Explainability Matters in AI-Driven Analytics

The AI Trust Problem

Artificial Intelligence (AI) has transformed how we analyze data, but there is one critical flaw in the system: users don’t trust the AI output.

And why should they trust it? To most business users, AI remains a black box that produces outputs without evidence or explanation, and I’m sure many remember the AI “hallucinations” that never completely went away. To make matters worse, most decisions made by business leaders are high stakes. They wouldn’t be thinking about it otherwise. This means that even if an AI could provide valuable insights, save time, or otherwise help the process, decision-makers may shun the AI because of the possible downside of relying on something they cannot validate themselves. This poses a challenge, because as the AI outputs become simpler and clearer for the business user, the outputs also become correspondingly less transparent and require a bigger leap of faith. In essence, trust and simplicity appear as opposites.

The field of Explainable AI (XAI) aims to address this. XAI aims to mitigate risks such as:

• Hidden biases baked into the AI generation process that go unnoticed until they cause major harm
• Failed regulatory or compliance audits due to the inability to produce necessary proof points
• Loss of credibility with users or stakeholders when the conclusions from the AI are not explainable

At Gigasheet, we believe in making analysis accessible to everyone. So, an AI for analysis should always show its work in a way that is easy to grasp even without deep knowledge of SQL or other technical fields. This is why we built the Gigasheet AI Agent to operate with full explainability and transparency at enterprise scale.

Explainability Matters in AI Analytics

Many AI-powered analytics tools promise to automate data analysis, but they often introduce more questions than answers. Consider the three main AI approaches used in enterprise data today:

1. AI Retrofits (Surface-Level Assistance, No Depth)

AI features bolted onto BI dashboards (e.g., Tableau’s AI Assistant) let users ask simple questions like, “Which region had the highest sales?” The problem is, they don’t go deeper than what the dashboard offered already, so they cannot help with complicated questions about “why” or “how” something changed.

Gigasheet’s AI Agent goes further than just pulling a surface-level number. It performs interactive, step-by-step analysis on the full row-level details, and users can inspect, modify, and refine the analysis in a spreadsheet-like interface.

2. Consumer AI (Great at Chatting, Not at Scaling)

AI like ChatGPT has become adept at analyzing small datasets, in great detail, with the ability to discuss deep questions and explain its process for transparency. The issue here is that it lacks enterprise-scale data integration, governance, and explainability. In addition, the typical approach of ChatGPT is to generate Python, which means a real validation of the process is only possible by reading the code.

Gigasheet eliminates this issue. Every step of the AI’s process is visible, editable, and interactive in a spreadsheet-like interface with no code, allowing business users to verify and refine in real time, and Gigasheet operates with enterprise scale and governance.

3. Code Copilots (Powerful, But Not for Business Users)

SQL-generating copilots (e.g., Snowflake Copilot) are great tools to help technical users produce code, but are useless to business leaders because these copilots operate entirely at the level of source code. 

Gigasheet bridges this gap, allowing business users to receive explainable and transparent answers from AI without reading or writing any SQL.
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How Gigasheet Makes AI Explainable

Gigasheet's AI Agent is designed for deep analysis with full transparency. Here’s how we do it:

1. AI That Shows Its Work

The Gigasheet Agent doesn’t just give you an answer, it also presents the analytical process visually on an interactive spreadsheet canvas that it shares with the user. All analytical actions, like filters, aggregations, transformations, etc. are applied on the spreadsheet and can be inspected and modified as desired.

2. AI Uses The Same Tools As You

Unlike AI that generates Python or SQL, Gigasheet’s AI uses the same actions as you would when doing data analysis. Users don’t have to trust the code, they can see in the visual interface how the AI filtered and aggregated data to arrive at its stated outputs. The AI collaborates with the user to build the analysis in the shared spreadsheet canvas, rather than producing unverifiable statements.

3. Enterprise-Scale, Secure, and Governed

Because Gigasheet integrates directly with enterprise data warehouses (e.g., Snowflake or BigQuery), it respects existing security and governance policies, and can operate at the scale of the warehouse. There is no special access for the AI, meaning all analytical actions by the AI and user collaborating together become API calls to Gigasheet’s scalable and secure backend.

The Gigasheet AI Advantage

Following these steps, the Gigasheet AI Agent provides several key advantages over all other existing AI solutions for data analysis:

• Explainability: See exactly how the AI reached its conclusions in a visual spreadsheet-like interface.
• Collaboration: Modify or refine the analysis with point-and-click controls, picking up right where the AI left off.
• Granularity: Interact with the AI to pick apart the data in full detail, with as much depth as needed in the analysis.
• Security: Data remains in the warehouse without exporting to third-party systems.
• Governance: No special access is given to the AI and warehouse data governance remains in place on the data at all times.
• Scale: All analysis actions are performed via Gigasheet’s backend and are executed on the warehouse, using the full computing power of the warehouse itself.

Gigasheet's AI Agent delivers the best of all worlds, bridging the gap between AI’s analytical power and the trust business leaders need to act on its insights.

Check out our whitepaper to learn more about applying AI for Big Data Analysis