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You may know that in addition to my work on innovation and creativity, I work as a professional Project Manager.

As I’ve just finished leading an 18-month project, I am reflecting on how project management and leading teams is changing as Artificial Intelligence becomes more common in the workplace.

In this article, I am going to show you:

1. The costs of projects and team leadership failing (and the incredible scale of project failure that rarely gets talked about)
2. The reasons why so many projects fail (and how to avoid these pitfalls)
3. How Artificial Intelligence (A.I.) and modern methods can ensure project success
4. The keys to delivering successful projects (and how I can help you achieve these)

Ready? Let’s dive in.

PS. If you are wondering why I am sharing these valuable insights here for free, I am currently looking for my next set of projects in the coming 12 months. If you have a project or team needing leadership and management, book an appointment with me now and we can speak about how I could help you.

The costs of projects and team leadership failing

Having worked on some of the largest projects for major clients, I’ve seen firsthand that project failure isn’t just an inconvenience—it poses a serious financial and reputational risk.

Here are some shocking statistics about project management and delivery:

• It is estimated that around 70% of projects fail to meet their original scope, schedule, or budget.
• For larger projects costing over $10 million, around 94% of projects fail entirely.
• Some of the worst failure rates come from IT, construction, and transformation projects, especially those deemed “Megaprojects.”
• 17% of large IT projects become “black swans”, exceeding budgets by 200–400% and threatening company survival.
• Innovation projects are some of the worst offenders, with around 96% failing to deliver value.

However, failure is not inevitable. Organizations that take proactive steps can drastically reduce their risk.

The True Cost of Project Failure

When a project fails, the immediate financial losses are only part of the problem. Other consequences include:

• Wasted resources: Time, labor, and materials invested in an incomplete or ineffective outcome.
• Loss of stakeholder trust: Repeated failures damage credibility and make it harder to secure buy‑in for future initiatives.
• Decreased morale: Teams that work tirelessly on a failing project often experience frustration and burnout.
• Competitive disadvantage: A delayed or failed project can allow competitors to move ahead, capturing market share and innovation opportunities.

Steps to Reduce the Risk of Project Failure

Over the years, I’ve helped organizations implement simple but effective measures to prevent costly failures. Here are four actionable steps you can take today:

1. Clearly Define Objectives: Ensure everyone understands the project’s purpose and expected outcomes. Ambiguity leads to confusion and misaligned efforts.
2. Establish Robust Communication Channels: Poor communication is a leading cause of failure. Regular updates, clear documentation, and stakeholder engagement can prevent misunderstandings.
3. Use Proven Frameworks Appropriate for the Project Type: Project managers who try to “wing it” often fail to keep teams aligned or on top of delivery. Proven frameworks, especially those aligned to the project’s ambition, can help ensure success.
4. Conduct Regular Risk Assessments: Identify potential risks early and develop mitigation strategies. Teams that actively manage risks are more likely to succeed.

The Common Pitfalls that Lead to Project Failure

Through years of managing complex projects, I’ve seen the same pitfalls appear time and time again, regardless of industry or project size. Often, organizations don’t realize they are making these mistakes until it’s too late. Recognizing them early allows you to take action and keep your projects on track.

Previously, I discussed some shocking statistics about project failures. Now, let’s delve into why they fail.

Common Causes of Project Failure

• Lack of Alignment with Strategy and Portfolio: Too many projects are started because a senior stakeholder thought it was a good idea without first testing how the project aligns with the overall strategy or fits into the wider program or portfolio.
• Lack of Clear Planning: Many projects start without a well‑defined scope or objectives, leading to shifting priorities and scope creep.
• Poor Stakeholder Engagement: When key stakeholders aren’t aligned or involved, misunderstandings and last‑minute changes become inevitable.
• Unrealistic Timelines and Budgets: Setting overly aggressive deadlines without considering resource availability leads to rushed work and missed milestones.
• Resistance to Change: Teams often struggle to adapt to new tools and processes, delaying adoption and reducing efficiency.
• Lack of Risk Management: Projects that fail to identify and mitigate risks early on often encounter avoidable obstacles that derail progress.
• Unclear Roles and Responsibilities: When team members are unsure of their responsibilities, accountability suffers, leading to duplicated efforts or missed tasks.
• Poor Resource Allocation: Insufficient staffing, a lack of critical skills, or misaligned priorities can leave teams struggling to deliver quality outcomes on time.
• Failure to Define Success Metrics: Without clear success criteria and performance measurements, teams may complete tasks without truly achieving business goals.
• Using the Wrong Management Framework: Some projects require a fully planned approach, others need a more agile method, and still others benefit from validating with fewer resources before investing more heavily.
• Failure to Identify and Kill Underperforming Projects Quickly: As circumstances change, a project that once looked promising may no longer deliver anticipated value. Organizations need to quickly identify and kill such “zombie” projects so that resources can be reallocated.
• Getting Too Emotionally Invested: Team members and sponsors can sometimes want to force a project to succeed by ignoring objective progress data.
• Outdated Tools and Methods: Relying on legacy systems like Excel, SharePoint, and Outlook can lead to missed opportunities for efficiency and speed gains through modern tools.

