June 28–30, 2026 | Las Vegas, Nevada
PMA Consultants will be attending the 2026 AACE International Conference & Expo. This premier global event brings together professionals in cost engineering, project controls, and risk management to share knowledge, best practices, and innovative solutions across the industry.
As part of the technical program, PMA is pleased to contribute to this year’s conference with a presentation by Francisco Cruz and paper by Saumil Maniar. We look forward to connecting with industry peers at AACE 2026!
RISK-4912 – Confidence: What Stakeholders Want and What We Need from QRA
Presenters: Francisco Cruz, PE, CCP and John K. Hollmann, CCP, CEP, DRMP, FAACE Hon. Life
Abstract
The primary outputs of project quantitative risk analysis (QRA) are probabilistic distributions of cost and schedule duration. These outputs are most commonly used to inform investment decision-making and to support project cost risk funding, budgeting, and schedule baseline decisions. Because many stakeholders are not statistical experts, these results are often simplified into measures of accuracy and ranges to effectively communicate the underlying risk narrative.
While AACE recommended practices address concepts such as confidence levels, accuracy ranges, and QRA methodologies, they do not fully address how to align these statistical outputs with stakeholder risk tolerance or organizational policy. This presentation explores the factors that influence organizational risk appetite and provides guidance on translating those preferences into practical decision-making criteria.
OWN‐4921 – Lessons Learned from a Large Scale PMIS Implementation Across a 4,000-User Organization
Presenters: Francisco Cruz, PE CCP, Ernesto Toxqui, EVP, and Daniel Posada
Abstract
Project management information systems (PMIS) have emerged as critical enablers of efficiency and transparency in large organizations, yet their successful deployment remains fraught with technical, contractual, and organizational challenges. This paper draws on a comprehensive scope of work and examines the end-to-end implementation of a PMIS across an enterprise environment that encompasses over 4,000 concurrent licenses. The presented case study underscores the importance of rigorous gap analysis, clearly defined implementation requirements, and robust contracting frameworks. Early identification of process-to-software misalignments, addressed through iterative validation workshops, proved essential for curbing inadequate training alignment, communication gaps, poor data mapping, and adoption issues.
Launched under a hybrid waterfall-and-agile delivery model, the rollout encountered significant delays stemming from protracted contract negotiations, ambiguous service-level agreements, and vendor-management complexities. A multi-stage testing regimen that includes unit, system-integration, and mock-migration trials, uncovered critical defects in data-migration planning and application programming interface integrations prior to go-live. Performance benchmarking further highlighted the necessity of proactive scalability planning and close vendor oversight to address change‐fatigue.
A series of lessons learned is presented: (1) embedding continuous gap assessment into governance structures to maintain alignment; (2) structuring contracts around milestone-based deliverables with explicit performance metrics; (3) investing in change-management and “train-the-trainer” programs to accelerate user adoption; and (4) leveraging iterative feedback loops to refine processes and accelerate value realization. These insights will offer a blueprint for practitioners embarking on large-scale PMIS implementations within complex organizational settings.
EST-4993 – AI-Aware Influences on Cost Estimate Maturity
Authors: Vishwambhara Kumbharathi, CEP PSP; Saumil H. Maniar, PE
Abstract
AACE International’s Cost Estimate Classification System aligns estimate purpose with project maturity to support stage‐gate decisions. Estimates traditionally progress from Class 5 to Class 1 as scope definition, engineering detail, and cost data increase, with accuracy refined through risk analysis and documented in the basis of estimate. The growing use of artificial intelligence is transforming this process. Applications such as automated quantity extraction, machine learning–based parametric modeling, and real‐time probabilistic analysis can accelerate estimate maturity and provide continuous confidence updates. However, they also introduce challenges related to data integrity, model reliability, and transparency that must be addressed to maintain credibility. This paper examines how AI may influence the classification of estimates and decision‐making practices. It proposes an “AI‐aware” framework with three dimensions: information fitness index (IFI), model risk grade (MRG), and automation level (AL) to integrate AI while preserving governance, accountability, and decision confidence.