It’s more than a year now. AI hovers on top of the discussion topic for leaders. Companies start believing in the tangible benefits of Generative AI. Despite glaring concerns, leaders aren’t afraid of experimenting with Generative AI possibilities for their business processes.

Given that Generative AI or LLMs will be integrated into every vendor’s product offering, Generative AI or Artificial Intelligence will drive people to speculate about their future possibilities, downsides, and opportunities.

Workativ, a heavily LLM-based chatbot development company, shares its insights on 2024 AI predictions. Discover our Generative AI or AI predictions and learn how you can design your perspectives.

Open Source LLM on Acceleration in 2024

2023 saw the proliferation of closed-source LLM models for outstanding performance with human-level proficiencies.

These models are built on a centralized cloud infrastructure.

The downside is that consumer-grade products such as ChatGPT and Bard provide limited access to their proprietary data.

They need to be more flexible for training and personal use.

2024 is the year of decentralized AI or open-source models.

It is observed that open-source LLM competitors quickly catch up with closed-source models in overall performance benchmarks.

If you look at the graph above released by ArkInvest, it exhibits steady progress by open-source models. The dominance is nothing less than closed-source models, which are getting closer in 2023.

Open-source LLMs are new yet much predictable in 2024. There are enough trends to support open-source LLM growth.

• Big techs and startups are major contributors

Meta and small players like Mixtral, Yi 34B, and Falcon put effort into research and development for their existing models. These models show significant performance similarity with their contenders, GPT 4.

The proliferation of open-source models is scaling at an incredible pace.

As the objective is to democratize Generative AI in a way similar to ChatGPT and others, Meta aims to give free access to its foundation model, Llama, and make it accessible across all its consumer products.

It is also predicted that Meta’s increased investment in hardware and reference to ‘we’re training’ significantly raises the speculation about the Llama 3 release.

In tandem with the previous cadences of six months to train Llama 1 & 2, Llama 3 is highly likely to be released in early March 2024.

• Ease of access to open-source LLM models

Currently, there are as many as 32500 open-source LLM platformson Hugging Face.

It indicates that open-source models are preferable for users for being more customizable, secure, cost-effective, and easy to iterate.

Also, quantization techniques will allow users to apply the Mixture of Experts model to work independently on a small subset of models rather than the whole models. So, with less compute power, open-source contenders can scale up fast. For example, Mistral largely uses this technique to build its open-source models.

AI Transparency and Regulations in 2024

The pace of foundation model development will increase in 2024.

However, more threats exist, such as deep fakes, transparency, and platform accountability nuances.

So far, we aren’t unaware of fiascos happening around security mishaps with regular GPT models such as ChatGPT.

Google’s latest GPT-4 variant, Gemini, raises accountability questions on its truthfulness. A video made to display Gemini’s capabilities turned out to be heavily doctored.

In 2024, AI governance and regulation policy sets new standards for AI companies.

• Friendly AI policy - In the US, new AI policies will allow AI agencies to craft their best practices to regulate how they want to apply and implement AI technologies. This allows for a more tailored approach for AI companies across different economic sectors.

• AI risk mapping - The US National Institute of Standards and Technology will likely roll out a framework that urges each agency to measure the risk factors their AI applications may pose to users.

• AI transparency for foundation models - The new EU AI Act will be stricter in 2024. Though it allows some AI applications to work independently, agencies developing foundation models need to comply with the strict policy of the AI Act. These AI companies must document how they train their foundation models so they can later be audited to ensure their systems are secure and minimize risks of biases.

• Content moderation on the Internet - 2024 is the year of elections worldwide. Deep fakes are an escalating threat to people’s identity. When AI can be used to fight fake content or deep fake videos, synthetic data is spurring for building evil bots or more such deep fake videos to bombard the internet with fake content. Meta, Discord, and TikTok are creating new tools to minimize the deep fake risks in the election atmosphere. Simultaneously, X proposes to charge some fee for every new bot on its platform to curb access to personal data.

AI Spending Soars

The AI race has just begun. CFOs want to drive more value for their businesses using Generative AI or its former version.

With varying use cases transforming various business functions across the enterprise and consumer sides, AI and cloud spending would increase by 20%-30% in 2024.

Also on this growth radar sits Nvidia.

Stock analysts are eyeing the chip and hardware maker as it continues to build massive iterations in the GenAI ecosystem.

Nvidia’s AI computing platforms are a go-to product for leading AI developer communities, including Hugging Face, Google, and Tesla.

With Nvidia’s stocks hitting $1 trillion, it demonstrates the significance of chip and data center solutions to augment AI development and companies’ interest in driving growth using AI.

