Why AI is the missing layer in your modern data stack
Business Intelligence
Jun 17, 2025
Explore how AI enhances modern data stacks by breaking down silos, speeding analysis, and simplifying access for faster, smarter decision-making.
AI can transform your modern data stack by solving key challenges like data silos, manual workflows, and slow decision-making. Here's why AI is essential:
Breaks Down Data Silos: Automates integration across systems, ensuring unified and consistent data.
Speeds Up Analysis: Provides real-time insights and predictive analytics, reducing delays.
Simplifies Data Access: Enables natural language queries, so anyone can ask questions and get instant answers without technical expertise.
Reduces Errors: Automates repetitive tasks like data cleansing, improving accuracy and saving time.
Enhances Forecasting: Predicts trends and risks, helping businesses plan proactively.
For example, companies like Mastercard and Walmart have used AI to improve efficiency, accuracy, and decision-making, cutting costs and boosting productivity. By integrating AI into your data stack, you unlock faster processes, smarter insights, and better outcomes.
[AI and the Modern Data Stack] #183 Adding AI to the Data Warehouse with Sridhar Ramaswamy
What Is the Modern Data Stack and What Problems Does It Have
The modern data stack is a cloud-based system designed to handle the massive amounts of data generated today. By 2025, data creation is expected to surpass 120 zettabytes [2]. To keep up, businesses need systems that can efficiently collect, process, and analyze this information. Unlike older, on-premise setups, the modern data stack prioritizes automation, flexibility, and user-friendly analytics, making it easier to manage a wide variety of data. However, even with these advancements, it doesn't completely solve the challenges of delivering insights quickly.
With its reliance on scalable cloud technologies, the modern data stack supports real-time insights and quicker decision-making. Companies like Snowflake, AWS Redshift, and Databricks have embraced this cloud-first approach, offering platforms that are more adaptable and user-friendly than traditional systems [1].
Main Parts of the Modern Data Stack
The modern data stack is made up of several interconnected tools and platforms that turn raw data into useful insights. Here’s how it works:
Data Sources: Information comes from databases, APIs, application logs, and external services.
Ingestion Services: These tools automatically gather and load data into a central location.
Storage: Cloud data warehouses, data lakes, or lakehouses store the processed data.
Transformation Tools: Tools like dbt clean and organize the data for analysis.
Orchestration Tools: These ensure data moves smoothly through the system.
Business Intelligence (BI) Platforms: Tools like Tableau and Power BI help visualize the data. Other components, like reverse ETL tools, data science platforms, and data catalogs, add further functionality.
Problems with Getting Insights at Scale
Even with all these tools, turning data into actionable insights remains a challenge. Many organizations struggle with the complexity of managing their data systems, highlighting the need for smarter solutions, such as AI, to simplify and improve the process.
One major issue is tool overload. Over 70% of companies rely on more than 5–7 tools, which disrupts workflows [3]. This forces teams to constantly switch between platforms - 40% of respondents say they spend more than 30% of their time navigating between tools [3].
Data silos are another big problem. Highly data-driven organizations often deal with over 50 separate silos, leading to inefficiencies that cost businesses around $3.1 trillion annually in lost revenue and productivity [5][6].
Madison Schott, Senior Engineer at ConvertKit, explains the practical challenges:
"I think a lot of data teams run into the problem of, you know, creating metrics within their BI tool, creating metrics within something like HubSpot, or maybe another growth or marketing tool like Google Analytics. And then, you have three different answers for the same question." [3]
The financial toll is staggering. Manual reconciliation of data across systems can cost millions annually in operational expenses [4]. Poor data quality alone costs organizations an average of $12.9 million each year [5], and 68% of enterprise data goes unanalyzed [4]. On top of that, 40% of companies report that maintaining integrations between various tools is their most expensive challenge [3].
This creates a frustrating paradox: businesses invest heavily in cutting-edge data infrastructure but still struggle to get timely, actionable insights. Data teams often spend too much time on maintenance and troubleshooting, while end users face steep learning curves and the complexity of juggling multiple tools. The result? Missed opportunities and inefficiencies that undermine the potential of the modern data stack.
