Understanding the AI Platform: What They Are and Why To Use Them

Your team drowns in reports. Your rivals launch products faster. Your data sits trapped in systems that won’t talk to each other. You’ve lived this reality for years. An AI platform solves these headaches.

Without one, the root issue runs deeper. Marketing creates data sales never sees. Support tickets hide patterns nobody extracts. Executives decide based on what happened, not what’s happening now. Band-Aid fixes from IT snap under pressure.

Companies with AI platforms break this cycle. They connect fragmented systems. They automate tedious tasks. They spot customer patterns humans miss. They act on real-time data, not last quarter’s report.

The pages ahead strip away confusion and hype. They reveal exactly how artificial intelligence platforms deliver business value — and how to choose one that works for your specific needs.

What is an AI platform?

An AI platform connects all your data, tools and AI models in one place. It takes data from everywhere: databases, applications, cloud services, spreadsheets and even unstructured sources like emails or documents. It runs AI models across departments, predicting sales outcomes, automating customer responses, optimizing supply chains and flagging unusual patterns in real time. Messy data goes in. Useful answers come out.

The three AI platform approaches

You have three main options for adopting an AI platform. Each aligns with a specific set of needs and capabilities. Choose based on the control you require, the expertise on your team, and the outcomes you expect.

Proprietary solutions

Proprietary platforms are closed-source products built and managed by a single vendor. You get a system that’s ready to deploy — no assembly required. The vendor controls updates, provides support, and handles security patches. Your team focuses on using the platform, not maintaining it.

The key benefit: speed. You launch quickly and don’t need deep AI expertise right away. But you trade flexibility. Customization is limited, and you adapt to the vendor’s roadmap, not the other way around. If priorities shift, your options narrow.

Proprietary fits best when fast results and predictable operations matter more than granular control.

Build your own

Building your own AI platform means designing, integrating, and maintaining every component yourself. You shape the solution to your exact business processes. Connect with proprietary systems. Customize everything — from data pipelines to user interfaces.

You gain maximum control and ownership, with the freedom to innovate on your terms. But the demands are high. In-house platforms require specialized talent, significant budgets, and patience. Expect to wait months — sometimes a year or more — before seeing production results. Maintenance and troubleshooting become ongoing, internal responsibilities.

Build-your-own is right when existing solutions can’t address your core requirements or when competitive advantage depends on tailored AI.

Enterprise-ready open source

Enterprise-ready open source bridges the gap between flexibility and reliability. These platforms start with open-source code, battle-tested by a global community, then add layers of security, support, and lifecycle management for business needs.

You stay independent — no vendor lock-in — and can adapt the platform for your particular use case. When an issue surfaces, you call on professional support teams instead of combing community forums. Security patches and upgrades arrive on a predictable schedule, not whenever someone volunteers.

Examples like SUSE AI show this model in action: open at its core, hardened for the enterprise, and backed by real accountability.

Enterprise-ready open source delivers freedom to customize, professional support to back you up, and resilience for demanding environments.

Key AI platform features

Not all AI platforms work the same. The best share key features that separate useful tools from expensive disappointments. Look for these capabilities when picking your platform.

Data connections that really work

AI needs data. Lots of it. Good platforms connect to all your data sources without endless coding. They smoothly pull from databases, files, apps and cloud services you already use.

Strong platforms handle messy data, too. When dates appear in different formats, they clean them up. Where values are missing, they fill them in. And whenever duplicates exist, they spot and fix them automatically.

The best create a complete view of your business. By combining customer records from different systems and matching product data across warehouses, they convert scattered information into one useful source.

Tools that don’t need data scientists

Your marketing team shouldn’t need advanced computer science degrees to use AI. Good platforms include tools regular business people can use daily.

What matters here are visual interfaces that clearly show what’s happening, along with drag-and-drop options for building workflows. Look for templates designed for common business tasks and plain language explanations that make sense of complex results.

Behind the scenes, the platform handles all the hard math. Modern genAI solutions make this even more accessible. This approach lets your team focus on practical business goals, like “predict customer churn” or “find unusual transactions” without needing to write complex code themselves.

Security that protects your data

AI works with your most sensitive information. Customer details. Financial records. Product plans. For these reasons, your platform needs serious security built in from the start.

Strong platforms carefully control who sees what. They continuously track who uses each piece of data. All information stays encrypted whether at rest or in transit. And when needed, they let you keep sensitive data local, right where it belongs.

Beyond basic protection, the best platforms include compliance tools for regulations like GDPR. They carefully document how AI makes decisions. And they defend your data and your private AI models from unauthorized access attempts.

Deployment that doesn’t need an IT overhaul

Good AI platforms work with what you have now. Instead of forcing you to replace existing systems, they add intelligence to tools your team already knows and uses.

Flexibility matters too. The right platforms run wherever you need them — in your data center, on major cloud providers or at edge locations. Sometimes they’ll even operate in all three places at once.

Connecting AI results to everyday business applications makes all the difference. Your sales tools should receive smart predictions directly. Support systems need customer insights delivered automatically. And the models themselves must integrate with workflows people use every day.

