In The Gentle Singularity, Sam Altman describes a world being gradually reshaped by AI. Not through one explosive moment, but through a steady, compounding evolution. A change that builds so quietly that we hardly notice it — until we look back and realise the foundations have shifted.

That same transformation is underway in project delivery.

We are not heading toward a new era. We are already in it.

AI agents are being embedded in assurance processes. Language models are supporting scheduling and risk analytics. Data pipelines are beginning to replace manual reporting. Project teams are augmenting their workflows with tools that didn’t exist 18 months ago.

But here’s the challenge: the pace of technological change is now far outstripping the pace of professional and organisational adaptation.

The window is closing

The recent UK Government paper, Data Analytics and AI in Government Project Delivery, was initiated in June 2023. That’s just two years ago yet it feels like a different era.

In those two years, we’ve seen a leap in capability. AI tools have moved from lab experiments to operational pilots. Models are more powerful. Interfaces are more accessible. Solutions that once took months to configure can now be built in days.

The intent behind the paper is clear. It rightly highlights the potential for AI and data analytics to transform project delivery. But recognising the opportunity is only the first step. We now need to turn ambition into implementation.

This is not a criticism. Government has an essential role to play as a catalyst. It can unlock access to data, create safe spaces for innovation, and build shared infrastructure that others can build upon.

But it cannot do it alone. Imagine what we can do if we collaborate at scale. 

Top-down will never be fast enough

We cannot rely solely on central strategies or policy-led initiatives. The scale of change we are facing demands a more open, distributed and community-driven approach.

If we are to close the gap between what is possible and what is practiced, we need to focus on enablement. That includes:

• IT environments that are flexible, secure and experimentation-friendly
• Access to data that is structured, discoverable and shareable
• People with the right skills in AI, data and delivery
• Space to test and learn, without fear of failure
• Mechanisms to share learning across teams and organisations

No single entity can do this alone. The complexity is too great. The pace is too fast. The resources are too scattered.

A new model for change: community-led delivery transformation

We need to stop duplicating effort and start collaborating at scale.

That’s the model being pioneered through the Project Data Analytics (PDA) Coalition. It’s not theoretical. It’s practical and focused. Organisations including Rolls-Royce, EDF, United Utilities, Ministry of Defence, Thales, Environment Agency, Hitachi Rail, Projecting Success and many others are already working together. Not just in principle, but in real-world projects.

We are building product groups to reimagine entire functional disciplines. Running hackathons. Sharing insights. Developing open-source solutions. Connecting apprenticeships to drive coherent product pipelines. And learning together as a profession.

It’s not about cutting-edge for the sake of it. It’s about improving outcomes... more confidence in delivery, earlier warning signals, smarter assurance, better return on investment.

Why it matters now

If we continue to approach transformation as a series of isolated pilots, we will fall further behind. Other industries are already accelerating. The tools are here. The ideas are proven. The barrier is not technology. It is coordination.

We need government to enable. Industry to engage. Academia to contribute. And delivery teams to take action.

A call to action

Let’s not allow the gentle singularity to pass us by. Let’s shape it together.

We need:

• Procurement principles that favour those who work collegiately, for mutual benefit
• Funding models that reward experimentation and scale-up
• Shared learning environments that move beyond organisational boundaries
• Communities that pool their efforts rather than reinvent the wheel
• Stronger alignment between skills development, delivery capability and technology deployment

The singularity in project delivery is already underway. The only question is whether we will shape the change or be shaped by it.

Now is the moment to act. Let’s move with purpose. Together.

Microsoft Copilot’s New Capabilities: Breakthrough or Hype for Project Delivery?

Microsoft is set to roll out the next wave of Copilot updates in mid to late May, and the expectations are sky-high:

• Reasoning models
• Persistent memory
• Specialist agents
• A redesigned user experience

But will these upgrades transform the way we manage projects—or are we facing another round of enterprise AI overpromising and underdelivering?

For those just starting with Project Data Analytics (PDA), this could be the breakthrough moment. But for others, entrenched in complex delivery environments, there are tough questions to answer.

What’s Coming — and Why It Matters

Reasoning Models: From Output to Outcomes

We’re not just talking about generating text anymore. These new models could enable structured, context-aware problem solving—bringing us closer to automating key elements of assurance and delivery.

