What are AI analytics? Your guide to unlocking data insights with AI

Today’s businesses have access to a vast pool of data, but it takes real energy to put it to good use. Traditional analytics methods are no longer up to the task. Teams spend hours wrangling data, running manual queries, and waiting on IT. The result: slow innovation and missing answers to critical business questions.

AI analytics change the game completely.

AI data analytics tools analyze complex raw data and transform it into clear, actionable insights in real time.

Instead of reactive reporting, businesses gain predictive foresight and prescriptive guidance. Instead of technical barriers, people explore data through natural language and simple visualizations. And for software creators who embed analytics directly into their products, AI analytics differentiate and deliver massive value.

Introduction to AI analytics

What are AI analytics?

AI analytics combine artificial intelligence technology with data analysis techniques to convert raw data into actionable insights in real time.

Built with a combination of automated data analysis, machine learning, and natural language processing, AI analytics accelerate data exploration and analysis. This makes it easier for end-users to uncover patterns, generate insights, and make data-driven decisions.

One of AI’s key strengths in analytics applications is its ability to handle large datasets and complex queries. Automation and machine learning analyze complex data more effectively and efficiently—so businesses can actually tap into the power of all the data at their disposal.

For software creators, AI analytics are also a prime competitive edge. Using AI-powered Analytics Platform as a Service (AnPaaS) solutions, they can quickly build, customize, and embed analytics into their applications, even without advanced data analysis or technical training.

Impact of AI analytics

AI is driving the future of analytics, and forward-thinking organizations are embracing it. Experts predict the global market for AI in data analytics will grow from $18.5 billion in 2023 to $236.1 billion by 2033 (a 29% compound annual growth rate).

AI analytics tools are particularly vital for software makers. Modern end-users expect real-time reporting and powerful data exploration features. (AnPaaS) solutions enable them to embed AI analytics directly into their apps, presenting the instant insights their customers demand.

Key terms in AI analytics

• Data modeling is the framework that shapes raw data into structured formats. Models ingest, prepare, and translate data, then deliver intuitive visualizations in the form of dashboards, tables, schemas, and graphs.
• Data connectivity links all your data sources. It’s the process of integrating your data across everywhere it’s stored—warehouses, third-party tools, and your native application itself—to feed a comprehensive data stream into your AI analytics solution.
• Deep learning is a specialized area of machine learning that trains neural networks to detect intricate patterns in data. By mimicking the human brain, these models excel at image classification, natural language interpretation, and identifying outliers in massive datasets.
• Predictive analytics leverage historical patterns to forecast what’s likely to happen next. Instead of only reacting to trends in hindsight, companies can anticipate demand, mitigate risk, and prevent churn before issues arise.
• Generative analytics go beyond returning data based on end-user prompts. Using advanced AI models, it automatically generates relevant reports, uncovers hidden relationships, simulates potential outcomes, and proactively suggests optimizations.
• Natural language processing (NLP) enables machines to understand and interpret human language. Rather than needing structured inputs, systems can extract meaning, intent, and context from plain language.
• Natural language querying (NLQ) allows people to ask questions the same way they speak. No need to navigate menus or figure out technical language. The system translates plain language into backend queries that retrieve relevant insights.
• Natural language generation (NLG) automatically turns numbers into narratives. Instead of leaving people to interpret charts, it provides clear, written explanations that summarize the key takeaways.
• Real-time analytics produce insights the moment data becomes available. As sources update, dashboards and models refresh instantly without the delays of old systems that require data to be manually re-run and refreshed.

Traditional vs. AI analytics

Comparing traditional and AI analytics is like comparing a hand crank to a battery-powered drill. Traditional data analysis requires expert human effort at every stage, whereas AI analytics deliver results with a few simple clicks. Instead of manually performing all the data analysis, your experts can focus on applying insights for strategic decision-making—where their skills are most valuable.

Limitations of traditional analytics

Traditional analytics depend on manual data handling and disjointed tools. Analysts gather data from isolated systems, clean it manually, and create reports using spreadsheets or BI software. This approach requires experts with advanced skills in statistical methods and tools.

It also takes a long time. Generating insights requires days (sometimes weeks), and reports often arrive too late to influence decision-making. Static reports lack the context and relevance that people need to extract meaningful intelligence they can act on. Even worse, outdated data can lead to inaccurate conclusions, resulting in decisions that may undermine business objectives.

Software creators can’t rely on traditional analytics for effective embedded reporting features. Delayed reporting and static insights offer little value to end-users, which undermines adoption, satisfaction, and retention. The huge lift for engineering and data teams requires creators to allocate many resources for minimal return.

Advantages of AI analytics

In traditional analytics, experts must manage every step of the process, from data connection to presenting and summarizing the findings.

