OSES-AISafety Effectiveness Survey

Organizational Safety Intelligence

Every catastrophe leaves warning signs. OSES-AI reads them first.

OSES-AI is an artificial intelligence enhanced Organizational Safety Effectiveness Survey. It turns everyday workforce feedback into an early warning system for aviation, aerospace, nuclear, and energy operations, built on more than two decades of proven safety science.

Schedule a Discovery Call See how it works
Live Risk Scan (Illustrative)
Monitoring
Safety Effectiveness Index
78 / 100
Elevated Precursors
2 Flagged
40+ yrs
of safety-science foundation
12
organizational risk dimensions monitored
24/7
agentic AI risk analysis
Built for safety-critical operations
Commercial & Military Aviation Aerospace Nuclear Power Oil & Gas Healthcare

Why Organizational Signals Matter

Major accidents rarely start with one operator error.

Four decades of accident investigations point to the same conclusion. Before the failure, there is a warning sign inside the organization: a safety concern that goes unheard, a risk that quietly becomes normal, a message that never reaches the people who could act on it. The six cases below show how consistent this pattern really is.

Smoke cloud from the Space Shuttle Challenger breakup shortly after launch 1986

Space Shuttle Challenger

  • Engineers repeatedly raised O-ring performance concerns.
  • Management normalized increasing levels of risk.
  • Communication barriers kept technical concerns from launch decisions.
Space Shuttle Columbia lifting off from Kennedy Space Center 2003

Space Shuttle Columbia

  • Repeated foam-strike incidents became accepted as normal.
  • Organizational assumptions overrode engineering concerns.
  • Safety review processes failed to challenge existing beliefs.
Recovered tail section of Air France Flight 447 being retrieved from the Atlantic Ocean 2009

Air France Flight 447

  • Training and automation-management gaps entered the accident sequence.
  • Procedural understanding and crew coordination were deficient.
  • Opportunities existed to strengthen organizational learning.
Wreckage field from a Boeing 737 MAX accident under investigation 2018-19

Boeing 737 MAX

  • Production pressure and business goals shaped safety decisions.
  • Communication gaps spanned engineering, management, and regulators.
  • Safety concerns were not fully raised before accidents occurred.
Explosion and fire at the Fukushima Daiichi nuclear power plant 2011

Fukushima Daiichi

  • Known vulnerabilities to extreme events were underestimated.
  • Risk assessments discounted low-probability, high-impact scenarios.
  • Organizational readiness fell short for severe contingencies.
Fireboats battling the Deepwater Horizon oil rig fire in the Gulf of Mexico 2010

Deepwater Horizon

  • Warning indicators and anomalies preceded the blowout.
  • Production priorities competed with safety considerations.
  • Multiple organizational defenses failed at the same time.

Across every one of these accidents, investigators identified organizational conditions that, recognized and corrected earlier, may have prevented escalation into catastrophe. OSES-AI exists to surface those conditions while there is still time to act.

The OSES-AI Solution

An organizational early-warning system.

OSES-AI anonymously gathers workforce views across twelve organizational dimensions, the same dimensions that research has linked to preventing or failing to prevent catastrophic failure. It then checks the results continuously against established safety science, real accident investigations, and High Reliability Organization principles.

Leadership commitment to safety
Reporting culture
Safety communication effectiveness
Procedural compliance
Production pressure
Operational discipline
Risk management effectiveness
Learning from incidents
Workforce trust & engagement
Hazard identification & mitigation
Training adequacy
Organizational resilience

How OSES-AI Works

Five steps, from a survey to a decision.

This is the same path every response takes, from the moment someone answers the survey to the moment a leader reads the report. No step is manual, and nothing sits waiting for someone to run a report by hand.

Watch the data move through the pipeline

Step 1

Anonymous workforce survey

Pilots, maintenance crews, dispatchers, cabin crews, and managers respond privately across 12 organizational risk areas.

Step 2

AI pattern detection

The AI scans every response for patterns tied to known accident precursors: normalized deviance, weak reporting culture, and complacency.

Step 3

Historical benchmarking

Results are checked against the organizational conditions documented in the Challenger, Columbia, 737 MAX, Deepwater Horizon, and Fukushima investigations.

Step 4

AI safety advisor

Leaders ask plain questions, such as "what's our highest risk area?", and get answers grounded in safety science, not guesswork.

Step 5

Automated reporting

Executive summaries, risk heat maps, and board-level briefings are generated on their own and sent to the right audience.

The survey runs on a recurring cycle, so it also tracks whether corrective action is actually working. Safety management becomes an ongoing loop, not a one-time report.

How Artificial Intelligence Adds Value

From survey data to safety intelligence.

A static survey only tells you where things stood on the day people answered it. Agentic AI keeps working after that: it reads the results, checks them against accident history, and briefs your leadership in plain language.

01
Detection

Finds accident precursors automatically

The AI reads every response for patterns tied to organizational risk, so a concern is flagged while it is still a concern, not after it becomes a failure.