How to Avoid These Pitfalls

I’ve helped clients implement practical solutions to these common challenges, such as:

• Developing a Comprehensive Project Plan (for waterfall projects): Establish a clear roadmap with defined objectives, timelines, and resource requirements to ensure alignment from the start.
• Using a Flexible Progress Validation Framework (for innovation projects): Employ a structured approach to identify what needs validation and how to check progress—like the L.I.V.E. (Lean Innovation, Validation & Execution) framework.
• Defining Success Metrics Early: Establish clear KPIs and measurable goals to prevent ambiguity and ensure the team understands what success looks like.
• Holding Regular Stakeholder Check‑ins: Continuous communication keeps expectations aligned and prevents last‑minute surprises.
• Setting Realistic Expectations: Use past project data to inform timelines and budgets, leading to more achievable goals and reducing unnecessary stress.
• Proactively Managing Risks: Identify potential risks early and develop contingency plans to navigate challenges with minimal disruption.
• Clarifying Roles and Responsibilities: Ensure every team member understands their role to improve accountability and prevent task duplication.
• Optimizing Resource Allocation: Match the right people to the right tasks to prevent skill gaps and keep projects on schedule.
• Ensuring Team Members Can Focus on Their Work: Avoid pulling high‑performing individuals into multiple projects simultaneously, which can lead to distractions, task switching, and burnout.

A.I. Tools for Managing Teams and Projects

Traditional project management methods often struggle to keep up with today’s fast‑moving business environment. Having worked on large‑scale programs, I’ve seen firsthand how A.I., process automation, and agile methodologies can drastically improve success rates.

Let’s explore some of the amazing A.I. tools I use to lead teams and manage projects—and how you can use them as well.

How A.I. and Automation Improve Project Management

A.I. tools go far beyond generating fun images or simple text responses. There are numerous tools designed specifically for critical, time‑consuming parts of managing teams and projects. Here are some of my favorite use cases and tools in action:

1. Task assignment and planning: A.I. can help create and assign appropriate tasks and allocate them to team members based on availability. A.I.-powered analytics can detect early warning signs of potential delays, allowing teams to take proactive measures. Examples: Wrike, Monday.com and Asana
2. Automation of Administrative Tasks: Automating routine tasks, such as scheduling and reporting, allows project managers to focus on strategic decisions. Example: Lindy
3. Generative A.I. for Document Preparation and review: A.I. can draft reports, meeting summaries, and project documentation, reducing time spent on manual writing. Examples: ChatGPT, Google Gemini, Microsoft Copilot, Claude
4. A.I.-Powered Research Assistants: Tools like Perplexity help teams conduct in-depth market research, trend analysis, and competitor insights.
5. User research optimisation: A.I. can speed up analysis of user research through automated analysis of interviews and usability tests (like Looppanel, Maze, Sprig), knowledge management (like Dovetail) and even generating video avatars for research (like Synthesia).
6. A.I. as a Sparring Partner for Idea Testing: A.I. can simulate scenarios, validate assumptions, and identify potential risks before a project even begins. Examples: Board of Innovation A.I. toolbox, ChatGPT, Google Gemini, Microsoft Copilot,
7. Streamlining Workflows with A.I.: A.I.-driven note-taking, meeting transcription, and automated action tracking ensure that nothing gets lost in communication. Examples, Otter, Zoom AI Companion, Microsoft Copilot and Google Gemini for Workspaces
8. Consolidating various updates and documents: I use tools like Google’s NotebookLM to automatically consolidate updates from numerous various sources, especially useful in mapping project progress from numerous stakeholders.
9. A.I.-Powered Project Management Tools: Platforms like Asana and Notion A.I. help optimize task allocation, collaboration, deadline management, and performance tracking.
10. THE FUTURE: A.I. Agents for Workflow Automation: A.I. can act like a personal assistant to automate manual processes, handle routine updates, progress tracking, and even stakeholder feedback collection, eliminating unnecessary bottlenecks. Program your own workflows in Lindy, Crew, Make, Zapier, n8n or Relevance

