Most discussions around Agentic Automation tend to focus on AI models, reasoning capabilities, or the level of system “autonomy.” In reality, however, these are not the decisive factors.
A system may have a powerful model, but without the right data, it will still produce incorrect or meaningless decisions. Conversely, with a strong data foundation, even simpler systems can deliver clear and tangible value.
Agentic Automation, therefore, is not primarily a technology problem. It is a data problem — how data is structured, connected, and utilized to turn automated actions into meaningful decisions.
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How Does Agentic Automation Work?
Unlike traditional forms of automation, Agentic Automation goes beyond executing predefined sequences of commands. Instead, it operates as a continuous loop: ingesting input data, analyzing and reasoning, taking action, and learning from outcomes to improve over time.
The key point is that data is not confined to the first step — it flows throughout the entire process. Data enables the system to understand context, maintain state, and build the foundation for continuous optimization.
According to Gartner, AI systems in the near future will move beyond simply “assisting” and become direct collaborators with humans in business operations. This shift is only possible when data is strong enough to support the entire decision-making loop.
Four Core Roles of Data in Agentic Automation
1. Data as Context for Decision-Making
To make the right decision at any given moment, a system must understand the current context — what is happening right now.
This context includes:
- Specific user requests
- The current system state (e.g., order status, ticket status)
- Relevant environmental factors at that moment
This is essentially a snapshot of the situation, enabling the system to respond accurately “in the moment.” Without this contextual layer, responses are likely to miss the point.
2. Data as Memory for Continuous Experience
If context enables correct decisions at a point in time, memory ensures continuity over time.
In traditional systems, interactions are often treated as isolated events, resulting in fragmented user journeys. Agentic Automation addresses this by turning data into an operational memory layer — where history, state, and user behavior are continuously stored and updated.
As a result, each interaction is no longer a fresh starting point, but a continuation of an ongoing, seamless journey.
3. Data as a Training Signal: Learning from Operations
Agentic Automation is not a static system. It continuously improves through feedback data — from successes, failures, and user behavior.
However, this is also where the gap between “deploying AI” and “creating value from AI” becomes evident. According to Boston Consulting Group (2024), only about 26% of companies generate significant value from AI initiatives, despite widespread investment.
The issue is not the model, but the absence of a complete data feedback loop. Without collecting, standardizing, and leveraging feedback, agents cannot improve over time.
4. Data as a Control Layer for Safe Operations
According to Gartner, 50% of failures in AI agent implementations stem from a lack of control mechanisms and governance.
As systems become more autonomous, the need for control increases — and this control is built on data.
Operational rules, access permissions, and activity logs are all forms of data that constrain and guide system behavior. Without this layer, risks extend beyond incorrect decisions to process violations and compliance issues.
Recent analyses also highlight that weak data governance is a major reason why Agentic Automation projects fail to scale.
The Current Reality: Data as a Bottleneck in Automation Agent Deployment
Despite rapid AI advancement, most organizations are still not data-ready. Data remains fragmented, inconsistent, and lacking real-time accessibility.
According to Boston Consulting Group, 74% of companies have yet to realize real value from AI, and only about 8% have reached a high level of data and AI maturity — revealing a significant gap between expectation and reality.
From another perspective, Gartner predicts that over 40% of agentic AI projects may be canceled before 2027 due to unmet expectations. These failures are not driven by algorithms, but by insufficient data foundations.
In the long term, Gartner also notes:
- AI will generate massive new data volumes, especially from physical environments — up to 10 times more than current digital application data
This creates a paradox: data is growing exponentially, but the ability to effectively leverage it is not keeping pace.
Conditions for Data to Truly Deliver Value
For data to serve as a true foundation for Agentic Automation, organizations need more than just “having data.” What matters is how data is structured, connected, and operationalized across the system.
Real-Time Data Access: The Right Decision at the Right Time
In continuous operations, timing is critical. A correct decision based on outdated data can still be wrong.
Agentic Automation requires data to be updated and accessible כמעט instantly. This is especially crucial in scenarios like order processing, customer support, and risk detection.
If systems rely solely on batch data, latency will undermine both accuracy and responsiveness.
Handling Unstructured Data: Unlocking the “Unused” Layer
A significant portion of enterprise data is unstructured — existing in emails, conversations, documents, and internal notes.
These sources often contain valuable insights into user behavior, needs, and issues. However, due to processing challenges, they are frequently ignored in traditional systems.
Agentic Automation requires the ability to leverage both structured and unstructured data to fully understand context. Relying only on structured data means missing a large portion of the information’s true meaning.
Data Quality and Consistency: The Foundation of Every Decision
Data is only valuable if it is accurate and consistent. Poor input data leads to flawed analysis and decisions.
In Agentic Automation, this becomes even more critical, as systems are not just supporting decisions but executing actions directly.
Therefore, data standardization, deduplication, and cross-system consistency are essential. A powerful AI system cannot compensate for a weak data foundation.
Data Governance: The Key to Safe Scalability
When data becomes the foundation of automated decision-making, the question is no longer just “Is the data correct?” but also “Is it being used correctly?”
Data governance includes:
- Access control
- Usage policies
- Monitoring and auditing
This layer ensures systems operate within defined boundaries while minimizing risks related to security and compliance.
In the long term, it is also a key determinant of whether Agentic Automation can scale within an organization.
Start with Data — Not AI
A common mistake is starting the AI journey by selecting tools or models. In contrast, successful organizations begin by building a strong data foundation.
The first step is to reassess the current data landscape:
- Where is the data stored?
- Is it connected?
- Is it accessible in real time?
- Is it of sufficient quality?
Platforms like Akabot enable organizations to gradually address these challenges — connecting data across systems, automating end-to-end processes, and evolving from rule-based automation to decision-capable systems.
Rather than rushing into AI implementation, a more effective approach is to build a robust data foundation first. Ultimately, the value of AI does not come from the technology itself, but from the data that powers it.
