AI agents are already transforming industries, automating high-level decision-making, and optimizing workflows at scale. From healthcare to software development, agentic AI is reshaping how businesses operate. These systems are digital workers handling complex tasks autonomously. Just as electricity turbo-charged industrialization, agentic AI is becoming the new infrastructure that will fuel the next era of enterprise transformation. But businesses must be ready to integrate AI seamlessly into their operations, or risk being left behind.
This article introduces how agentic AI is reshaping industries and what it takes for businesses to be agentic-AI-ready in an interconnected world.
From Software Tools to Autonomous Agents
Enterprise software has historically increased efficiency but always required human oversight. Agentic AI fundamentally shifts this by automating entire workflows and decision-making processes, reducing human intervention in every department in all vertical markets from customer service, coding to healthcare administration. 
According to Gartner, 33% of enterprise software will integrate agentic AI by 2028, automating 15% of day-to-day business decisions. This represents a massive shift from “shadow AI” as a chatbot support tool to AI as a central operational force.
The Last-Mile Problem in AI is 99 Miles
Despite advances in AI technology, many companies struggle to deploy these systems effectively across their operations. This challenge, known as the “last-mile problem” in AI, refers to the difficulty of taking sophisticated AI models and making them usable, accessible, and integrated within real-world workflows. Often, what remains in the final stretch of AI deployment—tasks like ensuring data compatibility, compliance, user adoption, and seamless integration into existing systems—ends up requiring substantial effort and resources. Some industry experts even refer to it as the “last 99-mile problem” to underscore the scale of these challenges. Solving this problem is essential to making AI practical and impactful for every enterprise.
Angie Ma, an expert in enterprise AI, notes that many companies are stuck in what she calls ‘pilotitis’—they succeed in creating Proof of Concept models but fail to scale them beyond pilot projects. Often, these projects focus on horizontal use cases with limited ROI, rather than core business challenges that could deliver transformative value.
The last-mile problem, which continues to hinder AI deployment, involves creating infrastructure that enables companies to deploy AI seamlessly without requiring massive engineering resources. By addressing accessibility and ease of integration, new agentic AI platforms are democratizing AI adoption, positioning every business to tap into its transformative potential.
The Role of the AI Ecosystem
Agentic AI is part of a broader AI ecosystem, leveraging advancements in cloud, edge intelligence, and machine learning. Success hinges on seamless integration across platforms to enable real-time data flow and decision-making. Tech companies likeOracle, Salesforce and SAP and others are building unified AI platforms that connect AI agents with other enterprise systems.
This interconnectedness ensures that agents can share information, coordinate actions, and make decisions that consider data from all parts of the business. These platforms allow companies to scale AI across functions, driving holistic digital transformation. As agentic AI becomes more accessible, it empowers enterprises to leverage AI’s potential without requiring extensive internal AI expertise, reshaping the operational landscape.
As businesses advance in their AI adoption journey, the demand for high-capacity data infrastructure continues to grow. Experts predict that data center capital expenditure could reach $1-$2 trillion by 2030, driven by the compute and storage needs of large AI models. This trend signals the significant investments required to support enterprise-scale AI, further emphasizing the need for robust, scalable networks that can handle the intensive data flows and new protocols which AI demands.
Unlocking New Efficiency: Agentic AI as Your Digital Workforce
AI agents now operate 24/7 across finance, logistics, and retail, autonomously managing tasks such as customer service and inventory optimization. Their ability to analyse data in real time and adapt to market shifts offers businesses a competitive edge in fast-moving sectors.
For example, SAP’s Joule Copilot enhances productivity and efficiency by automating routine tasks, providing contextualized insights, and streamlining processes across financial, HR, supply chain, and customer experience domains. With natural language processing and machine learning capabilities, Joule enables proactive decision-making, optimizes resource allocation, and predicts market trends and operational risks for up to 300m enterprise users.
In software development, 75% of executives surveyed by Capgemini believe AI agents will become indispensable for tasks like generating and debugging code. So-called TuringBot agents are already rapidly accelerating the software development lifecycle, allowing developers to focus on higher-level problem-solving and innovation while the AI agents handle routine tasks like code origination and refinement.
The Missing Piece: Why Enterprise Networks Are Critical for AI Success
The success of agentic AI depends on reliable, scalable networking infrastructure purpose-built for AI workloads deployed by companies such asStelia. AI agents need seamless, real-time data access across systems to make decisions. Without fast, secure networks, businesses risk delays in data processing that undermine AI efficiency.
Imagine a healthcare AI agent that pulls data from different hospitals to offer treatment recommendations. If those hospitals are not networked in real time, delays in data transfer could hinder the AI’s ability to make timely decisions, which, in healthcare, could be the difference between life and death.
The same principle applies across industries. In retail, AI agents need real-time access to inventory and sales data to manage stock levels effectively. Companies that want to be AI-ready must invest in upgrading their networks to handle the real-time data flows that agentic AI systems require to function at full capacity.
Agentic AI as the New Infrastructure of Work
Agentic AI is the future infrastructure of business, automating complex workflows, enabling 24/7 operation, and driving transformation across industries. Gartner forecasts that by 2028, agentic AI will automate 15% of daily business decisions. Companies must act now to build the networks and platforms necessary to stay competitive.
According to Sarah Guo, founder ofConviction venture capital firm, making AI truly transformative requires that it be as accessible and “unstoppable” as other core business tools, allowing companies to deploy it seamlessly across functions without needing specialized technical teams. This vision captures the future of agentic AI as not just a set of tools, but a universally deployable workforce that enhances productivity across every level of an organization.
The future of AI in business depends on focusing on high-ROI applications that address core business needs. The real transformation lies in moving beyond pilot projects and targeting high-impact use cases. For companies to fully benefit from agentic AI, they must build an infrastructure that is interactive, connected, and trusted, allowing AI to drive real operational value. Is your enterprise equipped with the networks and systems to thrive in this AI-driven future? Those who build the right foundation today will lead tomorrow’s digital economy, while others risk being left behind.
