Welcome to another edition of Project Flux, where we cut through the noise to bring you the AI insights that matter for project delivery.

We’ll take a closer look at:

Strategy & ROI: PwC’s finding that 56% of CEOs see zero returns on AI spending reframes AI risk as a delivery failure rather than a technology gap, underscoring that execution now matters more than investment.

Workforce & Capability: Anthropic’s prediction that up to 50% of entry-level roles may disappear within five years signals a structural shift for project teams, forcing a rethink of task design, talent pipelines, and progression.

Delivery & Evidence: Two major industry reports move AI in project delivery from experimentation to scrutiny, highlighting governance, skills, and process maturity as the real differentiators.

Platforms & Agents: Procore’s acquisition of Datagrid AI points to the rise of agentic intelligence inside construction platforms, where coordination, autonomy, and trust become central delivery challenges.

Data & Memory: ChatGPT’s extended memory capability raises new governance questions around persistence, consent, and data scope, particularly for project environments handling sensitive commercial information.

A PwC survey reveals a sobering reality: more than half of chief executives report zero return on investment from AI spending despite massive capital allocation. This isn't a technology problem—it's an execution problem. Organisations are investing heavily in AI tools without the foundational change management, skills development, or process redesign necessary for success. Project managers are uniquely positioned to change this. Read the full breakdown →

For project professionals, the 56% figure validates something you've likely observed: organisations are acquiring AI capabilities without implementing them strategically. The gap between tool acquisition and value realisation is precisely where project management disciplines create competitive advantage. Successful AI transformation requires stakeholder alignment, risk management, phased delivery frameworks, and measurable benefits realisation—all core project competencies. This is your moment to lead. Project teams that develop structured AI implementation approaches, establish clear success metrics, and manage change rigorously will deliver results whilst competitors burn cash on digital theatre. The profession must shift from responding to AI adoption to actively shaping it.

Dario Amodei, CEO of Anthropic, warned at Davos that AI could simultaneously drive 10% GDP growth and 10% unemployment—creating what he called a "dystopian" scenario. For project professionals already grappling with construction's skills shortage, this prediction demands urgent attention. The question isn't whether displacement will happen, but whether the industry will prepare for it. Read the full breakdown →

We believe Amodei's prediction is a wake-up call the industry cannot ignore. The construction sector, already facing skills shortages and graduate recruitment challenges, cannot afford complacency. However, we challenge the narrative that this is purely dystopian. For project managers, this represents an opportunity to fundamentally rethink project delivery models. If AI handles routine coordination, estimation, and scheduling, what becomes of the project manager's role? The answer lies in strategic value creation: stakeholder management, complex problem-solving, and ethical decision-making. Project-based industries must urgently develop AI-augmented career frameworks that prepare emerging professionals for hybrid human-AI teams. The alternative risks creating a generation whose skills are obsolete before mid-career.

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With portfolio-based assessment (offered as part of an Apprenticeship) and real industry insight, the course creates a clear pathway into digital construction for site teams, aspiring architects, engineers, surveyors, and project owners / funders. In partnership with the Digital Twin Hub and OCN London, the DTSA is shaping the next generation of talent and helping position the UK as a global leader in digital construction and innovation.

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Two industry reports—the Windsor Project Summit white papers and the AI4QS (AI for Quantity Surveying) Report—represent a significant milestone in elevating project management discourse. By bringing together Chief Executives, Senior Leaders, and sector experts, these initiatives have created platforms for strategic conversations about the future of the profession. For project professionals, these reports provide essential frameworks for understanding AI's implications at both board level and operational level. Read the full breakdown →

The Windsor Summit's focus on board-level expectations signals that project management is finally gaining recognition at the highest organisational levels. For project professionals, these white papers provide ammunition for conversations with leadership about AI transformation, future-proofing strategies, and the evolving role of projects in driving organisational success. The AI4QS report's emphasis on responsible AI adoption and governance addresses critical gaps where construction has historically lagged. Project managers can learn from the quantity surveying profession's structured approach to understanding AI's implications for professional practice, skills development, and service delivery. Our perspective is that these reports demonstrate how professional communities can collectively navigate technological change whilst maintaining standards and ethics. This is thought leadership with genuine practical application for the project community.

