Recent developments within artificial intelligence (AI) have demonstrated the scale and power of this technology on business and society. However, businesses need to determine how to structure and govern these systems responsibly to avoid bias and errors as the scalability of AI technology can have costly effects to both business and society.
As your organization uses different datasets to apply machine learning and automation to workflows, it’s important to have the right guardrails in place to ensure data quality, compliance, and transparency within your AI systems.
IBM can help you put AI into action now by focusing on the areas of your business where AI can deliver real benefits quickly and ethically. Our rich portfolio of business-grade AI products and analytics solutions are designed to reduce the hurdles of AI adoption, establish the right data foundation, while optimizing for outcomes and responsible use.
Our researchers are developing the next generation of advances in AI software and hardware to bring frictionless, cloud-native development and use of foundation models to enterprise AI.
The largest geospatial foundation model—made by watsonx.ai with NASA data—is now open sourced on Hugging Face
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Read about IBM's approach to AI ethics
Positioned as a leader within Gartner's Magic Quadrant for conversational AI platforms.
Stay at the forefront of responsible AI innovation with IBM Research driving the adoption and scaling of AI.
Improve productivity through faster model development and deployment, yielding USD 1.2—3.4 million in benefits.
Products
IBM has an AI solution to support where you are in your AI journey, allowing your organization to gain a competitive advantage in the marketplace. Our AI, data science and data management solutions support a range of real-world use cases from digital labor, proactive IT, cybersecurity automation, AI infrastructure and more. Take the next step in AI implementation and lifecycle management by learning more about our products and their pricing models.
Save time and effort for your digital workers. Automate tedious tasks to be faster and easier, changing the way your teams work.
Enable developers of all experience levels to write code with AI-generated recommendations.
Apply natural language processing to discover insights and answers more quickly, improving operational workflows.
Train, validate, tune, and deploy foundation and machine learning models, with ease.
Scale AI workloads, for all your data, anywhere.
Enable responsible, transparent and explainable data and AI workflows.
Respond more quickly—even proactively—to performance slowdowns and outages in your tech stack with end-to-end visibility and context.
Enhance your application performance monitoring to provide the context you need to resolve incidents faster.
AIOps Insights is an early product experience to validate and solve the problems central IT operations teams face in managing the availability of enterprise IT resources through event and incident management.
Reinvent critical workflows and operations by adding AI to maximize experiences, decision-making and business value.
Engage our team of hackers, responders, researchers and analysts to protect your organization from global threats through services such as penetration testing, crisis management services and more.
Co-create with our experts to make the most out of your technology. Implement with ease for faster results, stay ahead with the latest AI innovations, and upskill your team to drive broader adoption.
Use cases
IBM supports a number of AI applications to help your business to drive operational efficiencies, deliver insights, and create better employee and customer experiences. Some of our most common use cases include customer service, proactive IT, and cybersecurity automation, which have had strong adoption across industries such as supply chain, healthcare and financial services.
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Foundation models are general-purpose, large-scale models that can be fine-tuned to accomplish a wide set of tasks, creating an opportunity for enterprise.
IBM Consulting explains their approach when embarking upon new AI projects.
Ethical considerations for AI have never been more critical than they are today.
Learn how to operationalize AI across your organization in the latest chapter of the Data Differentiator.