I’ve tested these capabilities in ChatGPT and Gemini, and the early signs are promising.

Persistent Memory: Beyond the Shallow Chat

The days of disconnected prompts could be behind us. Persistent memory has the potential to support longer, more contextualised workflows—vital for navigating project complexity.

Notebooks for Project Intelligence

Microsoft’s move into Notebook functionality echoes Google’s NotebookLM. If done right, it could help bring together fragmented project data, turning raw information into a reasoning-ready workspace.

Analyst & Researcher Agents: Smarter Support for Messy Data

AI agents that can cut across systems to extract insight? That’s the dream. But we’ve been here before. Can they really deliver value across siloed data, or will organisational boundaries kill the potential?

But Let’s Talk About the Risks

Data Silos and Politics: Will these agents survive cross-functional barriers?

Model Context Limitations: Will reasoning models actually perform in real-world delivery scenarios—or collapse under complexity?

Security Constraints vs. Memory Use: Persistent memory sounds powerful, but how secure is it in reality?

Readiness of Organisations: Let’s be honest—can most teams even operationalise this without solid data governance?

Where I Stand Now

Personally, I still use both platforms:

• OpenAI for flexibility, custom GPTs, and experimental speed.
• Microsoft Copilot for secure integration and governance.

Yes, Microsoft is behind on raw functionality. But the audit capabilities and enterprise-grade oversight matter in high-stakes delivery environments.

One major blocker for me has been Copilot’s small context windows. If Microsoft has genuinely fixed this, it could be a game changer.

There’s a trade-off here:

• Speed and flexibility, or
• Security and integration

Strategic, Not Experimental

Copilot is no longer just a side experiment. It’s shaping up to be a strategic AI capability for project delivery, combining orchestrated agents, structured memory, and integrated tools.

But we need to stay grounded.

This won’t work for everyone—not without strong data foundations and change-ready teams.

What About You?

Are you still experimenting with Copilot and generative AI, or have you already gone all in?
What’s working—and where are you hitting walls?

👉 Follow us on LinkedIn to share your thoughts, keep up with the latest insights, and join the conversation on shaping the future of project delivery with data and AI.

Data analytics is revolutionising project governance by enabling better decision-making, real-time performance tracking, and risk mitigation. Organizations that integrate data-driven insights into their project management processes gain a competitive advantage in efficiency and success. Here’s how data analytics can enhance project governance.

1. The Role of Data in Strategic Decision-Making

Effective project governance relies on informed decision-making. By leveraging data analytics, organisations can:

• Identify trends and patterns that impact project outcomes
• Make evidence-based decisions instead of relying on intuition
• Optimize resource allocation and budget planning

By using historical and real-time data, project managers can adjust strategies dynamically, ensuring better alignment with business objectives.

2. Tracking KPIs and Performance with Real-Time Dashboards

One of the most powerful applications of data analytics in project governance is real-time dashboarding. These tools help project teams and stakeholders:

• Monitor project health with up-to-date metrics
• Track key performance indicators (KPIs) such as cost, timeline, and resource utilization
• Identify bottlenecks and inefficiencies early

With tools like Power BI, Tableau, and Google Data Studio, organizations can create interactive dashboards that enhance visibility and transparency in project governance.

3. Reducing Project Risks Through Better Insights

Risk management is a critical component of project governance, and data analytics helps mitigate uncertainties by:

• Identifying potential risks before they become critical issues
• Providing predictive analytics to forecast project challenges
• Enabling proactive decision-making to minimize disruptions

By continuously analysing data, project managers can develop contingency plans and improve resilience against unforeseen setbacks.

4. Ensuring Data-Driven Accountability

Clear accountability is key to successful project governance. Data analytics supports this by:

• Providing measurable insights into individual and team performance
• Automating progress reports for stakeholders
• Highlighting areas where additional support or resources are needed

With data-driven transparency, teams stay aligned with project goals and can make necessary adjustments in real time.

5. Investing in Data Analytics Training for Project Professionals

To fully leverage the power of data analytics in project governance, professionals need the right skills. Our Level 4 Data Analyst course equips project managers and teams with:

• Advanced data visualisation techniques
• Predictive analytics and forecasting methods
• Hands-on experience with industry-leading tools

Enhance your project governance with data analytics. Learn more about our Level 4 Data Analyst course here:

---

Final Thoughts

Integrating data analytics into project governance isn’t just a trend—it’s the future of efficient and successful project delivery. By using real-time dashboards, predictive analytics, and data-driven decision-making, organisations can achieve greater control, transparency, and performance in their projects. Now is the time to embrace data analytics and take your project governance to the next level.

