Title: Sprinting Towards Innovation: 4 AI Technologies Set to Shine in 2025
Venkat Viswanathan, serving as the Founder and Chairman of LatentView Analytics, has carved out a niche for himself in the realm of marketing analytics and decision science.
Approximately 1.1 million individuals yearly join the marathon race, covering 26.2 miles of uninterrupted running. That's a mere 0.01% of the world's population that can boast of such an endurance feat.
The marathon traces its roots back to the Greco-Persian Wars of 490 BCE. Legend has it that a Greek messenger, by the name of Pheidippides, ran 40 kilometers – approximately 25 miles – from the plains of Marathon to Athens. The purpose of his run was to announce Athens' victory over the Persians, who had outnumbered them substantially. After his triumphant shout of "We have won!", Pheidippides succumbed to exhaustion, marking the first marathon spectacle.
A decade later, in 1896, the marathon was reintroduced as a sporting event in the first modern Olympic Games in Athens. Fast forwarding to 1908, the present-day marathon distance of 26.2 miles or 42.195 kilometers was established at the London Olympics. To accommodate the presence of King Edward VII and Queen Alexandra, the event started at Windsor Castle, wound through London, culminating at White City Stadium, and ended in front of the Royal Box. The additional distance required to comply with the Royal Box's specifications, in turn, lengthened the early marathon's original 25-mile distance.
So, what's the connection between this historical anecdote and AI?
From Prototyping to...
The very first marathon can be likened to a proof of concept – a demonstration that structure and organization can be imposed on an idea or invention.
Fast-forwarding to modern days, this mirrors the last few years in the business world. Technologists experimented with the boundaries of generative AI (GenAI), trying out various ideas, investing resources, and cultivating success without the need for perfection.
The kinetic energy that propelled GenAI to new heights in 2024 echoes the 1908 Olympic marathon's revamped format – refined with a specific purpose while tuning components that preceded it. Just like how successful early concepts in GenAI were enhanced and popularized to cater to a wider range of users, the sport gained popularity by tailoring to the changing trends and preferences of its audience.
And now, what's next? 2025 serves as the year where GenAI graduates from the proof-of-concept stage to become a scalable solution, driving significant impact. Essentially, this is GenAI's leap from an inaugural race to its status as a widely-popular, global-level sport.
2025 Tech Trends to Watch
Following a study by David J. Deming, a professor at the Harvard Kennedy School, it is obvious that American adults have embraced GenAI faster than they did the internet or personal computers – another testament to this technology's potential.
Looking ahead, the following GenAI trends are likely to gain momentum and command the attention of executives in 2025:
Agentic AI
Agentic AI includes advanced AI systems capable of acting autonomously in real-world environments and can make decisions, plan actions, and learn from its experiences. AI agents thrive in settings that require problem-solving and planning, such as supply chains, emergency response, or advanced robotics. Additionally, they are already used in healthcare settings for triaging patients and resource management.
Small Language Models (SLMs)
Small language models (SLMs) are leaner versions of their large language model (LLM) counterparts. As businesses continue to explore the challenges with LLMs, they will likely opt for SLMs, which require far less computational power and are optimized for accomplishing specific tasks such as summarization or classification. By integrating SLMs into edge devices, businesses looking to conserve energy and sustainability can enjoy powerful, cost-effective solutions.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation (RAG) combines the strength of generative models with the retrieval capabilities of a search engine. It retrieves relevant information from external data sources to augment the responses generated by other models, leading to improved accuracy and reduced hallucination. In 2025, businesses leveraging RAG technology will enjoy improved AI-customer trust and automation in verticals such as legal and compliance.
Multimodal Intelligence
Multimodal Intelligence refers to AI systems that can process and integrate multiple forms of input (text, images, audio, and video) to generate insights. This technology is vital in unlocking the full potential of environments like metaverses and AR/VR, enabling richer interactions with AI.
As businesses brace themselves for the 2025 marathon of GenAI scaling, success will require focus, strategy, and adaptability. For the brave, the finish line promises scaling meaningful, impactful GenAI solutions while staying ahead of the rapidly evolving technological landscape. The race is on – it's time to set the pace.
Venkat Viswanathan, with his expertise in marketing analytics and decision science, played a crucial role in driving the success of LatentView Analytics. In a panel discussion at the 2025 AI Summit, Venkat Viswanathan emphasized the importance of Agentic AI in decision-making processes, citing its potential in emerging industries such as advanced robotics and healthcare.
