The Head of Delivery is the architect and steward of Capptus’ delivery operating system.
Their mission is to design a delivery environment where:
- AI removes execution friction
- Human judgment, taste, and systems thinking drive decisions
- Small, high-leverage teams consistently outperform larger, siloed ones
- Knowledge compounds across projects instead of resetting each time
Core Responsibilities (AI-First & System-Led):
1. Design the AI-First Delivery System (Primary Responsibility)
- Define how work flows from idea → delivery → learning:
- Where AI assists exploration, design, testing, and documentation
- Where human review and judgment are mandatory
- Where decisions must be explicit, documented, and reversible
- Ensure all delivery roles operate in shared mediums:
- Same artifacts
- Same tooling
- Same understanding of context
- Minimal handoffs
2. Elevate Judgment as the Core Delivery Skill
- Redesign delivery expectations so senior roles are evaluated on:
- Quality of architectural decisions
- Tradeoff clarity
- Ability to frame problems, not just solve tasks
- Institutionalize judgment rituals:
- Lightweight decision reviews
- Explicit assumptions and risk articulation
- “What would make this decision wrong?” discussions
- Protect time for thinking:
- Architects and leads are not fully utilized
- Slack is intentional, not waste
- Judgment degrades under constant execution pressure
3. Operate Delivery as a Living System
- Treat delivery as:
- Inputs (scope, constraints, talent, customer context)
- Flow (work in progress, dependencies, decisions)
- Outputs (value, quality, margin)
- Feedback (learning, reuse, improvement)
- Identify and act on:
- Bottlenecks
- Feedback delays
- Misaligned incentives
- Over-optimization of local metrics
- Use data (including Certinia) as signals, not commands.
4. Certinia as Observability, Not Control
- Surface patterns and constraints
- Track financial and delivery reality
- Enable fast, informed decisions
5. Knowledge as a System Output
- Every project must produce:
- Reusable patterns
- Decision rationales
- What-worked / what-didn’t insights
- AI is used to:
- Extract learning from delivery artifacts
- Summarize complex projects
- Connect current teams with prior context
- Knowledge ownership is explicit:
- Assets are curated, pruned, and reused
- Learning feeds back into future delivery design
6. Talent Development for Systems Thinkers
What the HoD Builds
- Consultants and developers who:
- Understand the full delivery system
- Can reason across data, platform, business, and customer context
- Specialize deeply in judgment-heavy domains
- Clear progression:
- From task execution → problem framing → system ownership → mentorship
- Juniors are onboarded into thinking, not just doing:
- Early exposure to decisions
- Explicit explanation of tradeoffs
- AI used as a learning accelerator
7. Customer as Part of the System
- Customers are treated as:
- Active participants in delivery
- Decision-makers with constraints
- Sources of feedback, not interruptions
- SteerCos are:
- Alignment forums
- Constraint-renegotiation spaces
- Shared judgment environments
Leadership Expectations
- Optimize for long-term leverage, not short-term output
- Make invisible work visible (decisions, tradeoffs, learning)
- Use AI comfortably without surrendering responsibility
- Protect buena onda while holding high standards
Think like a system architect, not a project manager.