Scaling that grows with your needs

Your first AI project won’t be your last. Good platforms start small but grow easily alongside your ambitions. What handles one department today can expand to serve your whole company tomorrow.

Resource efficiency separates strong platforms from the rest. They run initial tests on minimal hardware, then scale up automatically for bigger workloads as needed. As more data and users come online, everything continues working smoothly.

The top platforms grow in technical and organizational dimensions. They allow different teams to share knowledge. This way, departments can build directly on each other’s work. And over time, individual projects naturally develop into company-wide capabilities everyone benefits from.

Artificial intelligence platform benefits

AI platforms change how businesses work. While single-point AI tools solve one problem at a time, platforms create widespread benefits across your entire operation.

Faster answers when they matter

AI platforms turn data into answers when you need them. Not next quarter. Not next week. Now.

Your sales team spots which deals might slip before the quarter ends. Your support agents know which customers need extra attention today. Your warehouse sees inventory problems before they cause stockouts.

Companies using AI platforms make decisions in minutes that once took days. They spot problems while there’s still time to fix them. And they see opportunities while they still matter.

More output without more people

AI platforms handle routine tasks that eat your team’s time. The work still gets done. Your people just don’t do it.

Your data analysts stop making basic reports and start finding insights nobody asked for yet. Your customer service team stops answering the same questions and starts solving complex problems. Your IT group stops fighting fires and starts building new capabilities.

Business teams work on high-value tasks while AI handles the rest. Your staff size stays the same. But your results grow dramatically.

Smarter products your customers notice

AI platforms help you build products that stand out. Your offerings get smarter while competitors stay basic.

Your software learns how each customer uses it. Your equipment predicts when it needs service. And your website shows what each visitor wants to see.

Customers feel the difference. So, they stay longer. Buy more. And tell others about their positive experiences with your company.

Consistent quality at any scale

Humans make mistakes when they’re tired. When they’re rushed. When they’re doing the same task for the hundredth time. AI doesn’t.

Enterprise AI platforms bring the same careful attention to every transaction. Every customer interaction. Every quality check. They apply the same rules consistently across your entire business.

This consistency shows up in fewer errors. Happier customers. Better compliance with regulations. Less time fixing mistakes that shouldn’t have happened.

Teams that work as one unit

AI platforms break down walls between departments. They create a shared view of what’s happening across your business.

Your marketing knows exactly which products to promote based on inventory levels. Your product team builds features customers want based on support conversations. Your finance department spots spending patterns nobody else noticed.

Teams make choices based on the whole picture, not just their piece. They build on each other’s work instead of duplicating efforts. Plus, they move in the same direction without constant meetings.

Artificial intelligence platform challenges

AI platforms bring powerful benefits. They also come with real challenges. Knowing these hurdles helps you plan better and avoid costly mistakes.

• Messy data slows everything down. Your customer records have duplicates. Your inventory lives in three systems. Your sales data lacks AI-ready details. Fix your data first before expecting AI magic.
• Your team needs new skills. Regular IT staff know your systems, but not AI. Your data team builds reports but not models. Train existing staff or bring in specialists who understand both AI and your business.
• Old systems resist connection. Legacy apps lack modern APIs. Department tools use incompatible formats. Start with small integration projects and modernize critical systems first.
• Teams hesitate to trust AI. Sales leaders question predictions that contradict gut feelings. Operations won’t let AI make decisions alone. Build trust gradually with transparent models and early wins on non-critical processes.
• Hidden costs add up fast. Your purchase price doesn’t include data preparation. Cloud bills grow as models run daily. Budget for the complete journey, not just licenses. Start with projects that show quick returns.

These challenges aren’t permanent roadblocks — they’re hurdles to clear on your path to AI success. The right platform partner makes these challenges easier to navigate.

AI platform use cases

Companies across industries use AI platforms to solve different problems. These real-world examples show how platform AI moves from theory to business results.

Manufacturing gets smarter

Manufacturing companies use AI platforms to spot problems before they happen.

For example, a global auto parts maker connects sensors across 15 factories through their AI platform. The system spots unusual vibrations in equipment 72 hours before failures occur. Maintenance teams fix issues during planned downtime instead of emergency repairs.

This manufacturer prevents failures instead of reacting to them. They catch issues humans miss. Downtime drops. Costs fall. Production continues uninterrupted.

Healthcare saves lives

Healthcare organizations use AI platforms to improve patient outcomes and reduce costs.

For example, let’s say a pharmaceutical company has amplified its drug discovery process with AI. Their platform analyzes molecular structures and patient data to identify promising compounds 10 times faster than manual methods. It also predicts which patients will respond best to specific treatments.

This company finds breakthrough treatments in months, not years. Patients get better therapies sooner. The business gains market advantage while cutting research costs.

Financial services spot risks

Banks and insurance companies use AI platforms to detect fraud and assess risk.

For example, a global bank runs all transactions through its AI platform. The system spots unusual patterns that traditional rules miss. It catches 42 percent more fraud while reducing false positives by 65 percent. Customers experience fewer card blocks while the bank loses less money.

