Organizational Intelligence

Stop surveying. Start sensing.
See your organization as it actually is.

Cursus replaces periodic readiness surveys with continuous behavioral inference — ambient signals from communication platforms, calendars, and adoption systems scored into validated organizational constructs. You know what’s happening before it becomes a barrier.

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The intelligence gap

Change programs stall when leaders rely on
outdated information instead of real-time signals.

The tools exist to collect signals. What’s been missing is a platform that normalizes them into actionable organizational intelligence.

Stale readiness data

A readiness survey fielded three weeks ago reflects three-week-old sentiment. With single-digit response rates and social desirability bias, it was approximate when collected. By the time you act on it, the organization has moved.

Invisible saturation

Three programs targeting the same operations group in the same quarter. No aggregation across programs, no absorptive capacity ceiling, no collision detection. Change load isn’t tracked — it’s guessed.

No signal between programs

Organizational climate, network topology, and psychological capital don’t reset between programs. Yet most tools treat each initiative as an isolated workstream, ignoring the cumulative burden on stakeholder groups.

AI sprawl without intelligence

Copilot licenses are purchased, GitHub AI features are enabled, employees quietly adopt unauthorized tools — yet change teams have no visibility into actual adoption patterns, shadow IT risk, or whether absorptive capacity exists to support an AI transformation at all.

The Cursus approach

Behavioral inference from ambient signals — not annual engagement surveys.

Traditional approach

✕Quarterly surveys with 3-6 week latency and low response rates
✕Self-reported influence surveys subject to social desirability bias (Burt, 2005)
✕Stakeholder lists maintained manually in spreadsheets
✕Point-in-time snapshots, not trend data
✕Siloed by program, no cross-portfolio view

Cursus approach

✓Continuous behavioral inference from communication metadata and calendar patterns
✓Network analysis from observed interaction patterns, not self-report (Pentland, 2008)
✓HRIS-sourced org structure, always current
✓Trend lines that accumulate over time
✓Cross-program portfolio intelligence layer

Intelligence modules

Every dimension of organizational health, measured.

Signal Hub

Every signal, one normalized feed

Communication metadata, ERP adoption telemetry, calendar patterns, process mining events, and survey calibration data — all normalized into a single organizational signal feed. Multi-modal data collection — the same design principle that makes financial reporting reliable — applied to organizational intelligence. Cursus ingests continuously, not on a schedule, so your intelligence is always current.

Cross-source signal normalizationConfigurable signal weights per orgWebhook ingestion for real-time updatesSignal attribution for every scoreMulti-modal data collection by default

Network Intelligence

ONA from real interactions, not surveys

Influence networks built from Outlook, Teams, Slack, and calendar metadata — not self-reported surveys. Burt’s structural holes identify where change cascades will stall. Granovetter’s weak ties reveal cross-silo information bridges. Pentland’s honest signals research validates that communication patterns predict group performance.

Degree, betweenness & eigenvector centralityStructural hole detectionChange cascade simulationCross-team bridge identificationInformal influencer mapping

Climate Scores

Six dimensions, tracked continuously

Schneider’s organizational climate framework — the shared perceptions that predict collective behavior — operationalized into six dimensions tracked continuously. Psychological safety, trust in leadership, change fatigue, innovation readiness, collaboration health, and communication effectiveness. Computed from ambient signals and calibrated with targeted micro-surveys.

6 climate dimensions tracked continuouslyAggregated to group level (never individual)Trend lines and anomaly detectionBenchmark comparison across groupsMicro-survey calibration layer

PsyCap Index

Hope, Efficacy, Resilience, Optimism

Luthans’ Psychological Capital — Hope, Efficacy, Resilience, Optimism — tracked at the group level. Unlike personality traits, PsyCap is developable. Groups with depleted PsyCap need resilience-building interventions before they can absorb change. Cursus identifies them proactively, not after adoption stalls.

HERO framework operationalizedGroup-level PsyCap scoresPredictive risk flaggingIntervention recommendations per dimensionPre-launch readiness gating

AI Adoption Intelligence

AI adoption as a change management signal

AI transformation is the most consequential change initiative in most organizations right now — and the least instrumented. Cursus treats AI tool adoption as a first-class organizational signal: Copilot usage patterns, GitHub AI feature telemetry, and webhook-based data from any AI platform flow into the same behavioral inference pipeline that processes communication metadata and calendar patterns. Shadow IT detection flags unauthorized tool usage before it becomes a governance risk. AI adoption scoring assesses group-level readiness against absorptive capacity ceilings. Lumen surfaces executive-level orchestration recommendations — sequencing, saturation risk, and intervention priorities — grounded in the same organizational constructs that govern all change programs.

Copilot & GitHub AI usage telemetryWebhook ingestion for any AI toolShadow IT detection for unauthorized AIAI adoption scoring per stakeholder groupReadiness-gated rollout recommendationsExecutive AI orchestration briefings via Lumen

Absorptive Capacity

Can this group handle more change?

Cohen and Levinthal’s foundational construct, extended by Zahra and George into potential vs. realized capacity. High-ACAP groups can metabolize more change at higher velocity. Low-ACAP groups need sequencing, not stacking. Cursus computes both dimensions and uses the gap to set change velocity limits per group.

Derived from prior change historyAdjusted for current loadFeeds change load ceiling calculationsTrend analysis over program historyVelocity recommendations per group

Intelligence, not surveillance.

Cursus is architecturally designed to prevent individual-level monitoring. This isn’t a policy decision — it’s enforced in the data model.

Group-level only

Aggregation thresholds enforced before any metric is computed. No individual scores, ever.

Architectural constraint

Managers and executives cannot access individual behavioral data. Enforced by the data model, not policy.

Metadata only

Communication platform integration defaults to metadata. Content access requires explicit opt-in.

Learn about our privacy architecture

How AI drives value

AI that computes validated constructs from raw behavioral signals

Lumen applies scoring algorithms grounded in organizational science to ambient behavioral data — surfacing construct-level intelligence that no survey could deliver at this frequency.

Anomaly detection across climate dimensions — surface emerging issues before they become visible in surveys
Cross-source pattern recognition — correlate communication metadata, calendar shifts, and adoption telemetry automatically
Predictive risk scoring — flag groups approaching capacity limits based on historical patterns and current load trajectories
AI adoption orchestration — score group-level AI readiness, detect shadow IT, and generate sequenced rollout recommendations grounded in absorptive capacity data
Natural language querying — ask Lumen about any metric, trend, or group in plain language and get answers grounded in live data

Understand your organization the way you understand your finances.

Continuous. Ambient. Grounded in validated research. We’ll walk you through the intelligence layer with your own organizational structure.