Enterprises are shifting from traditional automation to agentic AI. These autonomous AI agents are becoming a core part of a wide range of internal workflows.
What is Agentic AI?
Agentic AI refers to AI systems that autonomously plan and execute actions to achieve goals. Agentic AI systems don’t just assist, they act. For example, an agentic AI system can identify high-intent leads from CRM data, launch personalized outreach emails, reply to follow-ups, and even book demos, all with no human intervention.
Why AI Agents?
AI agents understand context, learn dynamically and make decisions autonomously. Organizations across industries are leveraging AI agents to automate complex and dynamic processes that previous automation technologies could not address, with ongoing human oversight to maintain control and accountability. AI agents can amplify productivity and scalability while balancing cost, effectiveness and adaptability.
Driving the AI Agenda
AI is becoming a core part of enterprise strategy, with a focus on use cases that drive clear operational value. The most common applications include improving employee productivity (e.g., information discovery, content and idea generation, analytics, task automation), business process automation (e.g., compliance, risk management, workflow automation), and customer support and self-service.
Enterprise Agentic AI Adoption
89% of enterprises plan to increase their AI investments in 2026 and beyond, according to Kore.ai’s State of AI report. But there are challenges:
AI is making inroads in every function, but the readiness lags. AI is now embedded across most enterprises, with 71% of companies actively using or piloting AI across departments like customer service, IT, HR, finance, operations, and marketing. Yet, only 30% report being fully prepared, pointing to critical gaps in data, infrastructure, and talent.
Initial success is high, but scaling is tough. The study revealed that 93% consider their initial AI implementations (pilots) successful, however, scaling is constrained due to concerns over data privacy and regulatory compliance, LLM costs, and AI talent shortage.
The human-AI collaboration is the next frontier. AI success is increasingly tied to AI expertise and employees’ ability to collaborate with AI. Most companies plan to upskill or hire for data analysis and AI interaction roles, acknowledging that technical infrastructure alone won’t drive future gains.
The use of AI in the enterprise has entered a new phase—moving from experimentation to operationalization, but realizing its full value requires a renewed focus on readiness, scalable infrastructure, responsible governance, and a workforce empowered to work alongside intelligent systems.
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How agentic AI Can Change the Way Banks Fight Financial Crime. McKinsey & Company. August 2025. https://www.mckinsey.com/capabilities/risk-and-resilience/our-insights/how-agentic-ai-can-change-the-way-banks-fight-financial-crime
Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025. Gartner. August 2025. https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025
The rise of Collaborative Automation: How Autonomous AI Agents are Redefining Business Processes. Deloitte. July 2025. https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2025/generative-ai-agents-in-collaborative-automation.pdf
Research Report. Scaling AI: Practical Insights from AI Leaders. Kore.ai. 2025. https://www.kore.ai/ai-research-reports/scaling-ai-insights-report