May 9, 2023 By Kareem Yusuf, Ph.D 6 min read

Today is a revolutionary moment for artificial intelligence (AI). After some impressive advances over the past decade, largely thanks to the techniques of machine learning (ML) and deep learning, the technology seems to have taken a sudden leap forward. Suddenly, everybody is talking about generative AI: sometimes with excitement, other times with anxiety. But few doubt that the advances we are seeing are significant, or that they represent a huge opportunity for those businesses that act quickly — and strategically.

But why now? The answer is that generative AI leverages recent advances in foundation models. Unlike traditional ML, where each new use case requires a new model to be designed and built using specific data, foundation models are trained on large amounts of unlabeled data, which can then be adapted to new scenarios and business applications. A foundation model thus makes massive AI scalability possible, while amortizing the initial work of model building each time it is used, as the data requirements for fine tuning additional models are much lower. This results in both increased ROI and much faster time to market.

For decades, IBM has been at the forefront of breakthroughs in AI — from the world’s first checkers playing program to building an AI super computer in the cloud. Today we have one of the most comprehensive portfolios of enterprise AI solutions available. Our Watson suite is deployed to more than 100 million users across 20 industries, while the dedicated teams in IBM Research continue to push at the frontiers of the technology.

AI is already driving results for business. It makes our supply chains stronger, defends critical enterprise data against cyber attackers and helps deliver seamless experiences to millions of customers every day across multiple industries. But the foundation models that power generative AI will make these achievements seem like a prelude to the main act — and this will be especially true if we make the technology as accessible as possible. At IBM, we believe it is time to place the power of AI in the hands of all kinds of “AI builders” — from data scientists to developers to everyday users who have never written a single line of code.

Watsonx, IBM’s next-generation AI platform, is designed to do just that. It provides self-service access to high-quality, trustworthy data, enabling users to collaborate on a single platform where they can build and refine both new, generative AI foundation models as well as traditional machine learning systems. The early use cases that we have identified range from digital labor, IT automation, application modernization, and security to sustainability.

Watsonx has three components:, and watsonx.governance. It offers its users advanced machine learning, data management and generative AI capabilities to train, validate, tune and deploy AI systems across the business with speed, trusted data and governance. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring. And we believe it has the potential to scale and accelerate the impact of the most advanced AI on every enterprise.

Train, validate, tune and deploy AI across the business with is an AI studio designed for the business of today and tomorrow. It combines the capabilities of IBM Watson Studio, which empowers data scientists, developers and analysts to build, run and deploy AI based on machine learning, with the latest generative AI capabilities that leverage the power of foundation models.

Core to watsonx is the principle of trust. As AI becomes more pervasive, businesses need to feel confident that their models can be relied upon not to “hallucinate” facts or use inappropriate language when interacting with customers. Our approach is to establish the right levels of rigor, process, technology and tools to adapt in an agile fashion to an evolving legal and regulatory landscape. gives users access to high-quality, pre-trained and proprietary IBM foundation models for enterprise. They are domain specific and built with a rigorous focus on data acquisition, provenance and quality. In addition, IBM is making available a wide selection of open-source models through

Trust is one part of the equation. The second is access. For AI to be truly transformative, as many people as possible should have access to its benefits. To that end, we have designed with user friendliness in mind. is not just for data scientists and developers — business users can also access it via an easy-to-use interface that responds to natural language prompts for different tasks.

In a prompt lab, users can experiment with models by entering prompts for a wide range of tasks such as summarizing transcripts or performing sentiment analysis on a document. Depending on the task, will allow users to select a foundation model from a drop-down menu. Developers can build workflows directly in our ModelOps environment using APIs, SDKs and libraries, managing machine learning models from development to deployment. Advanced users will be able to use our tuning studio to customize models with labeled data, creating new trusted models from a pre-trained model.

But at IBM we believe that language is only the beginning when it comes to foundation models. We are also building models trained on different types of business data, including code, time-series data, tabular data, geospatial data and IT events data. Initial foundation models that will be made available in beta to select clients include foundation models for language (also known as LLMs), geospatial data, molecules and code.

