How can we transform generative artificial intelligence into an enterprise power with the IBM Watsonx platform?

Çağda Çelikbilek, BM Turkey Software Country Leader, spoke about transforming generative AI into a corporate power with the IBM WatsonX platform. Çelikbilek stated:

If we go back a bit, in 2022, when generative AI became available for individual use, we experienced a real boom. Following this, throughout 2023 and 2024, organizations began an experimental phase, as you mentioned earlier. That is, how can I integrate artificial intelligence, especially generative AI, into my organization? Because, as we know it, traditional methods of artificial intelligence, machine learning, and data learning have been a part of our lives for years. But generative AI started to take it to a different dimension. We began working on how to transform this from an experimental approach into something that would benefit our business lives. Initially, we started looking at what we could do within organizations with large language models, just as we individually accelerated our lives by communicating with ‘GPTs’.” However, we’ve seen that organizations realized things didn’t quite work out as planned. Large language models started causing problems for organizations in terms of accessing data within their own systems, providing erroneous or hallucinatory data, and incurring significant costs. I can even give an example: According to a recent study, 98% of organizations have now integrated AI, whether traditional or generative, into their lives. But they believe they’ve only seen a full return on their investment of about 25%. So, how do we at IBM approach this issue with the Watson X platform? IBM believes AI shouldn’t be built with the largest possible model, but rather with smaller, business-focused, topic-specific language models that we call “Fit for Purpose.” This isn’t just about the model itself; it’s about ensuring the data entering the model is accurate, relevant to the organization, transparent, and governed by the entire process from the data’s origin to the output from the model. “With the ‘Watsonx’ platform, we are trying to add value to organizations in this field from end to end.

The Rise of Generative AI

Artificial intelligence (AI) has become a rapidly developing technology in recent years, impacting many areas of our lives. The introduction of generative AI for individual use in 2022 led to a major boom in this field. People began using AI more in their daily lives, attracting the attention of organizations. Throughout 2023 and 2024, organizations began exploring how to integrate AI. This process stood out as an effort to go beyond traditional AI methods and discover the new opportunities offered by generative AI.

Integration of AI into Organizations

Organizations are experimenting with integrating AI into their business processes. Traditional AI methods, such as machine learning and deep learning, have already been in use for a long time. However, generative AI has attracted the attention of organizations by offering a different dimension. This new technology has the potential to accelerate business processes and increase efficiency.

The Role of Large Language Models

Large language models have become one of the most striking elements of artificial intelligence. Individual users have accelerated their lives by communicating using models like GPT. Organizations have also begun to leverage the opportunities offered by these models. However, it has been observed that large language models bring challenges such as accessing internal data and generating erroneous data.

Return on Investment

Investments in artificial intelligence are of great importance to organizations. However, research shows that while 98% of organizations have integrated AI into their operations, only 25% see a full return on investment. This indicates that AI investments need to be planned more carefully.

IBM’s Approach: Watson X Platform

IBM prefers to work with smaller, business-focused, subject-specific language models in the field of artificial intelligence, which it calls “Fit for Purpose.” This approach aims to overcome the challenges posed by large language models. The Watson X platform aims to add end-to-end value to organizations. This process includes elements such as using the right data, ensuring data transparency, and ensuring data relevance to the organization.

Data Governance and Transparency

The success of AI applications depends on the quality of the data used. IBM places great importance on data being accurate, transparent, and relevant to the organization. Data governance encompasses the entire process from where the data originates to the output from the model. This process ensures the effective use of AI.

Conclusion

Generative AI has the potential to create a major transformation at both individual and corporate levels. However, developing the right strategies is necessary for the effective use of this technology. IBM’s Watson X platform enables more efficient use of AI with business-focused and subject-specific models. This approach can help organizations derive more benefit from their AI investments. The future impact of AI will be shaped by how this technology is integrated and managed.