As businesses explore the potential of generative AI, they’re also at an increased risk of complications from complex data environments, a limited number of workers with AI skills and AI governance frameworks that holistically consider all compliance requirements (such as internal policies and procedures, industry standards like NIST AI and regulations).

Although these challenges are similar to those of past AI technologies, generative AI demands even more specialized skills, including management of large, diverse data sets and the ability to navigate heightened ethical concerns due to the unpredictable results it can produce.

IBM®, with its extensive experience in successfully deploying AI at scale, is well-equipped to help businesses address these challenges. IBM watsonx™ AI and data platform addresses skills, data and compliance challenges with tools that make AI more accessible and actionable, while granting access to data and providing built in-governance. The combination enables companies to unlock AI’s full potential to achieve their desired outcomes.

That’s why we are pleased to announce that IBM has been named a strong performer in Forrester Research’s The Forrester Wave™: AI/ML Platforms, Q3, 2024, by Mike Gualtieri and Rowan Curran, 29 August, 2024.  

The Forrester Report credits IBM with offering a “one-stop AI platform that can run in any cloud. IBM watsonx’s vision to be a one-stop AI platform is delivered through three core capabilities: 1) watsonx.ai™ to train and deploy models including foundation models. 2) watsonx.data™ to store, process and manage data for AI and 3) watsonx.governance™ to manage and monitor all AI activities.”

Closing the AI skills gap with watsonx.ai: a practical approach

A major barrier to AI adoption identified in IBM’s 2024 ‘Global AI Adoption Index’ is the shortage of skilled professionals, with 33% of companies identifying limited AI skills and expertise as a top challenge. Building and deploying AI models requires both specialized technical knowledge and the right tools, which many businesses struggle to obtain. IBM watsonx.ai is designed to address these issues by merging generative AI with traditional machine learning. It includes APIs, tools, models and runtimes to simplify and scale the development and deployment of AI applications.

Imagine a mid-sized retail company looking to implement AI-driven demand forecasting. Traditionally, this would involve assembling a team of data scientists to create, train and deploy ML models—a process that is both time-consuming and costly. With watsonx.ai, even organizations with limited AI expertise can quickly develop and refine models with “easy-to-use tools for model training and generative AI development” as mentioned by reference customers interviewed for The Forrester Wave AI/ML Platforms, Q3 2024 report.

IBM watsonx.ai has many tools for developing, training and tuning both generative and traditional AI/ML models and applications. For instance, AI developers can access the Tuning Studio to improve performance of pre-trained foundation models (FM) with parameter efficient fine tuning to train a model to perform a particular task. Watsonx.ai also provides an UI-based tooling environment (Prompt Lab), which leverages prompt engineering techniques and conversational interactions with FMs. This makes it easy for AI developers to experiment with and gain insight into the best fitting model or to determine additional fine tuning that may be needed. Model builders can also work with the AutoAI tool in watsonx.ai that leverages automated ML training to analyze a data set and applies algorithms, transformations and parameter settings to create optimal predictive models.

We believe the recognition from Forrester also validates the differentiated approach of IBM to deliver enterprise-grade foundation models, helping clients accelerate the adoption of generative AI into their business workflows while mitigating foundation model-related risks. The watsonx.ai AI studio includes pre-trained, open-source and custom foundation models from third parties and our own flagship Granite series, greatly accelerating AI deployment to meet business needs. By providing these powerful tools, watsonx.ai helps businesses bridge the AI skills gap and accelerate their AI projects, making AI more accessible and integral to their operations.

Tackling data complexity with watsonx.data: real-world solutions

Data complexity remains a major obstacle for businesses trying to leverage AI, with 25% of companies identifying it as a top barrier. The sheer volume of data generated each day can be overwhelming, especially when that data is scattered across different systems and formats. IBM watsonx.data addresses these issues with a fit-for-purpose data store that is open, hybrid and governed. It’s built on open data lakehouse architecture that centralizes data access and preparation to power analytics and AI workloads. For example, consider a global manufacturing company with data spread across multiple regional offices. Traditionally, consolidating this data for AI purposes would involve extensive manual effort, with teams spending weeks just to prepare it.

