A CIO arrives at the office and opens the company’s Analytics Hub. The management team has asked him to analyze the organization’s current IT spend and explain how it has evolved. The management teams’ interest is clear: IT spending has increased over the past few years, and they want to understand what kind of value they are getting in return.
The CIO types a prompt into a chat window and asks a BI assistant for analysis behind the increase: is it a one-off spike, or a systematic rise in the baseline budget and what value has the increased expenditure produced?
A simple prompt is enough to trigger AI-driven data discovery across the most relevant information streams: spend data, project data, IT usage metrics, and other key datasets. The assistant composes a clear data story, combining visualizations with concise narrative explanations and meaningful titles.
Reviewing the report, the CIO sees the picture clearly. Some of the cost growth has been driven by one-off initiatives to establish strong data foundations. But part of the growth reflects something more structural: a sustained increase tied to broader adoption of data and AI across the business.
Crucially, the report also highlights that the ROI has been positive and offers insights on which business units have been at forefront of AI adaptation and with which AI tools. This information can be used to pinpoint where the value is generated, and which initiatives should be prioritized.
Revenue and profitability have grown as teams adopt better ways of working and new data- and AI-enabled capabilities. With one click, the CIO generates the data story in presentation format, sends the presentation to the management teams, and is now ready for the management meeting.
The discussion that follows is no longer about isolated cost lines, but about how analytics and AI are changing the organization’s operating models and processes. Through AI-based analytics the conversation switches from what has happened to what does this mean and what the organization should do next. This is the essence of modern data-driven leadership.
Analytics is shifting from reporting to understanding
AI’s impact on organizational decision-making has been widely discussed, and business intelligence is no exception. Industry analysts consistently point in the same direction. For example, Gartner estimates that by 2027, 75% of new analytics content will be contextualized for intelligent applications through GenAI and by 2028, GenAI-powered narratives and dynamic visualizations will replace 60% of traditional dashboards.
These projections may seem ambitious, but the trajectory is clear: natural-language, on-demand analytics will increasingly become the default way to understand performance and support data-driven decisions.
The exact pace of this shift is difficult to predict. But for most organizations, the key question is no longer if analytics consumption will change, but when and how prepared they are when it does.
Analytics and BI platform vendors have responded quickly to GenAI’s disruption and now treat AI capabilities as table stakes. Gartner’s and BCG’s research emphasize innovation in agent and agentic capabilities, such as natural language query (NLQ) and natural language generation (NLG).
These advances enable users to create reports, insights, and even technical outputs like data models and dashboards through natural-language prompts via AI assistants. As a result, analytics is becoming more accessible to also non-technical users enabling true self-service analytics while still relying heavily on robust data foundations and governance underneath.
At Norrin, we have already seen this shift happen with our clients and Gartner reports the same. More than half of analytics and AI leader respondents report that they are already using AI-enabled tools for NLQ (54%) and automated insights (57%) in analytics development.
Six areas where AI will reshape BI
So, what trends and capabilities will AI enable next? From Norrin’s perspective, AI’s impact on BI will be felt most strongly in six areas:
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1. Data Storytelling In the future analytics is at the fingertip of every business decision makers. AI enables the ability to create on demand data stories with clear visualizations, evidence, rationale and insights. |
2. AI Driven Data Discovery AI helps users to automatically find relevant insights from the data. It highlights important trends, anomalies, and background factors to support decision-making. |
3. Data Sharing 72% of analytics managers sees data sharing a key. By demolishing silos and promoting data democratization organizations can capture full benefits of AI analytics. |
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4. Natural Language Query NLQ enables users to discuss with their data when conducting analytics or root cause analysis. It offers acts a sparring partner and provides clear answers for specific questions. |
5. Natural Language Generation NLG produces written, spoken or illustrated content allowing user to specify the tone and context. It support complex analysis and contextualize the output based on user preferences. |
6. Data Cleansing 30% of data collected by organizations is inaccurate. Utilizing AI to automate data cleansing effort can help organizations to keep single source of truth intact. |
Together, these capabilities are accelerating the move away from static reporting toward more contextual, conversational, and decision-oriented analytics.
The real shift in BI and what it means for data and analytics leaders
While it is tempting to frame recent developments as a move from dashboards to chat interfaces, that framing misses the point. The most important shift enabled by AI in business intelligence is the operating model.
As AI takes over more of the mechanical work, BI teams spend less time building static artifacts and more time designing trusted decision experiences. This includes governed semantic layers, reusable metrics, well-defined business context, and AI-ready data products. Skill requirements shift accordingly, toward promptable analytics design, deep business understanding, evaluation and monitoring of AI outputs, and strong stewardship of data quality and meaning.
This operating model shift is moving the BI conversation from tools to trust and from outputs to outcomes. The organizations that benefit most from AI-powered analytics will not necessarily be the ones that adopt new features first. They will be the ones that invest deliberately in data foundations, operating models, and decision-centric design.
Even with rapid progress, dashboards will not disappear overnight. However, next-generation BI tooling raises the bar for what sits underneath. To fully benefit from AI-enabled analytics, organizations need:
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Strong data foundations: clear practices for governance, access, and ownership
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Robust, scalable data platforms
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Appropriate processes, platforms, and governance for AI, so that solutions access data in a controlled, secure, and auditable way.
As with many AI-driven changes, organizations that treat this shift as purely technical will struggle. Those that approach it as an operating model question are far more likely to succeed.
Curious where your analytics stands today? Send me a message and let's have a chat!
Norrin works with organizations facing exactly this shift. Helping leaders move from tool‑centric BI to trusted, decision‑centric analytics. Whether you are strengthening your foundations or moving toward the cutting edge with conversational analytics assistants, Norrin can support you on the journey.
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