The Praxis Framework is a free and open-source framework designed to integrate the core elements of project, programme, and portfolio management (P3M) into a single, unified approach. Unlike traditional methodologies that rely on static books or proprietary content, Praxis is a living, evolving body of knowledge, freely available to all practitioners.

Praxis combines a body of knowledge, methodology, competency framework, and capability maturity model into a single structure, ensuring that organisations can develop their P3M capability in a coordinated way. The framework is continuously updated based on industry feedback, making it adaptable to real-world challenges and ensuring that best practices evolve over time. By removing barriers to access and adoption, Praxis democratises project management knowledge, making it accessible to organisations of all sizes and maturity levels.

Projecting Success is working with Praxis to extend its impact beyond traditional project management guidance. By integrating data schemas, standards, and reusable solutions, we can help organisations structure and manage their project data more effectively. This evolution will support the creation of a more data-driven, knowledge-sharing ecosystem where best practices are codified, analytics are standardised, and automation is embedded into project delivery.

Looking ahead, we will continue ingesting more books from renowned authors, further enriching Marvin, our AI-powered community agent, with deep expertise in project delivery. Additionally, we are collaborating with Praxis to develop an open-source solution centre. A space where members of the community and the Project Data Academy can contribute and share solutions. This initiative will enable organisations to access and build upon proven methodologies, accelerating innovation and improving project outcomes at scale. We’ll also be linking in Project Brain, an ontology based approach to transforming and reimagining project delivery.

All hugely exciting. By the community, for the community.

I had the pleasure of delivering a talk to the Association for Project Management NW network on 13 November on Next Gen Project Delivery - Disrupting the Status Quo. The opportunities presented by advanced data analytics and AI are vast and truly transformative. The project delivery profession will look very different very soon. The pace of this change is no longer constrained by the tools and technology, there is an abundance of choice. It is constrained only by organisational ambition, vision, people with the skills to make it happen and sheer determination.

I was delighted to share the stage with the brilliant Kathryn Jones from United Utilities who shared the vision for the Project Data Analytics Coalition. Working together and pooling resources to really move the dial on project delivery through the power of advanced data analytics and AI.  

Free - The ChPP wizard to help people to prepare for the APM’s ChPP application process. Levelling the playing field for those who may not have access to the sort of coaching available within larger organisations. I haven’t updated it in a while so please let me know where you’d like it improved. 

Free - My book on Next Gen PMOs, moving PMOs towards being the Centre for insights.

Free- My book on Next Gen Risk Management, removing the mundane elements of risk management and moving towards variance analytics

Free (subject to government rules) – Training on how to supercharge your career and unlock the opportunities in Next Gen project delivery.

Discounted – A senior leaders course that I deliver with Andy Murray, Exec Director of Major Projects Association and lead author of PRINCE 2, on the opportunities that advanced data analytics and AI present for project delivery

Free – Follow us on LinkedIn and my personal account

Free – Our YouTube Channel with hack pitch videos and educational webinars.

Free – PDA Task Force website

Martin Paver is CEO and Founder of Projecting Success. He is an internationally recognised expert and thought leader in advanced project data analytics. He secured a DataIQ award for most influential people in data for 2 consecutive years. He founded the Projects Data and Analytics Community with >10000 members with the objective of cross fertilising skills in P3M and data science, transforming project delivery. He was instrumental in founding the Project Data Analytics Task Force in 2020 and the Coalition in 2024. He has written books on Next Gen Risk Management, Next Gen PMO and a soon to be published book on Next Gen Assurance. He is co-author of the PRINCE2 v7 AI Practice Guide. He is also a highly experienced and qualified professional Portfolio, Programme, Project Director/Chartered Project Professional, Strategist and change management consultant with >30 years’ experience across government and the private sector within senior strategic and transformational leadership positions. He is challenging the status quo, driving advanced data analytics to reshape an entire profession.  Plus, he loves it!

