BI, AutoML, or LLM: How to Choose AI for Data Analytics​

There can be a lot of data. It’s not enough to simply scrape information from competitors’ websites – you still need to convert it into a convenient format for further analysis. Different approaches can be used for analysis – everything will depend on the context, goals, objectives, and the structure of the data itself. Fortunately, we live in the era of booming artificial intelligence. It’s hard to overestimate AI’s contribution to information analysis. AI for data analytics can not only speed up data conversion from one format to another, but also help with summarization, indexing, searching for hidden patterns, and drawing conclusions in other tasks.

At the same time, it’s important to remember that AI is not a “holy grail” – it can’t answer all your questions at once. Every neural network model has its own niche and area of application, and therefore a specific set of practical tasks it can solve. Let’s dive into this issue in as much detail as possible: we’ll break down how AI for data analytics is used, what approaches and tools exist, and which one to choose for your needs.

Context: What Exactly Do We Analyze After Scraping

Scraping is the process of collecting information – ideally with immediate conversion into a convenient format so the data is easy to work with in other programs, systems, and applied solutions. For example, the output info can be stored in databases, in CSV or XML, in JSON format, in Excel tables, etc. But scraping itself does not perform analysis; it only provides the foundation for it. “Raw” data has no inherent meaning on its own.

And this is where the first issue appears: what exactly do we want to obtain after scraping, and why? This will determine the dataset, its format, structure, completeness, timeliness, and other parameters. Data can be very different – just like the analysis process. For example, if we monitor competitors, scraping may be periodic – once a month or more often – and it can be convenient to store the data in tables or databases, logically collecting product prices and names. Then the aggregated information can be used for business analytics – displayed in dashboards and BI systems.

Check the Top 8 tools with AI for data analysis

But if we’re assessing market trends and customer sentiment, “numbers” alone won’t be enough – we’ll need to work with text: comments, mentions, meaning, sentiment, etc. These require completely different scraping and analysis mechanics. For instance, using AI agents for trend analysis or process reviews becomes a natural fit.

In general, the context of post–scraping analysis is formed at the intersection of three factors: data type, business goals, and the complexity of processing. This context is exactly what determines which AI tools for data analytics make sense: BI systems, AutoML, and/or LLMs.

Three Approaches of AI for Data Analytics: BI, AutoML, and LLM

In practice, three fundamentally different approaches are used most often: BI systems, AutoML platforms, and large language models (LLMs). They solve different problems, rely on different types of data, and require different levels of expertise.

BI (Business Intelligence, Business Analytics)

BI is a class of analytics systems designed for visualizing data, aggregating it, embedding it into workflows, and enabling timely control of key performance indicators. BI tools work primarily with already structured data: tables, metrics, and databases. At the same time, they can also include data preparation capabilities – normalization, formatting, transformation, etc. For this reason, modern BI systems actively integrate data analytics elements to automate a range of routine tasks.

BI answers the questions “what is happening?” and “how are the indicators changing over time?” In the context of web scraping, BI is used for:

• Monitoring and analyzing competitors’ prices over time
• Comparing product ranges, stock levels, and category overlap (niches)
• Tracking overall market metrics and KPIs
• Building detailed reports and dashboards for the business so a decision–maker can explore the data as deeply as possible

In essence, BI turns cleaned and normalized data into a clear picture of the current state of the market or business. The most suitable neural networks to pair with BI are specialized AI tools for analytics and data preparation.

Benefits of AI for data analytics in BI systems:

• Automating routine operations for data normalization and conversion
• Finding hidden patterns and calculating indicators, including using advanced mathematical models – expectations, correlations, sampling, and more
• Building realistic forecasts based on big data and accumulated statistics
• Providing a natural–language interface, where you can “talk” to the data through an AI assistant. For example, a manager can quickly request the strongest products versus competitors, a list of sales leaders, or ask the assistant to summarize the dataset, filter results, etc.

The limitations are built into BI systems. They are essentially a dashboard of indicators. A BI system does not answer “why did this happen?” – it only shows a snapshot at a specific point in time. Digging into details and drawing conclusions must be completed by an experienced manager or analyst. Even the best AI for data analytics can make mistakes because it can only rely on the metrics available in BI and does not see the full picture.

Examples of successful use cases for AI agents in data analysis:

• Hints and comments on the data
• “Dialog” with data: filters and queries in natural language
• Detecting and alerting on anomalies, which can be integrated into automated incident–response processes
• Automating the creation of summary reports and executive digests

Read also: Data collection without chaos – a systematic workflow for scraping.

AutoML (Automated Machine Learning)

AutoML is a class of platforms and tools designed to automatically build and evaluate machine–learning models. Unlike BI, AutoML is not about visualizing metrics – it focuses on discovering relationships, forecasting, and identifying influencing factors. The core focus of AutoML is turning data into structured inputs for business tasks – for example, metrics, features, or historical observations (linked sets of facts).

AutoML answers the questions “why is this happening?” and “what is most likely to happen next?” In the context of scraping, AutoML is used for:

• Forecasting prices, demand, sales volumes, and their changes
• Identifying factors that influence market and competitor behavior
• Classifying and segmenting products, categories, and offers
• Detecting anomalies and unusual patterns in large datasets
• Running “what–if” scenario evaluations when market conditions change

In essence, AutoML turns the accumulated post–scraping data into insights and forecasts that can support management decisions and planning. The best AI companions for AutoML are classic ML models and automated pipelines.

