Why agentic intelligence is the new competitive advantage

Organisations that harness agentic AI, reasoning LLMs and new economies of scale will set the pace

The rise of agentic AI—autonomous systems empowered by reasoning-capable large language models (LLMs)—is reshaping strategic decision-making across industries.

With breakthroughs such as DeepSeek’s cost-efficient Mixture of Experts (MoE) models significantly reducing training expenses, organisations now face both a unique opportunity and a critical imperative: to rethink their AI infrastructure and investment strategies fundamentally. Those who fail to adapt risk losing competitive advantage to agile competitors who rapidly embrace these transformative technologies.

Traditionally, large-scale AI systems demanded extensive high-performance computing (HPC) resources, creating a substantial barrier to entry. DeepSeek’s MoE architecture significantly lowers costs and accelerates deployment, laying the groundwork for integrating more sophisticated, reasoning-capable LLMs. These advanced LLMs underpin agentic systems, enabling them to autonomously manage complex tasks, solve nuanced problems, and make contextually informed decisions. This synergy between affordable foundational models and powerful reasoning LLMs creates unprecedented economies of scale.

Agentic AI impacts LLMs by necessitating their evolution from static text predictors to dynamic components of autonomous systems. This shift requires LLMs to handle interactive dialogues, maintain context over extended interactions, and make decisions aligned with human values, often achieved through advanced training techniques like reinforcement learning from human feedback (RLHF). Consequently, LLMs are being redesigned to support agentic capabilities, driving innovations in model architecture, training methodologies, and ethical frameworks to ensure their safe and effective deployment in autonomous applications.

Imagine deploying agentic AI that autonomously optimises supply chain logistics, manages dynamic pricing strategies, or conducts real-time risk assessment in financial services. The cost-effectiveness and agility provided by reasoning LLMs embedded within agentic AI solutions mean businesses can rapidly scale tailored applications, significantly enhancing operational efficiency and responsiveness to market shifts.

However, fully realising these benefits necessitates a strategic overhaul of existing AI pipelines. Traditional, inflexible infrastructures centred around capital-heavy HPC environments must give way to adaptive, modular platforms optimised for rapid deployment of agentic AI systems. Such platforms emphasise composable architecture, iterative development, and swift scalability, enabling organisations to experiment confidently and inexpensively.

Economies of scale emerge from this flexibility—lower upfront costs enable broader experimentation, fostering faster innovation cycles and reducing overall investment risk. Organisations can swiftly pivot between specialised applications, maximising both capital efficiency and strategic agility.

With these advancements, governance and compliance frameworks must also evolve. Autonomous decision-making introduces new layers of complexity, requiring stringent oversight mechanisms, robust data integrity measures, and comprehensive ethical guidelines. Effective governance structures ensure these powerful technologies are harnessed responsibly and sustainably, safeguarding organisations against unintended consequences.

The competitive implications are stark. Agile, lean entrants employing reasoning LLMs within agentic frameworks are already capturing market segments traditionally dominated by larger incumbents. These smaller firms leverage cost-effective AI innovations to quickly scale solutions in finance, healthcare, and retail—highlighting how swiftly the competitive landscape can shift.

Business leaders should prioritise the following actions:

• Shift investment focus from static HPC infrastructure toward flexible, modular platforms supporting reasoning LLM-driven agentic systems.

• Reconfigure AI pipelines to foster rapid experimentation, agile iteration, and seamless scaling of targeted AI solutions.

• Enhance governance frameworks to manage the risks associated with autonomous systems, ensuring robust ethical oversight and compliance.

I believe that in this era of heavy, inflexible AI infrastructure is over. Organisations that can harness the combined potential of agentic AI, reasoning LLMs and new economies of scale will set the pace for industry innovation.

Those that resist this shift risk their competitive edge, as leaner, smarter, and more adaptable competitors define the future marketplace.

Adnan Masood is chief AI architect at UST