Key Takeaways
* Real-life usage analysis of GenAI in the enterprise reveals that 73% of user queries reflect a need to deal with urgent, unexpected business situations.
* The adoption of GenAI tools leads to an “operational data boost.” Organizations using GigaSpaces eRAG report an 85% improvement in response speed to emerging situations, 60% faster crisis response times, and 40% better opportunity capture rates in sectors like manufacturing, logistics, retail, and pharmaceuticals.

The ability to respond to “business-as-it-happens” scenarios is no longer a luxury, but a critical competitive necessity. Traditional business intelligence (BI) systems, while excellent for planned reporting cycles, often fall short when urgent, unplanned events demand immediate data insights. 

GigaSpaces undertook an analysis of thousands of real-world questions submitted through eRAG, its GPT-style chat tool for operational business data in the first half of 2025. The research demonstrates that 73% of user queries reflect these urgent, unplanned business situations. This highlights a profound need for real-time decision support, especially at the operational frontlines, where delays can translate into significant revenue loss or customer dissatisfaction.

The challenge stems from a critical “operational structured data gap”. Even the most advanced Generative AI (GenAI) foundation models struggle to understand the context of complex structured business data and cannot execute SQL queries, the universal language of relational databases. This limitation prevents them from providing accurate, contextual responses, thereby limiting GenAI’s value in enterprise use cases. However, the same analysis shows that when the right tools are in place, forward-looking organizations are leveraging GenAI to transform their interaction with real-time structured data daily. This shift is driven by three distinct areas of real-time data demand: 

• Risk mitigation

• Opportunity optimization

• Real-time performance monitoring

Let’s explore how this transformation is unfolding across specific industries.

Manufacturing, logistics, and supply chain: how can GenAI drive agility and operational excellence?

The manufacturing, logistics, and supply chain sectors are constantly grappling with the inherent unpredictability of global supply chains. Here, the need for immediate, actionable data is paramount for maintaining agility and operational excellence. Users often query for freight margins by route, order status, and shipment delays. In manufacturing, questions frequently concern production volumes, raw material use, and output quality metrics, indicating an urgent need to monitor production in real time.

The key gaps identified in these sectors include a lack of real-time supply chain visibility during disruptions and the inability to rapidly respond to delivery issues and operational disruptions. Operations managers and procurement leads are often the “first responders” to such operational disruptions, requiring information that spans multiple systems and goes beyond pre-configured BI reports.

GenAI solutions like eRAG address these challenges by providing immediate, conversational access to inventory levels, supplier performance, and incident analysis. This capability transforms incident analysis and trend identification, allowing for proactive rather than reactive risk mitigation and crisis management. 

Retail: elevating customer-centric responsiveness

In the highly competitive retail landscape, customer-centric responsiveness is key to success. Retail professionals need to swiftly adapt to market shifts and capitalize on revenue opportunities in real time. A sales director, for example, might need to drive sales of excess or leftover inventory at a branch level. Their urgent question could be, “For all branches in the Midwest, show the slowest moving items per branch. Sort by highest to lowest margin”. This enables the quick launch of product promotions for high-margin, slow-moving items differentially across various branches, directly impacting revenue optimization.

eRAG offers immediate customer analytics and sales opportunity identification, enabling agile marketing responses. This transforms reactive relationship management into proactive opportunity identification and capture. 

Pharmaceuticals: how does GenAI Assist in ensuring compliance and performance?

The pharmaceutical industry operates under stringent regulatory requirements, where compliance and performance are non-negotiable. Access to accurate, real-time data is critical for both operational efficiency and regulatory adherence. The key gap identified is the need for rapid access to compliance data, especially during audits and inspections. Users are focused on the quick lookup of clinical and trial data relating to medications, upsell opportunities, and ongoing trials. Beyond compliance, sales directors in pharma also need real-time data for pipeline movement, top-performing reps, and customer growth numbers.

eRAG offers autonomous compliance monitoring and performance analysis. This enables quick lookups and analysis of clinical and trial data, sales performance metrics, and regulatory information, ensuring that organizations can respond effectively to audits and maintain high levels of performance. 

What is the broader impact of GenAI on operational data? 

