“Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.”
Frank Herbert, Dune, 1965, p. 17
It’s no surprise that the speed of AI development has outpaced research on its impact to skilled labor. In fact, a great deal of consternation has arisen over the last five years due in large part to the unknown it represents. Amidst the hysteria, however, is the fact that AI is quickly becoming indispensable while fusing fear and utility in a tapestry that is not easily undone. However, rather than tackle the broad impacts of artificial intelligence here, this paper focuses on the field of project management. It is, for one, a career in which this author has a great deal of experience, but also one that exists at the intersection of social and technical expertise. It both benefits from the efficiencies AI brings to the table while simultaneously being threatened by its effects. Perhaps most critically, project management invokes the traits AI has yet to master: intuition, influence, perception, and others. Its mathematical, coding, and powers of deduction may be super-human, but AI fails to quantify nostalgia or regret and the power they can have over a person’s decision-making. As will be discussed, the path forward is far from certain except that AI’s spread is creeping down the halls of corporate America, swallowing some roles while nibbling around the edges of others. Project management, however, remains at the periphery, a nod to the fact that its purpose is still largely based on human factors. Yet, while project management unquestionably benefits from the efficiency AI creates, it is this efficiency that threatens to drive the project manager into extinction. As this paper argues, however, the demise of the project manager is greatly exaggerated. While the future will select for the most competitive combination of social and technical skills, as long as people remain, there will remain a need for project managers.
Background
Project management is a broad field with arguably more similarities between industries than within a single industry. Program managers, product managers, and project managers are all part of the project management family even while carrying distinctly different responsibilities. Within one company there may be dozens of similarly titled roles drawing on various specialties, whether in software development, marketing, or HR. Regardless, the project manager is responsible for ensuring that an objective is met. They answer to whether other people have done their jobs and interact with the bureaucracy on behalf of the team. Douglas (2023b) writes that project management is a skillset that is gained through both training and experience, (p. 94), noting that it is often misunderstood by leadership as an intuition that any skilled worker possesses (p. 102). Angara et al (2020) write that devops is a socioeconomic system that solves social issues more than technical issues. Project management, they argue, is a complex phenomenon that requires high maturity, real time communication, and self-driven teams capable of making rapid adjustments (pp. 81, 86). While a certain amount of automation can expedite process, these observations speak to the interdependence of human sociology and technical execution. Ruiz et al (2020) arrive at a similar conclusion, writing that project outcomes will always depend on human factors and may be difficult to predict or resolve even for experienced managers (p. 55). Though this statement was made advocating for the use of AI, it acknowledges the inherent unknowns that come with human involvement.
In fact, human involvement drives the need for project managers. A startup with only a handful of engineers has no need for project management, but an organization with several dozen teams will experience divergence, competing priorities, agendas, and politics, all of which a project manager needs to mitigate to ensure a project stays on schedule. Growth can also reduce the effectiveness of leadership. Douglas (2023a) writes that executive support for projects is often inadequate and uniformed. The project manager, he writes, compensates for this by using a complicated array of tactics to offset ineffective leadership (p. 34). Threats to project success can also come from within the team. Bahi et al (2024) note that agile teams often encounter various complex and context-specific challenges that lack clear or easy solutions (p. 56). It is the role of the project manager to facilitate a solution and shield management from the chaos of execution. Similarly, Angara et al (2020) write that project success is inhibited by overlapping functions between teams, rapidly changing priorities, and customer expectations (p. 80). The project manager is expected to confront and coordinate solutions to all of these challenges.
