Project delivery expert says AI is exposing broken knowledge systems inside companies
When organizations adopt AI, they often uncover something unexpected: their knowledge systems are fragmented, undocumented, and broken. Project delivery expert Mark Burnett explains what that means for project leaders — and how to design adaptive systems where AI and human leadership thrive together.
Blending Governance: Mark Burnett combines traditional and agile project management for effective delivery in complex environments.
AI Amplification: AI enhances project management by improving thinking quality and exposing weaknesses in organizational knowledge.
Cognitive Load Shift: AI reduces time spent on documentation, allowing project leaders to focus on human aspects of delivery.
Iterative Systems: Adopting lightweight systems promotes adaptability and flow, favoring iterative documentation over extensive upfront control.
Future Leadership: Successful project managers will become orchestrators of teams, emphasizing human skills amid AI integration.
Mark Burnett is an engineer, transformation strategist, and the founder of The Ambidextrous Project Manager. His focus is on designing adaptive systems that enable teams to thrive in complex environments.
We caught up with Mark to learn more about his approach to project management and how AI is changing the way he works and leads. Here's what he told us.
Blending traditional governance with agile adaptability to find success
My name is Mark Burnett. I’m an engineer, transformation strategist, and the Founder & CEO of The Ambidextrous Project Manager (TAPM). My work sits at the intersection of strategy, technology, and human resilience.
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I help business leaders, project managers, entrepreneurs (SMEs), engineering and technology teams deliver complex initiatives — particularly in digital/ business transformation, PMO development, people and organizational change. Over the past two decades, I’ve worked across telecommunications, renewable energy, and IT & ICT ecosystems throughout the Caribbean, Latin America, and beyond. Today, I focus less on enforcing rigid methodologies in project delivery and more on designing adaptive systems that enable teams to thrive in complex environments.
I often describe myself as an ambidextrous delivery leader and strategic transformation partner — someone who blends traditional governance with agile adaptability. That means using structured frameworks where they add value, but also empowering teams with lightweight, human-centered delivery practices that allow innovation and speed. At its core, my role is to help people and projects succeed — especially when the odds are stacked against them.
Why AI amplifies "thinking quality" in project management
AI doesn’t replace thinking — it amplifies thinking quality. If your prompts are shallow, the results will be shallow. But when you combine domain expertise, structured reasoning, and contextual prompts, AI becomes a powerful intellectual collaborator.
AI also exposes weaknesses in organizational knowledge systems. Many companies realize that their information is fragmented, undocumented, or inconsistent once they adopt AI. AI adoption is forcing organizations to confront a deeper question: Do we understand our own processes?
AI doesn’t replace thinking — it amplifies thinking quality.
How AI reshapes the cognitive load of project leadership
AI fundamentally reshapes the cognitive load of project leadership. Traditionally, project managers spent enormous amounts of time on documentation, status reporting, meeting notes, risk logs, and communications. AI now generates, summarizes, or accelerates many of these tasks. I now spend significantly less time creating artifacts and more time designing delivery systems, coaching teams, interpreting insights, and making strategic decisions.
Ironically, as AI grows, project leadership becomes even more human.
AI helps me rapidly generate draft delivery plans, stakeholder communication templates, knowledge summaries, workshop materials, and risk scenarios. My attention now focuses more on the human side of delivery: stakeholder alignment, sense-making in uncertainty, ethical use of technology, and team empowerment. Ironically, as AI grows, project leadership becomes even more human.
How AI handles complexity in project delivery systems
We supported a digital initiative where stakeholders were overwhelmed by documentation and misalignment across teams. The first problem wasn't technology — it was information overload. This is how we solved the issue using AI.
- Step 1: Knowledge consolidation. We quickly generate summaries, themes, and risk insights. We did this by using AI tools to consolidate scattered project materials, including meeting transcripts, technical documentation, stakeholder emails, and delivery plans.
- Step 2: Decision-support workflows. Using AI-assisted prompts, we generated risk heatmaps, stakeholder communication drafts, and decision trees for leadership.
- Step 3: Rapid iteration. Instead of producing large static documents, we shifted to short iterative deliverables, such as print briefs, visual roadmaps, and decision memos.
Tools used:
- AI assistants for synthesis and drafting
- Collaborative documentation platforms
- Visual roadmap tools
Results: The most important outcome wasn’t speed — it was clarity. Leadership finally had a shared understanding of the priorities, risks, and delivery constraints. And the team regained momentum. Sometimes the biggest project risk isn't technical complexity — it's misaligned understanding. AI helped us reduce that friction dramatically.
