Editorial
Research Report
The AI Transformation 100: Concrete Ideas for Fixing How We Work
As we look at the future of the software business, change is taking hold across the industry. The question is: What will the markets within the software industry look like, and who will remain?
That is very much open at this time. One area that will be transformed but remain is self-service business intelligence. This market emerged as the speed of business accelerated and management needed to move beyond the machine-age model in which decisions were pushed to the top of the organization. When that model broke down, domain experts who were closer to the action needed to make decisions. In a world of real-time business, 30-day-old reports or dashboards became immediately obsolete.
Table of Contents
- Why Self-Service BI Became a Business Imperative
- Market Status: Only Slightly More Than ¼ of Organizations Have Obtained It
- How AI Will Transform Self-Service BI
- The New Promise of Data Democratization
Why Self-Service BI Became a Business Imperative
The strategic implication was clear: distribute both data and decision-making.
This shift gave rise to self-service BI, which at its core aimed at democratizing access to data at scale. Self-service business intelligence aimed to enable users to find, share and trust insights. Executed well, it drives faster, more consistent decision-making. And while the applications, data and agentic stack is still being resolved, I believe that self-service BI remains a critical input to humans and agents.
We need value-added data to make business decisions and enable agents. However, in contrast to the past, this will not be through a data catalog — data catalogs and semantic layers will become core supporting technologies of self-service BI.
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Market Status: Only Slightly More Than ¼ of Organizations Have Obtained It
So, what does the latest research say? Data from Dresner Advisory Services found that despite the difficulty delivering self-service through legacy solutions, 62% of large organizations rate self-service BI as critical or very important. In terms of numbers, only 28% indicate that bringing data together is easy or relatively easy within their organization.
For most organizations, domain experts struggle to find and use data effectively. Access for them often requires understanding where data resides, how it is structured and how to navigate complex system barriers.
At the same time, self-service collaboration remains an emerging requirement — present, but not yet as widespread as enterprise collaboration. Vendors lead customers in providing new capabilities to make things easier, though that gap should close as the market matures and access to new software-embedded capabilities becomes applied. And now a new force is accelerating urgency: the rise of agents is driving more organizations to recognize self-service BI as a critical investment.
How AI Will Transform Self-Service BI
Self-service BI is at a turning point. We are at an inflection point for analytics — how they are created, consumed and trusted. Generative AI and agentic AI are now beginning to fundamentally transform what self-service BI can deliver. At the same time, the distinction between generative AI, agents and natural language is becoming less relevant as these technologies converge.
The Foundation Still Matters
But before looking forward, the foundation matters. Success in BI and success in self-service are inseparable. A usable data platform cannot exist if users cannot find the data, and effective self-service is the mechanism to make data discoverable. Without it, every user and every model must independently hunt down the same information, creating redundant effort and slowing the entire pipeline from raw data to insight.
The numbers bear this out: among organizations that are completely successful with BI, 46% are also completely successful with self-service — and that figure climbs to 87% when including those who are somewhat successful.
Success, however, is not just about technology investment. It hinges on being explicit about the business problems self-service BI is meant to solve, and then systematically aligning the right data, governance and context to support the desired outcomes.
Using a semantic layer to power self-service BI is essential. Winners completely eliminate the last mile of technical barriers to data access.
Agentic AI Moves From Search to Action
This is where agentic AI enters. Self-service BI is increasingly extending beyond discoverability to the creation of tailored analytics. Agentic AI enables direct delivery of real-time, contextual data to decision-makers and intelligent agents that automate business processes.
These systems seek to abstract away the complexity of data retrieval and do away with the need to entirely understand data relationships and hierarchies — a simple, prompt-like interface surfaces exactly what a user needs, without requiring them to know where the data lives or how it is structured. This feature alone dramatically broadens the audience for self-service BI, putting analytical power in the hands of users who were previously locked out.
The emerging ability to autonomously identify relevant sources, construct and run scenarios, evaluate analytical outcomes and apply statistical models — not as isolated tools, but as a continuous, reasoning-driven workflow — can drive transformative change. Taken together, agents that are emerging will have the ability to find data, determine context, apply reasoning and close the loop, transforming self-service from a user-driven search into an intelligence-driven conversation. Analytics is no longer simply a product built and delivered by business analysts; it is becoming a dynamic process that agents can initiate, adapt and serve in real time.
Analytics Becomes a Conversation
In this new world, business users will expect agents to render analytics directly from conversation, turning a prompt into live analytical output without requiring navigation through tools or schemas.
This transforms self-service from a series of isolated queries into an ongoing, intelligent dialogue. The progression is clear: moving from natural language access to conversational rendering of analytics to a sustained dialogue with analytical content that builds context and deepens over time. The future of self-service is not a better search tool — it is an intelligent partner that understands context, retains knowledge and helps users act upon data.
Jung Suh, vice president and head of the digital commerce team at Samsung Electronics, said at Snowflake Summit that self-service BI is going through a shift — "from the data team as a bottleneck, to every leader as their own analyst.” More importantly, she said, a self-service agent “doesn’t just retrieve data — it reasons across it. Our agent sits in the middle of a process. It continuously watches signals, proposes actions and routes them to the right teams.”
Humans Still Need to Set the Guardrails
Suh’s remarks reflect the fact that knowledge workers will not hand over the wheel entirely.
Fully autonomous behavior — systems that act, decide, and deliver without human configuration or oversight — remains a low priority for the majority of organizations today. Comfort levels rise sharply when humans remain in the loop at the point of configuration, shaping the parameters, defining the boundaries and setting the intent before the agents execute.
Collaboration Becomes Part of the Analytics Workflow
Collaboration is undergoing a parallel transformation. Although roughly 80% of businesses now rely on digital collaboration platforms like Slack or Teams — a dramatic jump from just over 50% in 2019 — enterprise collaboration tools account for only 10% of analytics sharing.
Collaboration platforms were not originally designed with data discovery in mind, so workers defaulted to tools already embedded in their workflows. However, that dynamic is beginning to shift. Self-service and collaboration are both being fundamentally recast by generative AI, natural language interfaces and agentic capabilities. Think of agentic AI as the rocket and generative AI as the payload: one provides the intelligence, the other the autonomous motion.
Self-Service BI Now Serves Humans and Agents
As self-service takes on a new dimension, it must now serve two distinct masters: agents acting in service of humans, and agents acting in service of automated workflows.
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The vendors active in this space reflect just how wide-scale adoption of these technologies has become. They are no longer merely a competitive advantage, but a prerequisite for survival. And while we expect these categories to converge into a unified landscape within the next year, the broader opportunity lies ahead.
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The New Promise of Data Democratization
I expect momentum to accelerate as agentic solutions drive broader adoption and reduce the complexity that has held back self-service for so long. As these capabilities become widespread, accessing and using data should finally become much easier. This delivers on the original promise of data democratization.
While still early, the signal is clear: agentic AI is poised to fundamentally reimagine what self-service BI means, not as a set of tools that users query, but as an intelligent layer that works on user’s behalf. As this occurs, data will finally become the productivity enhancer that those of us in the data space imagined years ago.
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