Inherently, megaprojects fumble on budgets due to unclear organisational design requirements, and too ambitious a vision in the early stages of the project lifecycle can invalidate contingency assumptions which are often traditionally based on the 10% thumb rule. My paper will present proven methods on quantifying and tracking contingency calculations based on holistic quantitative analysis of risks, cost, and schedule.

This session explores how PMOs can evolve from oversight functions into enablers of delivery excellence. By embedding trust, transparency, and digital tools, PMOs can accelerate timelines, align stakeholders, and turn strategy into results. Practical insights and proven practices will be shared to help leaders transform governance into a driver of impact and value.

In every major capital project, time is the invisible architecture that determines success or failure. It defines not only cost and productivity but safety, quality, and stakeholder’s trust. Yet, despite decades of evolution in governance, technology, and standards, time remains the most misunderstood and mismanaged dimension of construction delivery. Across industries, the paradox persists even the most disciplined organizations continue to experience delays, disputes, and cost overruns. The cause is rarely the absence of governance but the absence of visibility. When the schedule is treated as a compliance document rather than a living control instrument, risk grows in silence until it becomes irreversible. The transition from traditional scheduling control to Digital Schedule Intelligence (DSI) marks a fundamental shift. It transforms the schedule from a static P6 file into a predictive system a shared language of accountability across disciplines. This evolution is not merely technical, it represents a cultural leap toward collective awareness, data integrity, and proactive risk management.

Most construction projects are let on fixed price contracts. At the outset, the scope is defined, the price is agreed, and the contract is intended to set clear boundaries around risk. In reality, anyone involved in project delivery knows that risk does not stay still. This presentation looks at how and why risk changes during delivery, how it is often absorbed without being recognised, and what can be done to manage it more effectively in real time. The session will cover: • How fixed price contracts were originally intended to work Changes were identified early, addressed through the contract, and assessed during delivery rather than reconstructed later. Example: Additional dewatering arising from unforeseen conditions was instructed and its time and cost impact assessed while the works were ongoing. • Why modern projects experience more moving risk Ongoing design development, tighter programmes, complex interfaces, and site constraints mean risk continues to change during delivery. Example: Foundation or temporary works details evolve after construction starts, requiring changes to sequencing and access. • What typically happens on site when things change Site teams make practical decisions to maintain progress, often outside the contract. Example: Dewatering is extended or temporary access added to keep follow on trades working. • How risk is quietly absorbed Minor decisions accumulate into unpriced work and productivity loss. Example: Labour and plant remain on site longer, work fronts fragment, and temporary measures remain in place without instruction. • Why problems often surface too late Commercial impacts appear in reports long after the decisions that caused them. Example: Preliminaries or plant costs overrun months after the original site changes were made. • What this means when projects move into claims or disputes Informal decisions are later tested with hindsight and limited records. Example: Entitlement for extended duration or disruption is challenged due to lack of contemporaneous notices or instructions. • What better commercial control looks like in practice Early recognition of risk, simple records, and timely use of the contract during delivery. Example: Notifying extended dewatering early and addressing its time and cost impact while the work is live. The presentation is not about turning every issue into a claim. It is about understanding when everyday site decisions have commercial consequences, and how managing those consequences as they arise can protect entitlement and reduce disputes later. The key message is straightforward: fixed price contracts only provide certainty if changing risk is actively managed. If it is not, risk will be absorbed by default, and the commercial outcome of the project will be decided long before anyone realises it.

While Artificial Intelligence (AI) offers transformative potential for contract and claims management, a critical challenge often overlooked is the prevailing "bad practice" of adopting AI tools without the foundational understanding of the science behind Contract, Claims, and Commercial management. Complex sectors like construction and oil & gas require a more fine-tuned approach to utilizing AI beyond just data processing. As we proceed further in the age of information, using traditional manual approaches are becoming obsolete and engineers must now face the challenges of leveraging AI ethically, responsibly, and without completely eliminating the human factor. This presentation will argue that true AI efficacy in contracts management hinges on first mastering the underlying commercial and contractual science. Firstly, examples will be given on certain situations wherein overly relying on AI may lead to undesirable results or even incorrect results, thereby cementing the fact that the human factor is always required at every step as AI is prone to hallucination. We will then delve into the practical applications of AI, exploring how capabilities like Natural Language Processing (NLP) and machine learning can revolutionize contract review, intelligent clause extraction, automated compliance monitoring, sophisticated risk identification, and data-driven claims analysis. Crucially, this session will provide actionable insights on "how to deal with" the responsible integration of AI. We will discuss critical considerations such as ensuring robust data quality, navigating integration complexities with existing systems, fostering effective human-AI collaboration, and addressing the change management hurdles essential for successful adoption. Attendees will gain a comprehensive understanding of how to ethically and responsibly integrate AI in a practical setting. By prioritizing the foundational knowledge of contracts management, understanding the limits of AI, and exploring the wide range of AI applications, project controls professionals will be better geared in leveraging AI for driving project success. 

