What is an AI platform?

What is an AI platform and how does it work?

An AI platform is an integrated technology environment that provides virtually everything development teams need to efficiently design, customize, and manage intelligent apps and agents. Users can securely access models, agents, tools, and infrastructure to support the entire AI lifecycle—from data ingestion and model training to deployment and monitoring—through a single, unified interface. An AI platform can deploy AI apps and agents using cloud services, on local devices, or in hybrid environments.

Key takeaways

AI platforms empower users to efficiently and securely design, customize, and manage AI solutions, all through a single interface.
AI platforms can deploy AI applications and agents using cloud services, on local devices, and in hybrid environments, depending on business needs.
AI platforms integrate data, models, machine learning, deep learning, predictive analytics, and other AI-powered capabilities.
Organizations that adopt AI platforms can streamline development, innovate at scale, reduce costs, and promote compliance.
Use cases vary by industry, including detecting credit card fraud, diagnosing diseases, and offering personalized retail services.
Trends to watch include increased focus on multimodal AI, AI agent orchestration, and edge computing.

What are three primary ways to deploy AI applications and agents?

Your organization can run an AI platform either on its own, on-premises infrastructure or using cloud services. Developer access to cloud services such as platform as a service (PaaS), AI as a service (AIaaS), and AI models as a service (MaaS) can help speed delivery of new AI apps and agents.

An AI platform also gives your organization flexibility to deploy AI apps and agents in three primary ways, each with distinct advantages and use cases:

Cloud environments
AI apps and agents deployed using cloud environments offer customers greater scalability and agility than other environments. They’re ideal for a variety of organizations, from startups to global corporations, that want to quickly adopt innovative solutions without making upfront hardware investments.

On-premises and private cloud environments
AI apps and agents deployed using an organization’s own data centers provide customers with greater control and data security. They’re often used by organizations in healthcare, finance, and other industries with strict data privacy and regulatory requirements.

Hybrid environments
Hybrid environments combine the flexibility and scalability of cloud-delivered offerings with on-premises control of sensitive data. By supporting workload allocation across environments, they’re well suited for organizations that must meet complex data governance requirements while optimizing resources.

Learn more about real-world use cases for deploying an AI platform.

Key Components

What to look for in an AI platform

Choose an AI platform that has a unified toolchain comprising these essential components:

Data integration

Connects to and ingests large sets of structured and unstructured data from diverse data sources. An AI platform with robust data integration capabilities automatically prepares raw data for AI modeling, helping ensure it’s clean, consistent, and accurate.

Built-in developer tools

Supports a wide range of development languages and frameworks and is built into environments such as GitHub, Visual Studio, and Microsoft Copilot Studio so that developers can build and scale AI solutions using their preferred tools. It also features tools for experimentation, evaluation, and debugging.

Machine learning models

Provides prebuilt and customizable machine learning models from various sources and for different use cases, such as natural language processing (NLP). It also features tools for training, fine-tuning and deploying models. Learn about artificial intelligence versus machine learning (AI vs. ML).

AI agent orchestration

Facilitates AI integration into existing operational processes and workflows by allowing users to create AI agents—autonomous systems that reason, make decisions, automate tasks, and interact with users or data. A complete AI platform should essentially serve as an AI agent platform.

Generative AI and predictive AI

Offers sophisticated AI tools for creating text, images, and other content, and predictive analytics for uncovering actionable insights. These capabilities require significant computing resources to run large language models (LLMs), which use deep learning to process large amounts of unstructured data.

User-friendly interfaces and APIs

Simplifies development workflows with easy-to-use interfaces and APIs. This allows developers and non-technical users alike to intuitively interact with AI models and integrate them into their solutions.

Deployment tools

Supports rapid deployment of AI solutions, from small prototypes to enterprise-level applications, at scale. This is especially important if your organization handles large datasets or runs AI apps in real time.

Monitoring tools

Helps maintain high-performance levels by monitoring AI solutions in real-time for potential issues, such as declining model accuracy and effectiveness. The AI platform should also streamline model updates and version management.

Enterprise-grade security and compliance

Features built-in safeguards for privacy, content safety, and regulatory compliance. This includes tools for detecting hallucinations, indirect prompt injections, and data leakage.

What are the advantages of an AI platform?

Here are some key ways that your organization can use an enterprise AI platform to innovate and compete more effectively:

Achieve faster time to value
An AI platform empowers teams to rapidly take new, innovative ideas from solution design to deployment using prebuilt models, easy-to-use tools, and automated workflows. Also, unlike traditional software solutions, it supports continuous innovation through iterative model development.

Support scalability
An AI platform running on cloud or hybrid infrastructure can quickly scale compute and storage resources to support large data volumes, changing user demands, and complex models.