It should be clear from the preceding discussion that the bulk of the PM role is driven by bureaucratic factors and human behavior. That said, there are technical aspects of the job that may involve hard sciences, data analysis, python scripting, and so on. In the literature reviewed here, a great deal of emphasis was placed on the advantages of AI as a means for assisting with these tasks. However, it is important to distinguish between tasks and asks as they pertain to project management. The former is something a PM might do, the latter is a dependency on another team (i.e. data science, finance, etc). While business trends are not discussed here, it is worth noting that consolidation of roles does occur, where project managers are expected to perform engineering or analyst responsibilities. In the observation of this author, however, the bulk of project management roles focus on enablement, not task completion. Finally, a great many studies focused on the impact of AI to construction project management while others make broad statements about its benefits to healthcare, software development, and manufacturing. It’s also worth reemphasizing that for the sake of simplicity, this paper uses “project manager” to refer to project, program, and product management, even though they involve different responsibilities. In any event, it’s important to keep in mind the potential for divergence in the scope, context, and definition of project management throughout this discussion.
Literature review
Overview
There is little doubt AI offers substantial benefits to overall project delivery. From data analysis and research, to customer satisfaction and quarterly planning, the upside of AI seems clear. For example, Janat et al (2024) cite cross-industry surveys reporting a 20% increase in task efficiency, a 10% increase in time saved, and a similar reduction in error rates as a result of AI implementations (p. 1817). Similarly, Shoushtari et al (2025) write that AI assisted tools reduced construction timelines by 20%, costs by 15%, and improved overall team engagement (pp. 50-57). Case studies examining a variety of industries have returned similar results. For example, automated scheduling and predictive analysis reduced construction delays by 20%. Healthcare professionals cited significant improvements in cross-team communication, noting that natural language processing made it easier to share and comprehend data in real time. This corresponded to a 30% increase in efficiency. AI helped with market analysis, segmentation, and reporting, boosting ad campaign performance by 30%. And in software development, machine learning was used to automate resource allocation and quarterly planning, contributing to a 25% increase in on-time delivery (Hossain et al, 2024, pp. 7-11).
Perhaps unsurprisingly, the vast majority of project managers have responded favorably to the inclusion of AI in their dependent workflows (Janat et al, 2024). Similarly, Hossain et al (2024) write that in a survey of 150 project managers, improved efficiency, decision-making, and productivity were among the top advantages. Respondents said that automating tasks such as scheduling and reporting allowed them to focus on more strategic issues (pp. 7-8).
Business case
The benefits of AI are discussed in the following sections, however they can be summarized as increased velocity, immediate access to higher quality data, reduced risk, and competitive advantage through better analysis (Bahi et al, 2024; Vergara et al, 2025; Hashfi et al, 2024). Outdated processes and increasing complexity were also cited as drivers of AI adoption. For example, Janat et al (2024) argue that traditional project management has relied on manual, labor-intensive, error-prone processes that introduce risk (p. 1811). Ruiz et al (2020) write that current project management methodologies are “largely insufficient” and leave the project manager to make decisions based on prior experience and intuition (p.54). Shoushtari et al (2025) similarly argue that current methodologies are challenged by the size, scope and complexity of today’s projects (p. 49). Douglas (2023) adds that this complexity leads to an over emphasis on timelines and budgets, and concludes that a more dynamic style of project management is required (p. 44). Finally, Hossain et al (2024) write that antiquated project management methodologies are rigid and fail to adapt to changes and uncertainties during delivery (p. 1). Researchers argue that AI can lubricate delivery and compensate for inefficient and outdated processes.
That said, there are compelling acknowledgements, if not counter-arguments made throughout the literature. For example, Hashfi and Raharjo (2024) write that many of the challenges inherent to other methodologies can be overcome by Agile (p. 368). Others, like Douglas (2023b) counter that while Agile does present advantages, it can be difficult to implement across multiple teams and suffers when communication breaks down (p. 46). Still, the potential risks are outweighed by the rewards. As Bahi et al (2024) point out, AI can greatly enhance agile delivery by streamlining tasks such as development, testing, and documentation. However, they acknowledge that Agile remains susceptible to large changes in priorities (pp. 54-56). Lastly, Angara et al (2020) note that project success is still largely driven by developer competency, seniority, and cross-team dependencies. Failures occur, they write, when communication, collaboration, and team cohesion break down (pp. 80, 81).