All chain links: proxies, scrapers, and pipelines in data processing.

Benefits of AI for data analytics in AutoML platforms:

• A significantly lower barrier to entry for machine learning without sacrificing model quality
• Fast forecasting based on historical data and statistics
• Automatic selection of the best–performing features and models by quality metrics
• Scalability – the ability to process dozens or even hundreds of features without manual tuning
• The ability to integrate results into BI systems and management dashboards

AutoML limitations are tied to configuration complexity and abstraction. Models often act as a “black box,” which makes it harder to interpret outputs and understand the reasons behind conclusions. Also, AutoML depends critically on input data quality: scraping errors, missing values, and bias directly affect the resulting forecasts. AutoML does not work with raw text and does not understand meaning – you need numeric representations only.

Examples of successful AutoML use cases for data analysis

• Predicting competitor and market–wide price dynamics
• Identifying factors behind sales decline or growth
• Segmenting assortments by behavioral and pricing features
• Automatically detecting anomalies in market data
• Supporting scenario planning and risk assessment

Data cleaning after scraping: why it matters so much.

LLM (Large Language Models)

LLMs are a class of neural–network models designed to work with text, context, and meaning. In data analysis, LLMs don’t function as classic analytics systems – instead, they act as an intelligent layer for interpretation, summarization, and interaction with information. They are especially effective when working with unstructured or semi–structured data.

LLMs answer the questions “what are the data talking about?” and “what conclusions can we draw from it?” In the context of scraping, LLMs are used for:

• Analyzing reviews, comments, mentions, and user–generated text
• Identifying topics, trends, and semantic patterns
• Assessing sentiment and customer attitudes toward products and brands
• Summarizing large volumes of information into concise conclusions
• Running fast, “on–the–fly” analysis and research through a conversational format

In essence, LLMs transform textual data into analytic entities that can then be used in BI and AutoML. The best for data analytics alongside LLMs are widely used language models with API access, as well as agent–based architectures and vector–database functionality. And anything that classic LLMs are missing can be implemented via intermediary services or frameworks.

Learn more about the LangChain and LangGraph libraries for scraping.

Benefits of LLM–Based AI for Data Analytics:

• Working with data that can’t be formalized using classical methods or simple algorithms
• Rapid summarization and structuring of large text volumes
• Discovering implicit meanings and contextual connections
• A flexible natural–language interface for analytics and research
• The ability to generate hypotheses and interpretations that BI and AutoML can’t provide

LLM limitations are tied to the lack of strict mathematical precision (there can be no guarantees here). Models may produce logical errors, distortions, or “hallucinations.” In addition, LLMs are not intended for accurate calculations or forecasting – they don’t replace BI and AutoML, they complement them. Output quality depends directly on prompts, context, and data sources.

Examples of successful LLM use cases for data analysis

• Analyzing customer reviews and identifying key pains and expectations
• Detecting market and consumer trends from text–based sources
• Generating analytical summaries and reports for leadership
• Answering analytical questions in natural language through dialogue
• Providing guidance during research and when exploring new markets or niches

Related: What is AI-based scraping, and what is its main drawback?

AI for Data Analytics Comparison by Criteria: BI vs AutoML vs LLM

What’s Better in AI for Data Analytics: One Approach or a Hybrid?

In real life, a hybrid approach almost always wins. The reason is that AI for data analytics is never used as the system’s only “node.” They’re just one component responsible for specific tasks and actions within a larger pipeline. The solutions discussed above – BI, AutoML, and LLM – are specialized tools meant to help managers and businesses in particular situations. You can’t say that relying on only one tool will deliver maximum efficiency. On the contrary, these systems can and should be combined so they complement one another.

With the right combination of BI, AutoML, and LLM, leaders can:

• see the fullest possible picture of what’s happening,
• understand the reasons,
• identify key factors and risks,
• forecast consequences,
• interpret meaning and context.

If you choose only one AI for data analytics approach, you’ll cover only part of this cycle.

Examples of successful combinations:

BI + AutoML. AutoML builds forecasts and explains the drivers behind changes, while BI captures the metrics and visualizes the data.

BI + LLM. BI provides the “numerical” view based on metrics, while the LLM interprets the data and helps draw conclusions.

AutoML + LLM. AutoML calculates and forecasts, while the LLM explains results and helps form hypotheses and scenarios.

BI + AutoML + LLM (full loop). The strongest and most effective model: BI answers “what is happening?”, AutoML answers “why and what’s next?”, and LLM answers “how should we interpret and use this?”.

Conclusion: What AI for Data Analytics Will You Choose?

It won’t be a surprise to anyone that adopting any of the solutions discussed is directly tied to a business’s maturity and level of digitalization. The more data flowing through a company, the harder it becomes to work with it. Equally important are data completeness, reliability, and timeliness. Setting up and maintaining such systems can be quite costly, so using AI for data analytics is not always justified everywhere. Still, it’s neural networks and AI for data analytics tools that often deliver the highest impact and efficiency.

As a reminder, scraping – the foundation for collecting data for subsequent analysis – requires a certain infrastructure and software stack. On our side, we can offer reliable residential, mobile, and datacenter proxies that ensure stable scraper operation and reduce the risk of access blocks.

We also provide ready-to-use scrapers so you can receive structured data outputs for one-time or recurring tasks and then use AI for data analytics.