Scale and manage AI with

For AI to drive truly impactful results across the business, it must integrate into existing workflows and systems, automating key processes across areas such as customer service, supply chain and cybersecurity. Enterprises need to be able to easily and securely move AI workloads around, and in today’s world that can mean across cloud, as well as modern and legacy software and hardware systems.

With, businesses can quickly connect to data, get trusted insights and reduce data warehouse costs. A data store built on open lakehouse architecture, it runs both on premises and across multi-cloud environments.

Optimized for all data, analytics and AI workloads, combines the flexibility of a data lake with the performance of a data warehouse, helping businesses to scale data analytics and AI anywhere their data resides. Through workload optimization, an organization can reduce data warehouse costs by up to 50% by augmenting with this solution.[1]

Users can access data through a single point of entry, with a shared metadata layer across clouds and on-premises environments. also comes with built-in governance, security and automation, enabling data scientists and developers to use governed enterprise data to train and tune foundation models, while also addressing enterprise compliance and security across the data ecosystem.

With, businesses will be able to build trustworthy AI models and automate AI life cycles on multicloud architectures, taking full advantage of interoperability with IBM and third-party services.

Build trust into your AI lifecycle with watsonx.governance

Trust is central with AI models, both while building and tuning and once they are inside your products and workflows.

Indeed, the more AI is embedded into daily workflows, the more you need proactive governance to drive responsible, ethical decision-making across the business.

Watsonx.governance can help build the necessary guardrails around AI tools and the uses of AI. It is an automated data and model lifecycle solution for creating policies, assigning decision rights and ensuring organizational accountability for risk and investment decisions.

Watsonx.governance employs software automation to help strengthen a client’s ability to mitigate risk, help meet regulatory requirements and address ethical concerns without the excessive costs of switching a data science platform, even for models developed using third-party tools. It empowers businesses to automate and consolidate multiple tools, applications and platforms while documenting the origin of datasets, models, associated metadata and pipelines.

By providing the mechanisms to help secure and protect customer privacy and proactively detect model bias and drift, watsonx.governance helps organizations meet ethics standards and proactively manage risk and reputation. Regulations can be translated into policies and business processes to help ensure compliance, while customizable reports and dashboards help maintain stakeholder visibility and collaboration.

Put AI to work in your business with IBM today

IBM is infusing foundation models throughout all of its major software solutions and services — embedding it in core AI and automation products and within our consulting practices. These include:

  • watsonx Assistant and watsonx Orchestrate: Core digital labor products have been supercharged with the NLP foundation model to enhance employee productivity and customer service experiences.
  • watsonx Code Assistant: Uses generative AI that allows developers to automatically generate code with a straightforward command, such as, “Deploy Web Application Stack” or “Install Nodejs dependencies.” 
  • IBM AIOps Insights: AI Operations (AIOps) capabilities are enhanced with foundation models for code and language processing to provide greater visibility into performance data and dependencies across IT environments, helping IT operations (ITOps) managers and Site Reliability Engineers (SREs) resolve incidents in a more expedient and cost-efficient way.
  • Environmental Intelligence Suite: IBM EIS Builder Edition, which will be available as-a-Service through the IBM Environmental Intelligence Suite (EIS) this year, is powered by the geospatial foundation model, allowing organizations to create tailored solutions that address and mitigate environmental risks based on their unique goals and needs.

Place trust at the core of your AI strategy

Possibilities that we are only beginning to imagine will soon become commonplace as these new AI models dramatically impact how people interact with technology, changing not only how we do business, but how we think about business.

But to fully realize its potential, AI must be built on a foundation of trust and transparency, and it must be as widely available as possible, so all can benefit. IBM believes that there are five pillars to trustworthy AI: explainability, fairness, robustness, transparency and privacy.

IBM has designed watsonx to adhere to these core principles of trust while being as accessible as possible. A future of trustworthy AI delivering boosts to productivity and enhancing innovation is not only possible, it is already here. These are exciting times. Let’s put AI to work and make the world work better — together.

See what’s coming with watsonx

Statements regarding IBM’s future direction and intent are subject to change or withdrawal without notice and represent goals and objectives only.

[1]When comparing published 2023 list prices normalized for VPC hours of to several major cloud data warehouse vendors. Savings may vary depending on configurations, workloads and vendors.

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