AI agents are software programs that use artificial intelligence (AI) techniques to interact with their environment, make informed decisions, and perform tasks without human intervention. Handling unstructured data – information that doesn’t follow a standard format or structure – is integral to fulfilling these roles. They do this through a multi-step process that involves:

1.    Data Preprocessing

AI agents must first prepare data that is unstructured for analysis by cleaning and standardizing it. This process is known as data preprocessing and is intended to reduce the noise and inconsistencies often present in this kind of data.

An AI agent preprocesses different types of data in different ways:

• Text Data: The AI agent breaks text into smaller components (tokenization), removes irrelevant words (stop-word removal), and standardizes word forms (stemming and lemmatization) to reduce data to its core elements. For example, a product review like “this product was amazing” might be reduced to keywords like “product” and “amazing.”
• Visual Data: The AI agent resizes, denoises, and enhances visual data to focus on its key features. For example, facial recognition technologies preprocess images to enhance their accuracy.
• Audio Data: The AI agent converts audio files into spectrograms or feature sets that represent sound patterns.

2.   Feature Extraction

Preprocessed data can then be transformed into a format that AI models can understand – typically a numerical representation. Again, this takes different forms for different types of data:

• Text Data: Natural Language Processors (NLPs) use word embeddings to represent words as vectors, capturing relationships between words and enabling the AI agent to understand the context surrounding data.
• Visual Data: Convolutional Neural Networks (CNNs) extract visual features such as edges, textures, and shapes to help, for example, a generative AI agent identify objects – such as trees, cars, or people – in images.
• Audio Data: Features like Mel-frequency cepstral coefficients (MFCCs) help the AI agent capture patterns in sound, such as features of speech or specific music genres.

3.   Advanced Analysis and Pattern Recognition

Once features are extracted, the AI agent uses machine learning (ML) and deep learning algorithms to identify patterns and derive insights from data. For text data, this involves using NLP to power sentiment analysis, summarization, and topic extraction; for visual data, this involves using deep learning to detect objects, classify scenes, or identify emotions; and for audio data, the AI agent typically conducts speech-to-text conversion, emotion detection, or keyword identification.

4.   Transforming Data into Actionable Insights

Ultimately, the goal of handling this kind of data is to gain insights that can inform actions and decisions. This goal is achieved through:

•  Classification: Assigning categories to data, such as labeling emails as spam.
•  Summarization: Condensing complex data into concise summaries.
•  Recommendation: Analyzing data to make recommendations for action, content, products, or services.

For example, an e-commerce AI agent might analyze customer reviews to identify common complaints, helping businesses refine their products, services, or business models.

Use Cases

A range of industries and organizations use AI agents that can handle this kind of data to enhance efficiency, improve decision-making, and facilitate innovation. For example:

• Healthcare Organizations: use them to analyze images like X-rays or MRIs and improve disease detection.
• Financial Institutions:  leverage them to monitor transaction logs for fraud detection and risk assessment.
• Marketing Agencies: often use an AI agent to evaluate customer feedback, sentiment, and trends to optimize campaigns.
• Entertainment Companies: use AI to recommend content based on user preferences and behavior.

Why is this Important?

An AI agent must be able to handle data that is unstructured because 80-90% of all data globally is unstructured. This kind of data can include anything from emails, social media posts, videos, audio recordings, or images, and, as such, handling it is crucial to the efficacy of an AI model.

This kind of data also plays a critical role in automation, enabling an AI agent to perform complex tasks – such as analyzing CCTV footage for security threats or converting speech to text for virtual assistants. AI models that lack the ability to handle it also lack the ability to perform these tasks, meaning they will be well behind the most advanced models.

Ultimately, organizations must build AI agents that can handle this kind of data because only they are able to unlock the insights that this data offers. Without these insights, AI models simply cannot function in the way they are designed to.