February 11th, 2026
AI in Analytics: What It Is, How It Works, and a Top Example
By Zach Perkel · 16 min read
What is AI analytics?
AI analytics uses artificial intelligence to help process, analyze, and interpret large, complex datasets. The technology relies on machine learning (ML), natural language processing (NLP), and related statistical models to find patterns and generate insights.
In practice, AI analytics is used for 3 main types of work:
Analyzes what happened: Processes historical data to identify patterns and correlations across many variables.
Predicts what will happen: Uses machine learning models to forecast outcomes like sales trends, customer churn, or equipment failures.
Recommends what to do: Suggests specific actions based on patterns, such as which customers to target or when to reorder inventory. I’ve used this approach in campaign planning to rank prospects by conversion likelihood.
For example, traditional analytics might show you that sales dropped 15% last quarter. AI analytics goes further by identifying which factors caused the drop, predicting next quarter’s performance, and recommending specific actions to reverse the trend.
4 Types of AI analytics
AI analytics breaks down into four main types based on the questions they answer. Each builds on the previous one, starting with understanding what happened and ending with recommendations for what to do next. Most AI analytics tools focus on one or two of these types, while some handle all four.
Let’s break down the 4 common types of analysis:
1. Descriptive analytics: What happened?
Descriptive analytics summarizes historical data to show what took place over a specific time period.
This is the most basic form of analytics and often serves as the entry point for AI analytics, answering questions like “which products sold the most in December?”
The AI processes raw data from your systems and organizes it into reports, dashboards, and visualizations. You get charts showing trends over time, breakdowns by category, and comparisons between different metrics. For example, a sales dashboard might show revenue by region, product performance, and monthly growth rates.
This type works well for regular reporting and monitoring business health. Marketing teams use it to track campaign performance, finance teams monitor spending patterns, and operations teams watch fulfillment metrics.2. Diagnostic analytics: Why did it happen?
Diagnostic analytics digs deeper to find the root cause behind trends you see in your data. When descriptive analytics shows sales dropped 20% in the Northeast region, diagnostic analytics explains why by examining correlations and relationships across your data.
The AI tests hundreds or thousands of variables to identify which factors contributed most to the outcome. It then might discover that the sales drop correlated with a pricing change, seasonal weather patterns, or increased competitor activity in that region.
I've found this valuable when something unexpected shows up in reports. You can ask "why did support tickets spike last week?" and get an analysis of contributing factors rather than manually checking every possible cause.3. Predictive analytics: What will happen?
Predictive analytics uses machine learning models to forecast future outcomes based on historical patterns. The AI identifies trends in your data and projects them forward to estimate what’s likely to happen next quarter, next month, or next year.
Common predictions include:
Sales forecasting: Estimates future revenue based on past sales trends, seasonality, and recent performance changes. Teams use this to set targets and plan budgets.
Customer churn likelihood: Predicts which customers are most likely to stop using your product or service, often based on usage patterns, engagement drops, or support activity.
Inventory needs: Forecasts how much stock you’ll need in upcoming weeks or months to avoid overstocking or running out, based on demand patterns and lead times.
Equipment maintenance requirements: Predicts when machines or systems may fail by spotting early warning signals in performance or usage data, helping teams schedule maintenance before issues occur.
For example, a predictive model might start with three years of sales data to understand baseline trends. It then adjusts for seasonality, recent marketing spend, and broader economic conditions to estimate what revenue is likely to look like next quarter.
The model’s accuracy depends on data quality and how stable your patterns are. Predicting next month’s website traffic works better than predicting customer behavior two years out because shorter timeframes involve fewer variables that can change.4. Prescriptive analytics: What should we do?
Prescriptive analytics can recommend specific actions to achieve your goals. This is the most advanced type because it combines pattern recognition with optimization to suggest the best course of action.
The AI analyzes multiple scenarios and their likely outcomes, then recommends which path gives you the best results. For instance, it might suggest which customers to target for upsell campaigns, how to allocate marketing budget across channels, or when to adjust pricing.
