How AI and Data Work Together

Artificial intelligence (AI) is the field of technology that can perform tasks, reason through complex problems, and make informed decisions typically requiring human intelligence. AI encompasses machine learning, natural language processing, and computer vision—all powered by data.

Data includes numbers, text, images, and audio from sources such as store transactions, online interactions, social media, and IoT sensors. When structured and interpreted correctly, data fuels AI systems with information and context. Generative AI and agentic AI depend entirely on high-quality datasets to learn and function.

Artificial intelligence and data are transforming industries by improving decisions, automating processes, and driving innovation. You can use data to train and ground your AI apps and agents, giving them context about your business, customers, industry, and competitors.

Let’s explore how data and AI spark fresh thinking and push boundaries.

AI can identify data patterns and help you generate insights that drive better decisions. You can use AI-powered tools, such as business intelligence dashboards and predictive modeling software, to gain deeper customer and market insights for smarter, timely decisions.

For example:

• Marketing: An AI dashboard analyzes audience engagement, campaign performance, and customer sentiment for an online publisher. The marketing team can use these insights to adjust publishing times and focus on the topics that are most likely to resonate.
• Sales: An AI sales tool deployed by a software technology company analyzes historical deal data, customer interactions, and customer social media reactions to score leads and recommend next-best actions. Account managers get real-time alerts like "Contact this prospect within 24 hours" for customers with a high probability to purchase.

Agentic AI is an intelligent system that can act autonomously in real-time to achieve specific goals with minimal or no human supervision.

AI agents make data-based decisions, reason, select the right tools to achieve an objective, and take action. For example, customer support AI agents can process service requests, resolve issues, and escalate to human support only when necessary.

Agentic AI has the power to transform entire industries by seamlessly integrating with data platforms, deriving context from data, and taking intelligent action, extending the capabilities of your labor force.

For example:

• Healthcare: An AI agent monitors patient vitals from electronic health records and wearable devices. It detects early indicators of disease, orders lab tests, and alerts the care team.
• Financial services: An AI agent processes loan applications by pulling credit data, income verification, and risk scores. It approves standard loans under $10K instantly, escalates complex cases with written case summaries, and updates records automatically.

AI automates repetitive tasks, boosting efficiency and cutting human error. By integrating it into your workflows, you can streamline operations, accelerate production, and increase productivity.

For example:

• Manufacturing: AI robots can carry out predictive maintenance and optimize repair timeframes, minimizing downtime and maximizing production.
• Financial services: AI analyzes massive datasets in real time to detect fraud, blocking suspicious claims instantly.

AI and agentic AI can help your organization personalize customer experiences by predicting and meeting your customers’ needs on the platforms or media they use.

Agentic AI goes a step further to activate marketing campaigns on advertising and social media platforms. An AI agent can identify precise customer segments, assemble targeted campaigns, launch them, and monitor how they perform.

For example, AI can scan purchase history, browsing patterns, and social activity to suggest personalized product bundles at a retail checkout—for example, "Pair this jacket with matching boots"—boosting average order value. Agentic AI then auto-launches targeted Instagram ads to cart abandoners, adjusting ads and bids based on click-through rates.

With AI on your side, you can experiment with new ideas and create groundbreaking products and services. From drug discovery in healthcare to algorithmic trading in finance, AI is driving transformative advancements.

For example, Johnson & Johnson used AI to create 3D maps of the human heart, helping surgeons navigate cardiac ablation procedures more effectively.

AI is available 24x7, delivering constant monitoring and resource optimization. It can help your organization minimize product waste, optimize inventory levels, and predict production issues before they disrupt operations.

For example:

• Manufacturing: AI analyzes sensor data to predict machine failures, auto-order parts, and schedule maintenance proactively.
• Retail: AI forecasts demand across stores using sales trends and weather data, dynamically adjusting inventory to prevent stockouts or overstocks.

Without high-quality data, AI models can’t train or learn. Below are a few of the ways that data helps AI and agentic AI.

AI models are trained on large datasets so they can recognize patterns and make accurate predictions. During the training phase, machine learning (ML) algorithms learn to analyze vast amounts of structured and unstructured data, make correlations, and offer recommendations.

The accuracy of AI models depends heavily on the quality, completeness, and diversity of the training datasets. High-quality, well-labeled data ensures reliable AI outputs, while diverse datasets help mitigate biases and improve AI performance.

Real-time, quality data is the fuel behind powerful AI and agentic AI applications. By continuously analyzing incoming data, AI systems can respond to changing conditions and circumstances, continuously improving their own expertise. For example, AI-powered traffic management systems use live traffic data to optimize signal timings that reduce congestion and improve urban mobility.

AI agents “want” a sense of the world around them before they can take intelligent action. Data feeds AI agents facts such as past decisions and user behavior (e.g., social media “likes”), so they can anchor their decisions in real-world situations rather than vague assumptions.

As data continues to grow, AI agents connect new inputs to stored data and decide on the best course of action rather than following a predetermined script.

AI models improve over time by learning from data and feedback. As they train through more examples and receive corrections, they adjust what they recommend, how they predict, and how they act.

For example, AI recommendation systems learn from how people actually use them. Over time, they adjust their suggestions to fit individual tastes, becoming more helpful and in tune with the users.

With the help of AI, data transforms into clear pictures, such as charts and maps, that you can understand and put to use right away. The AI system spots patterns in the data, such as which numbers rise or fall together, and then picks a useful way to show them.

For example, Tableau's AI-powered engine can analyze sales data and auto-generate interactive dashboards of sales growth by region. A healthcare provider can auto-generate the dashboards to reveal patterns, such as rising infection rates during flu season, and help administrators allocate staff and supplies proactively.