This bank inspects every transaction without delays. Fraud detection improves while customer experience gets better. The AI adapts to new threats automatically, staying ahead of fraudsters.

Retail understands customers

Retailers use AI platforms to personalize shopping and optimize inventory.

For example, a clothing retailer connects its AI platform to online and in-store systems. The platform tracks individual preferences across channels. It recommends products each customer wants. Its email campaigns generate three times more revenue than generic marketing.

This retailer knows what customers want instead of guessing. Every interaction becomes relevant. Sales increase while marketing costs stay fixed.

Telecommunications improves networks

Telecom companies use AI platforms to optimize networks and predict service issues.

For example, a telecommunications company uses AI to predict customer service needs. The platform analyzes network signals, device data and usage patterns. It identifies customers likely to call with problems and proactively fixes issues before they notice.

This company prevents problems instead of reacting to them. Customer satisfaction rises. Support costs drop. The network runs smoothly without constant firefighting.

How to choose between different AI platforms

Choosing an AI platform means finding one to suit your specific needs. These practical checks help you move past sales pitches to find a platform that works for your business.

Start with problems, not features

AI platforms come packed with fancy capabilities. Most businesses don’t need half of them.

Instead of comparing feature lists, start with your actual business problems. Define exactly what you need to solve. Identify which processes waste time. Determine which decisions need better information.

Strong platforms solve these specific issues. Weak ones dazzle with impressive demos that don’t address your real needs. The best AI investment targets your biggest pain points first.

Check your data reality

Every AI platform vendor assumes you have perfect, organized data. You probably don’t.

Don’t trust pretty presentations. Test platforms with your actual messy information instead. Watch how they handle missing values in real time. See how they cope with your inconsistent formats. Observe their connection to your existing systems without modifications.

Truly useful platforms expect messy data and come ready for it. They bring built-in tools to clean and organize information. And most importantly, they deliver results without demanding months of data cleanup projects first.

Watch for hidden costs

That impressive price quote? It rarely includes everything you’ll pay for an AI platform.

Dig deeper into the real costs. Ask about data storage charges that compound over time. Uncover the computing resource fees that hit after you scale. Find out which “basic features” actually cost extra. Factor in ongoing training and support your team will need.

Forward-thinking companies calculate total expenses over three years, not just month one. They identify every add-on required for practical usage. Then they run from platforms with pricing that mysteriously escalates once you’re committed.

Try before you commit

Would you buy a car without a test drive? Don’t purchase an AI platform without hands-on experience either.

Insist on a proper proof of concept using your actual data and business cases. Put your regular staff, not just IT experts, in front of the tools. Time their basic tasks. Watch their frustration levels. See if they can accomplish real work without calling for help.

Reputable vendors not only accept these tests, they encourage them. They actively support your evaluation process. They help tailor the platform to your specific challenges. And most tellingly, they prove concrete value before asking for major investments.

Plan for growth

Your first AI project won’t be your last. Your platform needs room to grow.

Look beyond the initial rollout to future expansion. Check how platforms handle 10 times the increases in data volume without choking. Test how they perform with hundreds of users instead of dozens. See how easily they adapt to new business cases you haven’t even imagined yet.

Truly valuable platforms scale in multiple dimensions. They expand technically as your data grows. They extend organizationally as more departments join. And they evolve capabilities as your AI sophistication increases, without forcing you to start over.

Should you be using an AI platform?

Most companies need an AI platform now, not next year. Your competitors already connect their data. They automate routine tasks, spot patterns humans miss and make faster, better decisions.

AI platforms solve real business problems, not theoretical ones. Manufacturing companies spot equipment failures before they happen. Healthcare businesses predict patient complications. And retailers personalize every customer interaction.

The right platform fits your specific needs. It works with your messy data. It connects to your existing systems. It grows as your AI maturity develops. Your business challenges determine whether buying, building or open source makes most sense.

SUSE provides the reliable foundation your AI platform needs. Our Linux and Kubernetes solutions power enterprise AI workloads with security, stability and performance built in. Contact SUSE today to build your AI platform on the infrastructure trusted by the world’s most demanding organizations.

AI platform FAQs

How much does an AI platform cost?

AI platform costs depend on the number of nodes or GPUs required for your workloads. The more complex your environment — and the more use cases you support — the higher the price. 

The advertised price rarely tells the full story — data preparation, integration work, training and computing resources often double the initial estimate. Start with a small project to validate ROI before committing to larger investments.

Where does the data for AI platforms come from?

AI platforms connect to your existing business systems, pulling data from databases, applications and files containing customer records, transactions and operational logs. They combine your internal information with external sources, such as market trends and public datasets, using the best platforms. These platforms also include tools to automatically clean and organize the data for analysis.

Are AI platforms secure?

Security varies widely between AI platforms, so look for ones that offer strong encryption, access controls and compliance features for regulations such as GDPR and HIPAA. Each system your platform connects to creates potential vulnerabilities, making strong governance and regular security audits essential. Established cloud-based platforms typically offer better security than hastily built internal solutions.