AI analytics replace those slow, manual processes with speed, automation, and intelligence. No more manual data wrangling—automated data processing yields actionable insights faster and with fewer resources. Machine learning identifies patterns and makes predictions, shifting your focus from describing the past to anticipating the future.

Additionally, AI doesn’t just create better analytics; it makes them far more approachable. With NLP and dynamic dashboards, people can surface insights intuitively without needing to be data science experts, democratizing data-driven decision-making.

For software creators, the shift to AI-powered analytics is a transformative development. End-users derive real value from your app’s embedded reporting, with intuitive experiences that improve engagement and drive higher ROI from your product. Creating analytics features for your application is also faster with AI. With the right AnPaaS platform, you can embed AI analytics and drive adoption with far less burden on your dev teams.

4 key elements of AI analytics

To deliver smarter analytics inside your software, you need AI that works under the hood, automating, personalizing, and translating complex data. Before AI, analytics were limited by time, expertise, and processing power. Now, the tools at your disposal are more powerful, intuitive, and integrated than ever before.

Sisense Intelligence is a prime example of AI tools: a suite of AI features to help accelerate building, embedding, and accessing analytics.

1. Automated data processing

AI automates every step of the data preparation workflow: collection, cleansing, and transformation. AI tools continuously ingest structured and unstructured data from multiple sources, apply data quality checks, and normalize the inputs for analysis. Instead of relying on analysts to handle tedious data preparation tasks, AI systems automate data pipelines with minimal to no manual intervention.

For software creators, automation removes one of the biggest barriers to scalability. Developers no longer need to script custom ETL processes or build extensive backend infrastructure to clean data before it reaches end-users. Automated pipelines ensure real-time accuracy and consistency, which helps product teams deliver always-fresh insights in their applications.

Build with Sisense Intelligence: Use automated data workflows to connect, cleanse, and prepare data in real time without the need for custom scripting. Your embedded analytics features continually draw from high-quality, analysis-ready data.

2. Predictive and prescriptive analytics

Predictive analytics use AI to analyze historical patterns and forecast future outcomes. Prescriptive analytics build on these forecasts by recommending optimal actions based on likely scenarios.

Together, these techniques shift analytics from reactive reporting to proactive decision-making. AI empowers this forward-looking approach with built-in pattern recognition and continuous learning. The more data it interacts with, the better your insights will be.

Software creators embedding analytics can employ these capabilities to provide highly valuable functionality. Instead of showing end-users what happened, predictive and prescriptive models show what’s likely to happen next and provide suggestions about how to address it.

Build with Sisense Intelligence: Integrate predictive capabilities directly into your apps. The forecast feature provides end-users with foresight and recommendations, directly in their app experience.

3. Machine learning and deep learning applications

Machine learning does just that: it learns. Over time, machine learning identifies patterns in large datasets and enhances your predictive analytics. Deep learning takes it further by using neural networks to identify complex, nonlinear relationships and anomalies that traditional analytics often overlook.

These technologies allow applications to autonomously detect shifts in customer behavior, flag operational risks, optimize resources, and more. Software creators can embed these capabilities into their apps to offer intelligent, self-improving experiences.

Build with Sisense Intelligence: With the trend and forecast features, you can easily include pattern detection, anomaly alerts, and future-looking analysis in your embedded analytics, enabling faster insights for your end-users.

4. Conversational data exploration: NLP, NLQ, and NLG

Natural language capabilities are re-shaping how people interact with data. NLP is the technology that allows machines to extract meaning from everyday human language and respond in easy-to-understand wording. It turns complex data interactions into smooth, intuitive conversations.

End-users experience NLP in two ways:

• NLQ makes data exploration easy: people query data in plain language instead of through a query syntax. They can just type a request like “What’s our churn this month?” instead of writing the request in SQL. 
• NLG closes the loop by translating data responses into plain-language narratives. NLG can respond to the query with a simple answer: “This month’s churn is 4%.”

AI-powered natural language capabilities turn complex data interactions into smooth, intuitive conversations. By embedding the full spectrum of natural language capabilities into analytics experiences, software creators can make insights more accessible for everyone.

Build with Sisense Intelligence: With the Sisense Intelligence assistant feature, you (and your end-users) can query data through conversation, rather than using the structured data languages of the past. Then, with the narrative tool, you can automatically generate plain-language summaries of the charts and widgets in your dashboards. By democratizing your data analytics, you are able to provide more effective insights, making it easier than ever for anyone to understand and analyze your data.

8 real-world industry use cases

AI analytics are changing the impact of data across industries. Instant, strategic insights influence how we understand behavior, health, operations, finances, and more. The impact is deep, and the applications are wide-reaching.

1. Healthcare: Improve patient outcomes

AI analytics transform healthcare by turning medical data into insights that improve health and even save lives. The global AI healthcare market is projected to surge from $14.92 billion today to $110 billion by 2030, driven by better patient outcomes and smarter care delivery. 

