October 20, 2023 By Anna Holman 9 min read

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually. 

AI plays a pivotal role as a catalyst in the new era of technological advancement. PwC calculates that “AI could contribute up to USD 15.7 trillion to the global economy in 2030, more than the current output of China and India combined.” Of this, PwC estimates that “USD 6.6 trillion is likely to come from increased productivity, and USD 9.1 trillion is likely to come from consumption-side effects.” When observing its potential impact within industry, McKinsey Global Institute estimates that in just the manufacturing sector, emerging technologies that use AI will by 2025 add as much as USD 3.7 trillion in value. AI technology is quickly proving to be a critical component of business intelligence within organizations across industries. Major cloud infrastructure providers such as IBM, Amazon AWS, Microsoft Azure and Google Cloud have expanded the market by adding AI platforms to their offerings. 

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. These development platforms support collaboration between data science and engineering teams, which decreases costs by reducing redundant efforts and automating routine tasks, such as data duplication or extraction. Some AI platforms also provide advanced AI capabilities, such as natural language processing (NLP) and speech recognition.  

That said, selecting a platform can be a challenging process, as the wrong system can drive increased costs as well as potentially limit the use of other valuable tools or technologies. Apart from pricing, there are numerous other factors to consider when evaluating the best AI platforms for your business. Gaining an understanding of available AI tools and their capabilities can assist you in making informed decisions when selecting a platform that aligns with your business objectives. 

What types of features do AI platforms offer? 

AI platforms assist with a multitude of tasks ranging from enforcing data governance to better workload distribution to the accelerated construction of machine learning models. Since achieving success with AI usually depends on an organization’s ability to deploy models at scale quickly, it is essential to look for the right capabilities in your AI platform to support your organization’s goals. These can include, but are not limited to: 

MLOps capabilities      

  • Orchestration pipelines: A single unified platform allows for teams to have a common set of tools across data analytics, data science, and ML, as well as supporting an extensive range of machine learning algorithms, including neural networks for complex predictive analytics. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency. 
  • AutoML tools: Automated machine learning, or autoML, supports faster model creation with low-code and no-code functionality. 
  • Decision optimization: Streamline the selection and deployment of optimization models and enable the creation of dashboards to share results, enhance collaboration and recommend optimal action plans. You can optimize trade-offs between business goals—such as reducing customer service costs or improving customer satisfaction—and determine the best course of action in each situation. 
  • Visual modeling: Combine visual data science with open source libraries and notebook-based interfaces on a unified data and AI studio. By exploring data from different perspectives with visualizations, you can identify patterns, connections, insights and relationships within that data and quickly understand large amounts of information. 
  • Automated development: With AutoAI, beginners can quickly get started and more advanced data scientists can accelerate experimentation in AI development. AutoAI automates data preparation, model development, feature engineering and hyperparameter optimization. 
  • Synthetic data generator: Synthetic data can be used as an alternative or supplement to real-world data when real-world data is not readily available, which can be particularly helpful in experimentation. Platform capabilities can assist you in generating a synthetic tabular data set that leverages the existing data or a custom data schema. You can connect to the existing database, upload a data file, anonymize columns and generate as much data as needed to address data gaps or train classical AI models.

Generative AI capabilities 

  • Content generator: Generative AI refers to deep-learning models that can generate text, images and other content based on the data they were trained on. AI platforms can generate content and assist with various tasks, such as crafting marketing emails and creating customer personas. 
  • Automated classification:  AI platforms can read and classify written input, such as evaluating and sorting customer complaints or reviewing customer feedback sentiment.
  • Summary generator: AI platforms can also transform dense text into a high-quality summary, capturing key points from financial reports, meeting transcriptions and more. 
  • Data extraction: Platform capabilities help sort through complex details and quickly pull the necessary information from large documents. It does this by identifying named entities, parsing terms and conditions, and more. 