How AI Makes Your Data Stack Better
AI takes a scattered data stack and turns it into a cohesive, efficient system. The results? Faster processes, fewer errors, and smarter decisions. Companies adopting AI-driven data solutions report significant improvements in productivity and accuracy. For example, generative AI and automation can save employees up to 60% to 70% of their time [9], while labor productivity can increase by as much as 40% [7]. These changes lead to lower costs and quicker decision-making, making AI integration a game-changer for modern businesses.
Automation and Time Savings
AI steps in to handle repetitive and error-prone tasks, freeing up your data team to focus on more strategic work. By automating processes like data validation and quality checks, AI ensures consistency and reduces human error.
The financial impact of poor data quality is staggering - businesses lose around $3.1 trillion every year due to errors, many stemming from manual data entry [10]. AI tackles this head-on by automating these processes, significantly improving data accuracy.
Industries like healthcare and finance have already seen the benefits. Real-world examples show up to a 40% improvement in data accuracy and notable reductions in labor costs [10].
"AI and intelligent automation have revolutionized the workplace, helping people - and the businesses they work for - streamline complex workflows with speed and precision." - Salesforce [8]
AI also enhances data cleansing, boosting accuracy by 40% [10]. This not only cuts costs but also allows your team to focus on driving innovation within your data stack.
Natural Language Queries for Business Users
AI makes data accessible to everyone, not just technical experts. With natural language processing (NLP), business users can ask questions in plain English and get instant answers - no need to learn complicated query languages.
This shift is transformative. Imagine a marketing manager asking, "What was our customer acquisition cost last quarter?" and receiving a clear, immediate response. AI-powered tools structure these queries automatically, simplifying the process further [11]. Guided NLP systems also enhance speed and accuracy by suggesting queries and structuring syntax [11][12].
Take Querio, for instance. It connects directly to your data warehouse, translating natural language questions into SQL queries. Users can explore live data and view visualizations in seconds, while data teams maintain control through governance layers that define metrics and business rules. This approach eliminates delays, enabling faster decision-making.
Predictive Analytics and Forecasting
AI doesn’t just provide insights about the past - it helps you anticipate the future. By integrating predictive analytics into your data stack, you can move from reactive reporting to proactive planning. AI identifies patterns and trends, allowing you to make decisions before challenges arise or opportunities disappear.
The impact is hard to ignore. AI-driven forecasting reduces errors by up to 50% [9][15], and 57% of CFOs report fewer sales forecast errors thanks to AI [14].
Walmart offers a great example of this in action. The retail giant uses AI to analyze sales trends, customer preferences, and supply chain data, optimizing inventory levels across stores. This reduces stockouts, minimizes waste, and enhances customer satisfaction [16].
Similarly, FedEx uses AI to predict shipment volumes and potential disruptions. By adjusting staffing, rerouting shipments, and optimizing delivery routes in real time, they maintain reliable service even during unexpected challenges [15].
Healthcare is also seeing life-saving applications. At Johns Hopkins Hospital, an AI system called TREWS analyzes electronic health records to identify patients at high risk of developing sepsis. The system detects 82% of sepsis cases with nearly 40% accuracy, reducing patient mortality by 20% [16].
AI-powered forecasting sifts through massive datasets in real time, spotting patterns that might otherwise go unnoticed. This ability helps organizations shift from reactive decision-making to proactive strategies, identifying risks and opportunities early [13][14]. Gartner predicts that by 2028, half of all organizations will rely on AI to replace traditional forecasting methods [14], making predictive analytics an essential part of any modern data stack.
"AI is no substitute for human judgment – it's a tool that enhances our capabilities." - Dr. Fei-Fei Li, Co-Director, Stanford Institute of Human-Centered AI [16]
AI Solutions for Common Business Intelligence Problems
Traditional business intelligence often struggles with challenges like isolated data, tedious preparation, and delayed insights. These issues, compounded by data silos and manual processes, can slow down decision-making and limit business growth. AI offers a way forward by automating repetitive tasks and transforming how businesses access and use their data.
