How a Seasoned PM Uses AI to Supercharge Delivery

I currently lead business operations and growth projects in the education sector, as well as implementation projects in healthcare technology, making sure clinical and business needs turn into scalable digital solutions. So, I run two completely different types of projects/programs for different companies.

Across all of that, my job is really about creating clarity around complex requirements from leadership, managing lots of different stakeholders, and making sure good ideas actually turn into delivered results through project management methods.

How AI is helping teams transition to lightweight project management

I started my career in very traditional waterfall project management with big MS Project plans, RAID logs in Excel, and long weekly status decks. In fast-moving digital/agile environments, that overhead quickly became a drag, and I realized I was focusing too much on process, as opposed to value.

That's why it's so important to transition to lightweight project-management methods. And that’s the direction the industry is moving. No one wants to engage in process-heavy systems anymore.

AI makes that transition easier because it does the grunt work, so you can deliver value as your stakeholders define it.

For me, the transition has looked like centralizing work into a single, living board instead of scattered documents. For example, I moved teams into a Kanban-style tool with columns like Backlog, This Month, This Week, In Progress, Blocked, and Done. Risks, decisions, and dependencies are tracked inside the same space. I imported only the truly critical milestones from the old Gantt, ran the new board in parallel for a short period, then retired the separate RAID log and slide decks once stakeholders were comfortable. The result is one source of truth instead of five competing versions.

Communication and governance have been simplified too. Instead of building heavy decks, I use the board + AI to draft concise updates for different audiences.

Ultimately, AI has made it very obvious that my value is in judgment, alignment, and change management — not in updating spreadsheets all day!

Share This Quote on:

• Share on Twitter
• Share on LinkedIn
• Share on Facebook

Melissa Khan-Blackmore

PMP-Certified Project Manager

How AI is changing modern project leadership and delivery

AI is affecting my role too. These days, my role definitely requires a lot less note-taking, task tracking, status updating, and even general project planning. I have a lot more time to dig into business problems and connect to higher-level strategic goals.

On the delivery side, AI is now woven into how we run projects. We use it to summarize client calls, cluster and find patterns in stakeholder feedback, and draft first-pass requirements, project plans/timelines, and even comms based on our project artifacts. For example, instead of me spending hours turning meeting notes and Jira tickets into a status deck, AI gives me a draft and highlights patterns or risks — then, I spend my time validating, prioritizing, and deciding what actually matters for the business.

Basically, I spend less time on manual status reporting, documentation clean-up, hand-translating the same update for different audiences, and combing through data to see what’s going on.

And that allows me to spend more time on higher-leverage work, like communicating with my clients, team, or business leaders, doing creative work like making processes better or improving project ideas, and creating strategy.

Ultimately, AI has made it very obvious that my value is in judgment, alignment, and change management — not in updating spreadsheets all day!

How AI simplified a complex rollout across multiple stakeholders

Here's an example. I was leading a product rollout for a large, highly regulated organization. We had tons of moving parts, operations, IT, end users, vendor engineers, and internal and external leadership, and all the feedback was scattered across email, chat, tickets, and meeting notes. It got to the point where my real job was just fielding complaints, issues, and requests.

We set up a lightweight system that layered AI on top of the tools we already used:

• Work management lived in Asana/Jira-style boards.
• Support requests flowed through a shared email inbox and an internal chat channel.
• Meeting notes and debriefs were captured in a shared doc.

I dropped that raw text into an AI workspace and asked it to:

• Cluster issues into themes
• Flag anything that looked high-risk or safety-critical
• Create status updates for different stakeholder groups

I did this — and I still do it — two times a week. It isn't 100% perfect, but it saves me hours of analysis and fielding different requests.

In terms of effort and results:

• It took maybe 2–3 hours to design the pipeline the first time.
• My weekly triage went from roughly half a day of combing through tickets and rewriting summaries to about 50 minutes of review and decision-making.
• This process really helps me summarize and capture EVERYTHING from every stakeholder group and catch risks easier.

That's why I think AI tools aren’t replacing the project manager; they’re stripping out a lot of low-value manual work so I can spend more time on prioritization, stakeholder alignment, and making clear, timely decisions in already complex environments.

Melissa's Tip

AI can and will radically transform how we plan for risk. It will also naturally reduce risk. But it can’t replace the parts that require real human judgment and accountability.

