Software is learning to act. Not merely to respond or execute commands, but to perceive context, reason through complexity and pursue outcomes on its own behalf. Agentic AI is the buzzword of the moment, but beneath the hype lies a meaningful shift in how organizations will work, support their teams and make decisions in the years ahead.

More than a passing phase, agentic AI represents the next stage in automation’s evolution — one that moves from efficiency toward adaptability, and from static workflows toward systems that can interpret intention, collaborate with human teams and actually think and act on their own behalf. With the promise of significant increases in productivity and efficiency, it’s easy to see why.

Gartner predicts that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024. Additionally, 33% of enterprise software applications are expected to include agentic AI by 2028, up from less than 1% in 2024. While this is exciting, it’s also not a one-size-fits-all solution. And before jumping in head-first, it’s a good time to weigh the benefits and challenges adopting agentic workflows will have on your own business.

The journey from automation to agency

The early days of automation were about repetition and consistency. Robotic process automation tools replaced manual data entry, basic scheduling and other repetitive tasks with rule-based scripts. This was revolutionary for its time, but it was rigid in its approach. When an input didn’t match the script, human intervention was required.

Over time, businesses layered intelligence onto these workflows. Machine learning allowed software to make predictions or classifications, nudging automation toward greater sophistication. Yet even then, systems remained reactive. They could respond, but they couldn’t initiate. For example, AI could flag an anomaly, but couldn’t decide whether it was significant or what to do about it.

Then came the generative AI era, where models could create language, images and code. Generative AI expanded what machines could produce, but not what they could decide. It generated possibilities, not priorities. The intelligence was expressive, but not yet agentic.

With agentic AI, we’re moving toward a phase where systems are not just reactive, but proactive. These AI agents are capable of perceiving changing conditions, reasoning through alternatives and taking action autonomously. They don’t just follow instructions, but move the needle in pursuing real business objectives. In other words, software now acts with purpose, albeit within the boundaries of human oversight.

What agentic AI is…and isn’t

Agentic AI blends intelligence and automation into a single operational layer that can manage outcomes rather than just execute steps. Instead of relying on humans to define every possible rule, agentic systems understand goals and context. They can reason through multiple inputs, choose the best path forward and adapt as conditions change.

For any kind of AI, data is critical, and this level of reasoning requires memory or a data source. For agentic AI workflows to be effective for a business, the AI solution must be anchored to a deep, unified system of record that is the one source of truth reflecting what’s happening across the organization in real time. Without that data substrate, an agent is just guessing. The system of record is what gives both the AI and business operations continuity. And understanding not only what to do next, but why that action aligns with the larger mission, and keeping every teammate up to speed on what’s going on, is imperative.

While traditional automation in customer support might categorize a service ticket and route it to the right queue. An agentic system can triage the issue, draft a response, update records and even resolve the problem if it falls within defined guardrails. In a sales environment, it can evaluate lead quality, tailor outreach based on behavioral data and move prospects through the funnel without waiting for manual triggers.

The distinction is subtle but powerful. Traditional automation executes rules*, whereas by contrast, agentic AI executes judgment.* It doesn’t just know what to do, but actually decides how to do it, based on context and evolving information. That capacity for self-direction is what makes the technology so transformative.

Make no mistake, though, this is not an argument in favor of cutting staff or quitting our 9-5s in favor of agentic replacements. Human oversight is still necessary — arguably more than ever before. While tech is advancing, so are end-users’ and consumers’ awareness and scrutiny of it.

As such, the most responsible use of agentic AI isn’t replacement, it’s reinforcement. It’s about freeing people from procedural work so they can focus on the complex, empathetic problems that require a human touch. This is especially important in industries like real estate or healthcare, where decisions are deeply personal.

Preparing for the age of agentic AI

Optimizing for agentic AI isn’t just about adding smarter tools; it begins re-architecting the environment those tools inhabit. Organizations that thrive will have integrated, high-quality data foundations and unified workflows. Fragmented systems or poor data hygiene can cripple an AI agent’s ability to reason effectively. For many enterprises, this means modernizing their systems of record — CRMs, ERPs and HR platforms — that make up digital operations.

Equally important is the need for well-defined guardrails. Businesses must define what good decisions look like, the limits of an agent’s autonomy and the ethical or compliance constraints that must be followed. This balance between freedom and control is critical. Too many restrictions, and the AI can’t act usefully, but too few and it risks acting outside the organization’s intentions.

Recent Deloitte survey findings reflect this. Nearly 60% of AI leaders report their organization’s primary challenges in adopting agentic AI are integrating with legacy systems and addressing risk and compliance concerns. On the flip side, unclear use cases/business value was the top answer for other respondents. While both groups cited risk and compliance concerns as a top challenge, it’s clear there’s a divide on where employees fit into the agentic AI puzzle.

But if history has taught us anything, it’s that the best results are realized when people, processes and tech operate together, as one collaborative network. Organizations that excel in this next era of AI will be the ones that take this into account when deploying their AI agents.

Why humans are a value add

While much of the conversation around agentic AI centers on automation, the deeper story is about augmentation. As intelligence shifts deeper into systems, the human role moves higher in purpose. Instead of being the operators inside every workflow, people become the designers of intent. This includes defining objectives, setting ethical and operational boundaries and interpreting outcomes through the lens of business strategy.

This evolution will inevitably reshape roles and organizational design. Teams will shift from micromanaging workflows to supervising AI agents, curating data and optimizing performance across systems. This will require new skill sets in technical fluency and delegating meaningful work to intelligent systems. In fact, recent research from PwC found that 67% of executives agree that AI agents will drastically transform existing roles within the next 12 months. In fact, 48% reported they will likely increase headcount due to change brought on by AI agents.

The leaders who embrace this shift early will gain more than efficiency. As agentic AI learns and improves, organizations can scale decision-making without scaling headcount. They can respond to customer needs in real time, anticipate and insulate their business from market shifts, and allocate human creativity and innovation where it matters most.

Building agentic workflows

Before organizations can fully realize the value of agentic AI, they need to lay the groundwork, much like systems and data had to be centralized before reaping the benefits of automation. Deploying agentic systems without the right operational and technological foundations will only amplify inefficiencies, not eliminate them. Companies must first align people, processes and technology by ensuring that their data infrastructure is unified, their processes are standardized and their governance frameworks are mature enough to support decision-making at scale.

To make that transition effectively, organizations should focus on a few key steps:

• Establish a deep system of record. Agentic AI depends on consistent, real-time access to information. Consolidating fragmented data across departments, systems and platforms ensures agents have the visibility and context to act intelligently.
• Create and enforce clear governance and ethical guardrails. Define the boundaries of agent autonomy, specifying where human oversight is required and how decisions should be audited. This builds trust and prevents rogue automation.
• Adopt orchestration layers for multi-agent collaboration. As AI systems grow, businesses will need orchestration tools that coordinate how AI agents communicate, hand off tasks to human teams and align on objectives.
• Reskill teams for AI collaboration. In agentic workflows, humans and AI don’t work in isolation, but as a team. Employees must learn how to supervise, interpret and refine agentic behavior, shifting from process execution to performance management and continuous improvement. Just as importantly, teams need to work alongside AI and pick up where it leaves off, handling the human moments that require empathy or judgment.
• **Centralization first for the biggest impact. **Agentic AI delivers its greatest value in organizations that have already centralized their operations — leaving behind same-store models in favor of specialized teams and shared systems. Centralization aligns people, processes and data around consistent, scalable workflows, giving AI the clarity and context it needs to act intelligently.

The alignment between data clarity, centralization, governance, orchestration and human oversight is the differentiator between experimentation and transformation. And for those willing to reimagine their workflows and take the right steps, the reward can be significant — an organization that doesn’t just run efficiently, but learns and evolves continuously.

Agentic AI isn’t a far-off concept; it’s here. The companies optimizing for it today will be the ones with a competitive advantage in the future.

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