In today’s complex construction environment, project controls and contract management must operate as integrated functions rather than independent disciplines. While planners and project controllers focus on scheduling, progress measurement, and performance forecasting, contract managers handle entitlements, risks, notifications, and claims. When delays and disruptions occur, the ability to connect both perspectives becomes critical to establishing factual, defensible positions. This session explores how Forensic Delay Analysis (FDA) acts as the essential link between time-impact assessment and contractual entitlement. Drawing on real-world case examples from major construction and infrastructure projects in the Middle East, the presentation demonstrates how analytical tools—such as Windows Analysis and But-For Analysis—can be translated into clear cause-and-effect narratives that directly support contractual claims and dispute resolution processes. The session focuses on three key methodological dimensions: Cause and Effect Analysis Demonstrating how to establish a traceable link between site events and their impact on the critical path. Emphasis is placed on the importance of contemporaneous records, logic review, and schedule integrity to produce reliable time-impact evidence. Entitlement Under Contracts Showing how delay analysis is converted into contractual arguments under frameworks such as FIDIC. The discussion includes practical treatment of concurrency, variations, late approvals, and risk allocation—highlighting where technical evidence aligns with contractual rights and where gaps commonly appear. Practical Lessons Learned Reviewing real cases to illustrate why certain delay claims succeed while others fail, with insights into weak substantiation, missing notice requirements, poor schedule quality, or unclear narrative structure. The objective is to offer clear, actionable guidance that practitioners can apply immediately. Special attention is given to the persistent challenge of concurrency, one of the most debated areas in delay claims. The session clarifies methodologies for identifying overlapping delays, assessing entitlement, and presenting conclusions in a balanced, defensible manner. The presentation also provides a forward-looking view on how digital record keeping systems and emerging project-control tools can support more accurate forensic analysis. While the core principles remain unchanged, technology is increasingly enabling better data quality, earlier detection of delay drivers, and stronger evidentiary support. By the end of the session, participants will gain: A clear methodological framework for performing and presenting forensic delay analysis A practical understanding of how time analysis aligns with contractual entitlement Tools and strategies for preventing weak claims and strengthening defensible ones Lessons from real dispute scenarios that practitioners can directly apply in their ongoing projects This session is designed for planners, project controllers, contract managers, claims professionals, and anyone seeking to strengthen the connection between project data, delay methodology, and contractual rights.