Sometimes the biggest project risk isn't technical complexity — it's misaligned understanding.
How AI and human skills complement each other in project delivery
We use AI to augment several areas. In documentation generation, we use AI to draft project charters, meeting notes, requirement summaries, and stakeholder reports. AI also excels at knowledge synthesis. We use it for things like extracting insights from large documents, summarizing meeting transcripts, and identifying patterns in project data. I also like to use AI to generate scenario simulations and risk lists that teams might overlook.
However, there are several areas that still demand a human touch. Project leadership remains deeply human. AI supports judgment — but it doesn’t replace it.
The areas that still require a human include:
- Stakeholder diplomacy
- Ethical decision-making
- Team motivation
- Conflict resolution
- Strategic prioritization
How an AI stack has evolved over the last year
AI is becoming the connective layer across tools, helping teams move faster between information, decisions, and action. My stack has evolved significantly over the past year:
- AI assistants: Used for drafting, synthesis, and research acceleration.
Impact: dramatically reduces documentation time, accelerates knowledge discovery - Collaborative knowledge tools: Used for shared documentation and alignment across teams.
Impact: improves visibility, reduces siloed knowledge - Visual planning platforms: Used for roadmaps, workflows, and delivery visualization.
Impact: faster stakeholder understanding, improved alignment - Communication tools: Still critical for coordinating distributed teams.
Impact: supports asynchronous collaboration
AI is becoming the connective layer across tools, helping teams move faster between information, decisions, and action.
How AI-powered summarization boosts project speed
I think AI-powered summarization of meetings and documents is underrated. This may sound simple, but it dramatically improves project velocity.
Instead of teams spending hours writing summaries, AI can instantly produce meeting summaries, key decisions, action items, and stakeholder updates. The real impact is decision speed. When leaders receive concise, clear information, they can make better decisions faster.
Why lightweight systems are replacing traditional methods
Traditional project management often emphasized documentation and control. Modern delivery increasingly emphasizes flow and adaptability. In practice, this means:
Instead of producing large upfront documentation packages, we now create:
- Lightweight roadmaps
- Evolving delivery briefs
- Iterative documentation
Tools like visual planning boards and collaborative documentation spaces allow teams to maintain living project knowledge. The transition requires cultural change. Teams must shift from documenting everything upfront to learning and adapting continuously.
Why agentic workflows have enormous potential
I’ve begun exploring agentic approaches, particularly around knowledge management and documentation. Right now, this experimentation is still early. But the potential is enormous. Agentic systems could eventually function like digital project analysts, continuously supporting delivery teams.
Agentic systems could eventually function like digital project analysts, continuously supporting delivery teams.
Instead of manually coordinating information across tools, AI agents can help:
- Retrieve relevant project knowledge
- Generate reports
- Summarize progress
- Identify potential risks
Why AI changes how we approach delivery rituals
AI is changing how we approach several delivery rituals. These are the ones most notably affected:
- Scope definition: Instead of static scope documents, we now use iterative scope framing with AI-generated scenarios.
- Alignment: AI summaries ensure everyone understands discussions and decisions.
- Validation: Teams can test ideas faster by simulating outcomes or generating rapid prototypes.
- Execution: AI supports delivery with quick insights, documentation drafts, and structured thinking prompts. It becomes a thinking companion within delivery workflows.
How AI will transform project management roles
Within five years, the role of the project manager will shift dramatically. The most successful leaders will function less like administrators and more like orchestrators of complex systems.
Project delivery will involve:
- AI copilots
- Automated reporting
- Real-time decision intelligence
- Adaptive planning systems
But the most valuable skill will remain human leadership. Empathy, resilience, ethical judgment, and storytelling will differentiate great delivery leaders from automated systems.
Why leaders should experiment early and stay curious
My advice is simple: experiment early and stay curious. Don’t wait until AI tools become perfect. Start small. Start off using AI for things like drafting documents, summarizing information, and brainstorming ideas. Most importantly, focus on improving thinking — not just efficiency.
The real opportunity isn't just doing things faster. It's in seeing problems differently.
My advice is simple: experiment early and stay curious.
How to maintain human focus in AI-driven projects
I often wonder how we will keep project leadership human in an increasingly automated world. Technology will continue evolving rapidly. But projects are about people working together to create change, and if we lose sight of the human dimension — trust, empathy, shared purpose — AI won't save a failing initiative.
The future of project leadership is not about choosing between humans and machines. It’s about designing systems where both thrive together.
Follow along
You can follow along with Mark Burnett's work on LinkedIn, and check out The Ambidextrous Project Manager.
More expert interviews to come on The Digital Project Manager!
