Despite optimism about AI’s potential and near-universal AI experimentation, Smartsheet research reveals a critical shortfall in trust, unified tools and consistent usage, underscoring the need for Intelligent Work Management platforms to drive measurable AI impact in project and portfolio management
Smartsheet, the Intelligent Work Management platform that unites people, data and AI, announced the results of its 2026 Project and Portfolio Management Priorities Report. The research reveals a key disconnect: the majority of project and portfolio management (PPM) professionals view AI as an opportunity to redefine their roles to focus on strategic work, but most organizations haven’t established the necessary infrastructure and governance to wield it effectively.
The majority of PPM professionals view AI as an opportunity to redefine their roles to focus on strategic work, but most organizations haven’t established the necessary infrastructure and governance to wield it effectively.
PPM professionals’ roles are defined by change, with 98% reporting the need to reprioritize work due to business shifts. While AI has the potential to help teams navigate these fluctuating priorities, organizations have yet to unlock its full power in this area. The vast majority still rely on fragmented tools that limit AI’s ability to help with navigating change, and 61% of PPM professionals say their current tools make it difficult to demonstrate measurable contributions to overall project outcomes. Another barrier is the need for constant oversight: 87% report that AI still requires human input at least some of the time, indicating that PPM professionals haven’t yet harnessed the potential of AI agents and assistants to understand their work and take action on their behalf.
While organizations rush to adopt AI, the research suggests that without a unified approach, they risk creating new silos instead of breaking down old ones. Intelligent Work Management platforms offer a way forward—connecting people, data and AI across the enterprise to build a collaborative system where humans and agents work together to extend team capacity and continuously learn from outcomes. This will help PPM professionals work smarter at speed so they’re not just keeping up with change but thriving because of it.
“Many businesses are jumping on the AI bandwagon out of FOMO but aren’t yet seeing real results,” said Pratima Arora, chief product officer at Smartsheet. “The challenge isn’t adoption, it’s effective implementation. What companies need is a unified approach that embeds AI directly into everyday workflows. That’s what the Smartsheet Intelligent Work Management platform delivers. Built on flexible data models, large language models, and a knowledge graph, it creates a collaborative system where people and AI agents work together to accelerate impact across the enterprise.”
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Key insights from the report include:
AI Integration is Lagging
AI is now part of everyday project work, but how it’s used and by whom varies widely. The research reveals that most professionals report experimenting with AI, and its adoption is uneven across roles. Executives are far more likely to use AI regularly (94% at least occasionally), while only 69% of individual contributors say they use it.
This divide reflects more than just comfort with new technology; it points to a lack of integration. Many organizations rely on standalone AI tools that sit outside core workflows, making it difficult for teams to use them consistently or effectively. By embedding AI into the systems teams already use, they can minimize fragmentation and increase adoption.
Workforce Optimism and Anxiety on AI’s Potential
The report also reveals a deep tension between optimism and concerns. While 87% of PPM professionals see AI as an opportunity to transform how they work, at the same time, nearly three-quarters (74%) worry their roles could be replaced by AI within five years. This paradox reflects a broader shift in mindset: PPM professionals aren’t resisting AI, they’re redefining their roles to focus on strategic, analytical and leadership work.
Fragmented Tools Limit Visibility and Impact
Even though AI adoption is nearly universal, many organizations struggle to connect it to measurable outcomes. Only 39% of respondents say their current tools make it easy to demonstrate their contributions to project success. AI can automate tasks and surface insights, but without the right system to connect them across organizational boundaries, it struggles to demonstrate measurable impact.
This lack of visibility is especially problematic for enterprise leaders looking to scale AI across departments. Fragmented tools and execution silos are preventing organizations from realizing the full business impact of their AI investments.
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Turning AI Potential into Real Results
The research uncovered several strategies that could help teams move from AI deployment to enterprise impact, including:
Unifying siloed systems: Unify fragmented tools with a single platform that connects people, data and AI across the organization.
Embedding AI into workflows: Integrate AI directly into the systems teams already use to eliminate friction and improve adoption.
Governing AI use at scale: Ensure AI capabilities are auditable, secure and aligned with enterprise standards.
Focusing on measurable outcomes: Use unified dashboards and reporting to demonstrate how AI contributes to strategic goals.
“What stands out in this report is confirmation that AI is not in and of itself a strategy, yet far too often, in the eagerness to experiment and adopt, AI is left isolated in silos with incremental efficiencies as its only possible outcome,” said Liz Miller, VP & principal analyst at Constellation Research. “Project leaders are grappling with fragmented tools, uneven adoption and a lack of visibility into outcomes. Intelligent Work Management platforms can help them shift from isolated experimentation to intelligent orchestration by embedding AI into existing workflows, enabling cross-system and cross-function coordination and providing the governance and auditability enterprises require. This is the difference between hoping for AI outputs and valuing AI outcomes.”
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