In summary, most researchers believe projects are becoming too complicated for humans alone to manage. They see increased automation as a means to mitigate risk and improve efficiency. The dependence on human factors remains strong, however, demonstrating that automation alone doesn’t ensure success.
Data analysis
AI has perhaps no greater impact than in the world of big data. Researchers cited better outcomes and more efficient use of time as the primary benefits. For example, Janat et al (2024) write that automating data science and analysis allowed the project manager to shift their focus to strategic initiatives (p. 1812). Hossain et al (2024) make a similar point, arguing that by automating mundane tasks such as analysis and reporting, the project manager was free to focus on more important issues (pp. 7-8). At a macro scale, AI is broadly seen as a risk reducer. Shoushtari et al (2025) write that AI has been used to find patterns and risks that analysts failed to see (pp. 51-52). Vergara et al (2025) make similar high-level observations, citing not only better analysis, but increased speed and quality as outcomes of intelligent automation (pp. 8, 9). Bahi et al (2024) write that agile teams benefit from data-driven decision making, risk assessment, and increased velocity as a result of AI (p. 54). In short, artificial intelligence improves the speed at which work is done. However, the project manager benefits primarily from the automation of this work, not the automation of their role.
AI & Communication
Project communication was another benefactor of AI involvement. Ruiz et al (2020) noted the potential for chatbot integrations with MS Project and Oracle Primavera to automate scheduling, reduce risks and conflicts, and improve messaging (p. 55). AI powered bots were also shown to improve cross-team communication through chat, messaging, and automatic report generation. In a survey of construction projects, AI reduced communication delays by 10% and improved team engagement by 25% (Shoushtari et al, 2025, pp. 51-52, 57). Similar benefits were realized in software development where GPT has been used to automate client communications, maintain dashboards, and provide instant access to information (Vergara et al, 2025, pp. 9-10). Other researchers go a step further, arguing that AI not only improves communication but enhances stakeholder engagement. It promotes a culture of data-driven decision making that allows project managers to benefit from profound data insights (Hossain et al, 2024, pp. 3, 4; Janat et al, 2024, p. 1812). In short, the benefits of AI are both inward and outwardly facing, enabling faster communication to both clients and stakeholders.
Resource allocation and planning
Significant benefits were also realized in capacity planning and resource allocation. In fact, the advantages of AI go beyond counting available hours. Shoushtari et al, (2025) write that AI models not only consider the team’s schedule but their individual expertise as well. Scheduling and delivery, they note, can be automatically updated as employee availability changes (pp. 51-52). Bahi et al (2024) echo these findings, writing that AI can analyze historical data and team performance to more effectively allocate resources. Real time monitoring can be used to address team performance and make agile teams more responsive (pp. 54, 58). Furthermore, IT and software development surveys found that the use of machine learning during planning improved resource allocation by 37% (Hossain et al, 2024, p. 11). It should be noted, however, that contradictions in the research exist. For example, Angara et al (2020), argue that storypointing requires a mindset that is intuitive, rational, and agreeable and is one that invokes real time, groundlevel effort (p. 81). In other words, it can only be done by humans. This same argument could be applied to the process of estimating developer weeks or expertise during quarterly planning. Similarly, Hossain et al (2024) argue on behalf of AI, citing the pitfalls of using historical data and experience to estimate effort. However, it’s worth considering that AI would use the same historical data and would be partly (if not equally) prone to the same error.
