As we move into 2025, the technology landscape is rapidly evolving, driven by significant macro and micro trends. These trends are shaping the way businesses operate, innovate, and compete in the market. In this blog post, we will explore trends in Artificial Intelligence (AI). Plus, we discuss recommendations on how to prepare for trends in AI.
Trend 1: Expect AI-Driven Product and Action Recommendations
What Are AI-Driven Product and Action Recommendations?
AI-driven product and action recommendations are personalized suggestions generated by AI algorithms based on a user’s past behavior, preferences, and interaction data, tailoring recommendations to enhance their experience and drive engagement.
Companies are increasingly integrating AI into their operations to enhance efficiency, drive innovation, and deliver personalized customer experiences. AI-driven product and action recommendations are becoming more prevalent as businesses leverage AI to analyze vast amounts of data and provide tailored suggestions to customers.
How To Prepare For AI-Driven Product and Action Recommendations
- Before implementing AI, ensure compliance with global and local data privacy regulations to build and maintain customer trust.
- Invest in AI tools and technologies that can enhance data preparation, product recommendations, and customer support. For more, check out our product, Tealium for AI.
Trend 2: AI-Powered Call Centers Will Grow
What Are AI-Powered Call Centers?
An AI call center is a customer service operation that uses artificial intelligence technologies such as Natural Language Processing (NLP), Machine Learning (ML), and voice recognition to automate and improve customer interactions. This approach provides a more personalized and efficient service experience compared to traditional call centers, handling large volumes of inquiries with less human intervention. Essentially, AI systems manage and respond to customer calls, analyze data to understand needs, and provide relevant information while streamlining agent workflows.
AI-powered call centers are set to grow significantly in 2025. AI-driven chatbots and virtual assistants can handle routine tasks, allowing human agents to focus on more complex issues. This not only improves efficiency but also ensures that customers receive timely and accurate responses.
How To Prepare For AI-Powered Call Centers
- Focus on collecting consented, real-time data and integrating it seamlessly across various platforms.
- Get up to speed and read more about AI call centers in our collection of blogs
Trend 3: Increasing Importance Of Data Collection For AI
What Is Data Collection for AI?
Data collection for AI is the process of systematically gathering large amounts of information from various sources to train and improve artificial intelligence models. This allows the models to learn patterns and make predictions based on the collected data. We dive more into this in our blog post, A Complete Guide to Data Collection for Artificial Intelligence (AI).
The demand for real-time, consented data is growing. This trend emphasizes the need for flexibility and interoperability in data management. AI requires vast amounts of consented, high-quality data to function effectively, making data collection a critical component of any AI strategy. You’ll want a solid Customer Data Platform (CDP) like Tealium for AI in your technology stack!
How To Prepare Your Data for AI
To prepare your data for AI, you’ll need 4 elements! AI-ready data needs to focus on quality, governance, understandability, and availability.
- High-Quality: Data must be accurate, complete, and consistent to ensure reliable model training and results.
- Governed: Compliance with data privacy and AI regulations is essential, requiring data to be trusted, consented, and auditable.
- Understandable: Adding contextual intelligence, metadata, and labels enhances data understanding, leading to better AI performance and outcomes.
- Available: Interoperability, availability, and real-time delivery are crucial for having the right data for AI training and activation at the right time.
Trend 4: Growing Importance Of Automating Data Collection Practices to Minimize Extract, Transform, Load (ETL)
What Does Automating Data Collection Practices Mean?
Automating data collection practices to minimize Extract, Transform, Load (ETL) refers to the use of automated tools and technologies to streamline the process of gathering, cleaning, and integrating data from various sources. This approach reduces the need for manual intervention in the ETL process, which traditionally involves extracting data from different sources, transforming it into a suitable format, and loading it into a data warehouse or other storage systems.
As companies seek to streamline operations and reduce costs, automating data collection practices to minimize ETL is becoming a key strategy. ETL processes can be time-consuming and resource-intensive, making automation an attractive option for businesses looking to improve efficiency. By leveraging automation, companies can reduce the time and effort required to collect, transform, and load data, allowing them to focus on more strategic initiatives.
What Does This Unlock?
By automating these practices, organizations can improve data operational efficiency, reduce integration time, and minimize the time-to-value for new systems. This means that data can be collected and processed more quickly and accurately, leading to faster insights and better decision-making. Additionally, automation helps in maintaining data quality and consistency, ensuring that the data used for analysis and reporting is reliable and up-to-date.
To learn more, explore our page, No Data Wrangling.