Here’s the reality: 82% of enterprises report that data silos disrupt their critical workflows [4]. These disruptions aren't just technical headaches - they translate into real costs and missed opportunities. AI-powered tools address these challenges by simplifying data processes and delivering insights directly to the people who need them. Let’s explore how AI connects fragmented data, reduces manual work, and speeds up decision-making.
Connecting Data from Different Sources
Data silos happen when departments store information in isolated, incompatible systems. This leads to inconsistent reporting, duplicated efforts, and missed opportunities that can hurt profitability [17]. AI breaks down these barriers by automatically aggregating and standardizing data from multiple sources, creating a unified dataset ready for analysis. Machine learning algorithms can then uncover patterns and insights that would otherwise go unnoticed [17]. Tools with natural language processing (NLP) make interacting with BI systems even simpler.
One standout technology here is data virtualization, which allows real-time access to diverse datasets without the need to create physical copies [18]. This eliminates the delays caused by transferring data between systems before analysis.
Take Querio, for example. This tool connects directly to major data warehouses like Snowflake, BigQuery, and Postgres. Instead of copying data, it allows users to ask complex questions that span multiple sources. For instance, a sales manager could ask, "How did our Q4 marketing spend impact customer acquisition across all channels?" and get a detailed answer that pulls from CRM, advertising, and financial systems - all through a single interface.
AI also enables real-time data sharing across departments, making collaboration easier. Marketing teams can access sales data, finance teams can pull operational insights, and leadership can view a complete picture - all without waiting for IT to generate custom reports [17].
Reducing Manual Work in Data Preparation
Data preparation is a notorious time sink, consuming over 40% of project time [19]. Analysts often spend more time cleaning and organizing data than actually analyzing it. AI flips this dynamic by automating tasks like data cleaning, transformation, integration, and enrichment.
AI algorithms excel at identifying errors and outliers, handling these tasks more quickly and accurately than manual methods [19]. They also match and merge related data from different sources, significantly reducing errors and saving time [19].
"AI can effectively automate both data preparation and data analysis, enabling business users in self-service BI environments to focus on strategic tasks that require their experience and business knowledge." – Donald Farmer, TreeHive Strategy [20]
By minimizing human error, AI ensures consistent data quality, which is critical for making sound business decisions [19]. It can also enrich datasets by adding new variables or insights from external sources, making the data more actionable [19].
Querio simplifies this process further by automating much of the behind-the-scenes work. When users ask questions, its AI agent translates plain language into SQL queries while applying a preconfigured context layer. This context layer includes table relationships, business definitions, and data quality rules, ensuring clean and consistent results without requiring the user to dig into the technical details.
Accelerating Decision-Making with Instant Insights
AI-driven BI tools deliver rapid analysis and real-time visualizations, cutting down on delays. These tools support anomaly detection, predictive responses, and data profiling while presenting complex information in easy-to-digest formats [21]. Interactive dashboards allow users to explore data on the fly, eliminating the need to wait for scheduled reports [21].
In December 2024, Pyramid Analytics integrated LLM tools into its BI platform, enabling non-technical users to get answers to complex questions. With GenBI, users can generate insights and dashboards in as little as 30 seconds [22]. This integration makes BI more accessible, allowing teams across departments - like product managers and finance directors - to ask questions in plain language and get timely, actionable answers [22].
Querio also delivers on this promise by connecting directly to live data warehouses. Users can generate accurate charts in seconds. Its drag-and-drop dashboards make it easy to track KPIs and tell data-driven stories, all without requiring technical expertise. Executives can explore performance trends or identify issues on their own, rather than waiting for pre-built reports.
How to Add AI to Your Data Stack
Integrating AI into your data stack requires a structured approach. According to a Gartner poll, 55% of organizations are now piloting or deploying generative AI [23]. This shift shows that many companies are moving past experimentation. However, achieving success hinges on proper preparation, selecting the right tools, and equipping your teams with the necessary training.