Watsonx.data can simplify this by offering a unified environment where data from various sources is more easily accessible and manageable. The watsonx platform also features 60+ data connectors to helps streamline the process of ingesting data. When viewing data assets, the platform automatically provides frequency and summary statistics. This helps in understanding the content of the datasets at first glance and allows a company to focus on improving its predictive maintenance models (as an illustrative example) instead of getting bogged down by data wrangling.

Additionally, through various client engagement projects, we’ve seen that watsonx.data’s workload optimization help businesses cut data processing costs, making AI projects more cost-effective.

Ultimately, AI solutions are only as good as the data underneath them. The watsonx platform has extensive capabilities for data ingestion, transformation and annotation that can be combined into an end-to-end data flow or pipeline. For instance, the platform’s pipeline editor enables seamless orchestration of processes from data ingestion to cleansing to model training and deployment.

This fosters a collaborative environment between the data scientists who develop data applications and the ModelOps engineers who deploy them within production settings. By supporting extensive data management and data preparation capabilities, watsonx can help organizations navigate the complexities of their data environments to ultimately reduce data silos and gain valuable insights from their AI initiatives and data projects.

Addressing ethical concerns with watsonx.governance: building trust through transparency

As AI becomes more embedded in business operations, ethical concerns have become a significant barrier, with 23% of companies identifying these issues as a top challenge. Key issues such as bias, model drift and regulatory compliance are especially critical in fields like finance and healthcare, where the consequences of AI decisions can be substantial. IBM watsonx.governance is designed to address these challenges by offering a structured approach to managing AI models with transparency and accountability.

Forrester further recognized watsonx in The Forrester Wave™: AI/ML Platforms Wave, Q3 2024 report for our model evaluation tools and model governance capabilities. These help data scientists, developers and businesspeople alike to effectively deploy, monitor, orchestrate and govern models used by production applications. For instance, imagine a financial institution that uses AI to recommend credit decisions. Ensuring that these models are unbiased and comply with strict regulatory standards is crucial.

With watsonx.governance, the institution can automate the monitoring and documentation of its AI model landscape, such as detecting bias and drift, running what-if scenario analyses, auto-capturing metadata at each stage and applying real-time HAP/PII filters. This helps institutions maintain ethical performance over time.

Watsonx.governance also helps businesses stay ahead of regulatory changes, such as the forthcoming EU AI Act, by integrating these requirements into enforceable policies. This not only mitigates risks but also fosters enterprise trust with customers, regulators and other stakeholders. By providing tools that enhance transparency and accountability, including designing and automating workflows to operationalize AI governance best practices, organizations can support responsible AI adoption and explainability that spans multiple environments and AI platforms.

Further, watsonx.governance helps organizations tackle ethical concerns directly, ensuring that their AI models are both compliant and reliable across every stage of the AI lifecycle.

The IBM commitment to enterprise readiness: a future with seamless AI integration

The IBM approach to AI is grounded in the practical needs of enterprise operations. As Forrester recognized in their report, IBM provides a “one-stop AI platform” designed to support businesses as they scale their AI initiatives across hybrid cloud environments. IBM provides the essential resources for effectively integrating AI into core business processes, from empowering AI developers and model builders with watsonx.ai to support AI application building, to simplifying data management with watsonx.data, to managing, monitoring and governing AI applications and models with watsonx.governance.

As generative AI evolves, companies need partners who completely understand the technology and the challenges it presents. IBM embraces open-source principles by design, as shown when we open-sourced a family of core Granite Code, Time Series, Language and GeoSpatial models and made them available on Hugging Face under permissive Apache 2.0 license to enable broad, unencumbered commercial usage.

With watsonx, IBM isn’t just facilitating AI adoption—it’s shaping a future where AI enhances everyday business processes and outcomes.