FI
FIS EngineeringIQ
Digital Engineering Intelligence · Banking & Issuing
AI learning from 1,284 release & incident outcomes
ILLUSTRATIVE DATA · ROLE: RELEASE LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 19
OntologyIQ 12.8k
KnowledgeIQ 6
Engineering Execution Intelligence
Release confidence, service reliability, and engineering productivity across banking and issuing products
◊ Knowledge graph: 12,840 linked engineering artifacts
Release Readiness Score
82%
↑ 6pts vs last release
Evidence Traceability
91%
↑ 4pts QoQ
Runbook-Matched Incidents
68%
↑ 9pts QoQ
Readiness Prep Time
-38%
↓ from 3.4 to 2.1 days
Open Release Risks
9
3 on the critical path
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
19 outcomes learned
Readiness Preparation Time — 12-week trend
Days to assemble release readiness, by product
Engineering Time Recaptured
Hours per engineer per week, before vs after assistance
Requirement-to-Test Traceability
Committed requirements with linked evidence
Signal Mix — Last 30 Days
By engineering domain
AI Confidence Distribution
Across signals and recommended tasks
Top Release & Service Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Engineering Signal Detection
Signals from requirements, code, tests, defects, approvals, and incidents — each with its source and timestamp
● Connected to 8 source systems (illustrative)
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 10
Patterns 5
Correlations 6
Root Cause Analysis 3
Recommendations 5
DecisionIQ — Initiative Management
Active engineering initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 8 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,284 historical release and incident outcomes to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with release manager or engineer signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at release and incident recommendations
● Continuous learning active
Outcomes Captured
1,284
↑ 96 this month
SME Corrections
47
→ model refinement
Initiative Success Rate
78%
↑ from 66% (Q1)
AI Recommendation Accuracy
87%
↑ 8pts vs baseline
Failed Closures Studied
6
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the recommendation model
Improvement Over Time
Recommendation acceptance rate by quarter
What's Working (Reinforced)
Patterns the AI is doubling down on
What's Not Working (Down-weighted)
Patterns the AI is moving away from
Add Custom Task
AI has pre-filled this based on the initiative's closure path
AI Pre-fill — Suggestions below are editable. Owner defaults to AI's recommendation.
OntologyIQ — Engineering Knowledge Graph
A living semantic model connecting every entity and risk relationship across active engineering work
◊ 12,840 entities · 31,200 relationships
Entity Types Modeled
8
Requirements, Code Changes, Tests, Releases, Services, Incidents, Dependencies, Owners
Relationship Types
12
verifies · contains · depends-on · owned-by · resolved-by…
Graph Freshness
96%
Entities updated within the last cycle
Query Accuracy (validated)
91%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where engineering and operations know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
23
↑ 4 this month
Experts at Retirement/Rotation Risk
6
2 not started
Capture Coverage
64%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
12d
↓ from 18d last quarter
Knowledge Graph — Experts, Assets, Services & Releases
Instance-level view of the graph: who holds the knowledge, what's been captured, and which services and releases it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from