Save costs through interoperability
Development tools and infrastructure resources are integrated into a single, cohesive platform that operates smoothly with existing IT systems, APIs, and third-party services.

Improve productivity
Teams can design, manage, and monitor machine learning and other AI models using automated workflows, giving them more time to spend on higher-value work. They can also create AI agents that can quickly and accurately perform repetitive manual tasks.

Reduce risks and promote compliance
An AI platform with enhanced data and threat protection capabilities helps ensure AI solutions follow security best practices. It also streamlines compliance with data privacy regulations through automation.

Foster cross-team collaboration
Data scientists, data engineers, and other users can communicate together and access shared workflows through a centralized environment.

Enhance decision-making
With an AI platform, your organization can more quickly deliver the real-time data and insights that individuals and teams need to make informed decisions.

AI platforms across industries

Organizations of all sizes and types are adopting AI platforms. Here are real-world examples of what they’re using AI agents, apps, and other solutions to do:

Banking and finance

Improve financial planning and risk management by generating actionable insights from analyst research and other financial reports and documents.
Detect credit card fraud by analyzing huge volumes of real-time transactional data for anomalies, then automatically notifying investigators when suspicious transactions occur.
Predict credit risk by determining the probability that potential borrowers will default on their loans.

Education

Support differentiated learning by generating lessons, assignments, and assessments that match each student's individual learning needs.
Increase teacher productivity by automating routine tasks such as taking attendance and sending reminders to students.

Government

Enhance emergency response by providing responders with real-time insights that improve decision-making in often life-threatening situations.
Improve citizen services by using conversational AI to answer queries and guide people to appropriate resources.

Healthcare

Speed disease detection by using computer vision technology to assist radiologists in reading diagnostic images.
Support patient self-care with AI-powered coaches that offer personalized health and wellness guidance based on analysis of patient profiles, lifestyles, and health goals and metrics.

Insurance

Streamline underwriting assessments by identifying a potential customer’s risk factors based on analysis of claim history and other relevant data.
Simplify claims processing by automatically routing claims to appropriate adjuster teams and using image recognition technology to interpret and classify damages.

Manufacturing

Accelerate product design by using generative design technology to explore different design options based on cost, materials, and other considerations.
Enhance quality control by using computer vision technology to analyze product components on assembly lines to determine if they meet specifications.
Proactively detect equipment issues by analyzing sensor and other machine data.

Retail

Personalize customer experiences by offering AI-powered services that assess photographs of a customer’s skin and recommend helpful products.
Optimize inventory management by automatically adjusting promotions or pricing strategies based on real-time analysis of inventory levels and sales performance.

What’s next for AI platforms?

Emerging trends
As AI technologies mature and use cases flourish, AI platforms will continue to evolve to meet new demands. Here are some key trends to follow:

Multimodal AI: Models will handle text, images, video, and audio, such as fully interactive virtual assistants, to enable richer AI-powered experiences.
AI agent orchestration: Multiple AI agents will work together to automatically perform complex processes that single agents cannot do on their own.
Edge AI: Computational processing will more often occur directly on devices rather than on servers, increasing speed as well as data privacy and security.
AI democratization: AI platforms will continue to expand user access to advanced AI capabilities with prebuilt AI models and intuitive tools.
Sustainable AI: Organizations will find new ways to optimize AI energy usage to support environmental, social, and governance goals.
Explainable AI (XAI): XAI tools will make AI decision-making more transparent and clear to end users.

Conclusion
An enterprise AI platform is an integrated technology environment that can help your organization build, deploy, and scale intelligent applications and models with greater speed, efficiency, and security. Whether you run it on a cloud, on-premises, or hybrid foundation, it can provide your developer and other teams with prebuilt and custom AI models, user-friendly tools, flexible infrastructure, and essential governance frameworks.

By supporting the entire AI lifecycle, a comprehensive AI platform is key to strengthening your competitive advantage in the AI era.

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FAQ

Artificial intelligence (AI) refers to computer systems that can replicate human tasks such as learning, reasoning, and problem-solving.
AI platforms are integrated environments used to develop, deploy, and manage AI applications and models, streamlining the adoption of AI technologies within businesses.
To set up an AI platform, select a cloud, on-premises, or hybrid environment that meets your business needs. Each environment delivers distinct advantages and supports different use cases.
The future of AI platforms includes multimodal and multiagent AI solutions, edge computing, explainable AI (XAI), low-code/no-code AI tools, and environmentally sustainable practices.
Yes, Microsoft Azure is a comprehensive AI platform for building and scaling AI solutions in cloud environments. It provides prebuilt machine learning models, tools, infrastructure, and governance frameworks.