Our Hack24 Employer Partner Event brought together thought leaders, industry experts, and forward-thinking professionals from Defence, Construction & Transport industries all with a common goal: to explore how data analytics and AI are shaping the future of project delivery. This event was more than just a discussion; it was a call to action for organisations to embrace data-driven project delivery.

Projecting Success has a bold vision: to increase project delivery success rates fivefold—from 0.5% to 2.5%—within five years. The key to this transformation? Empowering professionals with cutting-edge training, advanced data products, and a community-led approach to leveraging AI in project management. At the heart of this movement is the belief that AI-driven project delivery isn’t a luxury—it’s a necessity. From predictive analytics to real-time risk mitigation, embracing data-driven decision-making is the key to delivering projects on time and within budget.

The event outlined a four-phase journey that organisations must undertake to fully integrate Data & AI into their project delivery strategies:

Lightbulb Moment – Recognising that mature data analysis and AI are no longer optional but essential for competitive project delivery.

Education – Upskilling senior leaders to break down barriers to Data & AI adoption.

Upskilling – Training project delivery teams in data analytics and AI tools.

Implementation – Embedding Data & AI-driven decision-making into daily project operations.

A key part of this belief is us showcasing what is possible with our Hackathon challenges. As part of our we introduced Hack24’s Challenges and were lucky enough to have our challenge owners on hand to explain them:

Rolls Royce Submarines Resource Adjustment Challenge

The Problem: Project schedules often fail to reflect real-time resource availability, leading to inefficiencies.

The Solution: A tool that dynamically adjusts project activity durations, recalculates critical paths, and enhances resource allocation based on real-time data.

Thales Risk Foresight 1: Rapid Risk Recognition & Resolution

The Problem: Organisations struggle to identify emerging risks before they escalate.

The Solution: A predictive analytics system that tracks risk evolution, forecasts vulnerabilities, and provides real-time insights to enable proactive decision-making.

Thales Risk Foresight 2: AI-Powered Risk Management Chatbot

The Problem: Traditional risk registers are static and difficult to extract insights from.

The Solution: An AI chatbot integrated with Power BI that enables natural language queries on historical risk data, offering predictive insights for better risk mitigation.

The MoD/ MPA joint challenge; Red Team Agent: AI-Assisted Bid Assurance

The Problem: Poor bid quality and lengthy internal review processes.

The Solution: A Large Language Model (LLM) Red Team Agent that provides instant, AI-driven feedback on bid proposals, improving quality and reducing turnaround times.

Keynote & Industry Insights

A major highlight was the keynote by Marcus Elsom, Director of Bids & Programmes at Thales UK, who shared Thales’ journey in data-driven project delivery. He emphasised that Data & AI is not replacing project managers—it’s augmenting their capabilities.

He went on to talk about how Thales’ Predictive Edge Data Analytics Apprenticeship is creating the Next Generation of PMO and by launching the Projecting Success Level 4 Qualification in Project Data Analytics & AI to upskill its workforce in AI-driven project management. Saying that the 18-month program will integrate real-world challenges,

utilising Projecting Success’ eco system of Hackathons, Project Brain and Marvin and advanced analytics training will set a new industry benchmark for Data & AI adoption in project delivery.

What’s Next?

The journey doesn’t end here. As organisations like Rolls Royce, Thales, and EDF push the boundaries of Data Analytics & AI in project management, the ecosystem of data driven project delivery continues to grow. Whether you're a project manager, data analyst, or senior leader, now is the time to invest in data-driven transformation!

"Artificial Intelligence will have a more profound impact on Humanity than fire, electricity and the internet" Sundar Pichai, CEO Alphabet Inc. 

It feels almost like yesterday that our world became “data driven”. And no sooner have (some people) caught up with this, the world has changed again with the next BIG thing: AI.  

AI data specialists are at the forefront of this technological innovation- they're ahead of the curve and leaving most of us trailing in their wake.  

