The unprecedented pace of AI development over the past six months has made AI strategy a top area of discussion across organisations around the world.
The technology has defined the business landscape since the launch of OpenAI’s ChatGPT last November, promising to usher in the next generation of product and software innovation as AI discoveries and model improvements accelerate.
On the brink of a computing revolution, companies across industries are already exploring ways to enhance their operations and increase productivity.
According to Databricks' 2023 State of Data + AI report, not only is AI advancing rapidly, but companies are also building their own models in a way like never before.
The report, which analysed anonymised usage data from more than 9,000 global companies, revealed that the number of companies using SaaS Large Language Models (LLMs) like those used in ChatGPT has grown by 1310% over the past six months.
Companies are putting substantially more AI models into production as well, with the number of systems being produced growing by 411% and machine learning (ML) implementation growing over 50% year over year.
Don’t forget about data analytics
While this increasing production of AI models and LLMs demonstrates companies’ eagerness to gear up for the AI age, it does not necessarily mean that businesses are ready for the emerging technology.
As any engineer or CTO will know, data is the fuel that powers AI, and it’s critical to ensure that data is accurate, complete and reliable before producing any AI-powered system.
Without data, these systems can only develop in a fragmented way, lacing the underpinning that would allow them to be intelligent enough to make an impact.
To read more about data, visit our dedicated Data Management Page.
As Harvard Business Review puts it: “Promises of AI vendors don’t pay off unless a company’s data systems are properly prepared for AI. Data is locked in silos, inaccessible, poorly structured, and most importantly, not organised in such a way as to be used as the fuel that makes AI work.”
As Databricks notes, however, data integration is still the fastest-growing data and AI market on the Databricks Lakehouse, doubling in popularity since last year.
Last year, Power BI was the most popular program running on top of the Lakehouse, it said. The Lakehouse is increasingly being used for data warehousing, including serverless data warehousing with Databricks SQL, which grew 144%.
Democratising AI with open-source
When looking at the most popular data and AI products, Databricks found that while Microsoft Power BI and Plotly still reign above the rest, an increasing number of organisations are learning to open technologies.
8 of the 10 most popular data and AI products are based on open source software, it found, including dbt, Hugging Face and GeoPandas, among others.
One of the driving forces behind the rise of open-source AI is the growing recognition that collaboration is essential for advancing the field and allowing smaller businesses and organisations to harness these powerful technologies.
Many of the world’s leading tech companies, including Google, Facebook, and Microsoft, have embraced open-source AI and are actively contributing to the development of new tools and platforms.
Another significant development in this movement is the rise of open-source AI research organisations that are committed to open research and collaboration.
While no longer open source, ChatGPT creator OpenAI has been a prime example of this. OpenAI conducts cutting-edge research and shares its findings with the public, allowing others to build upon its work and accelerate the development of AGI.
This open research has the potential to revolutionize industries, empower small businesses, and enable new innovations in a variety of fields.
You can access Databrick's full report here.
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