Agentic AI and Enterprise Software Transformation

Artificial intelligence is reshaping nearly every industry, with enterprise software experiencing some of the most profound changes. As AI evolves from tools that assist users to systems that act autonomously, a new paradigm is emerging: agentic AI. This shift is influencing how companies operate, how investors evaluate opportunities, and how value is created across the software ecosystem.

The Current State of AI Investment

Over the past few years, public markets have been dominated by a small group of large technology companies associated with AI infrastructure, hardware, and foundational models. This concentration has drawn capital away from other asset classes, including private equity.

At the same time, private markets have quietly become the primary environment where AI-driven application innovation is occurring. The vast majority of enterprise AI applications today are developed by private software companies rather than public hyperscalers or model providers. This dynamic has created a disconnect between where attention is focused and where long-term value creation may ultimately occur.

The Three Phases of the AI Cycle

AI adoption in enterprise software follows a familiar technological cycle:

1. Hardware and Compute Providers - Early investment flows toward companies building chips, servers, and infrastructure required to support AI workloads.

   
   

2. Hyperscalers and Platforms - Cloud providers and large platforms expand capacity, tooling, and model access. These services increasingly resemble utilities that enable broader adoption.

   
   

3. Application Providers - Long-term economic value tends to accrue to software companies that embed AI directly into business workflows. These companies transform how work is done rather than merely supplying infrastructure.

Historically, application providers capture the largest share of economic value once new technologies mature and diffuse into everyday business processes.

What Makes Enterprise Software “Agentic”

Agentic enterprise software goes beyond automation. Instead of helping users complete tasks, AI agents perform tasks themselves within defined workflows.

Examples include:

• Agents that handle customer onboarding or scheduling without human intervention

• Systems that dynamically adjust pricing, inventory, or supply chains

• Agents that manage compliance, reporting, or operational decisions in real time

In these cases, software begins to replace service-based labor, effectively allowing enterprise software to absorb functions traditionally handled by people or consulting services.

Productivity, Margins, and the “Rule of 70”

Enterprise software has traditionally been evaluated using the “Rule of 40,” which combines revenue growth and profit margins. The introduction of AI is changing that equation.

By applying AI across product development, sales, services, and back-office operations, companies are seeing:

• Faster and cheaper software development

• Lower customer acquisition costs through AI-assisted sales

• Reduced service delivery costs via agent-driven support

• More efficient internal operations

These gains can significantly expand margins while maintaining growth, leading to performance levels that exceed historical benchmarks.

Why Data and Workflow Control Matter

Not all software companies are positioned to benefit equally from agentic AI. A critical differentiator is control over proprietary workflows and data. Most enterprise data has never been used to train large foundational models.

Companies that own specialized data and deeply embedded workflows can deploy agentic solutions that competitors cannot easily replicate. In contrast, businesses that rely on repackaging publicly available information or generic content face greater risk of displacement as AI commoditizes those capabilities.

The Role of the Agentic Factory Model

Scaling agentic AI across large software portfolios requires repeatable processes and infrastructure. The concept of an “agentic factory” reflects a structured approach to transforming existing cloud-based software into AI-driven, agentic systems.

This mirrors earlier transitions in enterprise software, such as the shift from on-premise systems to cloud delivery, which significantly increased efficiency and economic returns.

Key characteristics of this model include:

• Standardized tooling to build and deploy agents

• Integration into existing enterprise workflows

• Emphasis on high precision, reliability, and compliance

• Ability to scale transformation across many companies

Precision is particularly critical in regulated industries such as finance, healthcare, insurance, and automotive, where error tolerance is far lower than in consumer applications.

Real-World Applications of Agentic AI

Agentic AI is already being deployed across a wide range of industries, including:

• Healthcare, where agents assist with practitioner matching and appointment scheduling

• Supply chains, where agents manage tariffs, sourcing, and logistics decisions

• Sports and analytics, where agents run scenario modeling and performance analysis

• Enterprise management, where agents monitor KPIs and operational metrics

These examples demonstrate that agentic AI is not confined to a single sector but is broadly applicable wherever complex workflows exist.

Implications for Work and Employment

Agentic AI is likely to reshape job roles rather than uniformly eliminate them. Some tasks and job categories may disappear as agents assume repetitive or data-intensive work. Other roles may become more productive as AI acts as a capability multiplier.

A commonly cited dynamic is that AI may not replace a worker outright, but a worker who effectively uses AI may replace one who does not.

AI as a General-Purpose Technology

Agentic AI represents a general-purpose technology on par with earlier breakthroughs such as widespread computing and cloud infrastructure. Its impact is expected to be both exponential for some organizations and existential for others.

Companies that successfully embed AI into their core workflows stand to gain significant competitive advantages. Those that fail to adapt may struggle to remain relevant as productivity expectations rise.

Conclusion

Agentic AI marks a fundamental shift in how enterprise software creates value. The long-term winners are likely to be application providers that integrate AI deeply into proprietary workflows and data environments, rather than those focused solely on infrastructure or models.

As AI costs decline and capabilities expand, the strategic application of agentic systems will increasingly determine which companies thrive in the next phase of digital transformation.