The AI data Specialist is playing a crucial role in transforming vast amounts of data into actionable insights that the rest of us can benefit from.  

With organisations across various industries recognising the value of data, the demand for AI data specialists is skyrocketing and demand is likely to outstrip supply for a long time to come.  So in this blog post I explore some of the career opportunities available for AI data specialists and what you can expect in this exciting and dynamic field. 

The Role of an AI Data Specialist 

AI data specialists are professionals who collect, manage, and analyse large datasets to support AI and machine learning (ML) initiatives. Their expertise lies in ensuring that data is accurate, clean, and ready for use in training AI models. Key responsibilities include: 

• Data Collection: Gathering relevant data from various sources. 

• Data Cleaning: Ensuring data quality by removing inaccuracies and inconsistencies. 

• Data Analysis: Analysing data to extract meaningful insights and patterns. 

• Data Management: Organising and maintaining data for easy accessibility. 

• Collaboration: Working with data scientists, engineers, and business stakeholders to understand data needs and objectives. 

Career Pathways 

AI data specialists have a variety of career pathways to choose from, each with its unique opportunities and challenges. Some of the prominent roles include: 

1. Data Analyst 

Data analysts focus on interpreting data to help organizations make informed decisions. They use statistical techniques and software to analyse datasets, identify trends, and generate reports. 

2. Data Engineer 

Data engineers design and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are robust, scalable, and efficient. 

3. Machine Learning Engineer 

Machine learning engineers build and deploy machine learning models. They work closely with data scientists to turn raw data into actionable insights through model training and optimisation. 

4. Data Scientist 

Data scientists combine their expertise in statistics, programming, and domain knowledge to extract actionable insights from data. They develop predictive models and algorithms to solve complex business problems. 

5. AI Research Scientist 

AI research scientists work on advancing the field of AI through research and development. They explore new algorithms, methodologies, and applications of AI to push the boundaries of what is possible. 

Industries Hiring AI Data Specialists 

AI data specialists are in demand across a wide range of industries. Some of the sectors actively hiring include: 

• Technology: Tech companies are at the forefront of AI innovation, constantly seeking skilled data specialists to develop cutting-edge solutions. 

• Healthcare: AI is transforming healthcare through predictive analytics, personalized medicine, and improved patient care. 

• Finance: Financial institutions leverage AI for fraud detection, risk management, and algorithmic trading. 

• Retail: Retailers use AI to optimize inventory management, enhance customer experiences, and drive sales through personalised marketing. 

• Manufacturing: AI is revolutionising manufacturing with predictive maintenance, quality control, and supply chain optimisation. 

• Automotive: The automotive industry relies on AI for autonomous driving, vehicle diagnostics, and smart manufacturing. 

Future Outlook 

The future for AI data specialists looks promising, with continued growth expected in the coming years. As AI technology advances and more organisations recognise the value of data-driven decision-making, the demand for skilled data specialists will only increase. 

Key Trends to Watch 

• Automated Machine Learning (AutoML): Tools that automate the end-to-end process of applying machine learning to real-world problems are becoming more popular, reducing the need for specialised expertise. 

• Explainable AI: There is a growing emphasis on making AI models more transparent and understandable to ensure ethical use and compliance. 

• AI in Edge Computing: The integration of AI with edge computing is enabling real-time data processing and analytics, opening new possibilities for AI applications. 

Conclusion 

A career as an AI data specialist not only future-proofs your career but offers diverse opportunities and the chance to work on exciting and innovative projects that shape the future of the world in which we live in.  

With the right skills and a passion for data, you can be at the forefront of this amazing field and make a significant impact across various industries. Whether you're just starting or looking to advance your career, the path of an AI data specialist promises growth, challenges, and the satisfaction of contributing to groundbreaking advancements in AI. 

To find out how you could advance your career in AI, contact us about our Level 7 AI Data Specialist Apprenticeship at enquiries@projectingsuccess.co.uk