Agentic AI isn’t just a tech trend—it’s the next frontier in transforming business. By enabling AI systems to reason, plan, and act independently, companies can streamline operations and make smarter, faster decisions at scale.
Tracing the Origins of Agentic AI: How We Got Here
Let’s step back for a moment: since the 1950s, autonomous agents have transitioned from theoretical ideas to early real-world applications, like ELIZA and Shakey the Robot. Over the next decades, they evolved further progressing through expert systems and early autonomous vehicles in the '80s to multi-agent systems in the '90s.
Fast forward to today, and these agents are more capable than ever. Recent breakthroughs in deep learning, natural language processing, and vast data access are propelling autonomous agents into a whole new era of possibilities. European open-source leaders like Mistral AI, Hugging Face, and H are driving rapid innovation with adaptable AI models, underscoring that agentic AI is no longer theoretical but a transformative force in modern business. Major digital service companies see this potential as well and are investing in agentic AI to reshape operations and enhance decision-making across industries.
As this technology matures, the question for business leaders is clear: how will they integrate agentic AI to stay ahead?
Understanding Agentic AI: Intelligent, Adaptive, Autonomous
Agentic AI goes beyond traditional automation by autonomously reasoning, planning, and acting—without human oversight. Unlike standard chatbots or automation systems, which follow preset rules, agentic AI adapts in real time and handles complex tasks.
Let’s consider two cases:
- In customer service, traditional chatbots handle basic inquiries and escalate complex issues to human agents. Salesforce’s Agentforce, however, manages complex tasks end-to-end, significantly reducing the need for human intervention.
- Similarly, JPMorgan’s agentic AI enhances fraud detection by autonomously analysing real-time transaction data, quickly learning new fraud patterns and detecting anomalies with greater speed and accuracy.
In Action: Real-World Impacts
Agentic AI is already reshaping how companies tackle complex challenges.
Banking is a prime example. In traditional banking, customers often track transactions manually and receive generic product offers. Agentic AI can change that by proactively analysing spending patterns, predicting upcoming expenses, and providing real-time, personalised financial guidance. Rather than waiting for an account overdraft, the AI can forecast potential shortfalls days in advance and suggest actions like moving funds from savings or adjusting upcoming bill payments.
While traditional banks may send quarterly promotions based on broad customer segments, AI-driven systems can identify the perfect moment to recommend a higher-yield savings account—such as when a customer's balance consistently exceeds their usual needs. This shift from reactive to predictive banking goes beyond convenience; it transforms banking into a proactive financial partnership, helping customers make smarter decisions before issues arise.
In manufacturing, Siemens' Insights Hub (formerly MindSphere) is instrumental in enabling adaptive production by connecting assets and providing real-time data analytics across operations. With Insights Hub, manufacturers can implement self-optimizing processes that adjust autonomously to changing conditions, boosting flexibility and efficiency. This platform also supports predictive maintenance and resource allocation, empowering manufacturers to respond dynamically to fluctuations in demand and production requirements.
Unlocking the Potential of Agentic AI
In a previous article, I explored AI not just as an automation tool but as a lever for process reengineering. From my perspective, agentic AI is a key tool in this transformation.
The rise of agentic AI requires process owners, business analysts, and IT system designers to adopt a new approach. Unlike traditional automation, which follows predictable workflows, agentic AI operates in dynamic environments and makes independent, real-time decisions.
This shift requires new skills and mindsets in every role:
- Process owners need to design adaptable workflows that allow AI the autonomy to operate while maintaining essential controls. Strong change management skills help guide teams through the transition to AI-driven processes.
- Business analysts must deepen their data literacy, understand AI applications, and shift from traditional metrics to AI-specific KPIs, such as adaptability and trustworthiness.
- IT system designers should build AI-first architectures that support continuous learning and real-time data processing. They must also integrate ethics and governance frameworks to ensure compliance and address security concerns.
And for CEOs aiming to implement agentic AI, here are 4 key recommendations to consider:
- Identify High-Impact Areas: Start with processes that are labour-intensive or require real-time decision-making, such as fraud detection, supply chain management, or customer support.
- Adopt a Data-Driven Strategy: Make data the foundation of your AI strategy, as agentic AI relies on high-quality, real-time data. Invest in robust data management practices to support continuous AI improvement.
- Build Talent and Skills: Equip your team with the expertise needed to manage and refine AI systems. Consider hiring AI specialists and training current teams to fully leverage AI capabilities.
- Start with Pilot Programs: Begin with small-scale pilot programs to measure ROI and refine processes before expanding agentic AI across the organization.
Agentic AI presents a unique opportunity for businesses to reengineer processes, streamline operations, make smarter decisions, and accelerate innovation. With strategic planning and focus, CEOs can lead their organizations into an autonomous, AI-driven future.