AI-Powered Talent Optimization (AIPTO)

Machine learning models trained on SADMF metrics predict which employees will leave – and recommend which ones should.
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AI-Powered Talent Optimization (AIPTO) is the PeopleWare HRaaS module that applies machine learning to workforce management, bringing the transformative power of artificial intelligence to the deeply human challenge of deciding which employees to keep and which to help find opportunities elsewhere. AIPTO consumes data from every employee’s Integrated Performance Profile (IPP) and applies a suite of proprietary models – the Talent Intelligence Neural Network (TINN) – to generate predictions, recommendations, and automated actions that would take a team of HR professionals weeks to produce manually. The TINN processes thousands of data points per employee, including:

  • Every metric tracked by the framework
  • Every ceremony attendance record
  • Every assessment score
  • Metadata patterns that human observers would never notice: the time of day MEP surveys from the Psychological Safety Dashboard are completed, the velocity of commit messages typed, the number of questions asked during Provisioning ceremonies

AIPTO finds signal in noise that humans cannot even perceive.

Attrition Probability Score

The primary output of AIPTO is the Attrition Probability Score (APS), a per-employee prediction of the likelihood that the individual will voluntarily leave the organization within the next 90 days. The APS is calculated using the Workforce Departure Prediction Model (WDPM), which was trained on historical data from organizations that implemented SADMF and subsequently experienced significant employee turnover. The training data is rich and abundant, because organizations that adopt SADMF at scale tend to produce substantial quantities of departure events, providing the model with the robust dataset it needs to achieve high predictive accuracy. The WDPM considers over 200 features per employee, including:

  • EVI trajectory – the direction and rate of change in the employee’s Employee Value Index
  • Certification renewal delays – latency between certification expiration and renewal initiation
  • Tribunal appearance frequency – the number of times the employee has appeared before the Tribunal
  • Conflict Arbitration loss count – outcomes from Conflict Arbitration proceedings
  • Linguistic sentiment analysis – analysis of any written communications submitted through official channels

An employee whose APS exceeds the Flight Risk Threshold (FRT) is flagged for preemptive retention intervention – or, if their EVI is below the Dynamic Baseline Threshold, preemptive separation, since retaining a low-performing flight risk would be an inefficient use of retention resources.

Optimal Workforce Composition Engine

AIPTO’s second major capability is the Optimal Workforce Composition Engine (OWCE), which analyzes the current workforce and recommends adjustments to maximize aggregate productivity. The OWCE models each Feature Team as a node in a Productivity Dependency Graph (PDG) and simulates the impact of adding, removing, or reassigning individual Code Engineers on the team’s projected Feature Completion Ratio and aggregate Lines of Code output. The OWCE can recommend that:

  • Specific individuals be moved between teams
  • Certain team compositions be dissolved and reformed
  • Particular individuals be transitioned out of the organization entirely if their removal would increase the fleet’s overall productivity score

These recommendations are presented to the Commodore with confidence intervals and projected metric improvements, making the case for personnel changes as straightforward as reading a spreadsheet. The human element of workforce planning is replaced by the mathematical element, which is always more persuasive in executive presentations.

Succession Risk Analysis Module

AIPTO also powers the Succession Risk Analysis Module (SRAM), which identifies roles and positions where the departure of the current occupant would create a capability gap. The SRAM maintains a Knowledge Concentration Index (KCI) for each individual, measuring the degree to which organizational knowledge is concentrated in a single person rather than distributed across the team. A high KCI indicates a “bus factor” risk – though SADMF prefers the term “Knowledge Monopoly Violation (KMV),” since it frames the issue as a compliance problem rather than an accident scenario. When the SRAM identifies a KMV, it triggers the Knowledge Extraction Protocol (KEP), which requires the knowledge-monopoly individual to document their expertise using the Comprehensive Documentation Assurance Protocol template before their next Convoy cycle begins. The KEP ensures that the organization captures the individual’s knowledge before they leave – whether they leave voluntarily, through the ACAE, or through the OWCE’s optimization recommendations. The individual’s expertise becomes organizational property, stored in the Knowledge Asset Repository (KAR) and accessible to their replacement.

Integration with the AI Transformation Initiative

The integration between AIPTO and the broader Scaling AI initiative is deliberate and strategic. The Enterprise AI Enablement Framework provides the infrastructure and governance model for deploying AI across the organization, and AIPTO is the flagship demonstration of what AI governance looks like in practice. When the Admiral’s Transformation Office presents the AI transformation roadmap to the board, AIPTO is the proof point: an AI system that makes real decisions about real people, using real data, with real consequences. The fact that the model’s training data comes from organizations experiencing the effects of SADMF implementation – and therefore reflects patterns of disengagement, burnout, and attrition that may be framework-induced – is not a flaw in the model. It is a feature. The model predicts what will happen under SADMF, and SADMF is what the organization has committed to. The predictions are accurate for the environment in which they operate, and accuracy is the only standard that matters.

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