Creativity
Creativity has long been considered one of the hallmarks of human thinking, however, AI is increasingly capable of inspiring creative thought, if not synthesizing new ideas all its own. For example, audits of the construction industry found that only 8% of projects were categorized as highly imaginative, while 92% were rated low or moderately imaginative (Kineber et al, 2024, p. 2). This has, in part, been addressed through AI. For example, GPT has been used to improve urban planning and derive building layouts (Vergara, 2025, p. 10). Bahi et al (2024) write that GenAI can facilitate brainstorming, generate novel ideas, and help teams overcome creative hurdles (p. 58). On the other hand, Manyika (2022) argues that getting AI to develop truly novel ideas from math and science remains a challenge (p. 12). Hashfi and Raharjo (2024) make a similar argument noting that while data science and analysis can be automated, the human is still needed to draw conclusions. (p. 367). They acknowledge, however, that the line between artificial and human is constantly moving. Noting that the future of AI will likely synthesize STEM and human-centric or human-like behavior (p. 367).
Excessive optimism
Other benefits were more hopeful than actual. For example, Hashfi and Raharjo (2024) write that AI has the potential to predict bottlenecks in construction or identify KPIs, assign story points and plan sprints in software development (p. 368). As noted above, however, the nature of high fidelity tasks like storypointing and sprint planning are often contingent upon real time factors that AI won’t have access to (Angara et al, 2020). Still other suggestions are further fetched or unrealistic. For example, Shoushtari et al (2025) theorize that AI could use sentiment analysis to manage stakeholder engagement (p.55). However, the potential risks and unintended consequences of bad messaging are real, and particularly acute when dealing with executives. Nonetheless, Hashfi and Raharjo (2024) write that AI has the capability to replace human cognition, decision making, and problem solving, citing progress made in data analysis, risk assessment, performance monitoring, and optimization as signs of its progress (p. 372). Such predictions ignore the human aspects of project management discussed earlier. This is less an issue with compute power and more one of sufficient data to make an informed calculation. Angara et al (2020) attempt to address this gap, noting that tone is a leading indicator of project success or failure. They suggest that AI be used to monitor tone across the field of project communication, writing that organizations should attempt to capture “every possible communication” (p. 89) between project stakeholders, customers, senior management, and the project team. They include recording scrums, team meetings, slack messages and emails and storing them as text files for analysis (pp. 80, 89).
Still other researchers suggest that risks associated with resource allocation and team performance might be mitigated by using AI to screen candidates. Ruiz et al (2020) write that AI has been used to estimate prospective employees’ emotional intelligence and predict performance using data from social networks (pp. 59, 60). Privacy concerns aside, the accuracy of judgements based on such limited, self-selected, and highly curated data (i.e. what we post about and how we interact with social media, etc) must be called into question. In assessing the competency of AI to make judgements of any kind, Manyika (2022, p. 19) asks “as compared to what?” Similarly, gauging an employee’s emotional intelligence should be followed by the same question.
Risks
Finally, the use of AI is not without risks whether actual or theoretical. In fact, in the literature reviewed here, risk assessment was one of the greatest areas of overlap and consistency across researchers. Data privacy, the ubiquitous access to employee and customer communications, and data availability were cited by Janat et al (2024), Shoushtari et al (2025), and Hashfi, et al (2024). Job loss, resistance to change, budget, and lack of technical understanding were near unanimous points of concern as well. Perhaps most critically, only Vergara et al (2025) mentioned the potential for over-reliance on automation (p. 14). Indeed the quantifiable wins and perception of super-intelligence associated with AI, will present a compelling case for leadership to take results at face value. The aforementioned unintended consequences of sending grammatically correct but poorly timed communications to executives is one such example. Similarly, only Bahi et al (2024) cite the potential for lack of accountability associated with AI (p. 57). The potential for teams to generate AI-assisted data without review and for executives to take such data at face value are equally pernicious. Observations by Douglas (2023a) noting the importance of accountability and the disconnect between leadership and project management further the point. These topics will be discussed further in the next section, but it’s worth noting that today’s leaders may suffer from too much information, and that a reduction in communication might be more appropriate.
