Alteryx, Inc. (NYSE: AYX), the Analytics Cloud Platform company, has released its predictions for AI trends in the year 2024. These insights reflect a comprehensive understanding of the evolving business landscape and the revolutionary impact of data-driven AI technologies.
Fuelled by its potential to revolutionize industries, boost productivity, and spur unprecedented innovation, the excitement around AI and, more specifically, generative AI was unmistakable in 2023. The anticipation is not just about the technology itself, but the promise it holds for transforming the business landscape by accelerating data-driven insights capable of redefining the competitive dynamics of every sector. From data collection to analysis, interpretation to reporting, generative AI will revolutionise decision-making by enabling employees to take action and stay ahead of the curve.
As business leaders strive to take advantage of this era of decision intelligence, the focus for every sector in 2024 will be on harnessing AI’s power for business value by making AI-driven decision-making accessible and secure for the entire workforce.
Key Trends in AI and data analytics for 2024:
Generative AI to Reshape Competitive Dynamics:
Generative AI exposes new opportunities for many organisations to capture market share from their direct competitors and expand into adjacent markets. Alteryx recently surveyed data leaders globally and found that one of their top main drivers for adopting generative AI was to predict business performance and industry trends. As more organisations unlock the potential of generative AI, market share capture from direct competitors and expansion into adjacent markets will become the new norm.
Robust Governance Frameworks for AI Adoption:
While the top two reasons for not yet implementing generative AI came down to data privacy concerns and lack of trust in generative AI results. The year 2024 is anticipated to see the development of robust governance frameworks that facilitate responsible and effective implementation of generative AI across enterprises. These frameworks are vital for managing risks associated with AI applications, including their embedded large language models (LLMs), the end users of those applications, and the exchanges between the first two.
Reshoring of Work and Upskilling:
With AI and automation simplifying many tasks, more companies are expected to bring outsourced work back home. McKinsey believes the demand for these physical and manual tasks will decrease significantly by 2030 in favour of technological and cognitive skills. As organisations focus on upskilling and reskilling their workforce skills crucial in an AI-driven future, data analytics, data science, and machine learning will be critcal. There will also be a rise in new graduates skilled in data handling, reflecting the growing emphasis on data literacy in education.
ESG Challenges for Finance Offices:
In 2024, CFOs will be held accountable for driving conversations across the enterprise to ensure ESG becomes a corporate-level initiative. While ESG reporting remains murky, companies need to focus on operating more authentically, ethically, and confidently across the business, not just the office of finance. To further support this meaningful change, data analytics will be at the forefront of helping align their sustainability commitments with credible corporate finance strategies.
Multi-Modal Interfaces in Analytics:
The rise of multi-modal interfaces will empower every user in the analytics value chain to engage with data intuitively and successfully based on their role – all while increasing collaboration across the decision-making process, marking a significant trend in enterprise collaboration and flexibility.
Cloud-Smart Approach and AI:
The AI wave will create new data interaction paradigms—especially in the cloud. As more data becomes available and new AI use cases arise, systems will require more computing power to process this data, making a cloud-smart, hybrid approach more attractive than going all-in on the cloud.
The Datafication of Everything:
From text, images, audio, video, social media, and more – as much as 90% of all data in the world is unstructured. Generative AI has already shown us its potential to make sense of all this untapped data, and vendors will continue to unlock the capabilities of large language models (LLMs) and introduce different tools to help users analyse data in all aspects of life that might otherwise not fit neatly into a spreadsheet.