Procore Technologies' acquisition of Datagrid AI signals a fundamental shift in construction technology strategy—from passive data collection to autonomous decision-making. The combination of Procore's project management data with Datagrid's agentic AI could genuinely transform project delivery by automating submittal reviews and RFI responses. However, project professionals should approach this with healthy scepticism about vendor consolidation, data ownership, and professional liability. Read the full breakdown →

We have been saying for a while that platform consolidation often promises seamless integration but frequently delivers vendor lock-in instead. The key concern is whether agentic AI in construction management will genuinely augment project managers' capabilities or simply automate tasks whilst removing professional judgement from critical decisions. For project professionals, this acquisition raises important questions: Who owns the data? How transparent are the algorithms? Who bears responsibility when an AI agent makes a scheduling decision that leads to delays? The technology sector's "move fast and break things" approach doesn't align with construction's risk-averse culture and contractual frameworks. Project managers must demand clarity on how these AI systems make decisions, what data they use, and how recommendations integrate with existing professional workflows and responsibilities.

ChatGPT's extended memory feature now allows Plus and Pro users to retrieve conversations from a full year back—instantly searchable and permanently stored. While this enhances AI utility for long-duration projects, it creates urgent questions about data governance and professional liability when discussing commercially sensitive project information, design decisions, and stakeholder concerns. Read the full breakdown →

ChatGPT's memory feature is a double-edged sword. The ability to maintain year-long conversational context could genuinely improve how you manage complex projects—but only if your organisation has clear data governance protocols. Storing commercially sensitive information in ChatGPT's permanent memory creates exposure to data breaches and violates client confidentiality obligations under GDPR. The construction industry's tendency to adopt consumer AI without governance frameworks is creating significant liability. You need training not just on AI capabilities, but on professional boundaries when using persistent-memory AI assistants. Your organisations must establish clear protocols: what information can be shared with AI, how memory features are managed, and when data must be purged. The feature demands urgent policy development around AI tool usage.

Middle managers are caught in a contradiction they can't resolve. They're tasked with championing AI adoption whilst their own roles face automation. They must keep teams motivated through an existential threat they're experiencing themselves. And the data reveals a brutal pattern: the harder they embrace the tools, the faster their engagement collapses.

What the numbers show:

This isn't about weak resilience or poor stress management. When you hand managers responsibility for both people and machines without adequate support, clear boundaries, or time to master either, burnout is structural and predictable.

For project leaders navigating this: Your credibility comes from supporting your team through uncertainty—not from implementing tools flawlessly. Protecting time for genuine leadership and team development matters more than adoption velocity. That distinction separates leaders who build trust from those who just drive change.

Gallup (2025): State of the Global Workplace 2025 Report.
Gartner (2024): 2026 Organisational AI Predictions. Retrieved from Gartner Research
MIT Iceberg Index (2025): Measuring Skills-centered Exposure in the AI Economy.

The past week highlighted critical tensions between AI capability deployment and governance readiness. Google withdrew a medical AI product amid concerns about accuracy and safety in healthcare applications, highlighting the challenges of deploying AI in regulated industries. This cautionary development underscores why Miles Brundage, former OpenAI policy chief, launched AVERI (AI Verification and Evaluation Research Institute) with $7.5M to push for independent safety audits of frontier AI models, arguing AI labs shouldn't 'grade their own homework'.

For project professionals, the governance gap is immediate and material. Our perspective is that organisations deploying AI—whether in construction planning, cost estimation, or risk management—must establish independent verification frameworks before production use. The emerging playbook from Brundage and AVERI suggests third-party audits of model accuracy, bias, and failure modes should become standard practice. Project managers implementing AI tools carry professional liability for algorithmic decisions; independent safety audits provide necessary accountability mechanisms.

Mark Enzer: "If We Don't Define the Future, Someone Else Will"

This week's conversation with Mark Enzer (Prime Minister's Council for Science and Technology) and Gavin Spencer (Association for Project Management) explores a stark paradox emerging from the APM's Windsor Project Summit: individual AI productivity gains aren't translating into organisational returns.

Most organisations haven't defined what they're trying to achieve with AI. Enzer warns the profession risks "sleepwalking into a future" where technology dictates terms rather than serving human needs. His framework is outcome-first: a three-horizon approach anchoring decisions to purpose, federation over centralisation, and starting with decision-critical data rather than attempting to clean everything at once.

Both speakers emphasise the irreplaceable work is unglamorous—data readiness, security, access standards—but it's what separates success from failure. Spencer underscores that humans will always remain central to project delivery, equipped with skills to oversee AI decision-making and maintain control. The future isn't predetermined. It will be shaped by the choices professionals make today.

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Temporal reasoning continues to be a challenge for AI: Google Gemini is facing the heat!!

See you soon,

James, Yoshi and Aaron—Project Flux 

All content reflects our personal views and is not intended as professional advice or to represent any organisation.