Across all these industries, the adoption of GenAI tools like eRAG is creating an “operational data boost”. The ability to obtain immediate answers to unexpected situations, particularly those that require insights from diverse data sources, is transforming business operations. Organizations using eRAG report a remarkable 85% improvement in response speed to emerging business situations, leading to 60% faster crisis response times and 40% better opportunity capture rates compared to traditional BI-dependent processes. This autonomous data access with GenAI is fundamentally transforming how businesses interact with information, delivering on-the-fly insights from systems like Snowflake, Oracle, MSSQL, BigQuery, and SAP.

Operational Agility as the New Differentiator

The analysis of real-world data conversations reveals a critical gap that GenAI is uniquely positioned to fill: the need for real-time problem-solving beyond planned reporting. By enabling immediate, conversational access to live operational data, tools like eRAG empower professionals across manufacturing, logistics, retail, pharmaceuticals, and beyond to make agile decisions, proactively mitigate risks, optimize opportunities, and monitor performance continuously. As businesses navigate increasingly dynamic markets, the ability to engage with data conversationally and immediately positions them to stay steps ahead of both challenges and opportunities.

When businesses need ad-hoc answers to questions on their organizational data, a breakthrough approach is required. A solution such as GigaSpaces eRAG gives analysts, managers, and frontline staff a conversational interface to their operational data, allowing them to phrase questions in natural language and receive actionable answers on the spot, without schema redesign, backlog tickets, or lost momentum. Consider these real-world procurement examples:

• Tariff hike, freight spike and currency swing: If a procurement team had to wait for BI, model updates could take two weeks, leading to hours of manual spreadsheet work and potentially wiping out significant margin. With eRAG, a 3-minute natural-language conversation can show exact exposure, allowing renegotiations to begin the same morning, preserving margin.
• Lead-time extension, demand surge and tighter inventory policy: Relying on BI for this scenario could mean waiting for monthly safety-stock logic refreshes and planners burning days on calls and manual Google Sheets. eRAG enables a 2-minute conversation to recalculate stock-out dates, allowing purchase orders to be adjusted before the night shift, avoiding losses and extra costs.

While BI is an excellent solution when the market behaves as modeled, eRAG steps in the moment it doesn’t, transforming hours or weeks of manual scramble into minutes of clear, data-backed insights. 

• Conversational Abilities: Whereas BI is limited to the scope of its data model, with eRAG answers are derived from multiple data sources, requiring no modeling, and are built for ad-hoc, spontaneous conversations.
• User Accessibility: Requiring no technical knowledge, users can ask questions in plain English, without needing to learn proprietary programming languages like DAX for Power BI.
• Data Sources and Flexibility: Supports multiple data sources that can be easily updated, allowing blending of data from ERP, WMS, freight APIs, and FX tables in a single prompt without data-lake preparation. Adding new data sources is a matter of a few clicks, whereas changing BI’s data scope is a complex and lengthy project.
• Real-time Data: Delivers fresh, up-to-date answers based on operational data, in contrast to BI, which is primarily based on historical data.
• Rapid Deployment: Pilot environments can go live in less than four hours with a lightweight connector and metadata map. In contrast, BI backlog wait times for a new metric can be 10-30 days.
• Scalability: eRAG pushes optimized SQL to the database itself, scaling with the data warehouse rather than being limited by client memory, which can be an issue for in-memory BI models when extracts exceed RAM.
• Security and Governance: eRAG is read-only and honors existing database roles and row-level security, ensuring no extra governance is needed. All prompts and generated SQL are logged, providing tighter lineage than many BI tools.

The Interplay: Generative AI and the Data-to-Insight Workflow

Modern BI platforms are increasingly leveraging Generative AI (GenAI) to enhance productivity for both developers and consumers of analytics content. GenAI augments the data-to-insight workflow through low- and no-code features. For developers, GenAI assists in creating metrics, models, visualizations, reports, and dashboards. For consumers, its focus is on extracting insights and meaning from data to support informed decision-making. This pursuit of GenAI capabilities has lowered the barrier to adoption for BI platforms, democratizing functionalities and making them more accessible to a broader audience.

Which leading BI vendors are integrating advanced AI and NLQ/NLG capabilities? 

