With AI now integrated into everyday tools and platforms, the question is no longer whether companies will use AI, but how. The focus has shifted from breakthrough moments to widespread implementation. The next chapter of AI will be defined by scale, accessibility, and practical impact.
From LLMs to Real Business Impact
Enterprises are moving fast to adopt retrieval-augmented generative (RAG) AI, which combines internal company data with large language models. This pairing allows employees to ask natural-language questions and get tailored, accurate responses that reflect both public and proprietary information. It’s changing how teams access knowledge, make decisions, and stay productive.
As commercial, “Frontier” models from OpenAI, Gemini, and Anthropic continue to evolve, a parallel movement is growing. Developers and researchers are building open-source alternatives like Mistral, Llama, and DeepSeek. These models aim to reduce costs and dependencies, making advanced AI available to more organizations.
AI Will Be Everywhere
We are now living in a hyper-connected world. Nearly every device has computing power. Network bandwidth is massive. Consumers, especially younger generations, are starting to expect AI-powered digital twins to be embedded in intelligent systems that mirror real-world objects, tools, and environments.
This means no more digging through manuals or getting lost in complex systems. Everything from hospital navigation to classroom interaction will have smart, intuitive AI, layered into the experience.
The Next Evolution
The current AI boom is rewarding infrastructure players. But the next wave will favor lightweight, local AI that runs efficiently on edge devices. Innovations in AI-specific chips, Network Processing Units (NPUs), and quantum-inspired computing will push these capabilities further.
We’re also seeing advances in synthetic data generation, with companies like Fei-Fei Li’s World Labs leading the charge. These models use simulated environments and synthetic data and imagery to train systems more quickly and at lower cost. Technologies like NERF and holography help fill in missing visual data and build immersive, image-based simulations.
These innovations will transform how we train machines and how we interact with them.
The Rise of AI Agents
AI agents are already beginning to support workers by enhancing, accelerating, and in some cases replacing routine tasks. But even as automation grows, most systems will still keep a human in the loop for oversight and decision-making.
At GrowthPoint, we see this shift happening across every company we work with. Whether they are embedding AI into new products or reimagining legacy systems, the momentum is undeniable.
This is the era of Artificial Intelligence. And it’s only just beginning.
Continue watching and reading our series on the history of AI: