Editorial
The Gist
- Beyond chatbots. Agentic AI goes far beyond question-and-answer models, bringing reasoning, decision-making, and workflow execution into enterprise operations.
- Enterprise foundation. Clean data, orchestration layers, and rapid development environments are critical to embedding AI agents across systems.
- Outcome-driven design. Focus shifts from building interfaces to defining intents and outcomes, allowing AI agents to handle execution and optimization.
Because of the wide adoption of programs like ChatGPT, many people still think of artificial intelligence as only a glorified chatbot — ask a website a question, get a generated answer.
But this narrow view overlooks how AI solutions, specifically agentic AI, can transform how businesses operate more efficiently, make quicker decisions and even develop software.
The next wave of AI innovation isn’t about typing into a box. It’s about agentic AI — autonomous AI agents that don’t just respond, but proactively reason, make decisions, execute workflows and collaborate with both humans and other systems. It aims to alter how developers and companies build technology infrastructures. It’s a technology where business users can have the equivalent of a virtual graduate student researcher working alongside them to get their job done.
Table of Contents
- Moving From Interfaces to Intelligence
- Reimagining the Technology Foundation
- Designing Outcomes, Not Screens
- Evolving With Agentic AI
- Moving the Interface Toward Intelligence
- FAQ: Agentic AI in the Enterprise
Moving From Interfaces to Intelligence
Historically, software has relied on interfaces that humans must navigate. Developers build screens, menus and interactions, and users adapt their behavior to fit. Agentic AI reverses this paradigm. Instead of building more interfaces, enterprises are building systems that adapt to business needs and the changing data landscape in real time.
How AI Agents Orchestrate Business Processes
These agents can orchestrate processes that span functions — merchandising, pricing, marketing and supply chain — without requiring people to stitch systems together manually. In Product Information Management (PIM), for example, agents can ensure that every product lifecycle update automatically propagates through content, attributes, pricing and channels. Rather than waiting for teams to manually push updates, the system itself becomes proactive. AI agents learn how to bridge and interact with powerful tools and systems through exposed, robust APIs.
The same concept applies in broader retail operations. A grocer, for example, could set the intent as “minimize food waste while keeping shelves stocked.” Agentic AI agents would track sell-through rates, expiration dates, and local demand signals to proactively adjust replenishment orders, markdown schedules and promotions. The result is a more sustainable, profitable and resilient retail model without requiring constant manual intervention.
Related Article: Are You Ready for Agentic AI Shoppers as Customers?
Reimagining the Technology Foundation
To reach this level of enterprise intelligence, organizations need more than a chatbot overlay. They need:
- Clean, unified master data that every agent can trust.
- Flexible orchestration layers that allow agents to communicate and act across legacy systems and APIs.
- Rapid application development environments where companies can quickly build their own agent-driven apps.
- Data science labs where AI behaviors can be tested, refined and governed before being deployed at scale.
Critical Building Blocks for Agentic AI
With these foundations, agentic AI isn’t just an “add-on.” It becomes embedded in the fabric of enterprise technology, shaping how outcomes are defined, decisions are made, and execution takes place.
Designing Outcomes, Not Screens
In a modern, agentic AI infrastructure, the focus shifts from designing user interfaces to designing outcomes. Developers and business leaders spend less time perfecting user interfaces and more time defining intents, permissions and desired results. Agents then take on the heavy lifting of connecting systems, orchestrating data and ensuring consistency at scale.
From Manual Workflows to Intent-Driven Execution
For example, instead of coding a workflow for updating product data across multiple channels, an enterprise can set the intent: “All lifecycle changes must propagate in real time across every channel.” Agents then handle the execution.
This shift to agentic AI saves team members time and allows them to put energy into forward-thinking issues. For instance, instead of manually modeling every promotional scenario, AI agents collaborate with pricing and merchandising systems to test, recommend and execute optimized strategies. This allows teams to focus more on strategy overall.
Evolving With Agentic AI
While agentic AI is still evolving, being refined and increasing in use, forward-thinking organizations can begin preparing for the technology through some key steps, such as ensuring they have clean data pipelines and implementing extensible solutions that support intent-driven orchestration.
Steps Companies Can Take Today
Of course, companies should experiment with agentic workflows, especially in areas like pricing and promotion planning at retail, and product content creation. Companies should remember that moving toward advantages of agentic AI doesn’t require a rip-and-replace technology endeavor. Start with small tests and wins and build out broader action over time.
Lastly, developing internal governance models help define how agents act, communicate and improve, so a cultural change needs to happen that embraces agentic AI and has rules in place to keep the technology running smoothly.
With some of these steps in place, companies can create an environment where agentic AI ultimately thrives as a governed, enterprise-wide intelligence layer to drive speed, accuracy, and growth.
Agentic AI vs. Traditional AI
Key differences between chatbot-style AI and enterprise-grade agentic AI.
| Aspect | Traditional AI (Chatbots) | Agentic AI (Autonomous Agents) |
|---|---|---|
| Primary Role | Responds to user prompts | Proactively reasons, decides and executes workflows |
| Scope | Single-task interactions | Cross-functional, enterprise-wide orchestration |
| Dependency | Relies on predefined scripts or models | Adapts to changing business needs and data in real time |
| User Focus | Interface-driven | Outcome-driven |
| Value | Efficiency in responses | Transformational business impact |
Moving the Interface Toward Intelligence
Just as smartphones replaced buttons and cloud platforms replaced on-premises infrastructure, agentic AI will reshape how enterprises think about technology itself. The interface won’t disappear overnight, but the real competitive advantage will come from intelligence, not screens.
Companies that embrace this shift early can build on strong data foundations and open, AI-native platforms will redefine how work gets done. They won’t just react faster. They’ll move proactively, with intelligence at the core of every product, process and decision.
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FAQ: Agentic AI in the Enterprise
Editor's note: Agentic AI isn’t just another buzzword — it’s a shift in how businesses design outcomes and scale intelligence across systems. These FAQs spotlight where it matters most.
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