Start with AI, not a Project Framework.
From the AI IQ Blog
by Paul Boudreau
Technology offers an incredible opportunity to improve project performance. This blog shares the latest research and how organizations are implementing AI into their project methodology. Come with an open mind, increase your knowledge, share your concerns, and become a project manager with new skills to offer an organization.
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Most organizations still begin with the wrong question: What is the most effective project process: waterfall, agile, or a hybrid? That thinking reflects a pre-AI mindset. If artificial intelligence is going to reshape how projects are planned and delivered, then it should not be layered onto existing frameworks. It should be the starting point.
The Project Management Institute (PMI) currently positions AI as a tool within established processes. In this view, project managers continue to follow familiar structures, simply enhancing them with AI capabilities. That sounds reasonable, but it misses the critical point that those frameworks were not designed for an AI-enabled environment. They were built for human-driven planning, sequential decision-making, and limited data processing.
The results speak for themselves. Across industries, project success rates have remained stubbornly inconsistent for decades. Cost overruns, schedule delays, and unmet benefits are not rare exceptions but persistent patterns. If the frameworks were truly effective, there would have seen meaningful improvement by now. Instead, we continue to optimize within systems that were never designed for the level of insight, speed, and adaptability that AI provides.
This is not the first time organizations have faced this challenge. When enterprise resource planning (ERP) systems were introduced, companies quickly learned that simply automating existing processes led to poor outcomes. Real value came only when processes were redesigned to align with the capabilities of the technology. The same principle applies today. AI changes how decisions are made and enables continuous analysis rather than periodic review. It surfaces patterns and risks that traditional methods cannot detect. It enables dynamic planning rather than static baselines. Trying to force AI capabilities into rigid frameworks limits their impact.
The path forward is clear. Start with AI. Design your project approach around what AI can do, then determine which processes support that reality. This elevates the project manager's role as the focus shifts from managing process steps to orchestrating intelligent decision-making. The question is no longer which framework to use. The question is how to build a project environment where AI can deliver full value.
Posted on: May 25, 2026 08:00 AM |
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Aaron Porter
Community Champion
IT Director| Blade HQ
Payson, UT, United States
I think AI can help improve project delivery, with varying levels of success across organizations and industries, but I’m not convinced “AI first” is the right starting point for many of them. There are really two separate questions here that often get blended together, 1) What organizational capabilities are required for projects to succeed? and 2) What delivery approach best fits the type of work being done? AI can help with both, but it does not replace either.
For project success, the most important organizational capabilities are often things like leadership alignment, prioritization discipline, decision-making speed, cross-functional coordination, operational ownership, governance quality, and organizational incentives - things that exist independent of project management. Effective and intentional AI use can meaningfully improve visibility, forecasting, dependency analysis, communication flow, and benefit tracking. But if the organization cannot make decisions, resolve conflicts, stop low-value work, or sustain operational ownership after delivery, AI mainly accelerates the existing system and amplifies dysfunctions.
Choosing a project approach (predictive, adaptive, hybrid, etc.) can depend on variables like requirement volatility, uncertainty, regulatory constraints, dependency complexity, customer feedback cadence, release frequency, and coordination scale. AI may influence how these approaches are executed, but it does not eliminate the need to understand the variables and how they impact the work.
I suspect many organizations are not truly struggling with “which methodology should we use?” as much as they are struggling with broader operating model and decision-making challenges, even if they don't realize it. The methodology discussion often becomes a proxy for deeper organizational issues and can distract from solving them. I also think many organizations skip an important question: do they actually need transformation, or do they need targeted adaptation?
I bring up transformation and adaptation because those companies that truly are questioning which methodology to use are often undergoing or in need of a larger change. Starting by designing their approach around AI ignores the larger opportunity. Not every company needs to rebuild its operating model around AI. Some need selective augmentation and process improvement that might also leverage AI. Others may require broader transformation because AI materially changes their competitive environment.
Even then, transformation readiness matters. Many organizations attempt transformation without aligned leadership, adaptable governance, operational capacity, or clear decision ownership. Changing the tooling or methodology without changing the organizational system rarely produces the outcomes people expect. To me, AI is best viewed as an enabling tool inside a larger organizational and decision-making system — not as the organizing principle itself.
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