How companies deploy AI to maximize value

Artificial intelligence is critical, not only as a key enabler but also as a booster for enterprises in their digital transformation journey. It is the driving force behind business development today and in the future.

This is because artificial intelligence has the potential to reshape the Fortune 500, just like the Internet did. Decades-old established players may lose ground, while obscure, disruptive challengers may rise to become the next industry leaders.

Digital transformation driven by artificial intelligence has a huge impact on three important business areas. The most obvious is the technology stack, and ensuring it’s AI-ready. Next is the way AI will change company business processes and operations, with AI having the potential to transform established processes through automation. Third, and perhaps most important, is the transformation that artificial intelligence will bring to business.

The adoption and deployment of artificial intelligence will prove to be a key market differentiator in the coming years: To overcome the coming economic headwinds and stay ahead of competitors, enterprises need to embrace artificial intelligence as part of their digital transformation Key principles of transformation strategy.

With the rapid development of technology, the effectiveness of deploying artificial intelligence depends on maximizing benefits while minimizing the cost of implementing the model. For businesses that are exploring how to use artificial intelligence, there are three ways to maximize the value of their deployment.

1. Shift to data-centric computing

Many enterprises are undergoing technological changes, from model-centric computing to Data-centric computing. Simply put, we do not need to create an AI model and introduce data into the model, but rather apply the model directly to the data. As a result of broader digital transformation strategies, many enterprises are already going through this process, with enterprises turning to AI computing platforms as a single delivery point for service delivery across the enterprise.

This not only brings efficiencies, it also gives us larger, more transformative AI deployments that can work across departments and combine processes.

2. Focus on valuable models

The integration of machine learning models has undergone significant changes. Just three years ago, hundreds of new research papers were published every week discussing new machine learning models, raising concerns that the growth of models was getting out of control. Today, this trend is reversed. It is less specific and generalizable, which results in a more limited number of models. A single common-based language model can deliver functionality from multiple downstream tasks, not just one.

As models get smaller, they actually become more standardized. This has an interesting secondary effect, where the value of the intellectual property used to create new AI models is diminishing. Businesses now realize that their true value and intellectual property lies in the data they hold, further underscoring the shift toward data-centric computing.

3. Combine models and deploy multi-modal artificial intelligence

Of course, artificial intelligence has never been a specific, well-defined technology . It is a broad term for many related technologies. What we are seeing today is the rise of combining models and deploying them on different types of data. The fusion of different AI models and data types in a single pipeline will lead to greater operational efficiencies and new services offered.

One example is the combination of natural language processing and computer vision, which results in an image generation algorithm that creates images based on text input.

Another more practical example is that the language model extracts exceptions from the system log and then feeds them into the recommendation algorithm. E-commerce recommendation engines "You bought this, maybe you'll like this" are common, but in the context of NLP models they can be leveraged to provide support analysts with recommendations for the next best action to correct in text logs See the anomaly.

Artificial intelligence is being adopted across departments and enterprises, and C-suites and leadership teams don’t want to be left behind by competitors who are successfully implementing the technology. As AI is increasingly put into use, those businesses that can deploy it with the greatest efficiency will gain the next competitive advantage.

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