In most organisations, data is abundant, and yet trusted insights remain scarce. Nearly 40% of CFOs say they cannot fully trust their organisation’s data1. At the same time, consultancies note that dashboards are reaching the end of their usefulness, with static dashboards likely to give way to intelligent conversational systems2.
Against this backdrop, Snowflake Intelligence offers a simple idea: what if you could talk to your data directly and get clear, contextualised answers?
Enterprise Data Challenges that Snowflake Intelligence Addresses
Data Volume and Variety
As organisations grow, data becomes scattered across databases, spreadsheets, documents, and logs. Querying structured tables is no longer enough at enterprise level. From individual contributors to business leaders, data users are now looking to unlock insights from unstructured content and join it together.
Most enterprises still lack a unified way to do this.
Lack of Shared Understanding
Even when the data is there, aligning teams is difficult.
Decisions are made in conference rooms, but the context often gets lost as information moves through the organisation.
Many enterprises still have not even modernised their data estates, using legacy systems that make reporting slow and siloed.
With limited access to the insights and pressing data needs, the real question is: How do we democratise data safely and consistently?
Context and Business Semantics
There are business nuances that data users debate. ‘Quarterly sales’ might mean total product revenue to one team, but only services revenue to another. These inconsistencies easily cause confusion and erode trust in data.
Naturally, machines struggle even more. In order to reason accurately, an AI agent needs to understand the organisation’s unique business language. Many enterprises are yet to ensure both the infrastructure and the internal alignment that allows this.
Resource and Cost Pressures
Data teams are stretched thinly, and expanding them is not always feasible. It is for this reason that many organisations settle for dashboards and manual processes rather than adopting more intelligent systems, even if they can clearly see the benefits.
Trust, Traceability, and Observability
Once you deploy agents, you need to measure their accuracy and monitor evolution. Just like employees, AI agents need training, guidance, and improvement. This takes a robust feedback loop, which is the main driver of trust in machine-generated insights.
The Shift: From Dashboards to Intelligence
Dashboards show what has happened, but they cannot understand or reason about the data. Businesses are shifting towards agentic intelligence – systems that interpret context, analyse data, and act. By 2026, 75% of enterprises will be leveraging context-aware conversational data experiences3.
This is where Snowflake Intelligence enters the picture.
Introducing Snowflake Intelligence
Snowflake Intelligence reimagines how organisations interact with data. Instead of forcing users to navigate complex dashboards or write SQL queries, Snowflake Intelligence allows teams to ask questions in natural language and get trusted, contextual answers, directly from their enterprise data.
Snowflake Intelligence combines verified business concepts with AI-driven reasoning, so everyone speaks the same data language. From databases to PDFs and Slack messages, the solution unifies structured and unstructured data to offer the governance and security that Snowflake stands for.
How It Works
Everything you do in Snowflake Intelligence is powered by Cortex Agents, orchestrating tools like Cortex Analyst (text‑to‑SQL over Semantic Views), Cortex Search (retrieval over indexed unstructured content) and custom tools with governance enforced by Snowflake policies.
- Cortex Analyst tool:
- Translates natural-language questions into accurate business and data queries;
- Understands company-specific definitions in the semantic layer, such as ‘sales of products’ vs ‘sales of services,’ and ensures responses align with verified business metrics.
- Cortex Search tool:
- Scans unstructured and semi-structured data across internal and external sources, such as contracts, internal docs, emails, Slack threads, operational data, third-party APIs, and even weather or logistics feeds;
- Surfaces relevant context.
Together, these tools provide a 360-degree view of business performance and context, helping users ask better questions and take action quickly.
Measuring Agents Like People
One of the biggest barriers to enterprise adoption of AI is trust. Saying that leaders need AI to produce answers is an oversimplification. What they actually need is transparency, auditability, and the confidence that the system is improving over time.
Snowflake Intelligence addresses this head-on by observability, traceability, and feedback capture to evaluate and improve agent outputs over time.