How project managers can automate repetitive delivery work with AI

I’ve started to treat AI as a junior team member whose job is to do the first pass on anything that’s repeatable, text-heavy, or pattern-based. Then, I step in as the editor and decision maker. That mindset is reshaping how I deliver across the board:

• Repetitive and admin tasks: Meeting notes and recaps, tagging action items, and formatting them into readable formats.
• Status reports: Generating first drafts based on ticket progress, stakeholder updates, and project milestones.
• Task follow-ups: Smart reminders and check-ins to nudge stakeholders for overdue items or approvals. Automate these from your AI PM tool — I use Asana AI.
• Communication personalization: Translating the same update into different tones and formats (e.g. executive summary, team-level detail, etc.).
• Drafting client-facing comms or slide decks based on raw inputs
• Pattern recognition: Spotting blockers or risks by analyzing feedback, tickets, sentiment in updates, or response patterns.
• Clustering feature requests or complaints to highlight themes across teams or users: This is great in product development.
• Documentation and knowledge management: Organizing chaotic documentation into a clean knowledge base.
• Drafting or updating SOPs and process flows using transcripts or raw notes.
• Planning and forecasting: Drafting Gantt charts, roadmaps, or sprint plans based on high-level goals or backlog inputs.
• Predicting resourcing issues or timeline delays from early signals in tools like Jira, Confluence, or Slack.
• Identifying performance trends across sprints or workstreams.

How AI can and should transform risk management

AI can and will radically transform how we plan for risk. It will also naturally reduce risk. But it can’t replace the parts that require real human judgment and accountability.

Humans still have to:

• Set the risk appetite: I.e. what we think “bad” actually means.
• Make the final calls: Accept/avoid/transfer, go/no-go, and budget/scope tradeoffs.
• Handle the people-and-politics risks that never show up cleanly in data: Stakeholder resistance, trust issues, vendor reliability, and negotiation to get owners to actually mitigate.

Where AI in project risk management shines is the heavy lifting:

• Pulling risks out of notes/tickets/docs.
• De-duping and categorizing them.
• Drafting clear cause-event-impact statements.
• Suggesting probability/impact scoring.
• Proposing mitigations, triggers, and contingencies.
• Monitoring trends and early warning signals.
• Turning the risk log into exec-ready “top risks + what I need from you” updates.

In short, humans have to validate reality and decide what matters. AI can help us be more accurate and scan for risks more reliably.

All that being said, stakeholders and sponsors can be fearful of an AI model (even closed source), having all of the data, so there is often pushback. So, while risk management should be an AI job, that isn't always the case in practice.

Why privacy and closed-source AI matter in real project environments

A lot of people are against AI. They're really fearful of it "stealing" data. And I think that can be warranted. Like with anything, there are downsides if you are not careful.

If you are using a closed-source AI, it's much safer. It's all in how you set things up. You may need privacy internally as well. For example, if HR is using the same closed source tool that development is using, there is the potential for confidential HR data to leak into other parts of the organization.

That's why I think it's important to have a data privacy officer who is well-versed in AI and is overseeing exactly how your AI systems are set up internally.

How a practical AI tech stack supports faster, smarter delivery

Here's my delivery tech stack:

• MS Copilot: Every day emails, notes, summarizing my tasks. This sits on top of my Microsoft 365 at work.
• Fathom: Notes, recordings, follow-up items
• Asana: Project management planning, task tracking, predicting schedules
• SuperPrompt: Help with prompting
• Jasper AI: All things marketing

I love Copilot. I wish every company I work for would use it. It can pull all of your data from your emails, meeting notes, docs, etc., and is basically a second brain. It makes me 5x more productive.

Melissa's Tip

The Asana AI feature is basically like having ChatGPT sitting on top of your project plan, except it’s actually reading your real tasks, dates, dependencies, updates, and timelines.

And for one of the startups I work for, we now use the Asana AI feature. It's basically like having ChatGPT sitting on top of your project plan, except it’s actually reading your real tasks, dates, dependencies, updates, and timelines. This AI agent can:

• Do an “AI risk sweep” across your plan and notes to flag what’s quietly turning into a timeline or delivery risk.
• Draft smart status updates and project summaries that highlight what changed, what’s blocked, and what you need from stakeholders.
• Help you turn all that project noise into clean, exec-ready reporting.

The best part is it can support the annoying operational stuff too like spotting dependency impacts when something slips, helping you update plans faster, and even powering no-code AI workflows so the system prompts the right next steps automatically.

Why project managers should embrace AI — not fear it

The role of the project manager is changing already, and will continue to evolve with AI. So, my advice is simple: Embrace AI, don't be scared of it.

And use a closed-source AI for confidential data.

Oh, and AI will not take your job!

Follow along

You can follow Melissa on LinkedIn, Instagram, and TikTok as she continues turning good ideas into delivered results.

More expert interviews to come on The Digital Project Manager!