This project designs an end-to-end automated news publication system for a social media editorial team using AI integration and the DMAIC methodology.
Business problem: Manual news monitoring is slow, costly, and error-prone.
Solution: An AI system that monitors competitors, detects viral content,
auto-generates posts, and publishes after editor approval.
Competitor website
↓
AI monitors views in real time
↓
Views > 1,000 in 5 min?
NO → End process
YES ↓
AI generates: text + title + image
↓
Editor review
├── Approve → Auto-publish to Facebook & Instagram
├── Needs fix → AI corrects → back to review
└── Off-topic → End process
The current AS-IS process requires a journalist to manually monitor competitor sites (Onet.pl, WP.pl, Interia.pl), evaluate article popularity, write content, create visuals, and publish — all manually. This causes delays and missed trends.
Business goal: Reduce reaction time to trending topics, cut operational costs, and scale content production without increasing headcount.
Key performance indicators defined for comparison (before vs. after automation):
| KPI | Description |
|---|---|
| Reaction time | Time from competitor publish to own post |
| Daily output | Number of posts published per day |
| Engagement | Likes, comments, shares per post |
| Cost per post | Time and resources per publication |
| Rejection rate | % of topics rejected by editor |
Problem: The publication process is too slow and inefficient.
- Why is it slow? → Editor manually monitors competitor sites
- Why is monitoring manual? → No automated data collection system exists
- Why no filtering system? → Process relied on subjective editorial judgment
- Why subjective judgment? → No analytical tools or decision algorithms implemented
- Why no tools? → Process was never analysed for optimisation
Root cause: Lack of systematic content selection and over-reliance on manual work.
AI System (automation):
- Monitors competitor websites continuously
- Detects viral content (threshold: >1,000 views / 5 min)
- Generates unique text, title, and image
- Submits to editor for approval
- Auto-publishes after approval
Editor (quality control):
- Verifies factual and linguistic accuracy
- Evaluates title and image appeal
- Approves, rejects, or requests corrections
IT Specialist (implementation phase only):
- Configures monitoring system and social media integrations
- Periodic technical oversight — not required daily
Monthly KPI review covering: reaction time, daily post count, engagement rate, cost per post, and rejection rate. System parameters updated as market conditions evolve.
Expected benefits after automation:
| Benefit | Impact |
|---|---|
| Faster reaction to trends | Higher reach and competitiveness |
| Reduced manual workload | Editor focuses on quality, not repetition |
| Lower operational cost | Less time per post |
| Scalability | Multiple sources monitored simultaneously |
| Content standardisation | Consistent quality based on defined criteria |
| File | Description |
|---|---|
Projekt_Akkus.docx |
Full academic project report (Polish, WSZiB format) |
process_map.drawio |
Editable BPMN-style process diagram |
Course: Automatyzacja i Robotyzacja Procesów Biznesowych
Institution: Wyższa Szkoła Zarządzania i Bankowości (WSZiB), Kraków
Year: 2026
Author: Daryna Akkus
Daryna Akkus — Data Analyst | Business Analyst
GitHub ·
LinkedIn