I've used this for campaign planning where the system ranked prospects by conversion likelihood and recommended personalized offers for each segment. The recommendations included confidence scores so I could prioritize high-probability opportunities.
How AI in analytics works
AI analytics follows a basic workflow that turns your business data into analysis without requiring technical skills. The process varies slightly depending on the platform, but most follow these core steps:
1. Connect your data
You start by linking the AI analytics tool to your data sources. AI data analysis tools like Julius allow you to connect to databases like Postgres, Snowflake, or BigQuery. You can also upload CSV or Excel files, or integrate with platforms such as Google Ads and Stripe.
The system needs read access to your data but doesn't move it anywhere. The data stays in your database, and the AI queries it directly.
2. The system maps your data structure
Once connected, the AI analyzes your database schema to understand how tables relate to each other, what each column represents, and what data types you're working with. This mapping process happens automatically in the background.
Some platforms save this mapping and build on it over time. Others start fresh with each query. For example, Julius uses the persistent learning approach, which means the system gets better at finding the right data as you use it more. The AI remembers that your "revenue" field connects to the sales table and that "customers" links to orders, so future queries run faster and more accurately.
3. Ask questions in natural language
You type questions like "show me sales by region last quarter" or "which customers churned in the past six months." Natural language processing interprets what you're asking and translates it into a data query.
The AI determines which tables to query, what calculations to perform, and how to structure the results. You don't see any of this technical work happening.
4. AI generates the analysis
The system writes SQL or Python code to pull your data, runs the query, performs any necessary calculations, and creates visualizations. This happens in seconds for most queries.
Machine learning algorithms can also identify patterns you didn't explicitly ask about. For example, when I asked for sales trends across regions, the AI flagged that the Northeast consistently outperformed other regions during Q4. This led me to dig into seasonal factors I hadn't considered.5. Refine and explore further
Popular example of AI in analytics: Marketing performance analysis
To see how AI analytics works in practice, let's walk through a common business scenario where speed and flexibility make a real difference. In this example, a marketing manager needs to understand which campaigns drove the most revenue last quarter to decide where to allocate next quarter's budget.
Here's what the marketing manager could do using the traditional analytics approach:
The manager emails the data analyst with the request and waits 2-3 days for a response.
The analyst delivers a static report showing campaign performance.
If the manager wants to dig deeper or ask follow-up questions (like cost per acquisition or which customer segments responded best), another email goes out, and the wait begins again.
Getting the complete picture can take about a week.
And here's what would change with an AI analytics approach:
The manager opens an AI analytics tool, connects to the database, and asks, "which marketing campaigns generated the most revenue in Q4?"
A visualization showing campaign performance appears in 30 seconds.
The manager immediately follows up with "show me the cost per acquisition for each campaign," and then "what customer segments responded best to the top campaign?"
The complete analysis takes 10 minutes instead of a week.
The speed difference matters because marketing decisions often have tight timelines. When a campaign underperforms, waiting days for analysis means burning budget while waiting for answers. AI analytics helps teams spot problems and adjust quickly.
Traditional analytics vs AI analytics
The main difference between traditional analytics and AI analytics comes down to who does the work and how long it takes. Traditional analytics requires data analysts to manually write queries and build dashboards, while AI analytics automates much of that process so business users can get answers themselves.
Let’s compare them side by side:
| Traditional Analytics | AI Analytics |
|---|---|---|
Query process | No-code Q&A style data analysis | AI generates queries from natural language questions you ask it |
Speed | Search-driven analytics | Takes seconds or minutes to get answers |
Skills required | Interactive dashboards and associative analytics | Requires no technical skills for basic analysis, but basic literacy is still recommended |
Dashboard maintenance | Marketing and customer analytics | Dashboards update automatically with new data |
Access | Finance and planning teams | Available to anyone on the team |
Question volume | Limited by team capacity | Varies by platform |
How traditional analytics creates bottlenecks
Traditional analytics relies on data analysts to extract insights from databases. When you need to know something about your data, you submit a request to the analytics team, wait for them to write queries, and receive a report when they’re done.
