Martin Szerment
AuthorPublished on September 22, 2025
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Industry 5.0 is not just another stage of digital transformation – it's a fundamental shift in approach to manufacturing that places humans and sustainable development at the center of advanced technologies. In this new reality, analytical platforms like BigQuery AI become a key component of intelligent production systems, enabling the transformation of vast amounts of data into concrete business insights.
Industry 5.0: New Challenges, New Opportunities
While Industry 4.0 focused on automation and connectivity, Industry 5.0 introduces humanistic and ecological dimensions. Modern factories must be not only efficient, but also adaptive, sustainable, and focused on human-machine collaboration. This is where BigQuery AI plays a crucial role, offering comprehensive data analysis tools that support all these dimensions.
BigQuery AI: The Analytical Engine of the Future
Google BigQuery AI is a platform that combines the power of petabyte-scale data processing with advanced artificial intelligence capabilities. For industry, this means the ability to analyze data streams from hundreds of sensors, machines, and systems in real-time, while simultaneously generating predictive business models.
Key AI Functions in BigQuery
- ML.GENERATE_TEXT – automatic report and documentation generation
- AI.GENERATE – executive summary creation
- AI.GENERATE_BOOL – binary decision making
- AI.GENERATE_DOUBLE/INT – numerical value extraction
- AI.GENERATE_TABLE – risk table generation
- AI.FORECAST – trend forecasting
Case Study: Climate-ROI Copilot
A practical example of BigQuery AI utilization in industry is the Climate-ROI Copilot prototype application, which automates climate risk analysis and calculates return on investment (ROI) for adaptive ventures.
Solution Architecture
The application processes NOAA GSOD (Global Summary of the Day) data and generates business recommendations regarding investment profitability. In the Kraków case study, analysis of 128 months of data (2015-2024) for a 50 million PLN investment showed:
Business Parameters:
- Downtime cost: 1,800 PLN/hour
- Hours lost during heat waves: 2 hours/day
- Productivity drop: 8% during heat waves
- Adaptation CAPEX: 120,000 PLN
- Annual OPEX: 12,000 PLN
Analysis Results:
- Potential savings: 68,460 PLN annually
- Net profit: 32,460 PLN annually
- ROI: 90.2%
- Payback period: 1.1 years
Time Value
Traditional analysis: 2-4 weeks Climate-ROI Copilot: 15-30 minutes Analyst time reduction: 95%
If you want to learn more details about this case study, visit this link.
BigQuery AI in MES/MOM Systems
A particularly interesting application of BigQuery AI is its integration with MES (Manufacturing Execution System) and MOM (Manufacturing Operations Management) systems, such as OmniMES (www.omnimes.com). This integration transforms traditional data collection systems into intelligent platforms generating concrete business insights.
Transforming Data into Knowledge
The OmniMES system, enhanced with BigQuery AI capabilities, is no longer limited to just collecting production data. Thanks to advanced AI functions, it can:
Automatically Analyze:
- Machine performance patterns
- Product quality trends
- Material consumption optimization
- Equipment failure prediction
Generate Insights:
- Optimization recommendations
- Production forecasts
- ROI analyses for modernization
- Sustainability reports
Multidimensional Impact of BigQuery AI on Industry
1. Operational Optimization
BigQuery AI analyzes operational patterns in real-time, identifying opportunities to increase efficiency. Machine learning algorithms detect subtle correlations between production parameters that might be overlooked by traditional analysis methods.
2. Predictive Maintenance
Through IoT sensor data analysis, the system can predict equipment failures with enough lead time to allow for planned maintenance. This drastically reduces unplanned downtime costs.
3. Quality Management
AI can identify patterns leading to product defects, enabling proactive correction of production parameters before quality problems occur.
4. Sustainable Development
Analysis of energy consumption, emissions, and waste allows for process optimization in terms of ESG (Environmental, Social, Governance) goals, which is crucial in Industry 5.0.
5. Safety and Ergonomics
Monitoring working conditions and analyzing accident patterns helps create safer work environments, which is fundamental to the concept of human-centric manufacturing.
Implementation Challenges
Data Integration
Industrial environments generate data in various formats and protocols. BigQuery AI must be integrated with ERP, MES, SCADA, and IoT systems, requiring careful data architecture planning.
Security
Production data analysis requires ensuring the highest level of security, especially regarding trade secret protection and compliance with industry regulations.
Team Competencies
Effective use of BigQuery AI requires teams combining domain knowledge with analytical skills, which can pose recruitment challenges.
Future of BigQuery AI in Industry
Edge Computing Integration
Development of edge computing capabilities will allow moving parts of BigQuery AI analysis closer to data generation points, reducing latency and improving system responsiveness.
Augmented Reality (AR/VR)
Integration with AR/VR technologies will enable visualization of analysis results directly in the production environment, supporting operators in real-time decision making.
Autonomous Manufacturing
BigQuery AI can become the foundation for fully autonomous production systems capable of independent optimization and adaptation to changing conditions.
Economic Benefits
Cost Reduction
Automation of analyses can reduce analytical costs by 60-80%, while simultaneously increasing accuracy and speed of decision making.
Efficiency Increase
AI-based process optimization can translate to 10-30% operational efficiency growth, depending on industry and implementation sophistication level.
New Business Models
Advanced analytics open possibilities for new data-driven business models, such as Manufacturing-as-a-Service or predictive analytics for customers.
Industry Recommendations
1. Strategic Approach
BigQuery AI implementation should be part of a broader digital transformation strategy, with clearly defined business objectives and success metrics.
2. Pilot Projects
Starting with pilot projects in limited scope allows for learning and optimization before scaling the solution.
3. Technology Partnerships
Collaboration with technology and MES/MOM system providers, such as OmniMES, can significantly accelerate implementation and increase its effectiveness.
4. Competency Investment
Development of analytical teams and AI training are crucial for long-term success of BigQuery AI-based initiatives.
Summary
BigQuery AI represents a new generation of analytical tools that can fundamentally change how industry operates. In the context of Industry 5.0, where sustainable development, safety, and human-machine collaboration are key, platforms like BigQuery AI offer possibilities to achieve these goals while simultaneously increasing profitability and competitiveness.
Integration with production systems like OmniMES shows that the future of industry lies not only in data collection, but in intelligently transforming it into concrete business insights. Companies that invest in these technologies today are building competitive advantage for the coming decades.
Industry 5.0 is not just a vision of the future – it's a reality being shaped today. BigQuery AI is one of the key tools of this transformation, offering capabilities that just a decade ago seemed like science fiction.