Why the AI Boom Will Be Sustained Through 2026 and Beyond: The Rise of Agentic AI

The artificial intelligence (AI) boom, once fueled by generative AI and large language models, is entering a new phase defined by agentic AI-a paradigm shift where AI systems act autonomously, make decisions, and execute tasks with minimal human intervention. As enterprises grapple with the complexities of scaling AI, the emergence of agentic AI is reshaping business processes, redefining ROI metrics, and creating a sustained demand for infrastructure and software leaders like Alphabet, Microsoft, and IBM.

The Transformative Potential of Agentic AI

Agentic AI is no longer a theoretical concept but a tangible force in enterprise software. According to Gartner, 40% of enterprise applications are projected to incorporate task-specific AI agents by 2026, up from less than 5% in 2025. These agents are not mere tools for automation; they are evolving into outcome-owning systems that proactively manage workflows, optimize resource allocation, and drive business results. Salesforce highlights that by 2026, agentic AI will transition from "task-taking conversational agents" to systems that "own outcomes," embedding themselves into core business functions.

This shift is already evident in sectors like healthcare and financial services, where AI agents are delivering measurable ROI. For instance, inpatient monitoring systems powered by agentic AI have reduced operational costs by 20% while improving patient outcomes. Similarly, customer service automation in financial services has cut resolution times by 35%, enabling firms to reallocate human resources to higher-value tasks.

ROI and the Strategic Imperative for Infrastructure Investment

While the promise of agentic AI is vast, its ROI hinges on strategic infrastructure investment. IBM's research underscores that 78% of C-suite executives argue achieving maximum value from agentic AI requires a new operating model, not just incremental improvements to existing processes. This necessitates robust infrastructure capable of handling autonomous decision-making, real-time data processing, and seamless integration across hybrid environments.

Enterprises adopting agentic AI are prioritizing a "Crawl, Walk, Run, Fly" approach, starting with small, high-impact use cases to build confidence and demonstrate value. For example, IBM's watsonx Agents platform enables organizations to deploy AI agents for compliance monitoring and sales forecasting, generating early wins that justify further investment. Microsoft's Copilot Agents and Alphabet's Gemini-powered workflows are accelerating the adoption of agentic AI in enterprise software, with Microsoft reporting a 40% increase in user engagement with AI-driven workflows in 2025.

However, challenges persist. McKinsey's 2025 Global AI Survey reveals that only 39% of organizations report enterprise-level EBIT impact from AI, with most attributing less than 5% of their EBIT to AI use. This gap between potential and realization highlights the need for governance frameworks, data quality improvements, and cross-functional collaboration. IBM's Model Context Protocol (MCP), an open standard for secure AI-agent communication, is one solution addressing these challenges by enabling interoperability across platforms.

The Role of Enterprise Software Leaders

The rise of agentic AI is cementing the dominance of enterprise software leaders. Microsoft, IBM, and Alphabet are not only developing AI agents but also redefining the infrastructure required to support them. IBM's hybrid cloud ecosystem, for instance, is being industrialized to host agentic AI systems, with its IBM Cloud and IBM Storage platforms leveraging MCP to enable natural language interactions and AI-driven insights. Alphabet's Gemini models, integrated into Google Cloud, are similarly positioned to power autonomous workflows in logistics, healthcare, and enterprise resource planning (ERP) systems.

According to Gartner, by 2028, 33% of enterprise software applications will include agentic AI, a trend that will drive demand for platforms capable of managing complex agent ecosystems. Microsoft's Azure AI and IBM's watsonx are already seeing traction, with Azure AI's agent orchestration tools reducing deployment times by 50% for clients in manufacturing and retail.

Overcoming Barriers and the Path Forward

Despite optimism, risks remain. Gartner warns that over 40% of agentic AI projects may be canceled by 2027 due to unclear business value, cost overruns, and governance gaps. To mitigate this, enterprises must adopt a dual focus: technical readiness (e.g., scalable infrastructure, data governance) and organizational readiness (e.g., upskilling teams, aligning AI strategies with business objectives). IBM's emphasis on "reimagining operating models" and Salesforce's push for "trust in data" frameworks are critical in this context.

Moreover, the ROI of agentic AI is expanding beyond cost savings. High-performing organizations are leveraging AI agents to drive innovation, such as dynamic pricing models in e-commerce or real-time compliance monitoring in regulated industries. By 2035, agentic AI could account for 30% of enterprise application software revenue, surpassing $450 billion, a testament to its long-term strategic value.

Conclusion: A Sustained AI Boom

The AI boom is far from peaking. Agentic AI is set to sustain and accelerate growth through 2026 and beyond, driven by its ability to transform workflows, deliver ROI, and redefine enterprise software. For investors, the key lies in supporting infrastructure and software leaders-companies like Microsoft, IBM, and Alphabet-that are not only building the tools for agentic AI but also addressing the governance, interoperability, and scalability challenges that accompany its adoption.

As Gartner, McKinsey, and IBM collectively affirm, the next wave of AI is not a speculative future but an imminent reality. The question is no longer if agentic AI will reshape enterprises but how quickly investors and leaders can align with its trajectory.