AI innovator, Aware predicts that AI will make significant inroads into the enterprise in 2024.
2023 was dubbed “the year of AI” as over half of companies adopted large language models (LLMs) like ChatGPT into their workflows. However, LLMs are failing to live up to expectations in the enterprise and 2024 will see a shift towards more targeted, purpose-built AI models.
LLMs Falter in Solving Real Business Problems
LLMs promisd to fulfill numerous use cases but there are growing concerns around their quality, accuracy, and security. Models like ChatGPT can write a generic email but have limited capabilities to solve complex issues. And without a steady supply of data, LLMs are difficult and expensive to sustain.
As benchmarks focused on basic language tasks instead of business outcomes, companies now want AI driven by ROI. They will prioritize solutions tailored to specific problems versus broad, general models.
Securing Data Becomes Key
LLMs are fueled by ingesting swaths of public data, raising data leakage and compliance issues. To address this, 75% of companies now prohibit using ChatGPT.
Having recognized the value of their data, businesses will take a “hybrid cloud by design” approach to secure it. Data protection will be a pillar of AI strategies.
Companies will also use AI to analyze public data for insights into customers, employees, and competitors. Failure to harness such data gives others a competitive edge.
The Rise of Targeted Models
Rather than relying solely on LLMs, companies will pursue hybrid AI strategies encompassing open-source, closed-source, and custom targeted models.
Targeted models provide security and specificity. They can help teams develop IP around ML while reducing engineering teams and costs. Combined judiciously with LLMs when suitable, businesses get AI fulfilling their needs.
Managing Models Through MLOps
As more models are deployed, MLOps platforms will provide infrastructure to manage them efficiently. Data teams essentially become software teams, with product requirements, ticketing systems, and sprints to create scalable models.
MLOps brings simpler management and reduced expenses. It also creates a hot career path as the market size grows 40% by 2030.
AI Makes Data Actionable
With data expanding exponentially, AI helps make it actionable. But decisions, not dashboards, will define success.
The ability to enable faster, more confident decisions will fuel AI’s future. Humans and AI will partner in the workplace, with AI amplifying workers’ strengths.
Targeted models shine here too, with 85% higher accuracy over LLMs and sub $1,000 monthly costs versus upwards of $182,000.
ROI matters most. AI must provide clear benefits, not just budget line items. Internal data holds the key to success.
The shift to targeted AI has begun. 2024 will be the year businesses activate their most valuable asset – their own data – through purpose-built AI.