• Amazon Web Services (AWS) introduced scenario modeling in Amazon Q for QuickSight, enabling agentic trend analysis, forecasting, and solution exploration, with Amazon Q’s Q&A capabilities included in licenses.
• Domo is investing in secure and transparent AI workflows, allowing businesses to manage AI models and leverage prebuilt or custom agents to interact with data in natural language.
• Google’s Looker enhanced self-service analytics by incorporating Looker Conversational Analytics, allowing business users to easily interact with data, with Gemini included in all versions.
• IBM Cognos Analytics has invested in AI assistants and agents with IBM watsonx BI, a GenAI analytics agent intended to enable a wide range of analytics and reporting capabilities using natural language.
• Qlik recently introduced a new agentic AI framework to enhance user experiences, enabling features like conversational analytics, automated authoring, and autoML.
• Salesforce’s Tableau introduced Tableau Next, an agentic analytics platform with an AI semantic layer for interpreting data in a business context, delivering visualizations embedded in workflows, and supporting conversational analytics through agents.
• ThoughtSpot introduced Spotter, its agentic analytics feature for conversational analytics, and provides robust automated insights through SpotIQ, which automatically searches for anomalies in dashboards.

The Crucial Distinction: Where eRAG’s Agility Stands Apart

While many BI platforms now integrate LLMs and offer natural-language-first interfaces, a crucial distinction remains: the AI/NLQ features within traditional BI platforms often rely on the pre-defined semantic model.

• Power BI’s Copilot, for instance, “still relies on the semantic model; if the metric or table isn’t in the model, Copilot can’t invent it”. Moreover, it tends to answer each prompt independently, lacking the ability to maintain full conversational context across follow-up questions.
• Tableau’s Ask Data also “still hits a pre-defined BI model that is curated with extract refreshed nightly; if the field isn’t in the extract, you wait for a traditional BI release cycle”. Even Tableau’s Data Virtualization, while solving “plumbing,” still requires someone to design the BI model and perform data manipulations.
• Qlik Sense’s self-service capabilities operate “only on pre-loaded data”.
• ThoughtSpot’s search and NLQ are “limited to data-model objects pre-modelled by the BI team”.

This is the fundamental difference that positions eRAG uniquely for the “business-as-it-happens” world. eRAG can query any table or view the user has rights to, letting business teams adapt in real time without waiting for data-team sprints. Its multi-agent architecture is designed to:

• Pull live fields from any authorized source.
• Remember the full conversation, enabling a fluid dialogue for multi-step questions (e.g., “Show landed cost… now add the new tariff… now filter to EU suppliers only” runs without rebuilding visuals or queries).
• Run multi-step “what-ifs” and stitch results together on the fly.
• Auto-generate joins from a plain-language prompt, saving hours or days of data-engineering work each time variables change.

When real-life variables collide – tariffs, freight, foreign exchange rates, supplier lead-times – eRAG adapts in minutes without waiting for a new BI extract or data-model rebuild, ensuring businesses remain aligned with current reality rather than an outdated schema. This capacity to handle queries beyond pre-modeled data saves analysts hours of manual work per incident building one-off data visualizations or reports.

Dive into in the findings of this report in our webinar  Unlock 85% Faster Insights with Chat GPT & Operational Data.

What does the Future of Data Analytics look like? 

It’s crucial to understand that eRAG does not imply the obsolescence of traditional BI, it’s not an “either/or” decision.

• BI owns the planned routine: It provides the essential discipline for structured, routine operations, enabling deep historical analysis and standardized dashboards based on carefully designed data models.
• eRAG owns the rapid-change zone: It delivers the agility to respond to unexpected, unplanned situations, providing fast, ad-hoc answers drawn from live operational data.

The future of data analysis is not about choosing one over the other, but about intelligently integrating both to build enterprises that are both strategic and infinitely adaptable. They will continue to rely on robust BI platforms for strategic planning, performance monitoring against established KPIs, and in-depth historical analysis. Simultaneously, they will empower their teams with an eRAG solution to navigate the complexities of “business-as-it-happens,” turning unscripted challenges into immediate, data-backed actions. Together, eRAG and BI create a dynamic and resilient approach to decision-making in an increasingly fast-paced business world.