Key use cases for embedded AI analytics in healthcare applications:

• Help clinicians make faster diagnoses and increase personalized care
• Automatically detect anomalies in patient vitals and lab results
• Flag early signs of disease progression and recommend next steps for treatment
• Monitor population health trends across healthcare systems and optimize resource allocation 
• Detect opportunities for operational efficiencies in hospitals and care systems

2. Finance: Secure finances and detect fraud

Embedded AI analytics change the way we approach fraud detection and financial analysis. They make transactions safer, risk assessments more precise, and operations more efficient. As the financial services industry continues to digitize, AI is increasingly crucial for providing real-time insights and proactive fraud prevention.

Key use cases for embedded AI analytics in financial applications:

• Detect suspicious activity and flag potential fraud before it impacts customers
• Improve credit risk models by analyzing borrower behavior and history
• Forecast investment performance to help clients plan with confidence
• Automate compliance monitoring and reduce exposure to regulatory risk
• Deliver real-time insights to support smarter investment decisions

3. Supply chain and manufacturing: Optimize operations with predictive insights

AI analytics enhance manufacturing and logistics by increasing speed, accuracy, and foresight. With AI analytics tools, enterprises can transition from reactive management to proactive optimization, unlocking new levels of efficiency and uptime.

Key use cases for embedded AI analytics in supply chain and manufacturing:

• Forecast inventory needs and reduce costs tied to overstock or shortages
• Predict equipment failures and schedule preventive maintenance
• Detect production bottlenecks and workflow inefficiencies
• Trigger real-time alerts for disruptions to keep operations on track
• Support long-term planning with predictive models and scenario analysis

4. Marketing: Elevate campaign performance with real-time customer insights

Embedded AI analytics give marketing teams a precise, timely window into audience behavior. That real-time insight supports better segmentation, more accurate personalization, and deeper understanding what works and what doesn’t.

Key use cases for embedded AI analytics in marketing platforms:

• Analyze audience sentiment and behavior to support deeper segmentation
• Track real-time campaign performance and recommend optimizations
• Provide AI-driven personalization across emails, ads, and websites
• Keep stakeholders aligned with dashboards tracking ROI and KPIs
• Adapt messaging strategies dynamically to maximize engagement

5. Education: Advance education and training with adaptive insights

AI analytics enable more personalized, responsive, and measurable learning experiences. As more education software incorporates embedded AI analytics, students and teachers gain better insights and targeted interventions.

Key use cases for embedded AI analytics in education technology:

• Personalize learning paths based on individual performance and learning style
• Detect when learners are struggling and recommend targeted resources
• Dynamically adjust pacing to improve student comprehension
• Help instructors monitor cohort progress and curriculum effectiveness
• Provide administrators with data to improve efficiency and outcomes

6. Government and public sector: Provide smarter services at scale

AI analytics help governments meet rising needs with fewer resources. Embedded insights in applications empower agencies to make informed, data-driven decisions, enhance service delivery, and respond to citizens’ needs in real time.

Key use cases for embedded AI analytics in government applications:

• Analyze citizen feedback to prioritize services and detect pain points
• Use NLP to extract trends from open-ended surveys or public comments
• Forecast demand for social programs during economic or health crises
• Improve resource allocation with predictive modeling
• Enhance transparency and operational efficiency across departments

7. Retail and e-commerce: Drive sales and optimize operations

Embedded AI analytics make it easier for brands to personalize shopping experiences, optimize inventory movement, and respond quickly to market trends. Customers have better experiences and build more loyalty; brands operate more efficiently.

Key use cases for embedded AI analytics in retail and e-commerce:

• Analyze customer behavior to recommend relevant products in real time
• Forecast inventory demand across locations, seasons, and promotions
• Use NLG to explain product performance trends to internal stakeholders
• Detect and reduce cart abandonment with targeted interventions
• Provide consistent, personalized experiences across all channels

8. Software development: Enhance embedded analytics

Modern software is now an intelligence layer. Creators integrate AI-powered analytics directly into their applications to deliver more innovative experiences for their end-users. Customers get the insights they’re looking for, in easy-to-understand formats, right where they’re already working.

AI analytics don’t just improve end-user experiences. Creators can leverage AnPaaS solutions to bring AI into the process of creating embedded analytics. Build data models and dashboards with simple conversations. Speed up time to market and reduce engineering lift. And tap into the power of your own data with the same features that power accessible insights for your customers.  

Key use cases for embedded AI analytics in software development:

• Rapidly build and embed analytics features with fewer resources 
• Personalize end-user experiences with relevant insights and suggestions
• Improve retention by encouraging deeper interactions with app features
• Create monetization opportunities with premium analytics features
• Integrate predictive recommendations and forecasting into your tools

5 best practices for implementing AI analytics

Successfully implementing AI analytics requires a strategic approach that balances technology, data integrity, user experience, and security.