To build a strong foundation for AI integration, focus on three key areas: preparing your data, choosing AI-native tools, and empowering your team through effective training.
Check If Your Data Is Ready for AI
Before diving into AI implementation, assess whether your data infrastructure is up to the task. Poor data quality is a common reason why AI projects fail [24].
Start by auditing both your structured and unstructured data [26]. Since unstructured data accounts for 80% of enterprise data [25], you’ll need to address sources like emails, documents, and social media posts alongside traditional databases.
"Data is the lifeblood of AI." – Francesca Colenso, Director of Azure Business Group at Microsoft UK [23]
When assessing your data, focus on these critical areas:
Data Quality and Cleansing: Remove duplicates, fill in missing data, and correct errors to ensure consistency and accuracy [24]. Keep in mind that data preparation can consume up to 60–80% of a project's time [25].
Data Integration: Resolve discrepancies between data sources to enable seamless integration [27].
Infrastructure Assessment: Decide whether a cloud-first, on-premises, or hybrid setup works best for your business. Consider factors like data sensitivity, compliance needs, and existing tech investments [23].
Governance Framework: Develop policies to ensure data privacy, security, and compliance with regulations like the EU AI Act [28]. This includes tracking data lineage, setting metadata standards, and defining roles for data stewardship.
Regulatory compliance should be baked into your governance framework from the beginning. Once your data is optimized, you’ll be ready to select a platform that fully leverages your efforts.
Choose AI-Native BI Platforms
After preparing your data, the next step is selecting the right platform. Traditional BI tools retrofitted with AI features often fall short compared to solutions designed with AI at their core. Look for platforms that offer natural language querying, live data connections, and automated insights.
Querio is an example of an AI-native platform. It connects directly to major data warehouses like Snowflake, BigQuery, and Postgres without creating data copies. This reduces security risks and avoids delays caused by moving data. By eliminating silos, Querio provides real-time access to unified datasets, enabling users to ask complex questions in plain English and get accurate visualizations in seconds.
When evaluating platforms, prioritize these features:
Natural Language Processing: Allow users to interact with data through conversational queries.
Live Data Connections: Ensure the platform connects directly to your data warehouse, so users work with the most current information while maintaining security and governance.
Context Layer Management: Opt for tools that let data teams define business terms, relationships, and quality rules once, applying them consistently across all interactions.
Scalable Architecture: Select a platform that can handle growth in both user numbers and data volume without compromising performance.
Security and Compliance: Confirm that the platform meets your security needs, including SOC 2 Type II compliance, encrypted credentials, and read-only database access.
Train Your Teams to Use Self-Service Analytics
Having the right infrastructure and tools is only part of the equation. To fully unlock AI's potential, your teams need proper training. A lack of user training and change management is one reason why nearly 80% of AI projects never progress beyond proof of concept [31].
Training empowers your team to integrate AI into workflows and extract meaningful insights. Start with small-scale pilots to test AI applications before rolling them out organization-wide [29]. This approach allows you to identify potential hurdles, gather user feedback, and refine your strategy. Form cross-functional teams with diverse expertise to build comprehensive solutions [29].
Here’s how to set your team up for success:
Pilot High-Impact Use Cases: Focus on areas where AI can deliver immediate results, such as automated reporting, trend analysis, or anomaly detection [33]. Gradually integrate these tools into existing workflows [32].
Establish Best Practices: Encourage users to treat AI-generated outputs as drafts, refining them with human oversight and expertise [32].
Continuous Education: Provide ongoing training to keep stakeholders informed about AI capabilities and data governance. Share insights across teams to encourage responsible use [32].
Monitor and Optimize: Continuously track AI performance to address unexpected behavior and ensure the system meets expectations [30]. Regular updates to AI models will help them adapt to evolving business needs [29].
A Deloitte study revealed that nearly 70% of companies using AI plan to increase their investment in AI governance within the next two years [28]. This highlights the importance of establishing strong training and governance practices early in the process.
