The impending advantage in AI doesn't lie in models alone
In the ever-evolving landscape of technology, a new era of artificial intelligence (AI) is emerging—one that promises to revolutionize business processes and drive digital transformation. This new frontier is known as Agentic AI, a capability that acts like digital peers, overcoming the limitations of traditional automation.
Agentic AI, with its ability to coordinate sophisticated tasks, manage complex dependencies, and significantly amplify productivity, is set to redefine the way enterprises operate. By integrating into agentic workflows and multi-agent collaborative systems, AI can enhance resource efficiency, automate complex tasks, and introduce new business innovations. Gartner analysts predict these advancements will be a game-changer.
However, the effectiveness of AI is no longer solely reliant on models. Enterprises now prioritize greater control, reduced latency, predictable costs, and data privacy and compliance. This demand has fuelled the rise of private AI solutions, where models are hosted on-premises or within dedicated cloud environments, providing enhanced data security, controlled inference costs, and operational flexibility.
The future of AI lies in harnessing models intelligently within robust, adaptive frameworks that amplify human capability rather than simply replacing repetitive tasks. At Presidio, a leading innovator in AI, cloud, and next-gen infrastructure solutions, this vision is being brought to life by their CTO, Robert Kim. With a strategy-first, value-led approach, Kim is building secure, scalable ecosystems at the intersection of innovation and governance.
To ensure a more governed and grounded deployment, Agentic AI is often combined with Enterprise Retrieval-Augmented Generation (RAG). This capability, focusing on intelligence augmentation, uses smaller, dynamic knowledge bases for improved accuracy and relevancy. Furthermore, techniques such as optimized quantization, model distillation, and more effective inference engines are making private deployments practical and economical.
As inference costs drop and models become leaner yet more effective, organizations increasingly recognize that powerful, specialized, private AI environments are within their grasp. The emergence of open-source models like PHI, DeepSeek R1, and Qwen has made deployment requirements around token usage more affordable, expanding the set of use cases for private AI.
The dynamic between AI large language models today resembles the competition between chip manufacturers Intel and AMD in the early 2000s. As AI continues to evolve, the focus is shifting towards how models are curated, orchestrated, and applied. The worst version of any agent (or model) is the one you're using right now, because it constantly improves.
In customer support, for instance, an agent embedded within the system can adapt strategies and refine its actions based on interactions and feedback, improving over time. This adaptability overcomes the rigidity of robotic process automation (RPA), making AI a powerful tool for enhancing service quality and efficiency.
As we move forward, it is clear that Agentic AI will play a pivotal role in driving innovation and digital transformation across industries. With its ability to learn, adapt, and collaborate, Agentic AI is poised to become a digital partner that amplifies human capability, rather than replacing it.
