AI Predictions for 2024 — from More Embedded to More Proactive

In 2024, AI will take a major leap in four key areas:

1. AI will get embedded into most everyday software tools, making them smarter and more helpful.
2. AI integration will make adoption exponential, outpacing data democratization and expanding access and productivity.
3. Open-source AI will become the norm, enabling more customization and user control.
4. AI will go from just answering questions when asked to proactively taking action based on understanding user goals. 

Together, these trends signal a future of amplified productivity, customizable intelligence, decentralized progress and proactive assistance.

AI in everything

In 2024, most software products will have some level of AI, whether it's a chatbot, natural language search, or generative capabilities for text, images and analytics.

Today, Microsoft® Word and Excel already have Copilot AI assistance. Salesforce and ServiceNow have their own AI offerings. Coding platforms like GitHub and data platforms like Snowflake have AI functionalities.

A key impact of the AI infusion will be enhanced productivity. With AI assistance built into tools, users will be able to get information, take action and accomplish tasks more efficiently, without needing to code or program manually. This will drive productivity improvements across several industries.

What’s more, integrating AI into software tools improves access to AI, allowing everyday users to benefit from the advanced capabilities of the applications they use regularly.

AI democratization outpaces data democratization

In the coming months, we'll see AI capabilities become democratized at a much faster rate than data capabilities have been democratized.

While making data accessible and actionable by all users has been a focus for the past decade, relatively few people are truly data-aware and data-driven in their day-to-day roles. In contrast, adoption of AI is happening almost overnight. As a result, more people will rapidly become AI-ready and comfortable using AI tools compared to those who are data-savvy.

Driving this AI democratization is the integration of AI into virtually all software tools and platforms, making advanced AI functionality accessible without specialized skills.

What’s more, AI itself can help further data democratization by making it easier for users to interface with and extract insights from data. This will lead to productivity improvements, as workers leverage AI to work more efficiently.

Resurgence of AI open-source models

In 2024, we’ll see a renewal in the importance and adoption of open-source AI models. In the 2010s, open source was a major focus in many technology circles. However, over the past several years, as cloud and SaaS solutions have matured, open source has taken a backseat, while proprietary models and commercial solutions have gained prominence.

We’ll see open source regain traction, particularly for large language models. Key innovations are happening with open-source models, like LAMA and Mistral, that are becoming competitive with proprietary models from companies like OpenAI.

The open-source models have trended smaller in size, using fewer parameters. This makes them more cost-effective, power-efficient and easier for full deployment within private environments. Expect open-source models to rival commercial models in performance, and possibly even surpass them in the coming months.

This shift aligns with AI’s facilitation of greater collaboration. Open-source models lend themselves to community-driven development and iterative learning.

Also, data privacy and residency requirements are easier to address with open-source models that can run fully in private-cloud settings without external dependencies.

Generative AI and LLMs uplevel to semantic programming

The next major evolution in AI, particularly large language models (LLMs), will be a shift towards semantic programming for more action-oriented uses.

So far, LLMs have focused predominantly on information retrieval and content generation, like answering questions, summarizing documents and providing chat capabilities. We can expect a transition beginning next year from these reactive applications to more transactional, automation-driven applications. 

Rather than just querying information or producing text, next-generation LLMs will determine appropriate actions to take based on natural language instructions and semantic context. This entails process automation by programmatically interfacing with various systems to execute tasks.

For example, an LLM could update databases, make plans or optimize workflows based on a user's stated intent, without pre-programming rigid sequences of steps. This semantic programming would allow models to infer necessary actions despite variations in the order and presence of steps across related processes.

So, where the previous year saw LLMs supporting knowledge workers through content improvements, the next 12 months will unlock more impactful, prescriptive applications. This includes process enhancements, task automation, and leveraging AI assistants or agents to take actions versus just producing information. It marks a shift from reactive to proactive AI.

Taken together, these AI improvements mean that in 2024 software will get better at helping people work faster and smarter. AI systems will understand users better — and, from there, more accessible, proactive AI can advance even newer innovation.

This is just a glimpse of a future where AI makes complex tasks simple, serves users' needs better and takes initiative rather than just reacting.