From Exploration to Business Impact: The Maturity of Data Science and AIML

Data science and artificial intelligence (AI) have emerged as powerful tools in today's digital era, revolutionizing the way businesses operate. Over the years, the field has evolved from its early stages of exploration to now making a significant impact on various industries.

Data science and AI/ML have a long and influential history, stretching back many decades. However, it is only in recent years that these technologies have truly gained widespread attention and acceptance. In the early stages, data scientists were primarily focused on investigating the possibilities of AI/ML, conducting extensive research, and creating advanced algorithms. However, we are now witnessing a shift in the current era, where the real value of these technologies is becoming evident. Although there is still more progress to be made, we are already reaping the benefits of AI/ML in various domains.

So, get ready to be inspired as we explore a remarkable example of an AI/ML application in a major industry. This groundbreaking technology has not only reached a level of maturity but is also transforming businesses, delivering tangible benefits that will leave you feeling optimistic about the future.

Industrial Examples

Healthcare

CT-5300 scanner by Philips

The CT-5300 scanner by Philips uses AI-based image reconstruction to allow imaging with an 80% lower radiation dose, 85% lower noise and a 60% improvement in low-contrast detectability. AI also drives a smart positioning camera, reducing the time it takes to position a patient by up to 23%, while improving manual centering accuracy by up to 50% and increasing consistency from user to user by up to 70%. A suite of AI-enhanced workflow tools help improve dose, speed and image quality in scanning for various conditions, from cardiac imaging to trauma.

Finance

Fraud Detector by SEON

SEON, a European startup founded in 2018, specializes in combatting fraud and assisting businesses in industries such as banking, lending, FX and crypto trading, iGaming, and ecommerce. Their primary goal is to minimize risk and enhance conversions for their clients. Seon offers innovative tools that provide flexibility when integrating fraud prevention into your platform. You have the option to utilize individual modules for a multi-layered security approach or implement their complete end-to-end system. By leveraging real-time data enrichment, device fingerprinting, digital footprint analysis, and a powerful Machine Learning engine, Seon can effectively identify fraudsters, reduce user friction, and maintain control over risk thresholds.

Telecom

AVA – Energy efficiency by Nokia

Nokia has made significant progress in this field. Their Nokia AVA Energy Efficiency solution can effectively reduce energy costs and carbon footprint by up to 30% without compromising performance or customer experience. This advanced AI-driven solution can be implemented quickly, allowing CSPs to achieve substantial energy savings in a short period of time.

Nokia AVA Energy Efficiency is a telecommunications energy management solution that focuses on minimizing the power consumption of RAN equipment, both active and passive. It does not rely on specific hardware, OSS, or SON from any particular vendor. Instead, this AI-powered network tool optimizes both energy savings and network performance at the same time.

Airline

SabreMosaic by Sabre

SabreMosaic is an innovative airline retail platform that is set to revolutionize the industry. Built with a modular and open design, and powered by AI, it unlocks a new world of possibilities for offers and orders. With SabreMosaic, airlines can dynamically generate, sell, and deliver personalized content to travelers, driving revenue growth and enhancing the overall travel experience.

There are numerous other industries and examples that we can discuss here, highlighting how they are benefiting from AIML. However, to keep this article concise, the main purpose is to emphasize that AIML is no longer just an added bonus, but rather a significant and integral part of various industries.

Aspects of Maturity

We will also discuss the advancements in processes related to AIML. Businesses have come to realize that simply having AIML products or applications is insufficient. It is vital to establish processes for governance, privacy, security, compliance, validation, ethical considerations, data quality, accessibility, scalability, and other related factors. Companies are already giving attention to this and developing processes accordingly. In the following sections, we will explore these aspects in greater detail and provide relevant examples.

Watson AI Governance by IBM

We are all familiar with IBM Watson. Recently, IBM has placed significant emphasis on developing an AI governance module for Watson that addresses important aspects such as privacy, security, Sox compliance, and validation. IBM? WatsonX. Governance? has been created to assist in overseeing, managing, and monitoring the artificial intelligence (AI) activities of organization. It allows to govern generative AI (gen AI) and machine learning (ML) models from various vendors, including IBM? WatsonX.AI?, Amazon Sagemaker, Bedrock, Google Vertex, and Microsoft Azure.

Data Quality and Accessibility by Collibra

As data science and AI/ML continue to evolve, organizations have come to understand the significance of having high-quality and easily accessible data. Data governance frameworks play a crucial role in ensuring data integrity, privacy, and security. Collibra Data Quality & Observability follows a similar approach, offering a swift and sophisticated solution for managing datasets. It learns through observation rather than relying on human input, enabling automatic data quality without the need for explicit rules.

Scalability and Performance by Databricks

With the evolution of the field, the scalability and performance of AI/ML models have seen remarkable improvements. Organizations now have the capability to train intricate models on extensive datasets using distributed computing frameworks. This not only reduces training times but also enhances accuracy. Such scalability empowers businesses to implement AI/ML solutions on a large scale, resulting in significant operational improvements. Databricks serves as a prime example, as it is specifically designed to handle vast amounts of data and offers numerous features that can enhance performance.

Conclusion

The maturity of data science and AI/ML has transformed industries, empowering organizations to harness the power of data for strategic decision-making and operational excellence. From healthcare to finance and manufacturing, these technologies have made a significant impact across various sectors. As the field continues to evolve, ensuring data quality and accessibility, scalability, and addressing ethical considerations will be crucial for organizations looking to leverage the full potential of data science and AI/ML. With continued advancements, data science and AI/ML will continue to shape the future of businesses and pave the way for innovative solutions in the digital age. Finally, I would like to emphasize that Data Science or AIML is no longer just an embellishment or an added bonus. It has become an integral and substantial component of the overall enterprise landscape.