AI and Machine Learning Trends to Watch in 2023

By on
AI and machine learning

This article highlights 10 of the biggest trends triggered by technological advancements in artificial intelligence (AI) and machine learning (ML). These trends have collectively revolutionized the way businesses approach everything from education and economics to the environment. 

The broad AI and machine learning trends include the provisioning of cloud platforms for data activities – accelerating the use of AI and machine learning technologies and tools for business data and analytics. According to Gartner, “over 50% of enterprise IT spending in key market segments will shift to the cloud by 2025.”

Recent advancements in AI and machine learning technologies have led to a series of chain reactions in the global data technology market, which may be summed up as:

  • The growing advancements in AI and composable analytics solutions are enabling organizations to explore small and big, structured and unstructured data combinations, applying techniques that search for actionable insights in smaller – even microdata – tables. 
  • Stream-first architectures and streaming data analytics are seeing increasing adoption across a variety of companies, particularly within IoT and other real-time data ingestion and processing applications. Organizations have seen an increasing demand for real-time data in recent times, a trend that is set to continue through next year. 
  • Enterprises are still feeling increasing pressure to embrace Data Management strategies that enable them to extract actionable insights from a tsunami of data in order to make key business decisions. Various factors, such as the increasing need for compliance, increasing usage of Data Quality tools to manage the data, and increasing trends toward Master Data Management across multiple domains, are expected to lead to the adoption of automated MDM technologies and services. 

Gartner analysts forecast that 70% of enterprises will transition away from big data toward smaller, wider data (or data sourced from multiple sources) by 2025, thus providing greater context for analytics and smarter decisions. 

Here are 10 major trends that have been triggered by recent advancements in AI and machine learning technologies:

Trend 1: Increased Use of Cloud-Based Software Systems and Cloud Services

Thanks largely to the development of AI- and ML-powered, cloud-based software, organizations are now able to monitor and analyze volumes of enterprise data in real time, and make necessary adjustments to their business processes. As organizations keep moving to the cloud, and as data volumes and types continue to grow, outsourced Data Management systems could make businesses a lot more effective. ML-driven data integration across a multi-cloud or hybrid cloud ecosystem helps organizations retain flexibility in managing their data on an independent basis.

Trend 2: The Acceleration of Edge Computing Due to AI and Machine Learning

The rising popularity of AI and machine learning in enterprise Data Management has triggered fast adoption of  “edge computing.” In an edge analytics world, data storage and computations are brought closer to the source of data, making the data accessible and manageable, driving down costs, providing faster insights and actions, and enabling ongoing operations. 

Trend 3: Tremendous Rise in Automation of Business Processes

AI and ML platforms have jointly contributed to the rising importance of automation throughout the business value chain. All data-related processes are gradually shedding manual methods and becoming automated. This trend is very positive as it enables business staff to spend more time on business problems and come up with quick, accurate decisions. As data analytics grows in scope, automation will become a necessity to improve the quality, governance, and compliance around data-centric activities. 

Trend 4: Augmented Data Analytics

Thanks to the many AI-enabled data analytics platforms or solutions available today, “augmented data analytics” is a reality, where many of the critical phases like data collection, data cleansing, and data preparation are handled by smart tools, so that human data scientists or analysts are free to engage in complex data analysis issues. These superior analytics platforms use machine learning and natural language processing (NLP) to manipulate data and extract insights from data, which would otherwise take long hours to complete by a human data scientist or data analyst. 

Trend 5: AI-Enabled Business Intelligence (BI)

Using advanced AI and ML tools, today’s BI platforms are capable of maximizing the value of correlations, trends, and patterns guided by data. Today’s BI solutions drive more effective results and insights as Data Quality management, self-service BI tools, and advanced analytics capabilities are handled by AI and ML technologies.

Trend 6: Rise of Data as a Service or DaaS Practice

Thanks to cloud and advanced data technologies, now service providers can offer DaaS services to clients. Using DaaS for big data analytics, data analysts can streamline the task of reviewing information, and facilitate the sharing of data between departments and industries. 

Trend 7: Intelligent Automation (Analytics) and Automated MDM

Intelligent automation (analytics) is about activities in which businesses automate as many processes as possible using a variety of tools and technologies like AI, ML, low-code, or no-code tools methodologies. Also, AI and ML are now used for MDM, which makes Data Governance easy.  

Trend 8: Explainable AI – Tackling Bias in Data

Ethical AI or explainable AI tackles bias, diversity, and labeling in data more systematically within a Data Governance strategy – including using data textiles for automated data integration and metadata management.

Trend 9: Capturing and Storing Context-Specific Data for Analytics

Capturing, storing, and using “context-specific data” in data analytics require specialized capabilities and expertise to create data pipelines, X analytics techniques, and cloud services with AI capable of processing various types of data. 

Trend 10: Automation of Customer Data Management

This is a big trend, as it allows businesses to better engage with and manage customers. AL- or ML-powered tools that assist in managing customer data facilitate “smart automation,” another major industry trend. 

The Biggest Beneficiaries of Automated Data Governance: Small Businesses

Instead of big data, now businesses will adapt to “right data analytics,” and this trend is expected to make data and analytics accessible to all business staff across an enterprise. This kind of analytics aligns with the goal of making data practices more democratic.

By tapping data services, even small, under-resourced data teams can deploy Data Governance and integration by automatically automating pipelines, quality, and governance on demand.  Through institutionalized Data Governance, companies can control their data, guaranteeing that it is accurate, and maximize the value of their analytics. 

AI-driven MDM helps provide a 360-degree view of data and empowers users to provide better business insights with self-service analytics. AI-powered Data Management can also be used to build smart data catalogs, which in turn support active metadata (ML-augmented metadata that reacts and makes decisions) and self-service data preparation (a more advanced version of augmented analytics).

Image used under license from