One-Day Course 9am-5pm: Tailored for Senior Leaders

Next Dates:

26 February, 2025

21 May, 2025

1 October, 2025

Location: Victory Services Club, London

Cost: The course costs £595 per delegate

There is a further 25% discount if you have a delegate on our PDA apprenticeship.

Food and Refreshments: Provided Throughout the Day
Taught by Industry Experts: Gain Insights from Leading Professionals

A one-day no-waffle tour of the world of AI and data analytics, to demystify what it is, how it can be applied to projects to transform project performance, and the critical actions senior leaders must take to make it happen.

Who this course is for

“In any given moment, we have two options: to step forward into growth or step back into safety” Abraham Maslow

Senior leaders have been telling us they are swamped with offers of help to improve their project performance through using the new oil of business (data and AI). They also tell us they find the jargon baffling and that it is different to the language of projects, it can sound far too like science fiction, the solutions being pitched may involve buying expensive licenses for yet another project management tool or add-on, there is a worry that it requires investment in supercomputers or there is rightly a cautiousness regarding data and cyber security. Senior leaders tell us they fear missing out, of not taking the opportunity to use the power of AI and data analytics to tackle the stubborn issues affecting project performance. But as luring as OpenAI’s ChatGPT and other Generative AI platforms are, they are equally concerned about reputational risk or unintended consequences of using it. It’s right dilemma.

If that sounds like you, then this is the course that will help you cut through the hype to gain clarity about what’s possible right now, what will very soon be upon us and what you can do. This course is specifically designed for senior leaders with the following remits:

· Head of PMO or Head of Centre of Excellence

· Head of projects or Head of Profession

· Head of associated functions, e.g. assurance, risk

· Portfolio director/manager or project/programme director of a major project

Join like-minded people who have the same curiosity and desire to be at the forefront of transforming the profession.

What you will learn

You will learn how to tackle some of the following key challenges:

· How to differentiate the hype from reality. What is project data analytics and AI, with examples?

· What does this mean for the project delivery function and future roles? How should teams prepare?

· What is the state of the market?

· Who owns the data, rights of access, how do we contract for it?

· How do we drive up data quality? How can we trust it?

· How do we get the massive data sets that we need to train the AI models? Can we do it with just our own data or do we need to pool data and if so, how?

· How do we develop a strategy, roadmap and implementation plan? How do we take people with us when it could mean a fundamental change to their job or role?

What you will get

The course is limited to 20 attendees as it is delivered in a participative hands-on workshop format. You will be provided with a workbook that will help with your sense-making of the possibilities of AI and Data Analytics, the readiness of your organisations to use it and the steps you can take to fully exploit it. You will leave with an outline action plan specifically for your context, whether that is applying it to your project, to your function or your enterprise as a whole.

Agenda

The one-day course starts at 09:00 and finishes at 16:45 followed by networking.

· Module 1 – Objectives and introductions

· Module 2 - Demystifying AI and data analytics

· Module 3 - Opportunities to transform project delivery

· Module 4 - It’s all about the data

· Module 5 – What about tools/systems

· Module 6 – AI and data literacy/skills

· Module 7 – Making it reality

· Module 8 – Summary & Networking

Hosts

The Senior Leaders AI Playbook course is hosted by Andy Murray, the executive director of the Major Projects Association and renowned author of the IPA’s project routemap, P3M3 maturity model and PRINCE2 (among others). Andy is joined by co-host Martin Paver, CEO of Projecting Success, the founder of the Project Data Analytics Task Force and often cited as one of the leading figures in driving the adoption of AI and data analytics by the project profession.

Each course includes a different guest expert speaker who will showcase an example of AI/Data Analytics being applied and their experience with it.

As the course is delivered in ‘workshop’ style we encourage all participants to contribute - you will connect others on a similar journey and have the opportunity to continue to work collegiately to exploit AI and data analytics. We can go faster together than alone.

Join us!

In the world of project management, risk management is a critical function that often involves time-consuming and repetitive tasks. However, with the advent of advanced technologies like machine learning and automated workflows, it's possible to streamline these processes, allowing project managers to focus on more strategic activities. Below are several ways to automate the mundane aspects of project risk management, transforming it from a tedious chore into a more efficient and insightful practice.