I've seen this process take anywhere from two days to two weeks, depending on the team's workload. The analyst writes SQL to pull data, runs statistical analysis, and builds visualizations in tools like Tableau or Power BI. Only people with these technical skills can access the data, which creates a bottleneck.
AI analytics reduces this barrier by generating queries automatically. You type "show me sales by region last quarter," and the system writes the SQL, runs it, and creates a visualization.5 AI analytics tools for business teams in 2026
AI analytics tools vary widely in capability and who they're designed for. Some work best for technical teams, while others target business users who need quick answers without coding. Here are five tools that cover different use cases:
Julius: Ask questions in plain English and get analysis fast without SQL skills. Connects to databases like Postgres, Snowflake, and BigQuery, and learns your data structure over time for faster, more accurate queries.
Tableau: Traditional BI platform with AI features like natural language querying and automated insights. Best for organizations with dedicated analytics teams who need complex visualizations and advanced customization options.
Power BI: Microsoft’s analytics tool with integrated AI capabilities for automated insights and natural language Q&A. Works well if you use other Microsoft products and have some technical background or analyst support available.
Hex: Collaborative notebook platform for data scientists who write Python or SQL. The AI suggests code and helps debug errors, significantly speeding up technical analysis work for programming-skilled users.
Qlik: Enterprise analytics platform with AI-powered insights and strong data governance controls. Designed for large organizations with dedicated analytics infrastructure and formal compliance requirements.
Benefits of AI in analytics
Faster access to answers for non-technical teams
AI analytics reduces the need to wait on analysts for every question. Business users can explore data on their own, which leads to more questions being asked and better day-to-day decisions.
I've seen marketing teams go from asking 2-3 data questions per week to running dozens of analyses on their own. When you don't have to submit a ticket and wait three days, you naturally dig deeper into what's actually driving results.
Better use of existing data
Companies often have valuable data sitting in databases and tools that go underused. AI analytics makes that data easier to explore without building new dashboards or reports for every scenario.
In my experience, sales data, customer information, and operational metrics sit unused because accessing them requires too much work. AI analytics connects those sources so you can start asking questions instead of waiting for custom reports.
More flexible analysis than static dashboards
Scale insight across the organization
Lowers the barrier to data-driven work
Limitations of AI in analytics
AI analytics solves real problems, but it's not a perfect solution for every data challenge. Understanding where it falls short helps you set realistic expectations and know when you still need traditional approaches. Here are the limitations you can expect:
Output quality depends on data quality: AI can't fix messy, incomplete, or poorly defined data. If your tables are inconsistent or metrics aren't clearly defined, the results will reflect that. I've seen teams get excited about AI analytics only to realize their data foundation needed work first.
Harder to trust without context: AI-generated answers can look confident even when they're wrong or incomplete. You still need domain knowledge to validate results and catch incorrect assumptions. I always cross-check important findings before making decisions based on them.
Not all questions are a good fit: AI analytics works best for structured data and well-defined questions. Complex modeling, edge cases, or highly customized logic often still require analysts. When I need advanced statistical analysis or custom algorithms, I go back to the data team.
Learning curves still exist: While you don't need to code, you still need to learn how to ask good questions, interpret results, and understand limitations. Most platforms offer guided questions and suggestions to help you get started, so you're not guessing what to ask. The process gets easier as you become familiar with your data structure.
Governance and consistency can be a challenge: Without guardrails, users may interpret metrics differently or reach conflicting conclusions. Teams often still need shared definitions and oversight to stay aligned on what numbers actually mean.
Want insights without SQL skills? Try Julius
Using AI in analytics reduces the technical barriers that slow down data analysis. Julius is an AI analytics platform that connects directly to your databases and business data, so you can ask questions in plain English and get insights, charts, and reports fast.
Here’s how Julius helps:
Smarter over time with the Learning Sub Agent: Julius's Learning Sub Agent automatically learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering faster, more accurate answers without manual configuration.
Direct connections: Link databases like Postgres, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis always reflects live data so you're never working off outdated spreadsheets.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Ready to see how Julius can help your team make better decisions? Try Julius for free today.