Steps to Leverage A.I. in Your Projects

Before you begin replacing your entire project workflows with A.I., remember the most important thing about A.I:

A.I. is only a set of tools. It does not actually understand what it reads, and is still prone to make mistakes, called hallucinations. 

You still need to use your critical thinking and review what it produces, but you can use it to save significant time each day and week. 

So do not become lazy and just blindly trust the output it gives you. Use it as a tool to produce your best work, faster and easier.

I’ve worked with organizations to successfully integrate A.I.-driven tools by:

• Training Teams on A.I.-Driven Decision Making: Helping teams embrace A.I.-supported insights ensures faster, smarter project execution.
• Understanding what A.I. can and cannot do: By understanding where A.I. shines, and where it has significant risks, team members can identify the best ways to use A.I. in their context
• Adopting Real-Time Collaboration Platforms: Cloud-based A.I. tools facilitate seamless team coordination, even in remote settings.
• Eliminating Redundant Workflows: A.I.-driven automation removes unnecessary steps, streamlining project execution.

For all of these use cases, I can help your team learn about and adopt A.I. in their own management workflows. Get in touch and let’s talk about what you want to achieve.

The Key to Delivering Successful Projects

Over the past few sections, I’ve shared insights into why projects fail, common pitfalls to avoid, and how A.I. and modern methodologies can turn things around. These aren’t just theories. I’ve seen these patterns play out on some of the largest projects for major clients, and I know what works and what doesn’t.

Lessons from Successful Project Implementations

Based on my experience helping companies set up, plan, and run both transformational projects and challenging innovation ambitions, here is how I recommend any organization start their next project:

• Assess Your A.I. Maturity: Investigate your company’s current A.I. capabilities. Understand what tools and capabilities you already have, how they can be leveraged, and how different teams are currently using A.I. This baseline assessment is crucial for identifying opportunities to integrate A.I.-driven decision making and automation.
• Define Scope and Success with Precision: Get agreement on project scope from the outset and determine how success will be measured. Establish SMART goals and key performance indicators (KPIs) to ensure everyone is aligned. This clarity prevents ambiguity and keeps the team focused on delivering meaningful outcomes.
• Maintain a Clear Project Portfolio: Have a well‑defined portfolio that shows how each initiative aligns with overall business strategy. When projects are interconnected, it becomes easier to manage dependencies and ensure that each effort contributes to long‑term objectives.
• Set Up a Robust Stakeholder Communication Strategy: Develop a clear communication plan that outlines how information will be shared among teams and stakeholders. Regular check‑ins, transparent reporting, and proactive feedback loops are vital to keeping projects on track.
• Choose the Right Delivery Method: Embrace various approaches by selecting the method that best fits the project’s nature—whether it’s a traditional waterfall model, agile, hybrid, innovation‑focused, or the L.I.V.E. (Lean Innovation, Validation & Execution) method.
• Leverage A.I. and Modern Tools: As discussed earlier, A.I. tools are transforming project management. Use them for predictive analytics, automate administrative tasks, draft documentation, and adopt A.I.-powered project management platforms. These technologies streamline workflows and empower teams to focus on strategic decisions.
• Adopt a Holistic Approach: Successful projects result from integrating lessons learned across planning, communication, risk management, and the appropriate mix of project delivery methods. A comprehensive approach creates an environment where projects can thrive.

How You Can Apply These Lessons

The experience I bring from managing large‑scale projects has shown that success isn’t about a one‑size‑fits‑all model. It’s about understanding the unique challenges of each project and applying proven strategies to overcome them. Whether you are just starting to explore A.I., refining your project portfolio, or reassessing your project delivery methodology, each of these steps can significantly improve your outcomes.

If your organization is looking to enhance project success and overcome persistent challenges, let’s set up a time to chat.

I would be happy to share more details about these strategies and discuss how they might help your team achieve its goals.

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