Normalization of deviance Production pressure Weak reporting culture Leadership credibility Training gaps Complacency
02
Benchmarking

Compares you to past accident patterns

Your results are checked against the organizational conditions found in major investigations, so an emerging problem is caught before it turns into an accident chain.

Challenger-style breakdowns Columbia-style deviance 737 MAX pressures Deepwater conflicts
03
Advisory

Answers questions in plain language

The built-in AI Safety Advisor explains findings, interprets the statistics, and recommends what to fix first, in language a non-analyst can act on.

"What are our highest risk areas, and how do we compare to high reliability organizations?"
04
Reporting

Builds the report before you ask for it

Executive summaries, safety climate dashboards, risk heat maps, and board-level briefings are generated on their own and shaped for whoever is reading them.

Executives Safety officers Managers Regulators Operational staff

Primary Aviation Use Case

Catching the pattern before it becomes a headline.

A commercial airline runs OSES-AI every year across pilots, maintenance staff, dispatchers, cabin crews, and managers. Here is what that one year looked like.

1

Annual survey administered

Anonymous responses are collected fleet wide, across every operational role.

2

AI detects a developing pattern

Rising production pressure, less willingness to report, inconsistent management response, and falling confidence in corrective action.

3

Leadership is alerted

Immediate risk alerts, root cause analysis, and benchmark comparisons reach decision makers before any incident happens.

4

Corrective action, ahead of failure

Recommended fixes are put in place and tracked, closing the loop before an operational failure ever happens.

No accident happened. That is exactly the point.

The AI risk engine spotted a combination of organizational factors linked to higher accident risk, using nothing but workforce perception data. That is what prevention looks like: a decision made before there is anything to investigate.

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  • Immediate risk alerts
  • Root cause analysis
  • Benchmark comparisons
  • Recommended interventions

Strategic Value

From compliance activity to predictive intelligence.

Most safety programs look backward: they explain what went wrong after it happened. OSES-AI is built to look forward instead.

Identify accident precursors before accidents occur

Strengthen safety climate and culture

Improve leadership decision making

Detect emerging organizational risks

Enhance workforce trust and reporting

Support Safety Management Systems (SMS)

Improve operational resilience

Move toward High Reliability Organization performance

For more than twenty years, OSES has proven a simple idea: organizational safety risk can be measured before it shows up in accident statistics. Adding agentic AI takes that idea further, from measuring risk to actually predicting it.

Proven safety science, High Reliability Organization principles, and AI analysis work together in one platform. The result is a real, practical way for safety-critical organizations to catch accident precursors early, decide what to fix first, and build the kind of culture that prevents the next catastrophe rather than explains it.

Questions Before You Talk to Us

What people usually ask before scheduling a call.

These are the questions safety leaders and executives ask most. If yours is not here, ask it directly on the call.

Is the survey data really anonymous?

Yes. OSES-AI is built around individual anonymity from the ground up. Leadership sees organization-level and group-level results, not who said what. That protection is what makes people willing to report a real concern in the first place.

How long does it take to roll this out across our organization?

Most organizations run their first survey cycle within a few weeks of kickoff. Timing depends on headcount, the number of sites, and how many roles you want covered in the first pass. We will size a rollout plan for you on the call.

Does this replace our Safety Management System, or work alongside it?

OSES-AI works alongside your existing Safety Management System. It does not replace your reporting systems, audits, or compliance processes. It adds an organizational-level early warning signal that most SMS programs do not currently measure.

How accurate is the AI, and can we trust its risk calls?

The AI does not replace human judgment. It flags patterns and shows you the evidence behind each flag, including which survey responses and which historical accident conditions it is comparing you to. Every finding is meant to be reviewed by your safety team, not acted on blindly.

Can OSES-AI be adapted to our specific industry?

Yes. The 12 core organizational risk dimensions apply across aviation, aerospace, nuclear power, oil and gas, and healthcare. Survey wording, benchmarks, and reporting can be tailored to your operation during setup.

Who gets to see the results?

You control that. Typically, executives and safety officers see the full picture, managers see results relevant to their teams, and regulators receive whatever summary your organization chooses to share. Individual responses are never exposed.

Is OSES backed by independent research?

The underlying OSES survey was developed by Dr. Anthony Ciavarelli and Human Factors Associates and has been used for more than twenty years across military aviation, commercial aviation, and healthcare. OSES-AI builds agentic AI on top of that established foundation.

What does getting started actually involve?

A short discovery call comes first, followed by a rollout plan matched to your organization and a pilot survey cycle before a full deployment. We will walk you through timing and cost on that first call, based on your size and industry.

Bring Predictive Safety Intelligence In-House

See OSES-AI on your own organizational data.

Talk to the team about running OSES-AI at your organization, whether that is aviation, aerospace, nuclear power, oil and gas, or another safety-critical environment.

  • A walkthrough of the survey, the AI risk engine, and the safety advisor
  • A sample executive dashboard and risk heat map for your industry
  • Guidance on rollout across pilots, crews, maintenance, and management

Responses stay anonymous. OSES-AI is built to protect individual respondents while giving leadership organization-level insight.

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