AI Trends Shaping the Enterprise Landscape in 2024

AI began to look like it could make some sense in 1950 when Alan Turing published his work “Computer Machinery and Intelligence”. This later became the "Turing test" which is used by experts to measure computer intelligence.

However, the word Artificial Intelligence was first used officially in 1955 by John McCarty in a workshop he held at Dartmouth on “artificial intelligence”. This was when the word “Artificial Intelligence” first came into popular use

Fast forward to November 2022, with the release of ChatGPT, there was a significant turning point in the world of AI. In 2023, companies experimented a lot with AI using it in various aspects of operations to reduce cost, minimize errors, and improve efficiency and customer relationship.

In 2024 we expect to see great inclusion of AI in how we work and live. We are beginning to see trends like multimodal AI, agent AI, open source AI, and things like retrieval augmented generation (RAG). According to Dialpad, more than 69% of executives expect to spearhead their organization’s AI efforts, with 35% strongly agreeing and 35% somewhat agreeing.

In this article, we will look at AI trends shaping the enterprise landscape in 2024 and the impact of these trends on enterprises.

How AI is Changing The Enterprise Landscape

AI usage is rapidly expanding moving from more experimental stages to full-blown real-world use cases.

On the enterprise level, we can see a lot of B2B working together like OpenAI with Microsoft, its multiple APIs, and partnerships with other businesses. We also see Anthropic with Amazon and Google.

We are already seeing a lot of companies trying out Github Copilot Enterprise and by the end of 2024, we might see a full integration into people’s workflows and a lot of use cases with Excel and PowerPoint.

Already apps are integrating or planning to integrate GPT4 into their services, some example is Stripe which intends to use GPT-4 to detect user behaviors and potential frauds. Khan Academy is also another example of an app that has integrated GPT-4. This will only grow and many apps will start offering LLM/AI in not only their product but also for the development of other products.

As AI continues to evolve and become more accessible, enterprises must adapt their talent strategies. Reskilling and upskilling are crucial steps for enterprises to ensure their workforce is equipped to thrive in an AI-powered future.

However, the rapid adoption of AI also raises concerns about trust and risk management. Enterprises must develop rigorous processes to cultivate trust, measure user confidence levels, and ensure the reliable, secure, and ethical deployment of AI systems.

In the following sections, we will look at the AI trends to look out for in 2024 and their impact on enterprises

AI Trends Shaping Enterprises in 2024

Let us take a look at interesting AI trends that will shape enterprises in 2024

Merge between AI, Machine Learning and Automation

In 2024 we will see a merge between AI, machine learning, and automation to create hyperautomated processes that will enable company-wide AI adoption in enterprises. According to Gartner, it is expected that by 2024 65% of enterprises will have deployed some kind of hyperautomation.

Drag-and-drop automation tools like Activepieces enable, enterprises to translate complex operations into visual workflows to orchestrate AI workflows for more efficiency. The ability to visually translate workflows will enable swift AI adoption in enterprises because employees with domain expertise can now hyper-automate their specific workflows in a way that has real-world usefulness for them.

This will streamline and reduce the time spent on the repetitive tasks involved in applying machine learning models to real-world problems

Multimodal AI

Have you noticed that even the free versions of Gemini, Microsoft Copilot, or Claude now allow you to also upload images not just text when you are using them? This is called multimodal AI. They combine different modes of data including images and sound for more accurate and complete information. Although we have had text-to-speech models around for a while, they have been only trained to accomplish specific tasks.

At the moment, Gemini, can receive a photo of a plate of cookies and generate a written recipe as a response and vice versa.

Most AI models only have text and image models for now, however, it is expected that more models are going to be implemented to enable AI to process information from the same senses that we as humans use to process information. This expands the amount of information available for training.

According to Mark Chen, head of frontiers research at OpenAI, "We want our models to see what we see and hear what we hear, and we want them to also generate content that appeals to more than one of our senses."

The impact of multimodal AI on enterprises includes AI-powered virtual assistants that can understand and respond to customer queries in natural language, providing more human-like interactions. Another benefit includes tailored product recommendations based on customer preferences and past behavior, leading to increased sales and customer satisfaction. Multimodal AI will also improve the inspection of products for defects using image and video analysis, reducing human error and improving product quality.