As Saudi Arabia advances its Vision 2030 transformation, the giga projects driving national progress—such as Qiddiya, NEOM, and Diriyah—are redefining the standards of project delivery, governance, and performance. In such high-stakes environments, the traditional concept of a risk register—static, descriptive, and compliance-driven—can no longer keep pace with the dynamic realities of complex program execution. The future belongs to risk intelligence: an ecosystem that integrates data, technology, and decision-making into one connected framework. This presentation introduces a data-driven risk implementation model that has been developed and applied within the Qiddiya Investment Company Program, designed to transform how project risk information is captured, analyzed, and communicated across the enterprise. Rather than focusing on risk logs and periodic reporting, the model enables continuous insight through real-time analytics, predictive indicators, and integrated project controls. The session will outline the practical journey from concept to implementation, including: Defining the architecture of a Risk Intelligence Ecosystem that connects risk data with cost, schedule, and performance systems. Designing data models and taxonomies that ensure consistency across client, PMC, and contractor risk registers. Developing automated dashboards to visualize emerging risks and their quantified impacts on delivery confidence. Embedding governance and escalation protocols to translate data into timely management actions. Building a risk-aware culture supported by digital literacy, ownership clarity, and value-based decision making. Drawing on implementation insights from Qiddiya, the presentation will share the key enablers and challenges encountered in digitizing risk: managing stakeholder expectations, ensuring data quality, aligning with assurance frameworks, and integrating multiple legacy systems into a unified digital platform. The discussion will also demonstrate how risk analytics—when properly embedded—strengthen predictability, accountability, and investment confidence across giga projects. Ultimately, this session aims to show that risk intelligence is not a tool—it is a mindset and an ecosystem. By merging data science, project controls, and governance disciplines, organizations can shift from reactive problem solving to proactive value protection. This evolution supports Vision 2030’s mandate for transparency, digital transformation, and world-class delivery performance. Attendees will gain a structured understanding of how to: Move from manual risk reporting to automated, insight-driven intelligence. Design data frameworks that integrate risk with program controls and assurance. Apply predictive analytics to anticipate cost and schedule impacts. Use digital risk intelligence to inform executive decisions and portfolio governance. Through this lens, the presentation reinforces that the true measure of project success under Vision 2030 will not be how risks are recorded, but how intelligently they are understood, shared, and managed to drive resilient outcomes.

In today’s complex and fast-evolving business landscape, organizations are increasingly recognizing that risk management is not merely a compliance function but a strategic enabler of sustainable performance. Building a world-class risk management framework requires a holistic approach that embeds risk thinking into every layer of the organization—from strategic planning to daily operations and decision-making. This presentation explores how to design and institutionalize a best-in-class enterprise risk management (ERM) framework aligned with ISO 31000 and COSO principles. It highlights the key building blocks—governance, structure, processes, technology, and culture—that collectively transform risk management from a reactive exercise into a proactive value-creation discipline. By integrating risk management into project controls, financial planning, performance management, and operational delivery, organizations can enhance resilience, transparency, and accountability across all business functions. Embedding risk management within day-to-day business processes ensures that potential threats and opportunities are identified early, assessed objectively, and managed through informed decision-making. This integration fosters agility, improves cross-functional collaboration, and strengthens stakeholder confidence. It also drives smarter allocation of resources, enabling leaders to balance risk and reward while achieving strategic objectives. Ultimately, a world-class risk management framework empowers organizations to anticipate disruption, make data-driven decisions, and maintain a culture of continuous improvement—transforming uncertainty into a source of competitive advantage.

AEC teams still spend hours on manual QTO, brittle model checks, and one-off scripts that don’t scale. This session shows how to turn ad-hoc tooling into a maintainable automation pipeline grounded in BIM data and modern software practices. We’ll walk through a reference stack—Revit API (.NET/C#) → cloud services/workers → SQL/object storage → web dashboards (Blazor/React) → Power BI—plus patterns for parameter governance and ISO 19650 compliance.

