In 2023, generative AI emerged as a game-changer across various domains, offering heightened productivity and revolutionizing content creation. The extensive capabilities of these advanced tools created immense excitement and anticipation. Looking ahead to 2024, the focus is shifting towards a new phase: AI measurement. It involves moving beyond the technical prowess of Large Language Models (LLMs) and delving into their tangible impact on a company’s profit and loss (P&L) statements. This shift signifies a deeper exploration into these powerful AI technologies’ real-world implications and financial outcomes.
The integration of generative AI is expected to drive significant changes in 2024, with many companies experimenting or expanding with this technology. The pace of advancement in generative AI is crucial for technology executives to understand and keep pace with the year ahead. However, to make the most of AI, it is essential to consider more than just the technology itself.
Leaders will adopt a more evidence-based approach in 2024
In 2024, leaders are expected to adopt a more empirical approach to AI, recognizing the need to move beyond the hype and base their decisions on data-driven insights. This change indicates an increasing recognition of the significance of evaluating the advantages of AI tools and gauging their impact on organizational success. As a result, executive decision-makers will focus on identifying the most influential metrics for evaluating AI tools and delving deeper into generative AI tools’ return on investment (ROI). This strategic emphasis on empirical analysis is poised to guide organizations in making informed and impactful decisions regarding integrating and utilising AI technologies.
Everybody generally talks about AI, incredibly generative AI, which will have a real impact on everything data. It is here to change many things. So, if you look at the last 15 years of AI and bucket, it has largely been based on Supervised learning where supervised learning can sort of predict; people use people call it data labelling. So it’s very good at labelling stuff. Self-driving cars are in application, quality control applications. You’re labelling defective components. But now, with Generative AI, organizations will rush into processing far more unstructured data than ever. So, the analytics field was mainly about structured data that’s about to change with Generative AI.
– Shashank Garg, CEO of Infocepts
Data Regains Center Stage as AI Takes a Supporting Role
In the evolving landscape 2024, generative AI has soared in adoption, becoming less of a luxury and more of a competitive necessity for businesses. However, the rush to embrace AI comes with a cautionary tale—its outputs aren’t always accurate, highlighting the need for safety protocols and robust datasets. Surprisingly, only a fraction of AI-adopting organizations have implemented comprehensive usage policies, covering essential aspects like fact-checking, safeguarding sensitive data, and ethical content creation. Hesitancy in adopting AI might hinder organizations from leveraging its transformative potential. Emphasizing the significance of data quality, the focus is shifting towards refining training data to ensure precise and dependable generative AI outputs, steering businesses away from potential inaccuracies.
The second big thing that’s happened over time is that the ability to store and use data has increased tremendously. And therefore, it presented the real opportunity to move from insights and decisions to operationalizing whatever you do. So you start getting to this area of Enterprise Scale, AI-driven applications.
– Pranay Agrawal, Co-founder and Chief Executive Officer at Fractal Analytics
AI-driven chatbots are on the move, departing from their current state.
AI-powered chatbots are steering clear where their attempt to mimic human interaction often falls short, disrupting the flow of conversation. While excelling in imitating human speech, these AI, notably LLMs, occasionally reveal their artificiality, posing challenges in customer service interactions where even minor imperfections can derail smooth engagements. Progress in chatbot communication aims to bridge this gap, striving for nearly human-like interaction levels. However, perfection in AI communication might not mirror a single human response. Like skilled translators offering varied yet excellent translations, AI’s outputs will soon provide nuanced, context-rich responses, albeit not uniformly identical. The critical task in 2024 will be defining and evaluating the success of chatbot communication, ensuring enhanced customer engagements beyond mere conversation. Additionally, the integration of proprietary data securely remains a crucial concern for organizations as they navigate responsible AI deployment, aiming to elevate team efficiency, enhance user experiences, and drive positive business outcomes while carefully addressing ethical considerations and data security.
I am personally looking at the popular LLM-based chatbots more enthusiastically than a year ago. There are areas where these solutions can be very useful if somebody, a human, guides them. They can be a powerful tool with some human guidance and cannot be used as is. As we all know, these LLM-based chatbots have a very different purpose from a search engine and may not be used as one.
– Maharaj Mukherjee, Senior Vice President and Senior Architect Lead at Bank of America