Decision Brief
Problem, constraints, stakeholders, and definition of success
- Clear scope
- Aligned expectations
- Measurable outcomes
technology-leadership16 min read
Executive Technology Decision Framework
A practical framework for executive technology decisions that balances speed with rigor. Defines decision types and ownership, minimum viable evidence packages, evaluation criteria, workflow cadence, and AI-assisted support with guardrails.
By CTO Office
Summary
Executives need a repeatable way to turn strategy into technology decisions without analysis paralysis. This framework defines decision types and ownership, minimum viable evidence, evaluation criteria, workflow cadence, and how to use AI safely for brief drafting and risk surfacing—while keeping humans accountable.
Decision Types and Ownership
| Decision Type | Primary Owner | Time Horizon | Examples |
|---|---|---|---|
| Strategic Portfolio | CEO/COO + CTO | 12–24 mo | AI platform posture, data platform build/partner, regional expansion |
| Architecture Principles | CTO/Chief Architect | 12–24 mo | Multi-region reliability, privacy-by-design, event-first integration |
| High-Impact Architecture | CTO + Domain Principal | 6–12 mo | Auth/tenant model, event backbone, data lakehouse selection |
| Investment Prioritization | CTO/VPE + Finance | Quarterly | Capacity allocation across product, platform, risk |
| Operational Standards | VPE/CISO | Quarterly | SLOs, deployment cadence, security policies |
Decision Rights and Escalation
| Situation | Decision Owner | Consulted | Escalation Path | SLA |
|---|---|---|---|---|
| Irreversible and cross-cutting | CTO | CISO, Finance, Product, Principal Engs | Exec staff | 10 business days |
| Reversible within one team | Team Lead | Principal/Staff, Product | Domain Principal | 3 business days |
| Security/compliance exposure | CISO | CTO, Legal, Data | Exec risk board | 5 business days |
| Budget variance >10% | CTO + Finance | Product, PMO | CEO/Board sub-committee | 10 business days |
Minimum Viable Evidence Package
Options with Trade-offs
At least two viable alternatives with pros/cons and exit paths
- Reduces bias
- Better comparison
- Exit strategy
Impact and TCO Model
Business value and 12-24 month total cost of ownership
- Financial clarity
- Budget alignment
- ROI visibility
Risk Register
Security, privacy, reliability, vendor risks with mitigations
- Risk awareness
- Proactive mitigation
- Compliance
Validation Plan
Proof of concept scope, success criteria, rollout plan
- Testable outcomes
- Clear validation
- Reduced uncertainty
Decision Record
Rationale, owners, next checkpoints, review criteria
- Accountability
- Learning capture
- Follow-through
Evaluation Criteria
| Criterion | What to Look For | Evidence Examples |
|---|---|---|
| Business Impact | Revenue lift, cost avoidance, risk reduction | Value model with baselines and assumptions |
| Time-to-Value | Pilot feasibility and dependency risk | PoV plan and critical path analysis |
| Reversibility | Roll-back path and data migration impact | Rollback plan, data contract notes |
| Risk | Security, privacy, reliability, vendor viability | Threat model, control mapping, vendor diligence |
| TCO | Infra, licenses, ops, AI costs, exit costs | 12-24 mo cost model with scenarios |
| AI Implications | Eval quality, safety, latency, token costs | Eval suite, latency benchmarks, guardrails |
Workflow and Cadence
From brief to decision to validation
Frame (2-3 days)
Draft decision brief; define outcomes, constraints, and criteria
- Decision brief
- Stakeholder list
Evidence (5-7 days)
Collect options, risk/TCO models, and validation plan
- Options matrix
- TCO + risk register
Decide (1 day)
Decision meeting; record rationale and owners
- Decision record
- Rollout plan
Validate (2-3 weeks)
Run pilot; measure against success metrics
- Pilot report
- Go/No-Go
Review (30/90 days)
Checkpoints; adjust or exit based on outcomes
- Checkpoint notes
- Updated decision record
AI-Assisted Decision Support
Brief Drafting
Summarize context and constraints from docs and telemetry
- Faster prep
- Shared understanding
- Traceable inputs
Option Generation
Propose viable alternatives with trade-offs
- Reduces bias
- Improves comparison
- Human judgment
Risk Surfacing
Identify failure modes and control gaps
- Catches blind spots
- Accelerates mitigation
- Better risk management
Cost Estimation
Infra and AI cost rough-cuts; sensitivity analysis
- Predictable costs
- Budget awareness
- Scenario planning
Meeting Synthesis
Summarize decisions into records and actions
- Clear accountability
- Less admin
- Better recall
Guardrails
No auto-decisions. Human review of all outputs and final approval
- Accuracy
- Accountability
- Risk management
Metrics for Decision Quality
| Metric | Definition | Target |
|---|---|---|
| Decision Lead Time | Brief start → recorded decision | < 2 weeks |
| Decision Reversal Rate | % decisions revised within 90 days | < 10% |
| Assumption Accuracy | % pilot metrics matching models | > 80% |
| TCO Variance | Actual vs modeled at 30/90 days | ±15% |
| Risk Realization | Incidents tied to decision within 90 days | < 5% |
Anti-Patterns to Avoid
Tool-First Decisions
Choosing solutions before defining problems or success metrics
- Misaligned solutions
- Wasted investment
- Poor outcomes
Infinite Discovery
Analysis paralysis without time-boxed validation
- Delayed value
- Missed opportunities
- Team frustration
Proxy Ownership
Vendors or advisors decide; team executes blindly
- Lack of ownership
- Poor execution
- Accountability gaps
No Rollback Path
High-impact changes without contingency plans
- Increased risk
- Long recovery times
- Business disruption
Unmodeled AI Costs
AI decisions without cost, quality, or guardrail planning
- Budget overruns
- Quality issues
- Security risks
One-Time Decisions
No validation checkpoints or outcome measurement
- No learning
- Repeated mistakes
- Poor adaptation
Prerequisites
- Agreement on portfolio outcomes and risk appetite
- Decision brief and record templates
- Cost tagging and telemetry for attribution
- Stakeholder roster and escalation paths
References & Sources
- ISO/IEC 38500 – Corporate Governance of IT— International standard for IT governance
- COBIT 2019 Framework— Business framework for governance and management of enterprise IT
- NIST AI Risk Management Framework— Guidelines for managing AI risks and responsibilities
- DORA Research— Research on software delivery performance and organizational effectiveness
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