At this year's IBM Think in Orlando, IBM CEO Arvind Krishna fields questions from industry analysts. ... [+]
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 came with many announcements, but with AI in focus, its announcement of watsonx drew significant attention—with the market zeroing in on the substantial opportunities around AI.
After attending the event, and hearing from IBM executives as well as following the broad swath of recent AI and generative AI announcements, I believe that IBM’s announcement of watsonx is a significant milestone in the advancement of enterprise AI. Built on top of the Red Hat OpenShift platform, watsonx offers a full tech stack for training, deploying, and supporting AI capabilities across any cloud environment This move by IBM is indicative of the growing importance of supporting generative AI, and the potential for businesses to benefit from the ease and reliability of this technology. As I see it, this announcement is one of the more important announcements tying together much of the exciting generative AI news and analysis with the more practical connective tissues that will drive meaningful adoption in the enterprise.
watsonx features three different components: watsonx.ai, watsonx.data, and watsonx.governance. The first component, watsonx.ai, is a design studio for base models, machine learning, and generative AI. It can be used to train, tune, and deploy AI models including IBM supplied models, open-source models, and client provided models,and is currently in preview with select IBM clients and partners, and is expected to be available to the general public in July.
The second 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 for enabling AI capabilities. The watsonx.data store is open, governed, and hybrid, and is also expected to be available in July.
The last piece, watsonx.governance, is geared toward enabling transparent and responsible AI, and keeping data and AI workflows “explainable.” In the era of rapid AI innovation, it is critical that enterprises are able to not only explain what the AI is doing, but to manage bias and make sure models don’t drift from their intended use. This is a critically important part of the stack, as it will help users build AI capabilities responsibly. This component will be available in October.
These components while naturally synergistic, can be consumed individually or collectively, providing its enterprise users with flexibility as needed depending on what 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 widely accessible to all enterprises, not just those with advanced technical expertise. Its watsonx.ai tool features a model library that includes foundation models that have already been vetted and curated by IBM. These models are considered robust among the open-source community, and have been 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 auto-generating code through a natural language interface, planning for natural disaster patterns, or developing industry-specific use cases that can be easily customized per enterprise needs. Essentially, it’s a huge toolkit that enterprises can use to build AI capabilities according to their specific requirements.
In addition to the watsonx platform itself, IBM plans to “infuse” watsonx.ai models into its major product offerings. For example, Watson Code Assistant (available later in 2023) will use generative AI to help generate code with English language commands. AIOps Insights will feature models that offer visibility into IT performance across different environments. Watson Assistant and Watson Orchestrate will use a model that improves employee productivity as well as customer service experiences. Environmental Intelligence Suite will feature a geospatial model that allows companies to create solutions that help alleviate environmental risks.
Overall, IBM’s announcement of watsonx is an important step in supporting generative AI. With its full tech and services stack, watsonx offers businesses ease and reliability when it comes to deploying and supporting AI capabilities across any cloud environment. The availability of watsonx.ai models also makes building AI capabilities more accessible to all enterprises, regardless of technical expertise. As AI continues to grow and evolve, IBM’s initiative to enable responsible and transparent AI through watsonx.governance is a critical component for ensuring the safe and ethical use of this technology.
Critical Step for Wider Advancement
The announcement of IBM’s watsonx is not just a significant milestone in the deployment of generative AI, but also a critical step towards addressing some key challenges that have held back its wider adoption.
One of the biggest challenges in deploying AI at scale is the complexity and diversity of data sources. There is often no common data standard across disparate data sources, which makes it difficult to train models that can be used across different applications. watsonx addresses this challenge by providing a unified platform for training, tuning, and deploying ML models across any cloud environment. This means that businesses can now develop AI solutions without having to worry about the underlying infrastructure, thus reducing the time and cost required to get these solutions up and running.
Another major challenge with generative AI is the lack of transparency in its decision-making process. It is often difficult to understand how an AI system arrives at a certain decision or recommendation, which makes it hard to trust these systems. The watsonx.governance component is designed to address this challenge by enabling transparent and responsible AI development. This is critical for enterprises that need 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 is becoming more 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 track the performance of their AI models in real-time. This allows them to identify issues early on and take corrective actions before they impact business operations.
The availability of a comprehensive platform like watsonx can also help address the skills gap that currently exists in the industry. Many enterprises struggle to find employees with the necessary skills to develop, deploy, and maintain AI solutions. By providing a unified platform that includes pre-built models, AI services, and other tools, watsonx can help lower the barrier to entry for businesses that want to use AI but lack the internal expertise—IBM should also benefit here from its deep consulting expertise. I see both IBM consulting and GSIs like Accenture, Capgemini, and others being able to capitalize substantially 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 all industries. During its launch, IBM was able to point to a barrage of already in market use cases from Moderna to the PGA Tour to NASA. Furthermore, the company is leaning into its ecosystem as it announced a partnership with SAP, which is likely the first of what I expect to be many of these types of partnerships. I believe these strategies will lead to an explosion of new applications and use cases that are currently not possible with traditional approaches to software development. With the ability to train, tune, and deploy AI/ML models across any cloud environment, enterprises can now leverage the power of AI to drive innovation and competitive advantage.
While I truly expect an inflow of entrants to compete for the enterprise AI opportunity, I believe that IBM’s announcement of watsonx is a significant step towards realizing the full potential of generative and non-generative AI. By providing a unified platform for training, tuning, and deploying ML models across any cloud environment, watsonx makes it easier for businesses to develop and deploy AI solutions at scale. The inclusion of watsonx.governance also ensures that AI development is responsible and transparent, which is critical for 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 it should help to usher in a new era of innovation and growth.