1. Choose the right AI analytics platform

Selecting the right AI analytics platform is crucial for delivering scalable, embedded intelligence within your product. Prioritize solutions that support high concurrency, large datasets, and seamless integration across cloud environments. 

The platform you choose should align with business goals: Does it support your product roadmap, offer the right AI features, and scale with your user base? 

For many teams, AnPaaS options provide a flexible foundation by offering ready-to-scale infrastructure and a faster time to deployment, eliminating the need to manage backend complexity. But not all AnPaaS solutions offer the same set of features, user experience, and pricing options. It’s important to select the platform that aligns with your business structure and objectives.

Build with Sisense: Sisense is a developer-first platform designed to embed AI analytics directly into your software. It enables scalable performance, rapid customization, and streamlined integration through a rich set of AI-powered tools.

2. Integrate AI analytics with existing systems

Embedding AI into your product doesn’t mean ripping and replacing your current data stack. Choose tools that seamlessly integrate with your current systems, whether you’re using a cloud-native platform, legacy infrastructure, or a hybrid setup. 

Be sure the analytics platform you choose is compatible with modern data warehouses, RESTful APIs, and event-driven architectures. That way you can integrate analytics into workflows smoothly without disrupting existing operations. Robust compatibility reduces time-to-integration and enables AI to enhance, rather than complicate, your platform’s core functionality.

Build with Sisense: Sisense offers seamless integration into any application or data environment. Its cloud-native architecture supports major platforms like AWS, Azure, and Google Cloud.

3. Ensure data quality and governance

Even the best AI analytics platforms can’t work miracles with messy data. For embedded analytics to deliver trustworthy insights, your underlying data must be clean, consistent, and compliant. Look for AnPaaS platforms that make it easy to automate data validation, manage lineage, and enforce governance policies across data sources.

Your chosen analytics platform should also comply with relevant industry standards, such as GDPR, HIPAA, or SOC 2.

Build with Sisense: Sisense offers robust data modeling, governance, and regulatory compliance standards, giving software creators confidence in data quality across every layer of their analytics stack. Developers can maintain data integrity with features such as centralized data modeling, field-level access controls, and support for multi-tenant deployments.

4. Guarantee security and privacy

Data security is essential when integrating analytics into apps, especially if you handle sensitive data. Evaluate AI analytics platforms’ practices for encryption, authentication, and access control; they should be at least as strong as your own. If you’re in a highly regulated industry, make sure the platform you choose undergoes frequent audits to maintain compliance with the latest regulations relevant to your business.

Build with Sisense: The Sisense platform embeds enterprise-grade security into its core, featuring capabilities such as single sign-on (SSO), row-level security, encryption at rest and in transit, and fine-grained permissions. It also conducts regular independent audits to verify continued compliance with top security and privacy frameworks.

5. Empower end-users with intuitive, self-serve analytics

AI analytics provide the greatest benefit when people can effortlessly explore data and leverage insights. Software creators should create intuitive analytics experiences that allow both technical and non-technical end-users to access advanced features like predictive modeling and dynamic visualizations.

Choose an AnPaaS solution that offers extensive customization options so that your analytics features look and feel just like the rest of your app. As you implement AI analytics, look for opportunities to weave analytics experiences throughout the user experience. It’s all about helping people surface insights in a way that feels natural, seamless, and aligned with how they already interact with your product.

Build with Sisense: Sisense equips developers to tailor their embedded analytics with pixel-perfect precision. No-code and low-code options let you customize pre-built components quickly. Or take a pro-code approach for the deepest possible customization: leverage Compose SDK or create directly from your application code.

Transform your business with AI analytics today

AI analytics turn complex, scattered data into tangible value. Numbers that once sat behind an opaque wall become accessible, actionable business insights. Automation speeds up workflows, while natural language capabilities democratize access to data-driven decision-making. 

Most importantly, embedding AI analytics directly into applications puts vital intelligence at people’s fingertips. And that’s what Sisense is all about: making it easy to integrate AI analytics so your customers get powerful insights—and you gain a competitive advantage.

AI analytics FAQs

How do businesses maintain data quality in AI analytics?

Businesses automate data validation, continuously monitor datasets, and implement governance frameworks. AI-powered analytics can also enhance accuracy by automatically detecting and correcting errors.

Which industries gain the most from AI analytics?

The healthcare, finance, marketing, manufacturing, and software development industries reap the most significant benefits due to their complex data needs and demand for rapid insights.

What costs do businesses face when implementing AI analytics?

Costs include software licensing, cloud infrastructure, data integration, and training. AnPaaS platforms like Sisense offer scalable pricing models tailored to business size and needs.