Last week, around 4,000 IBM employees, customers and partners attended IBM Think, the company’s annual conference, to hear the latest innovations, updates and news from IBM. This year’s event had many announcements, but with AI in focus, its announcement by Watsonx attracted significant attention – with the market focused on the significant opportunities around AI.

After attending the event and hearing from IBM executives, as well as a wide range of recent AI and generative AI announcements, I believe that IBM’s watsonx announcement is a significant milestone in the advancement of enterprise AI. Built on top of the Red Hat OpenShift platform, watsonx offers a complete technology stack for training, implementing and supporting artificial intelligence capabilities in any cloud environment. take advantage of the ease and reliability of this technology. As I see it, this announcement is one of the more important announcements that ties together much of the exciting generative AI news and analysis with a more practical connective tissue that will lead to meaningful enterprise adoption.

watsonx has three different components: watsonx.ai, watsonx.dataand watsonx.governance. The first component, watsonx.ai, is a design studio for basic models, machine learning and generative artificial intelligence. It can be used to train, tune, and deploy AI models, including IBM-supplied, open-source, and client-provided models, and is currently in pre-program with select IBM customers and partners, and is expected to be available to the general public. in July.

Another component, watsonx.data, its data lakehouse offering, focuses on analytics and AI workloads. As data is what helps AI learn and grow, this component is critical to enabling AI capabilities. The Watsonx.data store is open, managed and hybrid, and is also expected to be available in July.

The last part, watsonx.governance, is aimed at enabling transparent and accountable AI, and keeping data and AI workflows “explainable”. In an era of rapid AI innovation, it is critical that businesses are able to not only explain what AI does, but also manage bias and ensure that models do not deviate from their intended use. This is a critically important part of the stack, as it will help users responsibly build AI capabilities. This component will be available in October.

While these components are naturally synergistic, they can be consumed individually or together, giving your business users flexibility as needed depending on which part of the AI ​​pipeline requires attention versus existing capabilities.

IBM’s goal in creating watsonx

IBM is creating the foundation to make AI more accessible to all businesses, not just those with advanced technical expertise. Its watsonx.ai tool contains a model library that includes foundational models already tested and curated by IBM. These models are considered robust in the open source community and are trained on language, code, tabular data, geospatial data, time series data, etc.

The models in the watsonx.ai library can be used for a variety of purposes, such as automatically generating code through a natural language interface, planning for natural disaster patterns, or developing specific use cases that can be easily adapted to business needs. Essentially, it’s a huge toolkit that businesses can use to build AI capabilities according to their specific requirements.

In addition to the watsonx platform itself, IBM plans to “bring” the watsonx.ai models into its main product offering. For example, Watson Code Assistant (available later in 2023) will use generative artificial intelligence to help generate code using English-language commands. AIOps Insights will feature models that offer insight into IT performance across environments. Watson Assistant and Watson Orchestrate will use a model that improves employee productivity as well as the user experience. The Environmental Intelligence Suite will feature a geospatial model that enables companies to create solutions that help mitigate environmental risks.

Overall, IBM’s watsonx announcement is an important step in supporting generative artificial intelligence. With its full suite of technologies and services, Watsonx offers businesses ease and reliability when it comes to deploying and supporting AI capabilities in any cloud environment. The availability of the watsonx.ai model also makes building AI capabilities more accessible to all businesses, regardless of technical expertise. As AI continues to grow and evolve, IBM’s initiative to enable accountable and transparent artificial intelligence through watsonx.governance is a critical component to ensuring the safe and ethical use of this technology.

A critical step for broader progress

The announcement of IBM’s watsonx is not only a significant milestone in the implementation of generative AI, but also a critical step towards solving some of the key challenges that have hindered its wider adoption.

One of the biggest challenges in applying artificial intelligence on a large scale is the complexity and variety of data sources. There is often no common data standard for different data sources, making it difficult to train models that can be used in different applications. watsonx solves this challenge by providing a single platform for training, tuning and deploying ML models in any cloud environment. This means that companies can now develop AI solutions without worrying about the underlying infrastructure, reducing the time and cost required to run these solutions.

Another major challenge with generative artificial intelligence is the lack of transparency in the decision-making process. It is often difficult to understand how an AI system arrives at a particular decision or recommendation, making it difficult to trust these systems. The Watsonx.governance component is designed to address this challenge by enabling transparent and accountable AI development. This is crucial for businesses that have to comply with regulatory requirements and ethical considerations, as it helps them build trust with their customers and stakeholders.

With the increasing complexity of AI models, it becomes increasingly difficult to manage and monitor the performance of these models. This is where the watsonx.data component comes in. It provides analytics and AI workloads that enable enterprises to monitor the performance of their AI models in real time. This allows them to identify problems early and take corrective action before they affect the business.

The availability of a comprehensive platform like watsonx can also help address the skills gap that currently exists in the industry. Many businesses struggle to find employees with the necessary skills to develop, implement and maintain AI solutions. By providing a unified platform that includes pre-built models, AI services and other tools, watsonx can help lower barriers to entry for companies that want to use AI but lack the in-house expertise – IBM should also benefit from its deep consulting expertise here. I see both IBM consulting and GSIs such as Accenture, Capgemini and others being able to significantly capitalize on the opportunity to help companies realize the potential of AI using this type of solution set.

By making e AI more accessible and easier to use, IBM’s watsonx has the potential to accelerate the adoption of a broad set of AI including generative technology across industries. During its launch, IBM was able to point to a number of cases already in the market from Moderna to the PGA Tour to NASA. Furthermore, the company is leaning on its ecosystem as it has announced a partnership with SAP, which is likely the first of many of these types of partnerships that I expect. I believe these strategies will lead to an explosion of new applications and use cases that are not currently possible with traditional approaches to software development. With the ability to train, tune and deploy AI/ML models in any cloud environment, businesses can now harness the power of AI to drive innovation and competitive advantage.

While I do expect an influx of entrants vying for the enterprise AI opportunity, I believe IBM’s watsonx announcement is a significant step toward realizing the full potential of generative and non-generative AI. By providing a single platform for training, tuning and deploying ML models in any cloud environment, watsonx makes it easy for enterprises to develop and deploy AI solutions at scale. Incorporating watsonx.governance also ensures that AI development is accountable and transparent, which is key to building trust with customers and stakeholders. Overall, watsonx has the potential to transform the way we build and deploy AI solutions, and if used to the extent of its proposed capabilities, should help usher in a new era of innovation and growth.