Collaboration between data and IT teams is essential for success [23]. By fostering open communication and feedback, you can ensure your AI implementation remains aligned with your business goals.
Security, Compliance, and Scaling with AI
When it comes to adopting AI, security and compliance are top priorities for executives. Over 75% of CISOs have expressed growing concerns about emerging AI-related security risks [39], while 81% of Americans feel that the risks of AI outweigh its benefits, particularly when it comes to data privacy [35]. To address these challenges, many AI-native platforms are being designed with built-in security features and scalable frameworks.
The goal is to choose platforms that prioritize security and compliance from the ground up, rather than treating them as secondary considerations. Modern AI-powered tools not only enhance organizational security but also scale effortlessly to meet growing demands. Let’s explore how these platforms incorporate security and compliance to manage risks effectively.
Built-In Security and Compliance
AI platforms are revolutionizing security and compliance by automating processes that traditionally required significant manual effort. For example, AI can reduce the time needed to detect and respond to security incidents by 60% [34] and help organizations achieve compliance rates exceeding 90% through continuous monitoring and early issue detection [34].
Take Querio, for instance. The platform adheres to SOC 2 Type II compliance and connects to data warehouses using read-only encrypted credentials, ensuring sensitive data remains protected through limited access and encryption.
Key security features offered by modern AI platforms include:
Data Anonymization and Pseudonymization: AI systems can automatically identify and secure sensitive data like personally identifiable information (PII). By 2024, AI-powered tools are expected to automate 70% of PII classification tasks [35], significantly reducing the manual workload.
Real-Time Monitoring: Continuous system analysis helps detect suspicious activity and mitigate potential breaches before they escalate [40].
Automated Compliance Reporting: AI can generate compliance reports automatically, cutting the time required for audit preparation in half and enabling faster responses to regulatory requirements [34].
A global retailer showcased these capabilities in 2024 by implementing an AI-driven data management system to comply with data protection laws across multiple markets. The system’s automated data classification, real-time monitoring, and predictive compliance alerts not only improved compliance but also enhanced operational efficiency and customer trust [35].
"AI cloud security compliance tools represent a significant advancement in safeguarding sensitive data while meeting regulatory requirements. By automating processes, enhancing monitoring capabilities, and providing valuable insights, these tools empower organizations to maintain compliance in an everchanging landscape." - Ashwin Chaudhary, CEO, Accedere [34]
The financial benefits are just as compelling. AI cloud security compliance tools can cut compliance costs by 30-50% [34], while proactive monitoring and real-time reporting reduce compliance breaches by 50% [34]. These platforms also scale effectively to handle growing data needs without compromising security.
Scaling for More Users and Data
Traditional business intelligence tools often struggle to accommodate large user bases and increasing data volumes. AI-native platforms solve this issue with advanced features like distributed computing, modular architecture, and intelligent resource management. By 2025, nearly 80% of enterprises are expected to adopt AI in some capacity, up from 55% in 2023 [39]. This highlights the growing need for scalable solutions that can expand without sacrificing performance or security.
For AI platforms, successful scaling depends on three core principles:
Modular Architecture: This allows individual components - such as query processing or visualization engines - to scale independently based on demand.
Distributed Computing: Tasks are spread across multiple machines, enabling the system to handle larger datasets and more users without bottlenecks.
Cloud-Native Design: Cloud platforms can automatically expand storage and processing power as needed, eliminating costly upgrades.
Querio exemplifies these principles. Its distributed query processing system supports unlimited viewer users while maintaining performance. Whether it’s a marketing team analyzing campaign data during a product launch or a finance team managing quarterly reports, Querio dynamically allocates resources to meet demand. Live connections to Snowflake, BigQuery, and Postgres ensure that scaling doesn’t compromise data freshness or security.
Additionally, Federated Learning allows AI models to train across multiple devices and data sources without centralizing sensitive information [38]. Organizations can also employ automated model retraining using new threat intelligence and drift detection algorithms to maintain AI performance as demands evolve [38].