Key benefits of an AI platform 

AI platforms can help you harness the power of AI technology, driving a range of benefits for your business, such as increased automation, scale, security and more. These platforms enable businesses to analyze vast amounts of data, derive valuable insights and adapt swiftly to changing market dynamics, ultimately fostering innovation and a competitive edge. 

Increased automation 

Automation plays a pivotal role in expediting both the scale and pace of activities throughout the data lifecycle. Once teams identify a successful, repeatable process, such as consistent data labeling, they can look for ways to automate it with machine learning. In this case, employing the AI platform’s capabilities to automate data labeling would lead to greater accuracy in predictions and enhanced usability of data variables. 

More scalability 

Scalability in both the training and production phases of machine learning models is vital, as constructing and training models on a local machine, such as laptop, has its limitations. This may be sufficient for smaller datasets, but data scientists will not be able to use this approach for more robust models. To scale, they will need a centralized workflow, which facilitates transparency and collaboration with fellow practitioners to align data to standards and monitor compute availability along with GPU and TPU usage.  

Better integration 

An  AI platform should also offer user-friendly integrations that facilitate the use of open source software and libraries. Most platforms are already compatible with popular open source frameworks like PyTorch, TensorFlow and Scikit-learn, but for a comprehensive AI ecosystem, seek an AI platform that offers seamless and convenient access to open source platforms such as MongoDB, Redis and PostgreSQL.

Moreover, the best AI platforms are developed and sustained by organizations and teams deeply involved in the open source community. They contribute to research, allocate resources and offer their expertise, thereby enriching the diversity of skills and research contributions while expanding the array of innovative technologies accessible to data science and machine learning professionals. 

IBM was one of the earliest champions of open source, backing influential communities like Linux, Apache and Eclipse, pushing for open licenses, open governance and open standards. IBM’s association with open source became even more prominent after its acquisition of Red Hat. 

Additionally, it’s crucial to consider the deployment and usage strategy for your AI platform. Will it be implemented on-premises or hosted using a cloud platform? Is it intended for internal team use or to be accessible to external customers? These factors are also important in identifying the AI platform that can be most effectively integrated to align with your business objectives. 

Enhanced security  

Open source packages are frequently used by data scientists, application developers and data engineers, but they can pose a security risk to companies. Security controls are vital to help identify and protect against rapidly evolving threats. The best AI platforms typically have various measures in place to ensure that your data, application endpoints and identity are protected.  

Key security measures include: 

  • Network security: Network security has three chief aims: to prevent unauthorized access to network resources, to detect and stop cyberattacks and security breaches in progress and to ensure that authorized users have secure access to the network resources they need, when they need them. 
  • Data security: Data security protects digital information from unauthorized access, corruption or theft throughout its entire lifecycle.  
  • Collaborator security: Collaborator security protects your workspaces by assigning role-based access controls to collaborators.

Improved governance 

AI governance seeks to guarantee the ethical, responsible and compliant development and implementation of an organization’s AI and ML models. An AI platform with thoughtful governance capabilities allows for improved collaboration and coordination on model approvals, monitoring and compliance governance. AI governance is essential to instill trust and reliance in the data-driven decisions made by organizations using the insights from these platforms. This trust extends to meeting both internal compliance mandates and external regulations. 

Lack of AI governance can lead to consequences such as inefficiency, financial penalties and significant damage to brand reputation. It can also hamper scaling of ML processes, making results harder to reproduce and risking errors due to incorrect or incomplete data. Penalties can be substantial, with bank operators receiving seven-figure fines for biased loan eligibility models, and potential GDPR fines of up to 20 million euros or four percent of annual revenue.

Moreover, ensuring appropriate user access is an essential element of governance within an AI platform as it can prevent certain roles from inadvertently making an error that impacts the whole system. IT administrators should have the ability to allocate accounts based on job roles, monitor user activities and facilitate seamless sharing and collaboration among practitioners. 

Seek a platform that implements robust governance practices to ensure the standardization of data, the mitigation of bias and compliance with industry regulations. 

Technical support 

If you require assistance with training and education, reliable bug reporting and tracking, resolving issues or responding to emergencies, it is wise to choose an AI platform capable of offering the support you need. 

