
The key is building these foundations comprehensively while progressing with agentic use cases, allowing teams to introduce agents with confidence and scale their capabilities as supporting infrastructure evolves.
The New Landscape of Self-Service BI
Self-service BI is shifting toward a more fluid, conversational experience. Structured and unstructured data sit side by side. Dashboards, narrative explanations, and visualizations appear when needed. Analysts guide quality and structure, and agents extend reach and reduce the friction between a question and its answer. This creates a more connected and capable analytical environment, one aligned with the speed and complexity of modern decision-making.