"AI as a technology is revolutionary and transformative, but it requires maintenance and care to ensure it meets its full potential. Factoring in two key design principles, scalability and security, will enable your AI to grow with your business and adapt to new challenges as they emerge. To drive value and innovation for your business, build an infrastructure prepared for the future." - Redapt Marketing [36]
The investment in scalable AI solutions is already paying dividends. In 2024, spending on AI-native applications surged by 75%, with organizations averaging $398,271 in annual investments [37]. This growth underscores the value AI systems bring when designed to scale across enterprise environments.
When evaluating AI platforms, look for solutions that can meet both your current needs and future growth. Platforms with transparent pricing, flexible user management, and seamless integration with existing infrastructure are best positioned to support long-term success.
Conclusion: Get More from Your Data Stack with AI
The modern data stack has come a long way, but challenges like manual analysis, fragmented data, and slow decision-making can prevent organizations from fully thriving. AI isn't just another addition - it's the game-changing layer that elevates your data infrastructure.
According to Gartner, by 2025, 75% of organizations will have operationalized AI, a sharp rise from just 15% in 2022 [42]. This shift is fundamentally altering how businesses handle data and make decisions. Companies are moving beyond traditional analytics to adopt prescriptive intelligence, powered by AI, which enables real-time insights and boosts operational efficiency [41].
AI offers immediate, tangible results by automating data integration and delivering real-time predictive analytics [42]. For example, AI-driven solutions are helping organizations cut costs by up to 20% and reduce downtime through predictive maintenance [43].
This is where platforms like Querio stand out. As an AI-native solution, Querio simplifies the gap between business users and complex data systems. With natural language querying, teams can ask straightforward questions like, "What was our customer acquisition cost last quarter?" and instantly receive clear, accurate visualizations. Live connections to data sources like Snowflake, BigQuery, and Postgres ensure insights are always up-to-date, while SOC 2 Type II compliance ensures security remains a priority.
AI’s strategic importance is undeniable - nearly 90% of business leaders acknowledge that AI is either a key part of their strategy today or will be within the next two years [44]. The real question isn't whether to adopt AI, but how soon you can get started. Start small with pilot projects to demonstrate value, prioritize data quality and governance, and focus on platforms that make AI accessible to everyone in your organization.
Organizations that embrace AI-driven intelligence early will set the pace for tomorrow’s market. By integrating AI into your data stack, you're not just organizing information - you’re unlocking a sustainable edge. Your data already holds the answers; AI is the key to turning it into the advantage you've been waiting for.
FAQs
How does AI help break down data silos in a modern data stack?
AI addresses the problem of data silos by using natural language processing (NLP) and machine learning (ML) to bring together data from various disconnected sources. This process creates a unified system, making it easier for teams across different departments to access and analyze shared information.
By automating the process of combining data and streamlining collaboration, AI breaks down the walls that often block information sharing. The result? Quicker decisions and more precise insights that can drive your business forward.
How does integrating AI into a modern data stack benefit non-technical business users?
Integrating AI into a modern data stack transforms how non-technical business users interact with data, making complex analysis much simpler and more approachable. AI allows users to quickly identify real-time trends and tap into predictive insights, all without requiring a deep technical skillset.
Beyond simplifying access to insights, AI takes over repetitive tasks, minimizes human bias in analysis, and accelerates decision-making by delivering clear, actionable recommendations. The result? Faster, more precise outcomes that help businesses navigate the demands of a data-driven world with greater efficiency and confidence.
How can organizations effectively integrate AI into their modern data stack?
To bring AI into your modern data stack, start by pinpointing specific use cases that directly support your business goals. Examples might include real-time product recommendations or predictive analytics for customer behavior. Make sure your infrastructure is ready to handle AI by setting up scalable hardware, reliable software, and strong data management systems that ensure data quality and security.
Take an iterative approach when rolling out AI applications. This means testing, refining, and keeping a close eye on performance to ensure everything stays in line with your objectives. Also, encourage collaboration between your technical teams and business leaders. This teamwork helps build a clear framework, making AI adoption smoother and ensuring it delivers real value to your operations.