How Agentic AI is Revolutionizing Enterprise Software and Why You Must Act Now

The Evolution of Enterprise Software

Software as a Service (SaaS) changed enterprise operations by delivering tools online via subscription. Vertical SaaS introduced industry-specific features, and robotic process automation (RPA) brought virtual bots to automate repetitive tasks. However, the arrival of agentic AI marks a new era.

What is Agentic AI?

Agentic AI uses autonomous agents that do more than mimic humans — they analyze data, make decisions, execute tasks, and self-orchestrate workflows in real time. Unlike traditional SaaS or RPA, agentic AI acts as a digital labor force integrated across technology stacks to drive measurable business outcomes. These agents leverage large language models to perform sophisticated reasoning without human prompts.

Versatile Applications and Capabilities

Each AI agent can be assigned specific goals, such as driving sales, managing customer service, or adjusting manufacturing processes dynamically. Unlike generative AI models that primarily generate content, agentic AI agents can independently crawl databases and build workflows to complete tasks.

Market Adoption and Industry Insights

Gartner predicts that by 2028, about one-third of enterprise software will incorporate agentic AI, up from less than 1% in 2024. A survey by Cloudera of 1,484 IT leaders revealed that 83% see AI agents as critical for competitive advantage, and 60% fear falling behind without adoption this year. Moreover, 96% plan to increase deployments within 12 months, with many expecting large-scale rollouts.

Addressing Workforce Challenges

Salesforce CEO Marc Benioff describes agentic AI as a new labor, productivity, and economic model. With labor force participation below pre-pandemic levels and more job vacancies than candidates, AI aims to eliminate rote tasks while boosting employee productivity. Agentic AI bridges the gap between personnel and products, enabling continuous, automated customer interactions and sales management, greatly enhancing efficiency and revenue potential.

Pricing and Deployment Challenges

Agentic AI pricing is evolving from traditional 'per seat' to 'per task' or value-based models, linking costs directly to outcomes. Salesforce’s Agentforce platform exemplifies this shift with its consumption-based pricing tied to results.

Responsibility, Accountability, and Risk Management

Selecting AI vendors now requires assessing reliability, responsibility, and alignment with company goals. Organizations must consider legal and compliance risks, auditability, and clearly define accountability for AI agent actions, including protocols for managing rogue agents.

Practical Steps for Businesses

Reevaluate your technology stack to identify rule-based functions suitable for AI automation. Anticipate interoperability needs and involve cross-functional leaders from legal, IT, and operations to avoid siloed decisions. Develop policies ensuring safe and responsible AI use. Understand AI agents' capacities and reconsider cost models focusing on volume and efficiency rather than seats or licenses.

The Future of Enterprise Software

Agentic AI will complement rather than replace SaaS, creating a collaborative technology environment aimed at workforce augmentation. Enterprises must adapt software strategies promptly to leverage agentic AI, securing their competitive edge and future success.