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Adel Nehme
VP of Media at DataCamp
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Richie Cotton
Data Evangelist
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Data & AI Trends & Predictions 2024

January 2024

Summary

As we prepare for 2024, the field of data and AI is set for significant shifts. The webinar transcript highlighted the transition from the excitement of AI breakthroughs in 2023 to the practical application of these technologies in 2024. Large language models continue to be influential, yet the conversation is moving towards practical and cost-effective applications. The year ahead promises the widespread use of generative AI, with companies investigating both open and closed-source models to balance innovation with security. The focus is increasingly on incorporating AI into regular business operations, emphasizing efficiency and value creation. As organizations aim to utilize AI's potential, data governance and literacy become vital to ensure successful implementation and ethical use. The changing role of AI in professional settings emphasizes the need for a workforce skilled in both technical and strategic aspects, highlighting the importance of aligning technological abilities with business objectives.

Key Takeaways:

Generative AI is moving from excitement to mainstream, with a focus on practical applications.
Organizations are balancing between open-source and closed-source AI models to optimize performance and security.
Data governance and literacy are essential to ensuring effective and ethical AI deployment.
Practical AI use cases, which focus on efficiency and automation, will generate significant value.
AI literacy is becoming a necessary skill across various professions.

Deep Dives

Generative AI Goes Mainstream

The widespread use of generative AI in 2024 signifies an important moment for technology integration in business. While tools like ChatG ...
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PT have become well-known, with even non-technical users interacting with them, the real challenge lies in applying these technologies within organizational frameworks. According to the WaveStone 2024 Data and AI Leadership Executive Survey, a large portion of Fortune 1000 companies are still in the experimental phase with AI, indicating a vast opportunity for growth and implementation. The focus is on moving from experimentation to deployment, ensuring that AI applications provide tangible value. The webinar highlighted the need for organizations to overcome obstacles such as data management and compute costs to effectively integrate AI solutions.

Open-Source vs. Closed-Source AI Models

The discussion between open-source and closed-source AI models is heating up as organizations aim to use the best of both worlds. Open-source models like Mistral and Lama 2 offer transparency and community-driven improvements, which can enhance security and performance. However, closed-source models often provide more comprehensive support and proprietary innovations. The choice between these models often depends on data security concerns and organizational needs. Haga Lopesco from Mosaic ML highlighted the risks associated with closed-source providers, emphasizing the importance of data privacy. As companies weigh these options, the trend towards a blended approach, using both open and closed models, is expected to grow.

Practical AI Use Cases

While advanced AI applications grab headlines, it is the routine, yet essential, AI use cases that are set to deliver substantial value in 2024. These include automating standard tasks such as data entry, document processing, and customer support. AI's ability to simplify these processes can lead to significant efficiency gains, freeing up human resources for more strategic tasks. For instance, AI tools can handle tasks like summarizing meeting notes or generating responses to customer inquiries, enhancing productivity. The emphasis on applying such 'practical' AI use cases emphasizes the potential for AI to transform business operations in practical ways.

Data Governance and AI Literacy

As AI technologies become more embedded in business operations, the importance of data governance and AI literacy cannot be overstated. The accuracy and reliability of AI outputs are heavily dependent on data quality, necessitating comprehensive governance frameworks. Scott Taylor's concept of 'artificial stupidity'—a result of poor data quality—highlights the risks involved. Moreover, AI literacy is becoming a fundamental skill across professions, with a growing need for individuals to understand AI's capabilities and limitations. As organizations invest in data and AI literacy, they empower their workforce to effectively use AI tools, promoting a culture of innovation and informed decision-making.