What’s Ahead for AI In 2024: The Transformative Journey Continues
Organizations increasingly recognize AI’s role in driving decision-making and fostering growth. Here’s what we can expect from AI in 2024.
- By Fern Halper
- December 21, 2023
From advancing disease diagnosis and drug discovery to enhancing precision farming and energy conservation, AI has become a pivotal force in the global technology landscape. Demand continues to grow; in a recent TDWI survey, respondents cited rising demand for machine learning, with over 70% of respondents acknowledging its increased necessity. OpenAI's ChatGPT reaching 100 million weekly active users marks a significant milestone in AI's evolution.
As organizations increasingly recognize AI's role in driving decision-making and fostering growth, what does 2024 hold for the technology?
The Top 5 Trends for AI in 2024
Trend #1: Vendors will infuse AI into more products
In 2024, the trend of vendors incorporating AI into their products will accelerate, with the potential to significantly impact the data and analytics life cycle. From augmented tools for data quality checks and cleansing to advanced features in analytics tools, AI's presence will become more pronounced. These tools will enhance the efficiency of data management and bring new insights and automation capabilities to analytics. Additionally, with the integration of generative AI, BI tools will evolve to offer more intuitive and interactive experiences, allowing users to engage with data through natural language interfaces.
This integration can streamline processes, augment human decision-making, and potentially unlock new avenues for innovation and growth. The inclusion of generative AI in these products may open new possibilities for content creation and data analysis, marking a significant shift in how businesses interact with -- and use -- AI.
Trend #2: Generative AI will shift to using organizational data
The coming year will see a shift in how generative AI is employed by businesses, with a greater emphasis on using organizational data. Companies are increasingly cautious about sharing sensitive data on public platforms, opting instead to host private foundation models within their four walls. This move is driven by concerns over data security and the desire to customize AI applications to specific organizational needs. By using their own data, companies can ensure that AI output is relevant and in context.
This trend will lead to innovative applications of generative AI in a variety of business functions. For instance, customer support chatbots could be trained using specific company data, providing more accurate and relevant assistance. Similarly, marketing departments might use generative AI to craft personalized messages based on customer data to help enhance the effectiveness of their campaigns. The focus on using proprietary data with generative AI will enable businesses to maintain control over their data while leveraging the capabilities of AI for customized solutions.
Trend #3: AI’s infrastructure will evolve
As AI applications become more widespread, the underlying infrastructure that supports these technologies will need to evolve. In 2024, many businesses will revisit their architectural frameworks, particularly for generative AI applications. This might involve adopting new types of databases (such as vector databases) that may be better suited for managing the complex data representations used in AI models. The efficient storage and retrieval of these high-dimensional vectors are crucial for the performance of many AI applications.
These infrastructure changes represent a move towards more sophisticated systems capable of handling the demands of AI. For businesses, this means investing in new platforms and possibly rethinking their current data management strategies. These infrastructure developments may enable more efficient data processing, better performance, and greater scalability.
Trend #4: Tuning mechanisms will advance
New tuning techniques such as prompt tuning and retrieval augmented generation (RAG) will gain popularity next year. These methods provide more context-specific adjustments to AI models without the need for extensive retraining. Prompt tuning, for example, uses smaller pre-trained models to encode text prompts; RAG combines specific information with prompts to enhance the relevance of the model's output.
These advancements are crucial for businesses that want to tailor AI models to their specific needs. By employing these new tuning techniques, organizations can provide their AI models with up-to-date and relevant data, ensuring that the outputs are aligned with current business scenarios and objectives. Moreover, these methods are less computationally expensive, making AI more accessible and practical for a broader range of businesses. This evolution in tuning mechanisms is a key step towards making AI more adaptable and effective in diverse business contexts.
Trend #5: Businesses will increase their attention to responsible AI
The ethical and responsible use of AI will become a central theme in 2024. The focus is increasingly on establishing practices that protect privacy, ensure fairness, reduce bias, and enhance diversity and inclusion, all within the realm of ethical business conduct. In the past, although data quality, privacy, and security were the main business concerns, the tide is turning towards a stronger emphasis on data ethics, equity, and the explainability of AI systems.
This shift is a response to ethical concerns as well as a reaction to emerging legislative pressures. In the U.S., proposed legislation such as the Algorithmic Accountability Act is pushing businesses to critically assess and report the impact of automated decision systems, particularly in areas affecting healthcare, housing, and education. Meanwhile, the EU AI Act aims to ensure AI systems are safe, transparent, and non-discriminatory, aligning with environmental and ethical standards. As we head into 2024, these legislative developments are expected to drive a significant change in how companies approach AI ethics, making responsible AI a cornerstone of technological advancement and application.
Conclusion
The AI landscape is set for significant evolution, shaped by technological advancements and a growing emphasis on ethical practices. The trends outlined above shine a light on the potential directions AI might take and underscore the need for organizations to adapt swiftly and responsibly to these changes.
The journey ahead for AI is as exciting as it is challenging.
About the Author
Fern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is VP and senior research director, advanced analytics at TDWI Research, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at [email protected], on Twitter @fhalper, and on LinkedIn at linkedin.com/in/fbhalper.