On a different note, according to IDC’s Worldwide Artificial Intelligence Systems Spending Guide (August 2023), businesses already invested $ 166 billion in AI development in 2023.

Surprisingly, a new investment trajectory will surpass the previous growth rate by 26.9% of the CAGR growth rate. With that, AI investment will grow to $423 billion by 2027.

Leaner Alternatives to ChatGPT or Branded LLM Tools

With the growing interest in democratized AI tools, non-AI companies want to expand into LLM GPT development.

NVIDIA leads as the powerful GPU and CPU hardware maker. Spearheading innumerable LLM model developments for its customers, they want to expand into LLM development.

Experts also predict that alternative models are possible with AI’s self-learning capabilities.

This could be a cost-effective way for users to work with slightly cheaper GenAI or LLM models than leading brands.

AI Integration into Daily Applications

It’s a race to capture maximum value in the AI age. Product-based companies want to win the competition with a strong customer profile.

To give elevated experiences to app users, brands prefer using AI or Generative AI tools inside apps to double down efficiency and experience for them.

The core idea is to personalize offers and increase customer retention.

Apps to trade, play music, create images, or binge-watch will heavily infest AI properties without our knowledge.

As per Gartner, a third of all new apps will integrate Generative AI-powered user interfaces by 2026. This will help elevate user experience and drive revenues for businesses.

Monetizing GPT Isn’t Easy on the ChatGPT Store

AI developers can have free access to developer tools to create new GPT applications for ChatGPT stores.

However, experts believe the experience will not be as authentic as with the Apple store.

The security is the primary reason. On top of that, GPTs may look similar and offer similar kinds of experience or task accomplishments.

So, what’s the need to pay for a similar experience if users can access ChatGPT without spending a single penny?

More AI-related Jobs Than Only ML Engineers

Until recently, we only knew a few AI jobs—data scientists, ML engineers, etc.

Going forward, job seekers will have more options in the AI field to scale their careers.

• Prompt engineers - Generative AI training requires particular prompts to guide GenAI solutions to learn and generate outputs as users desire. Prompt engineers must carefully check training data before ingesting it inside a model.

• AI product manager - This role involves overseeing AI products from ideation to execution. For AI product managers, ensuring the product works well as desired is essential.

• AI ethicist - We all know about hallucinations and misinformation LLM can spread. AI ethicists closely oversee AI performance to avoid such indifferent behavior.

• AI compliance specialists - AI models or applications must work with the evolving trends. AI regulatory executives ensure their models follow the correct set of governance to stay competitive.

Database Stores Will Emerge as Vector Stores

Semantic search or vector search is synonymous with Generative AI search.

GenAI interfaces need vector databases to improve search performance, an integrated requirement.

Databases significantly help with data storage and model training. However, the evolving needs demand data conversion into vector data or mathematical representation to improve search results.

To solve the purpose of the business, database platforms are ready to transform into vector databases and increase the usage of their platforms for RAG development or vector searches.

Databricks joined the bandwagon. However, we can expect some big players, such as Snowflake, to emerge as vector stores.

AI Agents Will Proliferate

2024 will bring significant growth for AI agent development.

Generative AI agents are no longer known for everyday NLP tasks such as writing emails or generating documents.

We can see more specialized agents who can talk to each other and perform tasks by making decisions together.

Big companies tend to use their existing data to build personalized AI agents and monetize them.

We already see Bloomberg has built its own LLM model for its clients using its massive data repository. Who can say that Bloomberg can launch more intelligent AI agents for global users to derive financial insights and improve investment decisions?

AI for Customer Support

We already see the best use cases of Generative AI in content creation, AI-backed chatbots, and the automation of human tasks.

According to Gartner, the wide use of Generative AI is better optimized by customer support. 38% of leaders surveyed confirmed using Generative AI to improve customer experience and retention.

With Generative AI helping customer support to generate call scripts, enable self-service, and drive AI personalization, IBM indicates that Generative AI will be the brain of the contact center.

Similarly, Generative AI aims to transform employee support for IT incident management, IT support, HR support, and employee retention and engagement.

Workativ makes it easy for business leaders to leverage Generative AI employee support and transform their workplaces. Contact us if you want to bring out your LLM-powered chatbot for employee support.

AI-powered solutions are a significant technology that can reshape our work processes and add value to employee experience that helps grow revenue.

Businesses are undeniably emphasizing adapting to the evolving changes on the technology side to remain competitive.

Generative AI and other AI developments aim to bring tangible value to businesses of any size. Staying abreast of changes is critical to surviving business challenges and improving sustainability.

It is essential to look at AI predictions and act appropriately to create an adaptive AI environment and leverage its benefits.

If you are looking for AI solutions for your employee support, Workativ can help. Let’s connect and discuss what suits your business use cases best.