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Artificial Intelligence

AI is not mysterious. It’s smart decision-making over data. In industrial environments, AI helps operators and engineers make faster, safer, and more informed decisions.

Whether it’s filtering alarms, predicting maintenance needs, or accelerating troubleshooting, AI works best when it assists people, not replaces them.

Common Myths about AI

AI is a powerful assistant, not a substitute for people.

AI is only as good as the data you feed it.

AI is not magic — it has boundaries.

With caution and the right approach, AI can significantly improve industrial operations.

Learn more about AI myths:

AI will replace humans

AI can automate repetitive tasks, analyze large volumes of data, and speed up decision-making — but it lacks human intuition, responsibility, creativity, and accountability. AI works with humans, not instead of them. Think of it as a reliable assistant, not a boss. Humans still set goals, define context, and make final decisions.

AI needs no data

Quality data is the foundation of meaningful AI output. Poor, incomplete, or outdated data leads to poor answers. Even the smartest AI cannot generate accurate results when the data behind the scenes isn’t there. No data → no relevance.

AI can solve any problem

AI cannot fix problems you don’t understand yourself. It cannot bypass missing processes, broken logic, or physical limitations. It can support analysis, speed up troubleshooting, or highlight patterns you might have missed, but it won’t instantly deliver miracle solutions. AI works within the constraints you give it.

AI isn’t suitable for Industrial Automation

When used responsibly — with verified data, proper safety measures, and clear boundaries — AI can assist operators, support diagnostics, reduce downtime, and analyze alarms or historical trends far faster than a human ever could. The value is real, and we can prove it.

Conclusion

Bringing AI into industrial environments is not easy. Real-time data, safety-critical decisions, and massive volumes of contradictory information make implementation a challenge.

Many companies struggle with complex integrations, high hardware requirements, and unreliable results.

AI is not a magical solution — it is automatically smart decision-making over data that supports humans. It analyzes patterns, predicts outcomes, and assists operators, engineers, and managers in making faster, safer, and more informed decisions.

Explore by Use Case/Trouble

tyto ikonky rozdělit doleva, doprava jako máme v produktech + nějaké vhodné ikony

anticipate failures, reduce downtime

continuous checks with AI assistance

cluster, prioritize, suppress irrelevant alarms

transfer expert know-how via AI copilots

faster troubleshooting, fewer errors

Learn more about using AI for different issues:

Predictive & Prescriptive Maintenance
AI anticipates failures, suggests corrective actions, and prevents unplanned downtime by analyzing real-time and historical sensor data.

Alarm & Notification Intelligence
Reduce operator fatigue by clustering, prioritizing, and contextualizing alarms. Only the relevant alerts reach the human operator.

Operator Training & Knowledge Retention
AI copilots transfer expert knowledge to new or less experienced staff, ensuring consistent operational standards.

Documentation & Log Search
AI performs semantic searches across manuals, SOPs, logs, and technical bulletins — drastically reducing troubleshooting time.

Safety Compliance Monitoring
Continuous supervision ensures operational limits and safety rules are not violated.

Technical Notes:
DataTalk’s advanced Agentic RAG system evaluates each problem, selects the right tools (live data, historical logs, alarms, documentation), fuses results, verifies consistency, and produces actionable recommendations. This prevents hallucinations, resolves contradictory inputs, and maintains safety and compliance standards.

Explore by Role

AI in industry helps different roles in unique ways:

Less fatigue, faster troubleshooting, AI-assisted guidance.

Prioritize alarms, monitor operations, reduce unplanned downtime.

Automated insights, trend analysis, data-driven decisions.

Integrate AI with PLCs, SCADA, historical databases safely and efficiently.

Learn more about use of AI for each role:

Operators / Technicians

  • Filter, cluster, and prioritize alarms to reduce fatigue.
  • AI-assisted troubleshooting guidance based on manuals and historical data.
  • Quick access to knowledge previously held only by experts.

Shift Leaders / Supervisors

  • Monitor real-time operations and deviations.
  • Predict and prevent equipment failures before they happen.
  • Reduce downtime and optimize resources.

Managers / Executives

  • Automated reports and dashboards with actionable insights.
  • Trend analysis over time, helping with strategic decision-making.
  • AI-driven understanding of process efficiency and risk.

AI / IT Specialists

  • Maintain cybersecurity and compliance with on-premise or secure deployment.
  • Seamlessly integrate AI with PLCs, SCADA, historians, and alarm systems.
  • Avoid hallucinations via Agentic RAG orchestration.

How AI Works

DataTalk’s AI uses an advanced Agentic RAG system to combine live and historical data, operator feedback, and documentation — producing actionable, grounded recommendations instead of guesses.

This approach ensures safety, reliability, and explainability in every decision.

Build vs. Buy AI

Do It Yourself or Plug & Play

What are the pros and cons of both variations?

AspectOwn ImplementationReady-to-Use Product (DataTalk)
Flexibility✔︎ Full control over architecture, models, and integrations.
✔︎ Can be highly customized to unique processes.
– Predefined framework may limit extreme customizations.
✔︎ Still adaptable with connectors and APIs.
Time to Value Long development cycles (months to years).
✘ Requires in-house AI + industrial expertise.
✔︎ Deploys quickly — weeks, not years.
✔︎ Pre-integrated with documentation, live data, and alarms.
Cost✘ High upfront investment in infrastructure, tools, and talent.
✘ Continuous maintenance costs.
✔︎ Lower entry cost, predictable pricing.
✔︎ Shared development costs across customers.
Expertise Required✘ Needs skilled AI engineers, data scientists, and domain experts.
✘ Hard to recruit and retain talent.
✔︎ No AI expertise required to operate.
✔︎ Built-in industrial know-how.
Reliability✘ High risk of hallucinations without grounding.
✘ Error handling must be custom-built.
✔︎ Agentic RAG ensures grounded, explainable answers.
✔︎ Proven reliability in industrial use cases.
Integration with Industrial Systems✘ Must custom-develop connectors for SCADA, PLCs, historians, alarm systems.✔︎ Ready connectors for live, historical, and alarm data.
✔︎ Semantic search via vector store built in.
Security & Compliance✘ High risk if data is sent to cloud.
✘ Must ensure own cybersecurity, backups, and compliance.
✔︎ Runs safely on-premise or in secure environments.
✔︎ Built with industrial IT/OT security in mind.
Scalability✔︎ Can scale to any size with enough investment.
✘ Scaling requires ongoing optimization and hardware upgrades.
✔︎ Scales smoothly with modular architecture.
✔︎ Optimized for performance on industrial hardware.
Support & Updates✘ All responsibility on internal teams.
✘ Risk of outdated models/tools.
✔︎ Continuous updates and improvements.
✔︎ Vendor support and roadmap.
Knowledge Transfer✘ Risk of losing know-how if key employees leave.✔︎ Knowledge embedded in product and continuously evolving.

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Blog/Insights

AI Confessions & Stories

Stay up to date with industrial AI: myths, lessons learned, and practical tips from the field.

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AI Module in DataTalk: Industrial Automation

The AI module within DataTalk utilizes a Large Language Model (LLM) to perform advanced data analysis and provide intelligent system insights for industrial processes.

Read More →

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AI Module in DataTalk: Cloud or On-Premises Options

The AI module in DataTalk is designed to work in two flexible configurations, tailored to meet your specific needs: a cloud-based AI LLM solution or a fully on-premises setup.

Read More →

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