What’s next for AI?
While large language models continue to advance, new models and agents are proving to be more effective at discrete tasks. AI needs different horses for different courses.
Blink and you’ll miss it: The speed of artificial intelligence’s advancement is outpacing expectations. Last year, as organizations scrambled to understand how to adopt generative AI, we cautioned Tech Trends 2024 readers to lead with need as they differentiate themselves from competitors and adopt a strategic approach to scaling their use of large language models (LLMs). Today, LLMs have taken root, with up to 70% of organizations, by some estimates, actively exploring or implementing LLM use cases.1
But leading organizations are already considering AI’s next chapter. Instead of relying on foundation models built by large players in AI, which may be more powerful and built on more data than needed, enterprises are now thinking about implementing multiple, smaller models that can be more efficient for business requirements.2 LLMs will continue to advance and be the best option for certain use cases, like general-purpose chatbots or simulations for scientific research, but the chatbot that peruses your financial data to think through missed revenue opportunities doesn’t need to be the same model that replies to customer inquiries. Put simply, we’re likely to see a proliferation of different horses for different courses.
A series of smaller models working in concert may end up serving different use cases than current LLM approaches. New open-source options and multimodal outputs (as opposed to just text) are enabling organizations to unlock entirely new offerings.3
In the years to come, the progress toward a growing number of smaller, more specialized models could once again move the goalposts of AI in the enterprise. Organizations may witness a fundamental shift in AI from augmenting knowledge to augmenting execution. Investments being made today in agentic AI, as this next era is termed, could upend the way we work and live by arming consumers and businesses with armies of silicon-based assistants. Imagine AI agents that can carry out discrete tasks, like delivering a financial report in a board meeting or applying for a grant. “There’s an app for that” could well become “There’s an agent for that.”
