Closing the Gaps in Industry: From Bureaucracy to Data-Centered Agility

In manufacturing industries one of today’s biggest challenges is closing the knowledge and data gaps between business management, procurement, production, and IT systems teams.

Traditionally, operational implementation has been driven by the type of information that needed to be managed, but with systems that are extremely costly to implement, difficult to maintain, data-redundant, and overly fragmented across applications. The result: bureaucracy, lack of continuity, and a loss of shared vision.

Today, replacing those core systems (OLTP, ERP, PLM, CRM…) is unfeasible. The real path forward is different: reusing what already exists, but in an agile, cost-effective, data-centric way.

A New Approach: From Systems to Data

Current trends show we need to give less importance to classical applications and more to ultra-fast layers of integration and analysis, where:

• Data is the center of gravity.

• AI is applied to operational management (not design or engineering).

• The key is to close gaps through small, agile developments that deliver value quickly.

This enables:

• Natural language queries.

• Self-documentation systems.

• Lightweight and flexible user interfaces.

• Projects where technical teams and business experts collaborate seamlessly.

Case Study 1: Interface Documentation Assisted by AI

One of the most critical pain points in industry is the lack of clear and updated documentation of interfaces between systems.

• The problem: Procurement, production, logistics, and quality systems exchange data through dozens of interfaces. Documentation is often outdated or missing, and knowledge sits in the heads of a few specialists.

• The consequence: Excessive dependency, costly integration projects, and limited visibility for business leaders.

How AI can help

1. Automatic inventory of APIs, logs, messages, and database links to generate an initial interface map.

2. Dynamic documentation generation where technical details are translated into business language (e.g. “The Procurement system sends the daily parts list to Production in JSON format”).

3. Continuous updates so documentation evolves with each change.

4. Natural language queries, e.g. “Which systems consume real-time production data?”, returning a clear diagram.

Benefits

• Closes a critical gap between IT and business.

• Reduces bureaucracy and dependency on individuals.

• Accelerates decisions and new integration projects.

• Builds the foundation of a more agile, data-driven enterprise.

Case Study 2: Git and Jira.  A Knowledge System with RAG

Another strategic opportunity is applying generative AI with Retrieval-Augmented Generation (RAG) on top of Git and Jira.

How it works

• Git: the system ingests code repositories, documentation, commit history, and recent changes.

• Jira: it ingests issues, user stories, tasks, comments, attachments, and workflows.

• The content is normalized, chunked into manageable fragments, and indexed using both semantic (vector-based) and keyword search.

• A user can then ask in natural language:

  • “Which commit changed the login validation?”

  • “What issues are blocking the current sprint delivery?”

  • “Who last modified the payments module?”

• The system retrieves the relevant fragments and feeds them to the generative AI, which produces a clear, contextualized answer with links back to Git commits or Jira tickets.

What is automated

• Documentation: summaries of commits, issues, and project changes.

• Traceability: linking code changes with the Jira tasks that motivated them.

• Cross-search: a single point to query both Git and Jira without switching tools.

• Smart notifications: alerts for dependencies, blockers, or critical changes.

• Automated reporting: daily or weekly project status summaries.

Benefits

• Saves time: no need to manually search across repositories and projects.

• Improves collaboration: both business and technical users can ask questions in plain language.

• Reduces risk: better visibility of dependencies between code and tasks.

• Keeps documentation alive: always up-to-date, without extra manual effort.

• Faster decision-making: managers can ask “Which tasks are blocked by code dependencies?” and get immediate answers.

Conclusion

Digital transformation in industry is not about replacing legacy systems, but about closing the knowledge and data gaps with agile, data-driven, AI-assisted solutions.

The first step may be to improve interface documentation with AI.
The second step could be applying RAG on top of operational repositories like Git and Jira.

Both approaches empower teams to collaborate better, reduce bureaucracy, and unlock faster, more informed decision-making.

The industries that succeed in combining agility, data, and applied AI for operations will be the most competitive in the years ahead.