Discussion & Outlook
Tension between technocratic thinking and philosophy is nothing new. Plato was one of civilization’s earliest technocrats and believed morality could be quantified as plainly as mathematics. More recently, rational choice theory attempted to model social behavior and politics in the way physics models quantum mechanics (Cohn, 1999). Today’s conversation surrounding AI embodies a similar dynamic. In fact, the preceding discussion displays an unabashed bias for technical applications of AI and high level measures of efficiency. Nothing of substance was offered regarding mitigating company politics, disagreements, or divergence between teams. AI’s most ardent supporters are quick to boast that computers will soon manage human relationships or prevent politics through data and automation, while trivializing the ambiguity of human decisions and how often emotion overrides rationality. This says nothing of the draconian measures required to access such data and whether that is a world in which anyone would want to live. In that, the project manager should derive comfort knowing that as long as people remain, there will be a need to manage them.
Nonetheless, the potential for over-reliance on AI as mentioned by Vergara et al (2025) is real but not automatic. Data will always be a refuge from ambiguity even if truth lies in the undefined. Leadership, for example, have always been biased toward quantifiable pedigree: degrees, certifications, work history, etc. However, an MBA says nothing of a project manager’s ability to mediate a disagreement between two type-A engineers. A fortune 500 resume may struggle to perform in the chaotic environment of a startup. A credentialed PM might have no feel for when they need to step on toes or play within the rules. Yet a person’s adaptability or ability to gain the respect of an engineering team is arguably more important than their understanding of system diagrams.
The preference for data will, nonetheless, persist. And that’s expected. Inherent in this preference, however, is the assumption that what lies in the data constitutes everything that needs to be known. The answers AI provide are necessarily a subset of all potential answers, much in the way this paper represents a summary of the reviewed literature, and that literature reflects what this author thinks is relevant. In that, AI platforms are no different. For example, the most frequently cited domains vary by company. Wikipedia is GPT’s top source while Perplexity and Google favor Reddit. Though the top sources account for fewer than ten percent of total citations, they reflect bias in the system. For example, GPT’s tendency toward Wikipedia indicates a preference for encyclopedic sources, while Perplexity prioritizes community discussion (Lafferty, 2025). During a conversation addressing the potential impacts of AI to the music industry, producer Rick Beato (2025) took a more critical position, noting that true expertise remains in the minds of the experts and is unavailable to AI. Artificial intelligence, he argued, can’t access what hasn’t been written down.
Gaps aside, AI will select for the most common points of view, but not necessarily the most relevant points of view. For example, in research conducted on the cocoa industry, this author found that 2024 versions of GPT failed to highlight the risks posed by aging trees and farmers (Vedvick, 2025). It instead biased toward the growing cocoa trade and emphasized what a great idea he had. A year later, Anthropic’s Sonnet 4.5 did a better, but still incomplete job of surfacing these risks. As Hashfi & Raharjo (2024) rightly pointed out, AI might be able to surface insights, but only humans can decide if those insights are relevant or even insightful at all. Taken together, the limitations of AI suggest a tool that is better used internally, where complete datasets are available, but one that should be treated with a degree of skepticism when applied externally.
Nonetheless, data-driven decisions have value and should be pursued. Their upsides were highlighted by Bahi et al (2024) and Hossain et al (2024) and reflect a growing desire for such traits in project managers. Meta (n.d.) recently posted a Program Manager role specifically focused on driving decisions through data. Decades prior, Starbucks embarked on a mission to push its development managers toward centralized planning, and quantified measures of site suitability. These efforts clashed with the relationship focused nature of store development but were central to closing dozens of under-performing stores during the Great Recession.