1. Suggesting Risks Using Risk Libraries and Machine Learning

 One of the first steps in risk management is identifying potential risks. Traditionally, this has relied on the experience and intuition of project managers and their teams. However, by integrating risk libraries—repositories of common risks associated with similar projects—into your project management software, you can automate the suggestion of potential risks. Machine learning models can take this a step further by analysing past projects and suggesting potential sources of variance that may not be immediately obvious. These models can scan through historical data, identifying patterns and anomalies that could indicate emerging risks, providing a proactive approach to risk management.

2. Automating Risk Updates with Workflows

Another mundane but essential task in risk management is ensuring that risks and their associated management actions are regularly updated. This often involves chasing down team members for updates, a task that can be automated using workflows. Automated workflows can be set up to send reminders and requests for updates at specified intervals, ensuring that risk information remains current without the need for constant human intervention. This not only saves time but also improves the accuracy and timeliness of the risk data.

3. Developing Automated Dashboards on Risk Status

Keeping stakeholders informed about the status of project risks is crucial, but manually compiling and updating reports can be laborious. By automating the development of dashboards, you can provide real-time insights into the status of risks, including which risks are most pressing, what mitigation actions are being taken, and where the project stands in relation to its risk tolerance thresholds. These dashboards can be tailored to different audiences, ensuring that everyone from the project team to executive stakeholders has the information they need, when they need it.

4. Analysing Who is Effective at Risk Management

One of the more advanced applications of automation in risk management is using data analytics to assess who within the team is particularly effective at managing risks. By analysing historical data, you can identify patterns that show which individuals or teams are most successful at identifying risks early and mitigating them effectively. This can inform future team compositions, training needs, and even recognition programmes, helping to build a culture of proactive risk management.

5. Monitoring Engagement and Effectiveness in Risk Processes

Finally, automating the analysis of engagement in the risk management process can provide valuable insights into how effectively your team is managing risks. Dashboards can be developed to track who is engaging with the risk management process, how often updates are made, and the outcomes of those updates. This data can be used to refine processes, identify areas where additional support or training may be needed, and ensure that the risk management process is as effective as

Pushing the Boundaries of Automation in Risk Management

These examples represent just the basics of what can be achieved by automating parts of the risk management process. As technology continues to advance, the potential for further automation—and the insights it can provide—only grows. Imagine a system that not only suggests risks but also predicts them with high accuracy, develops mitigation strategies, and automatically adjusts project plans to account for emerging risks in real-time. The future of risk management is one where data-driven insights take centre stage, enabling more informed, proactive decision-making and ultimately leading to more successful project outcomes.

By embracing these automated solutions, organisations can significantly refine their current approaches to risk management, making the process not only more efficient but also more effective. The future of risk management lies in leveraging technology to handle the mundane, allowing human expertise to focus on strategic decision-making and innovation.

In today’s dynamic business environment, effective risk management is crucial. Leveraging advanced data analytics and AI can significantly improve your ability to identify, assess, and mitigate risks. Here are five practical tips to elevate your risk management strategy:

1. Automate Workflows for Risk Status and Management Actions

Implement AI-driven workflows to streamline the updating of risk statuses and management actions. This automated process ensures that risk information is current and that management actions are tracked and adjusted as needed. Furthermore, use these workflows to create dynamic dashboards that not only provide a real-time view of risks but also highlight who within your organisation is proactively engaging with these risks. This visibility encourages accountability and ensures that risks are being actively managed by the right people.

2. Leverage Libraries of Common Risks with AI Assistance

Establish and maintain libraries of known or commonly occurring risks, drawing on historical data, industry benchmarks, and relevant reports like NAO (National Audit Office) findings. Enhance these libraries by creating a Large Language Model (LLM) to assist users in identifying potential areas of risk. This model can prompt risk managers on what to consider, offering tailored suggestions based on the specific context of their projects or operations. Where possible, train the LLM on lessons learned reports, NAO reports, or utilise a community-driven model like Marvin. This AI-powered guidance ensures a more comprehensive risk identification process.

3. Maintain a Repository of Mitigation Methods

Develop a repository of effective mitigation strategies tailored to specific risks. By cataloguing these methods, your organisation can quickly deploy proven responses to new threats. Advanced analytics can be used to continuously evaluate the effectiveness of these mitigation strategies, allowing for refinement and adaptation over time. This repository becomes a critical tool in ensuring that risk responses are both timely and effective, reducing the likelihood of repeat issues.