Background The construction industry across the Gulf Cooperation Council (GCC) region faces persistent challenges with cost overruns, schedule delays, quality issues, and resource inefficiencies. While Lean Construction principles have demonstrated significant value globally—reducing project delivery time by 10-20%, improving quality by 15-25%, and eliminating non-value-added waste—their adoption remains limited without enabling technologies. Objective This paper presents a comprehensive framework for implementing Lean Construction Management best practices through Project Management Information Systems (PMIS), specifically addressing the integration of digital-first approaches in construction portfolio and program management. The study bridges the gap between theoretical Lean principles and practical implementation in the regional context of large-scale real estate and infrastructure projects. Key Findings The research demonstrates that successful Lean Construction requires five critical enabling mechanisms: 1. Visible, governed collaborative workflows that eliminate email-driven chaos and create transparent accountability 2. Single source of truth data architecture that ensures consistency across portfolio level projects 3. Data-driven continuous improvement replacing memory-based decision making with analytics and KPI tracking 4. Intelligent resource optimization through role-based workflow intelligence and smart allocation algorithms 5. Cross-functional integration combining PMIS with FIDIC contract compliance, BIM models, and enterprise systems Results Case studies from healthcare programs, corporate office rollouts, and industrial facilities show PMIS-enabled implementations achieving: • 40-50% reduction in change order processing time • 30-35% improvement in schedule predictability • 25-30% reduction in rework and quality issues • Doubled work volume capacity per PMO team member • Portfolio-wide standardization of workflows and best practices across geographically dispersed projects Implementation Approach The paper outlines a three-phase PMIS implementation strategy: • Foundation: Workflow design and role-based security configuration • Integration: BIM model integration, CDE/EDMS alignment, and enterprise system connectivity • Optimization: KPI dashboard development, continuous improvement protocols, and ROI demonstration Conclusion Lean Construction is not merely a philosophy—it is a discipline requiring disciplined systems, visible processes, and data-driven governance. PMIS platforms, when architected with Lean principles as the foundation, serve as the essential digital backbone enabling construction organizations to achieve measurable efficiency gains, improve stakeholder accountability, and deliver superior project outcomes in competitive GCC markets. Keywords Lean Construction, Project Management Information Systems, Digital Transformation, Construction Portfolio Management, PMIS Implementation, Waste Elimination, Process Optimization, GCC Construction REFERENCES [1] Lean Construction Institute. (2024). Lean Construction principles and value stream mapping methodologies. [2] Project Management Institute. (2024). PMBOK Guide and portfolio management standards. [3] Al Samman, B. (2025). Lean Construction Management: Enabling Efficiency, Accountability, and Continuous Improvement. CMCS Publications. [4] Aguilar, J., et al. (2023). Digital transformation in construction: PMIS as the enabling backbone. International Journal of Construction Management, 23(4), 567-585. [5] Womack, J. P., & Jones, D. T. (2003). Lean Thinking: Banish Waste and Create Value in Your Corporation. Free Press. [6] FIDIC. (2017). FIDIC Conditions of Contract for Construction: Red Book. Federation Internationale des Ingenieurs-Conseils.

Most projects measure success at practical completion—yet the value curve is decided in operations. This session shows how to extend project controls into O&M using BCA (FCA) outputs as a living baseline for cost, risk, and performance. We translate condition findings (defects, CRV, residual life) into an FCI trend, a risk-weighted renewal portfolio, and time-phased cashflows. The talk outlines a light digital workflow (CMMS/BIM-lite, NDT, drone/IR inputs) and a quarterly governance cadence: inspect → quantify → prioritize → approve → deliver → verify. A short case vignette from water/desal assets demonstrates how condition → risk → scope → schedule → budget is tracked with familiar controls (P-curves, burn-down of deferred maintenance, outage windows, and SLA KPIs). Attendees leave with a 90-day starter plan and templates that owners can deploy immediately. Learning outcomes: Map BCA outputs (defects, CRV, FCI) to post-construction cost/schedule controls and risk registers. Build a 5-year renewal portfolio with P10–P90 estimates and outage planning. Set O&M KPIs (FCI trend, backlog age, risk exposure, SLA incidents) and link them to budgets. Stand up a quarterly governance cadence with auditable gates and board-ready dashboards.

AI agents and dashboards do not fail because of poor prompts or visuals; they fail because they are built on unreliable, fragmented, and misaligned data. For decades, the construction industry has relied on reports and dashboards to explain what already happened. While digital tools have improved visualization, most projects still operate in silos, with engineering, procurement, construction, and project controls each managing their own data, logic, and priorities. Progress measurement is often subjective, data is inconsistent, and decisions are made too late. As project complexity increases, especially in large programs, this reactive approach is no longer sufficient. This presentation explores the evolution from spreadsheets to dashboards to AI data agents, with a clear message: AI only works when project data is reliable, structured, and aligned across disciplines. The session addresses a common misconception: AI does not fix poor data or broken processes. Real value comes only after the fundamentals are in place. Drawing on real project delivery experience, the presentation shows how aligning Engineering, Procurement, Construction, and Project Controls, through Advanced Work Packaging (AWP), a model-based structure, and objective rules of credit, turns physical progress into consistent, machine-readable data. Built on this foundation, AI data agents can analyze schedule, cost, progress, constraints, and procurement information. Unlike static dashboards, these agents rely on trusted data to identify early risks, explain performance drivers, and recommend corrective actions aligned with the Path of Construction and rolling look-ahead planning. Rather than replacing professionals, AI data agents support better decisions, shifting project controls from reporting past results to predictive, data-driven performance intelligence.