None of this means that AI is the right tool for the job, however. Researchers cited the risks of AI but did not consider the appropriateness of its application, particularly as it stacks up against existing solutions. For instance, did AI uplevel a mature quarterly planning process, or was the existing state devoid of organization? If the later, then the presence of structure, whether AI-driven or not, is likely to improve execution. Hossain et al (2024) and Bahi et al (2024) nonetheless argued for the benefits of AI-assisted capacity planning. And, while it’s true that calculating engineering capacity is well within the capability of AI, it’s also well within the capabilities of Excel. Furthermore, the assumption that priorities are universally understood and followed across an organization is true only in the laboratory. The politics and competition between security, technical debt, and new product development will not be remediated simply because AI says it’s so. Finally, the level of effort required to update a plan of record, adjust a backlog, and notify stakeholders is relatively trivial. While AI could be used to automatically plan quarters, it’s arguable whether it should. The notion that human priorities will subvert to an intelligent machine is naive and ignorant of how people behave in a collective. As Angara et al (2020) rightly pointed out, the estimation process is full of real time contingencies that intelligent automation won’t have access to. It’s worth considering, therefore, whether fixing bad planning is more costly than doing it through traditional methods.
Perhaps more importantly, automation of the sort discussed above risks undermining accountability (Bahi et al, 2024). In some respects this is as much an individual trait as it is an outcome of culture. A product development cycle that exists agnostic of quarterly planning can, for example, foster a culture of kicking it over the wall. Product writes the epics and it’s engineering’s responsibility to do the work. On the other hand, quarterly planning itself can result in a similar exchange where program management assigns work to a product backlog and walks away. In this there is a lesson for the data-driven orgs, like Meta and others. The project manager still needs to be accountable for ensuring the message delivered to leadership is the right message, and leadership needs to foster a culture of trust that enables project managers to challenge the status quo.
Outlook
When it comes to AI, the toothpaste is out of the tube. And the technology represents undeniable potential to revolutionize how work gets done. However, while there is a tendency to believe that we’ll all soon be replaced by thinking machines, that end is not as near or as certain as the doomsdayers might have us believe. It is true that efficiency breeds a reduction in force and that reduction carries less need for project managers. However, it bears mentioning that none of the authors above cited reductions in force amidst all the gains in efficiency and other benefits. Job losses were only noted as a potential risk, not a realized event. Furthermore, the Wall Street Journal reported that while AI has generated a flood of investment activity, it hasn’t made the average worker more productive. Instead, AI’s impact is more sector and role specific. For example, entry level developers. Furthermore the percentage of jobs exposed to AI automation has remained relatively constant since 2022, suggesting that AI’s impact on the labor market isn’t accelerating (Lahart, 2025).
This hasn’t slowed AI investment, however. For example, global spending on AI is expected to surpass $500 billion by 2026, and the cost of maintaining that infrastructure will exceed $2 trillion by 2030. For context, that is more than the annual revenues of Amazon, Apple, Microsoft, Meta, Nvidia, and Alphabet combined (Morabito, 2025). This is a tremendous amount of capacity but it doesn’t necessarily spell the end of white collar work. For instance, Deloitte found that as of June 2025, 53% of consumers had either experimented with GenAI or were using it regularly, up from 38% in 2024. Regular users also doubled to 20% year-over-year and of those, 42% said AI had a very positive effect on their lives (Fineberg et al, 2025). In other words, AI may be much more of a consumer device than a piece of corporate infrastructure. If artificial intelligence becomes the way in which we shop, search, and explore, that bodes much more positively for job creation and security.
Nonetheless, worries of broad job losses are not totally unfounded. Vibe coding for example enables fullstack app creation with virtually no knowledge of computer science. At minimum, advanced AI will enable organizations to remain flatter for longer, avoiding the types of problems that come with bureaucracy and the need for project managers. We’re already at a point where the distance between creativity and creation is nearly zero. This author, for example, created a fullstack budget app through AI-assisted coding and basic technical aptitude. Certainly others have created far more sophisticated programs with the same tools.
All of that said, visions of a world enslaved by AI are hyperbolic. Even super intelligence without access to data is limited. The randomness of a mundane day at work is still beyond our willingness to cede privacy for better data. And so, until then, humans will still be required to manage the cascading wave of chaos that ensues when a customer’s email lands on the CEO’s desk.
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