As the conversation around Agentic AI powered by LLMs grows, one question dominates every enterprise and investor discussion: Where are the real value pools? The technology is advancing fast, but not all applications are producing tangible business outcomes. To invest wisely, organizations need a clear understanding of where real value is emerging, why it matters, and what stands in the way.
This piece will highlight two distinct layers in which AI is creating value inside enterprises, each with different characteristics, risk profiles, and returns timelines.
The first and most visible value pool sits inside the tools enterprises already use at scale. CRM systems, service desks, and productivity suites are rapidly adding agentic interfaces directly into products that enterprises already use at scale. Instead of navigating menus and screens, users can ask for what they need in natural language and increasingly take action the same way.
A sales manager might ask for accounts most at risk this quarter. The software explains, presents charts and recommends that once required hours of manual digging. An HR leader might say: ‘Start the onboarding workflow for the new engineer in Berlin.’ The system launches tasks for IT, payroll, facilities and books welcome lunch.
These capabilities build on a unique combination of assets that existing SaaS platforms already hold. The proprietary data, embedded workflows, daily user engagement, and deep integration into enterprise operations create an extremely strong moat which is reinforced by a powerful, closed-loop feedback system where every user interaction, correction, and approval implicitly trains and refines the AI and creates a continuous improvement cycle.
As AI interfaces become table stakes, differentiation will shift from ‘having AI’ to the quality of outcomes, reasoning transparency, and how well the system handles edge cases and errors.
Beyond pre-packaged SaaS, organizations are building their own agentic AI to tackle specific functional needs. This value pool exists on a spectrum, from optimizing existing tasks to orchestrating entire processes.
Customer service teams use AI to handle common inquiries. Security teams use it to triage alerts that once overwhelmed analysts. AI focuses on removing operational drag from workflows that have long been burdened by manual effort. These areas were well suited for AI since they involve high volume repetitive tasks, have extensive historical data, and experience significant pressure to improve efficiency.
Unlike traditional RPA which automates fixed sequences of clicks and rules, Tier 1 systems interpret intent and operate on unstructured data. Instead of breaking when a screen or field changes, they adapt to new phrasing, new information, and evolving business logic. RPA removes mechanical effort. Tier 1 removes cognitive effort.
To build these effectively, enterprises generally choose between two technical approaches based on their data needs:
| Method | When to Use | Why It Works |
|---|---|---|
| RAG | Data changes frequently or content is too large to embed in the model | Reduces hallucination by grounding responses in internal data |
| Fine-tuning | Organization/Domain specific language or decision patterns stay stable | Aligns model behavior to enterprise standards |
Underestimating data quality requirements, lacking sufficient change management, and trying to automate workflows that are not well defined can lead to implementation failures.
The advanced stage looks very different. Here, AI stops supporting a human and begins orchestrating an entire workflow. These workflows span multiple systems, long chains of approvals, and weeks of manual work.
Agents understand the goal, connect information across systems, make decisions, and complete the work from start to finish.
For instance, a collection of agents in the procurement process monitors inventory levels, selects vendors based on requirements and historical performance, negotiates pricing within approved parameters, creates purchase orders, updates the ERP, and notifies stakeholders across the supply chain.
In pharmaceutical R&D, a discovery agent synthesizes vast scientific literature, patent databases, and experimental data to propose new molecular pathways or formulations, then generates hypotheses for lab validation.
While success here is mixed, it transforms how enterprises operate. Key enablers include strong exception handling, defined escalation routes for unclear situations, and redesigned approval processes that support autonomous decisions. Attempting full automation too quickly and lacking clear accountability when issues arise can lead to implementation failures.
| Layer | Payback Timeline | Risk | Value Profile |
|---|---|---|---|
| Tier 1 | Short | Low | Cost savings, productivity gains |
| Tier 2 | Longer | Higher | Cycle time compression, quality improvements, operational transformation |
The longer payback timeline ties directly to high process variability and decision entropy. Successful teams phase autonomy, targeting high volume, low entropy steps first to deliver early returns while building toward full orchestration.
In the next part of this series, we will examine additional value pools that are reshaping how decisions get made across the enterprise. Interested in exploring how Agentic AI can advance your strategy? Let’s talk.
In order to help our clients find their edge, we work closely with them to identify high RoI use cases, define the problem sharply, use appropriate algorithms and tech stack to deliver scalable solutions, and support the implementation and consumption of solutions.