How an Experienced Chief of Staff Leverages AI to Accelerate Delivery

What happens when a veteran chief of staff treats AI like an entry-level teammate? This piece shows how AI streamlines delivery work—reducing busywork, support planning, clarifying priorities, and letting teams focus on outcomes.

Summary

• AI removes planning, analytical and low-value admin tasks without replacing the strategic value of a chief of staff: it handles notes, status updates, pattern detection, and reporting so human leaders concentrate on judgment, prioritization, and stakeholder alignment.

• Lightweight, centralized delivery outperforms heavy process: a single AI-enabled source of truth (boards + concise summaries) can replace decks, RAID logs, and scattered docs while preserving governance.

• AI improves risk management but does not replace human accountability: it surfaces, groups, and tracks risks; humans still set appetite, manage politics, and make final go/no-go calls.

My background as Chief of Staff and Global PMO Leader

I lead business operations and growth initiatives in fintech, retail, e-commerce and education and run implementation projects for healthcare and greentech, ensuring clinical and business requirements become scalable digital products. That means I manage two distinct program types across different organizations. My core job is creating clarity from complex leadership requests, coordinating many stakeholders, and driving ideas to delivery through project practices.

How I leverage AI to Accelerate Delivery in Operations and Projects?

How AI supports the shift to lightweight project & operations management I began with waterfall practices—big MS Project plans, Excel RAID logs, and long weekly decks. In fast-moving digital environments, that overhead slowed us down. The industry is moving away from process-heavy approaches, and AI makes that shift feasible by doing much of the grunt work so teams can deliver value as stakeholders define it.

My practical change: consolidate work into one living board instead of dispersed files. I set-up Project & Operation targets with OKRs, planning and coordination roadmaps and migrated teams to a Kanban-style board (OKRs, Backlog, This Month, This Week, In Progress, Blocked, Done) and tracked outcomes, risks, decisions, and dependencies there. I migrated critical milestones from the old Gantt, ran both systems briefly, then retired separate RAID logs and slide decks once people adapted. The payoff: one source of truth instead of multiple competing versions.

Communications and governance got simpler too. Instead of crafting long decks, I use the board plus AI to produce concise updates tailored to each audience. Ultimately, AI makes my contribution clear: judgment, alignment, and change management—not spreadsheet upkeep.

Lead smarter with AI Join the DPM community for exclusive content, templates, events, and weekly leadership insights—free to join.

How AI reshapes modern operations and project leadership towards strategy implementation

AI has reduced the time I spend on bottleneck planning, risk discovery, result / task tracking, status updates, and basic planning, freeing me to tackle business problems and link work to strategic goals. On delivery, AI is embedded in workflows: analyze KPI / OKR and project portfolio updates, forecasting impacts and risks, summarizing meetings and client calls, clustering stakeholder feedback, drafting initial requirements and timelines, and producing comms from project artifacts. Rather than spending hours turning notes and tickets into an analytical overview, AI provides a draft, highlights patterns and risks, and I validate, prioritize, and decide what matters.

This reduces manual status reporting, doc cleanup, and repetitive audience translations, allowing more time for client and leadership communication, process improvements, and strategy work. Again: my highest value is judgment and alignment—not updating spreadsheets.

Business Case to Leverage AI

Simplifying a complex rollout I led a product rollout for a large, regulated organization with many stakeholders—operations, IT, end users, vendor engineers, and leadership—where feedback was scattered across email, chat, tickets, and notes. My role had become triage.

We built a lightweight pipeline using the tools already in place:

• Strategy and OKR planning with Oboard in Jira.

• Work management on Asana/Jira-style boards.

• Support via a shared inbox and internal chat.

• Meeting notes in a shared document.

  
  

I fed that raw text into an AI workspace and twice weekly asked it to:

• Build programs and delivery dependencies

• Cluster issues into themes

• Flag high-risk or safety-critical items

• Generate status updates for different audiences

  
  

The setup took about 2–3 hours initially. My weekly triage dropped from half a day to roughly 50 minutes of review and decision-making. The process captures all stakeholder input and surfaces risks more reliably. AI didn’t replace me; it removed low-value manual work so I could focus on prioritization, alignment, and timely decisions.

Tip AI will transform risk planning and often reduce risk, but it cannot replace human judgment and accountability.

How do I treat AI as a team member or consultant

I think of AI as a junior teammate that does first-pass work on repeatable, text-heavy, or pattern-based tasks. I then edit and make final decisions. Typical uses I’ve automated:

• Analysis and planning: Analyse conflicting or dependent OKRs / KPIs into a logic roadmap priority.

• Status reports: first drafts from ticket progress and milestones.

• Task follow-ups: automated reminders and check-ins.

• Communication personalization: same update rewritten for different audiences.

• Drafting client comms or slide decks from raw inputs.

• Pattern recognition: spotting blockers, sentiment, or response patterns.

• Clustering requests/complaints to show themes.

• Documentation: organizing into a knowledge base and drafting SOPs.

• Planning and forecasting: initial Gantt/sprint plans from high-level goals.

• Predicting resourcing or timeline risks from early signals.

• Identifying performance trends across sprints.

• Repetitive admin: meeting notes, recaps, tagging action items.

AI Planning, Monitoring & Analysis

How does AI change risk management

AI is excellent at extracting risks from notes and tickets, de-duplicating and categorizing them, drafting cause-event-impact summaries, suggesting probability/impact, proposing mitigations and triggers, monitoring trends, and turning risk logs into exec-ready “top risks + ask” updates.

As a Human, we should still:

• Define risk appetite (what “bad” means).

• Make final decisions: accept/avoid/transfer, go/no-go, budget/scope tradeoffs.

• Manage people-and-politics risks—stakeholder resistance, vendor reliability, and negotiations.

How to treat privacy and sensitive data regarding stakeholder concerns?

Sponsors and stakeholders can fear centralized AI models holding sensitive data, so adoption varies in practice. Privacy and closed-source AI in real projects Concerns about AI and data are valid. Using closed-source AI and careful internal configurations reduces leakage risk. It’s important to have a data privacy officer knowledgeable about AI to oversee internal setups—for example, preventing HR data from mixing with engineering data. Proper governance and boundaries are essential.

My practical AI stack

• Microsoft Copilot: daily emails, notes, task summaries on Microsoft 365.

• Fathom: meeting notes, recordings, and follow-ups.

• Oboard / Mooncamp: Strategy and OKR / KPI planning

• Jira / Asana: project planning, tracking, schedule predictions.

• SuperPrompt: prompting assistance.

• Jasper: marketing content.

  
  

I find Copilot especially powerful—it integrates across emails, notes, and docs and acts like a “second brain,” boosting productivity significantly

.

Tip Asana’s AI acts like a contextual assistant on your plan: it can run risk sweeps, draft smart summaries highlighting changes and blocks, turn project noise into clean executive reporting, surface dependency impacts, speed plan updates, and enable no-code AI workflows that prompt next steps.

Conclusion

Why chief of staff or PMO leads should leverage AI to accelerate delivery. My advice: learn it, embrace it, do not fear it. Use closed-source solutions for confidential data. And no—AI will not take your job; it will remove low-value tasks and complicated analytical processes for recommendations so human leaders can focus on judgment, relationships, and decisions