AI Agents

Agentic AI models have some autonomy. They can analyze their environment, set goals, and achieve them on their own without human intervention. Unlike traditional AI, which is primarily reactive or predictive, agentic AI can independently plan, execute, and adapt its actions to achieve specific objectives.
The implications for enterprises include, Agentic AI can automate complex tasks, reduce human error, and optimize workflows. For instance, Agentic AI can accelerate innovation by exploring new possibilities and generating creative solutions. It can analyze vast datasets to identify patterns and trends that humans might overlook. Understanding individual customer preferences and behaviors, enables agentic AI to deliver highly personalized experiences, increasing customer satisfaction and loyalty. AI agents can monitor systems for anomalies, detect potential threats, and respond proactively to mitigate risks. According to Markovate, AI agents can analyze market trends, make investment decisions, and detect fraud, enhancing profitability and risk management. For example, a financial AI agent can actively manage an investment portfolio using adaptive strategies.

Retrieval-Augmented Generation (RAG)

You might have basic knowledge of a topic and when asked basic questions, you can easily reach out to the knowledge you already have and provide an answer. However, when you are asked deeper questions, you might have to conduct deeper research from external sources to ensure that you provide a more accurate reply. This is the concept of how retrieval augmented generation

Retrieval-augmented generation (RAG) is a technique that combines the strengths of large language models (LLMs) with external knowledge sources to improve the accuracy and relevance of output. It involves retrieving relevant information from an external knowledge base when a prompt is fed into an AI. This could be a database, a document repository, or even the entire web. The aim of this AI development is to reduce wrong AI outputs otherwise known as AI Hallucinations which have been one of the major roadblocks to full enterprise AI adoption. RAGs retrieve information from external sources when the LLM data is not sufficient.

According to Nvidia Retrieval-augmented generation gives models sources they can cite, like footnotes in a research paper, so users can check any claims. An example of a RAG AI model is Perplexity AI. RAG systems can continuously update their knowledge base with live data sources, ensuring that they remain current and adaptable to evolving situations which enables enterprises to stay ahead of rapid AI advancements

According to Tonic.ai 2024 is the year RAG systems mature, transitioning from research labs to real-world applications. This trend emphasizes the need for enterprises to bridge the gap between research and deployment.

Open- Source AI

Linux was able to gain more popularity and adoption by allowing developers to modify its code the way they wanted. This made it a more affordable option for cloud computing. In 2024 Open-source AI is predicted to develop similarly.

The cost of building and deploying large language models can be alarming. According to TechTarget, using open-source models enables developers to build on other people’s works thereby reducing expenses. In 2023, we saw powerful open-source AI models like Meta Llama 2 and Mistral AI's Mixtral models.

This benefits enterprise because the source code for AI models that are built on open source is available for all to see. This ensures more transparency and enables people to easily spot issues and fix them before they cause any significant damage.

According to Mark Zuckerberg, Amazon, Databricks and Nvidia are launching full suites of services that support developers in fine-tuning and distilling their own AI models. This means that in the coming future, enterprises can fine-tune their own AI models and not get locked with a particular vendor. This enables them to maintain cost while running a future-proof system

Customized GenAI for Enterprises

While generic AI models have proven to be really helpful in increasing efficiency and reducing costs for enterprises, models that cater to niche markets will be more efficient.

This will be helpful for specific industries like healthcare, finance, and legal. In 2024, enterprises will explore a more diverse range of AI models. This is because maintaining the privacy and security of data has become a big concern. The ability of enterprises to customize their own models will help them to have more control over privacy and security.

According to Gillian Crossan, risk advisory principal and global technology sector leader at Deloitte, stricter AI regulation in the coming years could push organizations to focus their energies on proprietary models.

Gen AI is coming to enterprise software, it is speculated that there could be a battle between vendors who want to charge per user and IT people who believe that generative AI features should be free. In 2024, Deloitte predicts that enterprise spending on generative AI will grow by 30%. More companies are expected to develop their own generative AI models to drive greater productivity, optimize costs, and unlock novel insights and innovations.

Rise in Demand For AI Related Skills

The scarcity of AI talent creates intense competition for skilled professionals, driving up salaries and making it difficult to fill critical roles. Many existing employees lack the necessary AI skills which creates a skills gap that hinders AI adoption and innovation in enterprises.
Organizations with a strong AI talent pool can gain a significant competitive edge by developing innovative AI applications. In a recent report by O’Reilly , AI programming, data analysis and statistics, and operations for AI and machine learning were the top three skills organizations needed for generative AI projects.