A strong user community along with support resources (e.g., forums, documentation, customer support) can also be invaluable for troubleshooting issues and sharing knowledge. 

Best examples of AI platform use cases 

Embracing AI is essential for organizations to stay competitive and avoid the risk of falling behind. The following use cases demonstrate how organizations have integrated AI in their respective industries. 


AI’s strengths can help meet the myriad challenges involved in the delivery of healthcare—challenges that are only growing. 

Addressing challenges in radiology 

As patient data is increasing in volume and complexity, there is also growing pressure on radiologists to be more efficient and tackle larger patient volumes. The shift to value-based care makes reimbursements more elusive, driving organizations to look for ways to boost efficiency and productivity in order to meet their financial goals. As one would expect, these changes and growing demands have led to mounting provider frustration and burnout. 

With its robust image and data analysis capabilities, AI can aid radiologists in: 

  • Image acquisition 
  • Initial reads and interpretations 
  • Study prioritization and triage 
  • Recommendations of relevant findings from patient records in the EHR 
  • Recommendations of relevant findings from literature or clinical guidelines 

Financial services 

Today, the banking industry is undergoing a transformation with the use of AI. For bankers working with consumers, AI-powered research systems can significantly enhance their access to key information about various products the bank has available, such as data on features, benefits, terms and conditions, pricing and other important information, empowering the bankers to deliver better service. 

This improved access to information, enabled by AI, equips bankers with a powerful toolkit to provide superior service. With in-depth knowledge of the bank’s product offerings and a clear understanding of individual customer profiles, they can tailor their recommendations and solutions more precisely, aligning them with each customer’s unique financial goals and circumstances. 

One bank found that its chatbots, which were managed by IBM Watson, successfully answered 55 percent of all customer questions, requests, and messages—which allowed for the other 45 percent to be referred to human bankers more quickly. Part of effectively implementing AI is determining when it’s time for the AI to pass the baton. 

The financial industry has effectively harnessed AI to assist in additional areas, including: 

  • Automating credit assessments 
  • Real-time fraud detection 
  • Money laundering prevention 
  • Claims processing 


Over the past two years, we have all needed to adopt new hybrid approaches to working, parenting, socializing—and shopping. The emergence of “hybrid shopping,” which blends in-store digital and physical touchpoints, has become mainstream. Hybrid shopping is the primary buying method for 27 percent of all consumers and 36 percent of Gen Zers. Across all ages, nearly three in four (72 percent) consumers overall depend on stores as part of their primary buying method. 

This creates both a challenge and an opportunity: how can retailers seamlessly weave together hybrid shopping experiences that comprise online, in-store, mobile and virtual channels within a single customer journey? 

The retail industry is digitally transforming, embracing AI at its core to enable key capabilities across five primary areas: 

  • Personalized shopping experiences: AI delivers hyper-localized insights and real-time recommendations. 
  • Superpowered associates: AI-assisted store associates engage with consumers across all touchpoints. 
  • Intelligent workflows: AI optimizes in-store processes, inventory management and deliveries. 
  • Operations center: AI technology monitors and resolves store incidents efficiently.  
  • Store operating platform: Scalable and secure foundation supports AI at the edge and data integration. 


Manufacturers often encounter various challenges, such as unforeseen machinery breakdowns or issues with product deliveries. By harnessing the power of AI, manufacturers can enhance operational efficiency, introduce new products, tailor product designs and strategize future financial decisions, advancing their journey toward digital transformation. 

Key AI solutions that directly address these challenges include the following: 

  • Predictive Maintenance: AI helps manufacturers detect equipment issues through sensor data, enabling proactive maintenance and cost savings. 
  • Quality assurance: AI-driven machine vision on data-driven assembly lines identifies product defects, issuing alerts for corrective actions to maintain quality. 
  • Inventory management: AI-powered demand forecasting apps and tools improve inventory control, reducing excess stock and stockouts compared to traditional methods. 

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