Explore PI System AI readiness with lessons on signal fidelity, data health, traceability, and practical next steps from our joint webinar.
Your historical PI data can contain the process behavior needed for AI-led initiatives. Before building a model, you need to understand what your archive preserved, whether critical inputs are healthy, and how those inputs connect to the equipment and calculations behind them.
On October 7, 2026, Pattern Discovery Technologies Inc. and Tycho Data explored these questions in “Is Your PI System Ready for AI.” Speakers Paul Sheremeto, president and CEO of Pattern Discovery Technologies Inc., and Jie Chou, founder and CEO of Tycho Data, shared complementary approaches to PI data readiness.
For process engineers and PI administrators, the session offered a focused way to assess the data supporting one AI use case.
Key Takeaways: PI System AI Readiness
Why Does PI Data Quality Matter for AI?
PI data quality determines whether your AI inputs represent the process you intend to analyze. Unsuitable compression settings can remove meaningful changes from historical signals, while stale values and broken references can undermine the reliability of operational inputs.
The webinar organized readiness around three questions: did you retain the signal, can you rely on the data, and can you trace its origin and impact? Each question addresses a different weakness in the operational data lifecycle.
Pattern Discovery Technologies brings industrial analytics experience to this challenge. The session connected archive fidelity with the operational trust and governance needed to support AI workflows.
How Does CompressionInsight Evaluate Historical Signal Fidelity?
CompressionInsight compares raw snapshot behavior with recreated archived values to evaluate how much signal information the PI archive retains. Pattern Discovery Technologies uses an information-theory-based measure called Data Fidelity to support this assessment.
Over-compressed tags can lose meaningful variability. Under-compressed tags can retain more values than necessary. CompressionInsight recommends exception and compression settings that balance fidelity with archive volume, helping you evaluate both situations using observed tag behavior.
The webinar showed how users can review recommendations, export results for team discussion, or update selected PI points. These recommendations guide tuning; they do not restore historical information that was never retained.
How Does Osprey Support Data Health and Traceability?
Tycho Data’s Osprey monitors operational data quality and helps you understand the dependencies behind your AI inputs. Its data monitoring capabilities address stale tags, gaps, invalid values, broken references, and configuration issues.
Osprey also connects tags with assets, calculations, displays, and downstream consumers. This context helps you investigate questionable values and understand how changes in the PI environment may affect an analytics workflow.
Through the Tycho Data partnership, the webinar brought these capabilities together with CompressionInsight’s archive-fidelity analysis. You can evaluate the retained signal and the operational environment supporting it rather than treating either assessment as sufficient on its own.
What Should Your Engineering Team Review First?
Your first review should establish which inputs are critical and how you would detect a problem with them. The webinar encouraged engineers and PI administrators to ask:
A PI System health review can provide additional context for identifying operational weaknesses. Keep your initial assessment tied to the chosen use case so findings lead to specific actions.
Explore the Webinar and Assess Your PI Data
The webinar resources on the Pattern Discovery Technologies website provide a starting point for revisiting the session.
Pattern Discovery Technologies helps you evaluate archive fidelity with CompressionInsight. You can explore the CompressionInsight evaluation to investigate tuning opportunities in your own PI environment.
Bring your findings to the people responsible for the process, PI administration, and model development. Agree on remediation priorities and how you will monitor the inputs once the AI workflow is operating.
FAQs About PI System AI Readiness
What Does Data Fidelity Measure in CompressionInsight?
Data Fidelity measures the information retained in archived data relative to the raw snapshot signal. Pattern Discovery Technologies uses this measure in CompressionInsight to evaluate current settings and recommend tuning that balances signal retention with the amount of data stored.
Can CompressionInsight Restore Previously Lost Historical Data?
CompressionInsight evaluates signal retention and recommends settings; it does not recreate historical observations that were never stored. Pattern Discovery Technologies’ tool helps you identify tuning opportunities using available snapshot and archived behavior, supporting better-informed decisions about future data collection.
How Do CompressionInsight and Osprey Complement Each Other?
CompressionInsight addresses archived signal fidelity, while Osprey addresses operational data health, traceability, and dependencies. Pattern Discovery Technologies and Tycho Data presented these as complementary checks: preserving meaningful signal information and understanding whether the data supporting your AI workflow remains trustworthy.
Do You Need to Assess Every PI Tag First?
You can begin with the critical tags supporting one defined AI use case. Identify the asset and model objective, then assess fidelity, health, and lineage for those inputs. Expand your assessment as you understand the issues and dependencies involved.