Structured Data, AI and Automation.

At a recent business lunch, the topic of AI as a tool for improving productivity took centre stage. The conversation was filled with excitement, with many believing AI could provide a quick fix to their business challenges.

To me this is the problem of hype. The reality is that for most companies, there’s far more immediate value in organising and structuring their data well before jumping on the AI bandwagon.

Why Structured Data Should Come First

Structured data refers to information that is neatly organised, making it easy to search, analyse, and automate. Think of it like a well-ordered filing cabinet where every document is properly labelled and can be easily retrieved. In contrast, unstructured data—emails, PDFs, audio recordings—can feel like an overstuffed drawer of random papers, with no clear system to quickly find what you need.

The advantage of structured data is its ability to facilitate repeatable processes.

By focusing on structuring data, businesses can create systems that automate routine tasks, reduce errors, and enable faster decision-making. This is especially important when looking to scale operations or create consistent outputs.

The Practical Benefits of Structured Data

Example 1: Inventory Management

Take a retail business, for instance. With structured data, essential tasks like managing inventory can be automated in ways that make life easier for everyone:

Automated Reordering: When stock levels fall below a set point, the system can automatically reorder items based on real-time structured data. This keeps inventory levels optimised without manual intervention.

Sales Insights: Structured sales data can help you instantly identify trends—what’s selling, what’s not—and make decisions on promotions or stock adjustments quickly and confidently.

Storage Efficiency: Tracking product turnover rates through structured data allows businesses to organise storage efficiently, ensuring high-demand items are accessible and reducing unnecessary stockpiles.

Structured data allows for smoother, more consistent business operations by minimising the room for error and guesswork.

Example 2: Streamlining Customer Service

In a customer service environment, structured data simplifies the process of handling cases:

Automated Case Routing: Service requests can be automatically assigned to the right agent or department based on the structured data entered when a customer logs a query.

Status Updates: Structured data enables real-time tracking, allowing customers to receive accurate status updates without having to chase your team.

Performance Metrics: Key performance indicators (KPIs) such as resolution times or customer satisfaction rates can be easily monitored using structured data, helping businesses refine their service strategy.

By structuring data effectively, businesses can offer faster, more reliable customer service.

Example 3: Optimising Manufacturing Processes

In a manufacturing setting, structured data can play a critical role in automating key tasks:

Production Scheduling: Automatically align production schedules with real-time demand and inventory data, ensuring output meets market needs without overproduction.

Quality Control: By integrating structured data from IoT devices and sensors, businesses can monitor product quality and trigger adjustments when something goes off-course.

Predictive Maintenance: Using structured machine data, businesses can predict when maintenance is needed, reducing unexpected downtimes and costly repairs.

In all these cases, structured data helps reduce variability, ensuring processes run smoothly and consistently.

AI: A Tool, Not the Answer

There’s no denying that AI is a powerful technology. It can sift through unstructured data, find patterns, and make sense of large datasets. For example, AI can analyse customer feedback across millions of records to identify general sentiment or emerging trends. But it’s important to keep expectations realistic, especially when it comes to using AI in a business context.

AI works well when you need to analyse trends or process vast amounts of unstructured data. For example, it can help identify customer sentiment from thousands of reviews or recognise recurring patterns in customer complaints or feedback.

However, the hype around AI as a productivity tool often overlooks one key point: AI struggles when it comes to making decisions in individual cases.

Relying on AI to automate processes based on unstructured data leads to inconsistencies and mistakes because the data itself is too unpredictable. For instance, automating responses to customer service emails without structured data is a risky move—it’s far too easy for AI to misinterpret tone or context.

Structured Data vs. Unstructured Data: Automation’s True Enabler

When it comes to automating business processes, structured data will always beat unstructured data. Structured data offers clear, reliable, and consistent inputs that can be processed with precision. Unstructured data, on the other hand, introduces too much uncertainty. No matter how advanced AI becomes, the reality is that it still requires a solid foundation of structured information to deliver consistent, scalable results.

The Bottom Line: Focus on Building Structure, Not AI

There’s no doubt that AI has a place in the business toolkit. But before diving headfirst into AI-driven automation, most businesses would benefit from taking a step back and focusing on the fundamentals: building a repeatable, scalable process using structured data.

With properly structured data, you’ll already have a system in place that reduces errors, increases efficiency, and delivers consistency.

Only then can AI complement your existing systems, adding value where it truly makes sense. In other words, structured data is the real foundation of business productivity. If you get that right, the rest—including AI—will follow.

Until next time.