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Exploring the AI ML Landscape – Trends That are Shaping Our Future

Exploring the AI ML Landscape - Trends That are Shaping Our Future

AI ML Trends That are Shaping Our Future

Introduction
AI Global Market and Predictions
Top AI/ML Trends
Summary

Introduction

In today's fast-evolving technological landscape, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as driving forces that reshape how we interact with the world. From virtual assistants anticipating our needs to fraud detection systems safeguarding our finances, AI/ML trends have seamlessly integrated into our daily lives.

Statista says the value of the AI market is projected to reach US$242bn in 2023. With an annual growth rate (CAGR 2023-2030) of 17.30% and making the market volume of US$739bn by 2030.

Be it AI’s effect on your routine tasks, employment, productivity, healthcare, or investments, the world of big data, robotics, virtual assistants, and voice recognition is here to stay with the potential to shape our futures.

Generative AI applications such as Copilot, ChatGPT, and many others have captured the topic of daily conversations recently. Due to their broad utility and ease of usage, anyone can utilize them to create content, image codes, and much more.

In this blog, we'll dive into the most prominent AI/ML trends that are not only transforming industries but also promising a future where innovation knows no bounds. But first let us have a look at some figures about the global AI market.

AI Global Market and Predictions

According to Forbes Advisor, the AI market size globally was valued at $136.55 billion in 2022, and it will grow exponentially in the upcoming years. This will be backed by huge investments in AI technologies, from the global economy.

Here is supporting data to prove this further. The AI market size is projected to expand at a CAGR of 37.3% from 2023 to 2030 and reach $1811.8 billion by 2030. The same research indicates that AI will contribute more than the combined current output of India and China to the international economy by 2030.

The biggest benefactors of this technology are China with an expected boost of 26% to GDP in 2030, followed by North America with a 14.5% boost accounting for almost 70% of the global economic impact! This is a huge number to ignore for any sector or government.

Artificial Intelligence is expected to contribute $15.7 trillion to the global economy by 2030, more than the current output of China and India combined. Having understood the potential of this technology, let us now look at what are the current trends around AI/ML.

Top AI/ML Trends

The Generative AI Trend

The generative AI applications that are available in the open market help with the reorganization and classification of data. But their USP lies in their capability to generate content, compose music, and create digital art that can persuade users to experiment on their own.

As per an article by Mckinsey impact of Generative AI on productivity can add trillions of dollars to the global economy. Our latest research estimates that generative AI could add the equivalent of $ 2.6 trillion to $ 4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $ 3.1 trillion.

This in turn can increase AI impact by 15 to 40%. This estimate does not include the impact of embedding generative AI into software used for other tasks. Customer operations, software engineering, marketing and sales, and R&D sum up to around 75% of the value that generative AI use cases could deliver.

Increased Use of Cloud Services and Cloud-Based Software Systems

Due to the development of AI and ML-powered, cloud-based software, companies can monitor volumes of enterprise data in real-time to make the required adjustments in the business processes.

Companies globally are now moving to the cloud. Data volumes are increasing and outsourced DMS help businesses to be more effective.

Big organizations maintain flexibility in managing their data on an independent basis with the help of ML-driven data integration across a multi-cloud or hybrid cloud ecosystem.

Augmented Data Analytics

“Augmented data analytics” has become a reality due to the various AI-enabled data analytics platforms and solutions. Data collection cleansing, and data preparation are critical phases of the process and are handled by smart tools. It helps human data scientists or analysts to freely engage in complex data analysis issues.

These analytics platforms utilize machine learning and NLP to utilize data and extract insights from data, which otherwise can take hours or even days to complete by a human data scientist or analyst.

Amplification of Edge Computing Due to AI and Machine Learning

Edge computing put simply is a distributed information technology architecture where client data is processed at the edge of the network, as close to the originating source as possible.

The increased popularity of AI and ML in enterprise Data Management has initiated the quick adoption of “edge computing.”

This brings data storage and computations closer to the source of data. Again, this makes data accessible and manageable, reducing costs, while still providing quicker insights and actions.

Automation of Business Processes

To survive the cut-throat digital transformation happening all around them, businesses find it inevitable to embark on their digital evolution. AI and ML platforms contribute to the importance of automation in the entire business value chain.

With the digital evolution, all data-related processes are eventually being moved from manual methods and becoming automated. This trend helps staff spend more time on real-time business problems and come up with quick decisions to resolve them.

The more the scope of data analytics, the more automation becomes necessary for quality, governance, and compliance improvement around data-centric activities. What fuels automation better than AI?

Enable Business Intelligence with AI

With the advanced use of the latest AI and ML tools, BI platforms can maximize the value of trends, correlations, and data-guided patterns. BI solutions drive effective outputs and detailed insights as a part of Data Quality management.

Self-service BI tools, the ones where no SQL is required to generate reports, can connect to multiple data sources, and agility in analytics and other advanced analytics capabilities are handled with AI and ML technologies.

This is the main reason why many governments and the public sector have started adopting AI in their routine processes.

Data as a Service or DaaS a New Trend

DaaS is a data management strategy used to store data and analytics. Due to the cloud and advanced data technologies service providers can offer DaaS services to clients.

DaaS is used for big data analytics, which streamlines the task of reviewing information and facilitates data sharing between departments and industries.

DaaS outsources many operations which include data storage, integration, and processing operations to the cloud. This is like SaaS, which removes the requirement of installing and configuring software locally.

Building ML platform teams for the smooth running of business-critical models

Companies persuading real-time machine learning prefer having an ML platform team in place. It ensures that the models perform at high levels of reliability and availability.

The ML platform teams manage and improve the tooling that keeps models running smoothly in production. The only challenge here is hiring skilled talent. Equipe them with operational AI tools to improve performance from AI/ML projects.

Industrialization and Standardization of ML processes

In many big companies, AI/ML is used in separate parts, each with its tools. This is fine for small projects. But for big customer-focused AI projects, as in Banking or Transportation or education sector, we need rules to work efficiently and safely.

A good process to follow here is choosing a team to make rules for AI/ML in the whole company. It could be the Enterprise Architecture group, a Data Science Center of Excellence, or an ML Platform Team.

This team's job is to make rules that help AI/ML to be fast and big, but with safety measures to reduce risks. This way, everyone follows the right steps, and problems are avoided through good governance.

Summary

The rise of AI is set to revolutionize how we all work and operate, from business owners to content creators, developers, and regular tech users. To not miss out on the incredible potential to simplify tasks and improve processes, it's crucial to embrace AI and learn how to use its tools that matter to you.

Unless you are ready to embrace this new technology, you are going to miss out on lots of ways to ease your business processes and tasks related to your day-to-day life. This transformation is not limited by location or industry – it is a global shift that affects everyone. Whether you are in any sector, joining this trend can lead to innovative solutions and smoother business operations.

Get in touch with us, and we will assist you in creating personalized apps, solutions, and services for your business in a remarkably short time. Embrace the future with us!