2025 Gen AI Predictions: What Lies Ahead?

In 2024, organizations realized the revolutionizing business potential of gen AI. They accelerated their gen AI operationalization processes: explored new use cases to implement, researched LLMs and AI pipelines and contemplated underlying ethical issues.

And with the seeds of the AI revolution now planted, the market is maturing accordingly. This means that in 2025, we’re likely to already see organizations offering gen AI services to customers, embedded in commercial applications - from SaaS CRMs to chatbots.

To support this leap forward, the underlying tech stack will mature as well. Multi-agent systems and multimodality will become widely available, gen AI service providers will expand features and offerings and open-source will challenge commercial vendors with new capabilities.

And with LLMs becoming more accurate and intelligent, and new guardrails are introduced, the risk of hallucinations diminishes, perpetuating trust that drives this entire ecosystem forward.

Two years after OpenAI released ChatGPT to the public, will 2025 finally be the year gen AI becomes an inseparable part of business? What kind of obstacles do organizations need to overcome? Who will thrive in this new ecosystem? Here are my predictions for the upcoming year:

1. Gen AI Embedded as a Service

Forward-thinking enterprises have been working on implementing gen AI as part of their innovation strategies. Excited about the opportunities and vast potential in gen AI, they are ramping up their efforts to implement gen AI in their applications. These could be either to support internal operations, (like McKinsey’s Lilli or a co-pilot agent to support human customer service representatives during calls), or as part of their SaaS commercial offering to customers and prospects (like AI suggestions on social media or a customer-facing agent that assists shoppers with purchases).

Currently, many of these gen AI initiatives are in pilot or PoC phase. They are also mostly focused on certain use cases, like chatbots/agents, code development and content creation.

But in 2025, the embedding of gen AI directly into SaaS commercial applications is poised to take off in multiple directions. Businesses across industries will move from the PoC phase to operationalization, and seamlessly integrate gen AI capabilities into everyday SaaS apps and services, as part of their customer-facing offering.

For example, SaaS CRM platforms could predict customer needs based on real-time interactions, email clients may offer intelligent drafting suggestions, social media platforms will provide AI capabilities for writing and distributing content, and e-commerce websites will dynamically hyper-personalize user experiences.

By incorporating gen AI as an inherent feature, SaaS companies will be able to provide advanced services and help users better interact with their digital ecosystems. This is expected to enhance the user experience and drive a competitive advantage for these organizations. 

That being said, I believe that over time (not next year but over the upcoming 5 years) gen AI will become a staple capability, and users will come to see it as foundational in any service or product they use. Therefore, embedding gen AI in your SaaS service should be a part of any enterprise software strategic plans.

2. Exponential Leap in LLM Intelligence

One of the prominent challenges of operationalizing gen AI applications is de-risking them. Issues like hallucinations, bias, toxic content, security, privacy and accuracy have been significant obstacles on the way to implement gen AI.

Some widely publicized cases include the Canadian airline chatbot that mistakenly offered a passenger a discount or the chatbot that was prompted into selling a car for $1. Obviously, organizations are wary of taking that risk without model transparency or an understanding of how to de-risk their applications.

But over the past year, gen AI models have undergone an exponential evolution, achieving remarkable gains in intelligence and accuracy. These improvements encompass a wide range of capabilities, including  a significant reduction in hallucinations, nuanced language understanding and precise contextual responses. (And for other cases, it’s always important to add guardrails).

This leap makes LLMs a viable option for enterprises where accuracy is non-negotiable. For example, for regulated industries like financial services or for customer-facing chatbots that close deals. These trustworthy models pave the way for broader adoption, garnering trust among organizations and users alike.

3. Open Source Grows as a Viable Contender to OpenAI

AI has been developed over the past decades. But, truth be told, it was OpenAI that induced us all to cross the AI Rubicon. And yet, open-source gen AI projects are gaining momentum and are set to rival proprietary models like OpenAI’s offerings.

These initiatives provide a more flexible and cost-effective alternative for developers and businesses, particularly for niche or highly customized applications. Organizations can train, fine-tune and customize the model for their needs, in a much more resourceful manner. This helps them drive much more business value.

In addition, there is growing concern over ethical issues related to copyright over training data, security and privacy concerns over data storage policies and a general suspicion towards commercial vendors’ ethical stance. To these, open-source provides an alternative that is transparent, customizable, and user-driven. By allowing individuals and organizations to inspect, modify, and improve the underlying code, open-source models address some of the opacity issues inherent in proprietary systems.

Overall, open-source gen AI solutions like LangChain, LLaMA, Falcon, BLOOM, Mistral,  ModelScope, StarCoder, WhisperAI, Hugging Face's multimodal models, TensorFlow, PyTorch, MLRun, and many others democratize AI, fostering a culture of innovation and inclusivity and providing all organizations and users with the innovative capabilities AI has to offer.

4. The Rise of Multi-Agent Systems

Multi-agent systems are AI frameworks where multiple models or agents collaborate to solve complex tasks. These systems promise more robust performance through specialization and coordination, akin to teams of experts tackling different aspects of a problem.

Instead of having a model solve a complex task, tasks are divided into smaller tasks and each model assumes and specializes in a specific task. The models communicate with each other, resulting in an automated application that provides a much more complex and sophisticated capability, with minimal human intervention.

Emerging standards and frameworks will make these systems more accessible and reliable in 2025. From advanced customer service bots that coordinate across departments to autonomous systems in manufacturing and logistics, they will bring newfound efficiency and precision to complex workflows.

5. The Era of Multimodality

Multimodal AI is the AI systems that are capable of processing and generating content across multiple formats such as text, images, audio and video. For example, they can analyze a dataset, process a document with text, charts, and images, generate visualizations, write a report, and even create an accompanying presentation—all seamlessly.

These capabilities have peeked at us in 2024, and they are expected to peak in 2025, where we'll be able to provide the model with multiple types of inputs (text, documents, images, etc.) and to generate multi-model results. This will drive greater value and productivity. Next year or the year after, they might even be able to handle large-scale projects, from full-scale movie scripts to comprehensive business plans, creating incredible efficiencies for creators and businesses alike.

6. Service Providers Expand Their Value Propositions

As gen AI models reach maturity (see #2), the challenge is no longer the model’s intelligence but how effectively it can be applied to real-world problems. As a result, service providers will pivot from focusing solely on the models themselves to delivering a broader suite of complementary services, which can help unlock the innovative potential of gen AI.

This means that providers will offer tailored solutions that complement LLMs in gen AI pipelines. For example: graphical gen AI pipeline designers, integrations with external data and RAG, application templates, tools libraries, guardrails, test and CI/CD facilities, etc.

From a business perspective, these value-added services will serve as a competitive edge, helping them position themselves in an ecosystem that will become saturated with AI and gen AI services and solutions. In addition, it will help users develop and adopt gen AI faster.

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As a veteran in the AI and ML industries, the past year has been inspiring. No longer limited to labs and research, LLMs and AI models are starting to bring widespread business value across industries.

Knowledgeable and efficient organizations are planning their gen AI strategies, implementing the right process for operationalization and de-risking. This means efficient scaling, automation and resource optimization while ensuring compliance and reducing risk of data privacy breaches, bias, AI hallucinations and IP infringements.

This will allow businesses to maintain a competitive edge in a world that is bringing immense value from (gen) AI.

Here’s to a successful 2025!