4. Utilise Predictive Analytics for Risk Forecasting

Leverage predictive analytics to anticipate potential risks before they occur. By analysing historical data, predictive models can identify trends and forecast threats, providing valuable foresight. This allows your organisation to take pre-emptive actions, reducing the likelihood and impact of unexpected disruptions. Predictive analytics ensures that your risk management is forward-looking, rather than just reactive.

5. Implement Real-Time Monitoring

Adopt AI-powered tools for real-time risk monitoring across all operations. These tools can provide instant alerts to emerging risks, allowing for immediate responses. Additionally, real-time monitoring should extend to tracking regulatory changes, cybersecurity threats, and operational anomalies. By staying informed in real time, your organisation can mitigate risks before they escalate, ensuring continuous protection against potential threats.

Conclusion

Integrating advanced data analytics and AI into your risk management processes can dramatically improve your organisation's ability to identify, assess, and mitigate risks. By automating workflows, leveraging AI-driven libraries and models, and utilising predictive and real-time monitoring, you can ensure that risks are managed proactively and effectively. Embrace these technologies to safeguard your organisation against the complex and evolving risks of today’s business landscape.

In today's rapidly evolving job market, staying ahead of the curve means continuously enhancing your skills and knowledge. For those looking to specialise in advanced fields like artificial intelligence (AI), a Level 7 Apprenticeship offers a unique and valuable pathway. But what exactly is a Level 7 Apprenticeship, and why should you consider enrolling in one? Let’s dive into the details. 

Understanding the Level 7 Apprenticeship 

A Level 7 Apprenticeship is a prestigious program equivalent to studying a master’s degree. It is designed to provide a high level of training and education in a specific field, combining practical work experience with academic learning. This type of apprenticeship is particularly beneficial for those who want to gain in-depth knowledge and advanced skills without the traditional full-time study route. 

The benefits of a level 7 apprenticeship

• Real-World Experience: One of the main advantages of a Level 7 Apprenticeship is the opportunity to gain hands-on experience in your chosen field. Unlike traditional education paths that focus heavily on theoretical knowledge, apprenticeships allow you to apply what you learn directly to real-world situations. 

• Earn While You Learn: Financial constraints are a significant barrier for many when considering further education. Level 7 Apprenticeships address this by allowing you to earn a salary while you study, making it a financially viable option. 

• Career Advancement: Completing a Level 7 Apprenticeship can significantly boost your career prospects. The combination of advanced skills, practical experience, and the academic rigor of a master's degree makes you a highly attractive candidate to potential employers. 

• Industry-Relevant Skills: Apprenticeships are designed in collaboration with industry leaders, ensuring that the skills you acquire are directly relevant to the current job market. This alignment with industry needs enhances your employability and readiness to tackle job-specific challenges. 

• Networking Opportunities: Being part of a Level 7 Apprenticeship program connects you with professionals and peers in your field. These networking opportunities can open doors to new career possibilities and collaborations. 

 

comparing to traditional education paths

While traditional master’s degree programs offer valuable academic knowledge, they often lack the practical experience component that is crucial in many industries. Level 7 Apprenticeships bridge this gap by integrating academic learning with on-the-job training. This holistic approach ensures that you not only understand the theory but also know how to apply it effectively. 

why you should consider a level 7 apprenticeship

Artificial intelligence is one of the most dynamic and impactful fields today. A Level 7 AI Specialist Apprenticeship equips you with the advanced skills needed to excel in this cutting-edge area. You’ll learn from industry experts, work on real AI projects, and gain insights that can propel your career to new heights. 

By enrolling in this apprenticeship, you’re not just learning AI; you’re preparing to shape the future of technology. The practical experience, combined with the depth of knowledge provided, ensures that you are ready to meet the challenges and opportunities in the AI landscape. 

get involved

If you’re looking to advance your career and specialise in AI, consider joining our Level 7 AI Specialist Apprenticeship program. It’s a unique opportunity to earn while you learn, gain valuable industry experience, and achieve a qualification equivalent to a master’s degree. 

Stay tuned for more information, and don't miss the chance to be part of this transformative journey. For those interested, please reach out, and let’s discuss how this program can help you achieve your career goals. 

Explore the future of Risk Management with our new book. Read how to mitigate the risk of your role being reimagined. Download our free E-Book today and let's transform the future of project delivery together.