AI relies on data to deliver valuable outputs. Below are a few key AI technologies and how they use data.

Machine learning (ML) is a subset of AI that can recognize patterns and make predictions by analyzing large datasets. ML encompasses methods such as decision trees.

Decision trees follow a logic that splits data into yes/no branches based on certain criteria; for example, "Is income over 100K?" Decision trees can guide decisions such as loan approvals by checking income first, then credit score.

Netflix uses machine learning for personalized movie recommendations. Netflix’s algorithm analyzes your viewing history, ratings, and even pause patterns to predict what you'll enjoy next.

LLMs form the “brains” of generative and agentic AI systems. They can reason, plan, execute tasks autonomously, and generate human-like content.

In healthcare, for example, LLMs can assist clinicians by analyzing patient symptoms, medical records, lab results, and medical literature to suggest potential diagnoses and highlight overlooked patterns. They can generate differential diagnoses (e.g., from vague symptoms like fatigue and chest pain), prioritize tests, and explain their reasoning—boosting accuracy while leaving the final calls to clinicians.

NLP is how we get computers to actually get human language. NLP uses machine learning plus linguistic rules to break down text or speech; figure out grammar, sentiment, and context; then do things like translate or summarize.

Customer service chatbots rely on NLP to read and understand, for example, "Where's my order?" on a customer chat. They can detect the frustration and reply appropriately.

Generative AI uses huge datasets and pre-trained LLMs to create entirely new content, such as text, images, code, or music, from simple human prompts. Unlike traditional AI that just classifies or predicts, genAI generates original outputs with a human-like grasp of language, drawing patterns from training data to produce personalized emails or digital art.

Key techniques for genAI include GANs (two competing neural networks refining their outputs until they are realistic) and transformer models (which proces sequential data rather than unique datapoints). ChatGPT, which stands for Chat Generative Pretrained Transformer, is such a transformer model.

Agentic AI is the evolution of generative AI. It gives genAI the autonomy not just to create or chat, but to plan, decide, and act on its own to fulfil a predefined goal. Agentic AI scans its environment (for example, emails and other data), reasons with an LLM as its brain, and executes tasks. AI agents can make plans, adjust plans, or collaborate with other agents—all with minimal human hand-holding.

For example, AI marketing agents can spot a sales dip, research trends, draft targeted ads, launch them on various ad platforms, monitor clicks, and tweak budgets on the fly to hit your company’s revenue targets.

Computer vision is AI that lets machines "see" and make sense of images or video, much like human eyes and brain. It starts by breaking down pixels into features—edges, shapes, textures—using techniques like convolutional neural networks (CNNs), which scan for patterns to detect objects or faces without hand-coded rules.

For example, self-driving cars use computer vision to spot pedestrians, read signs, and track lanes in real time, through CNNs trained on millions of road images.

Edge AI runs AI models on devices such as cameras, sensors, or phones instead of sending data to the cloud. It uses lightweight neural networks optimized for low-power chips to process data on these devices without internet delays. One common application is a wearable fitness tracker.

To make the most of your AI and data, consider these five best practices. They can help you improve predictive accuracy, personalize customer experiences, and get the most ROI from your AI investment.

Make sure you have clean, unbiased, well-structured data from the start. Implement automated validation pipelines to remove missing values, duplicates, and outliers. Just as importantly, conduct regular audits and add data governance frameworks for reliable, high-integrity AI results.

Keep regulatory compliance front and center when feeding data into AI models. You can protect sensitive information throughout the data lifecycle with encryption for storage and transmission, de-identification, and role-based access controls (RBAC), which block unauthorized views. These methods will keep you compliant with privacy laws and regulations, slashing the risks of breaches and fines.

AI apps demand serious computing power to train models and crunch massive datasets. A scalable infrastructure consists of systems that flex up or down on demand, adding GPU and CPU resources during peak loads without downtime.

Cloud platforms and hybrid cloud/on-premise systems can also provide scalability. They can handle petabyte-scale data through distributed processing while controlling costs with pay-as-you-go pricing, keeping your AI humming as your needs grow.

Many cloud solutions offer cost-effective scalability combined with AI capabilities. For example, Salesforce Data 360 integrates all your data in a cloud-based system that powers AI and agentic innovation.

AI success hinges on IT, business, and data teams working as one unit. You can break down organizational silos with regular workshops and shared dashboards so everyone aligns on goals, accelerates adoption, and helps your organization get the best ROI from your AI projects.

AI and data integration can unlock huge business gains. They also come with real hurdles.

Fragmented data in different departments require significant integration efforts and can hinder your AI deployment. Without unified data, AI models may produce incomplete or inconsistent results. You can move away from data silos by adopting data platforms such as data warehouses, data lakes, or data lakehouses, which consolidate information from multiple sources into a single repository.

AI applications can spark ethical concerns, including biased algorithms and data misuse. You can build customer trust by explaining your AI processes clearly, publishing ethical guidelines (e.g., fairness checks and human oversight), and complying with data protection laws and regulations.

AI implementations can be costly. AI expertise, new infrastructure, and ongoing maintenance can add up quickly. You can cut costs by using cloud-based AI services, which offer scalable and cost-effective solutions. You can also adopt AI by starting small with pilots that show ROI before increasing the scope of your projects.

Legacy systems often lack compatibility with modern AI tools. Consider phased upgrades, replacing one module at a time, or hybrid architectures running old and new systems in parallel.

Unlock the full potential of AI and data to transform your business decisions, streamline processes, and drive innovation. Learn more about Salesforce Data 360, the platform that unifies and transforms fragmented data, and powers intelligent agents.