Just like employees and performance reviews, agents are evaluated and improved over time, across metrics such as:
- Answer correctness: Accuracy of final response;
- Logical consistency: Reasoning follows expected logic;
- Drift: Deviation from expected reasoning;
- Latency: Speed of response;
- Traceability: Visibility into how answers were produced;
- Recall & precision: Correct and relevant use of data;
- Tool selection: Picking the right tool for each task.
Trust, Traceability and Continuous Improvement
In traditional AI tools, answers can feel like a black box. Snowflake Intelligence makes all output explainable, emphasising visibility into reasoning steps and auditability when it comes to decisions.
It also ensures that the agent learns from experience, which turns weaknesses into training opportunities.
This empowers enterprise AI to evolve from a black box into an explainable, fully auditable system.
The Business Value
The tangible business value that Snowflake Intelligence delivers is based on:
- Faster time to insight: Snowflake Intelligence enables enterprise data teams to reduce dependency on manual data work;
- Actionable intelligence: The technology moves strategically from descriptive to prescriptive recommendations;
- Democratisation of data: Every employee is empowered to leverage the system to ask relevant questions and get reliable answers to act on;
- Operational efficiency: Analysts’ manual workloads decrease, so they can focus on strategic tasks and accelerate insights;
- Governed AI: The system maintains security, compliance, and transparency within Snowflake’s trusted architecture.
Despite years of investment in analytics, most organisations still don’t feel genuinely insight-driven.
In fact, less than half of finance and operations leaders report feeling “very satisfied” with their ability to make informed, data-based decisions, and only 40% feel confident combining data across functions to generate deeper insight4.
If we look back to the years before AI became widely available, we can see that enterprises have not been making considerable progress. In 2019, fewer than 4 in 10 executives considered their company advanced in analytics maturity. Although it was only the organisations with the strongest data-driven cultures that were twice as likely to significantly exceed their business goals5, investment alone would not guarantee success.
Now, while 82% of organisations increase their investments in data & analytics, only 37% of leaders feel that the efforts to improve data quality have been successful. For this reason, it is not surprising that only as little as 4.7% of organisations have implemented Generative AI in production at scale6.
Clearly, there is a gap between data and usable intelligence. This is exactly what Snowflake Intelligence aims to address.
How to Get Started with Snowflake Intelligence
Getting started with Snowflake Intelligence is easy when you have the infrastructure to enable enterprise AI.
Here is an overview of the process:
- Assess your data readiness: Ensure key data sources (structured and unstructured) are accessible and governed in Snowflake;
- Start small: Pick a single, high-impact question or workflow to pilot Snowflake Intelligence;
- Build your first agents: Define business concepts clearly and test how agents reason with your data;
- Monitor and refine: Use Snowflake Intelligence’s built-in traceability features to evaluate agent performance and continuously improve results;
- Scale responsibly: As trust grows, extend Snowflake Intelligence across departments, emphasising strong governance.
Final Word
Snowflake Intelligence goes beyond analysing data to fully understands it, reason through it, and helps teams make better decisions, faster. In doing so, it redefines what it means to be a data-driven organisation.
The business journey from dashboards to intelligence is well underway. Enterprises that thrive in this AI-powered era will be the ones that can talk to their data, trust their insights, and act with confidence.
Infinite Lambda is a Snowflake Intelligence Launch Partner. We help modern enterprises migrate to robust data platforms, enable AI, and adopt well-governed agentic capabilities. Reach out to get started.
References:
- Brewer, Kevin. “Finance leaders have trust issues with their data”. Journal of Accountancy. 2024.
- Spiegel, P. "A future without dashboards?". KPMG. 2025.
- Ellis, S, et al. “IDC FutureScape: Worldwide Manufacturing 2024 Predictions”. IDC FutureScape. 2023.
- “Value of connection”. KPMG. 2022.
- Davenport, T., Guszcza, J., Smith, T., Stiller, B. “Analytics and AI-driven enterprises thrive in the Age of With”. Deloitte Insights. 2019.
- Bean, R. & Davenport, T. “2024 Data and AI Leadership Executive Survey”. Wavestone. 2024.
