Integrating AI as a Virtual Team Member: A Strategic Guide for Project Management and Delivery Teams

AI Support for Project Management and Delivery Teams

A project manager's core competency lies in building and maintaining relationships with stakeholders and team members to achieve project objectives. Success in both relationships depends on the project manager's ability to understand and fulfil their partners' needs by sustaining productive collaboration.

With stakeholders, this involves demonstrating tangible project benefit delivery and effectively selling necessary changes to ensure benefits realisation. The project manager must maintain the delicate balance between time, cost, and quality expectations; keeping stakeholders engaged and supportive throughout the project life cycle.

For team members, effective project management means enabling their success by ensuring access to essential knowledge, appropriate tools, and sufficient time to fulfil their roles. A servant leadership approach proves effective, particularly in matrix organisations where resources are seconded from other departments or managed through separate reporting structures.

Project teams can be divided into two groups: the delivery team responsible for project outcomes and the project management team focused on creating necessary project management deliverables. Delivery teams are typically managed in specialist organisations within matrix organisations or business units. For project management teams this organisation is usually the organisation's project management office.

Given this organisational context, AI can support project managers and both delivery and management teams in several ways: enhancing stakeholder communication through automated reporting and presentation generation, streamlining project documentation and administrative tasks, supporting delivery teams with technical problem-solving and content creation, and providing project management teams with improved planning, tracking, and analysis capabilities.

Development of AI in recent years

Since ChatGPT became publicly available in November 2022, there has been a significant surge in AI applications across various problem-solving domains. This widespread adoption stems from several key factors: the user-friendly nature of these tools and their remarkable ability to generate good-quality content across multiple formats.

The driving force behind AI's growing popularity lies in its capacity to produce text, images, and code that closely mimics human-created work. These AI-generated outputs often achieve such sophistication that they become nearly indistinguishable from content created by humans, making the technology particularly valuable for users seeking efficient, high-quality results across diverse creative and technical tasks.

The models have been rapidly developed and update to address the challenges of the real world and issues identified by a more widespread adoption of AI. Techniques, such as prompt engineering, have been developed craft the questions in such a way as to assure better quality in the output.

AI as a Productivity Tool

Generative AI serves as a valuable asset for teams seeking to create high-quality content that aligns with project standards. Teams can leverage these tools to develop comprehensive project documentation, compelling presentations, detailed reports, and various project management materials. Additionally, AI can enhance external communications by generating content for project websites, social media platforms, and marketing campaigns.

AI-powered copilots streamline communication workflows by automatically capturing and synthesising project meeting discussions. These tools create detailed meeting summaries that serve as official records, documenting key decisions and outcomes while ensuring consistent distribution of information to team members and stakeholders across the project.

Beyond administrative and communication tasks, AI can directly contribute to project deliverables and outcomes. In software development projects, AI tools can generate code snippets, assist with debugging, create test cases, and even produce entire software components. These capabilities enable development teams to accelerate product delivery while maintaining code quality standards. AI can also support the creation of user interfaces, database schemas, and technical specifications, effectively becoming a collaborative partner in the actual production of software products rather than merely supporting project management activities.

What is the value of giving another tool to a team

The value of providing teams with additional tools lies in their ability to enhance work completion and improve project outcomes. These tools can serve as bridges between different project stakeholders, helping teams better understand and align with the requirements of project managers and stakeholders. By facilitating clearer communication and understanding across all project participants, these tools enable teams to work more effectively toward shared objectives and deliver results that meet everyone's expectations.

However, introducing new tools to unfamiliar teams presents significant challenges that require careful consideration and planning. Teams need adequate support during the transition period, including comprehensive training, ongoing guidance, and access to resources that help them navigate the tool's features and capabilities. Organisations must allocate sufficient time for the learning curve, recognising that initial productivity may decrease as team members adapt to new workflows and processes. Without proper support and realistic timelines for adoption, even the most valuable tools can become sources of frustration rather than productivity enhancers, potentially undermining team morale and project success.

Projects must assess the team's AI tool proficiency and determine how this capability level will affect delivery timelines and output quality. Additionally, projects should define the specific responsibilities of project managers and team members in supporting and facilitating AI tool adoption and usage.

AI as a Virtual Team Member

If AI is an artificial intelligence (though not yet generally intelligent), then it might be considered to be a virtual team member.

Early AI iterations resembled an overly enthusiastic newcomer—quick to respond and occasionally embellishing details to support its narrative. While AI capabilities have significantly matured with each generation, maintaining a healthy scepticism toward AI outputs remains essential. Just as you would verify the work of any new team member, AI-generated content requires careful review and validation to ensure accuracy and reliability.

From a cognitive perspective, AI functions primarily as a System 1 tool—delivering rapid, intuitive responses without the deliberate reasoning characteristic of System 2 thinking. While this speed and efficiency prove valuable, the lack of careful analytical processing can lead to errors. In human learning and skill development, these two thinking systems work in tandem. High-performance training typically involves using System 2's deliberate, methodical approach to establish correct mental pathways, enabling System 1 to produce accurate instinctive responses during real-time performance. This mastery develops through slow, intentional practice that engages analytical thinking.

For AI to function effectively as a team member, it must possess relevant domain expertise in its assigned area of contribution. This requirement can be fulfilled by providing the AI with specific, high-quality domain knowledge, ensuring it has access to authoritative information within its specialised field. This knowledge foundation is fundamental to the AI's ability to deliver meaningful value to the project team.

Impact of AI Team Members on Team Dynamics

Integrating AI as a team member requires careful oversight to ensure proper knowledge application and output quality. This supervision can be assigned to existing human team members or designated specialists, but ultimately the project manager remains accountable for ensuring adequate supervision and maintaining quality standards across all deliverables.

A significant concern emerges when AI team members are positioned as replacements for junior human staff, particularly when tasked with creating initial document drafts based on historical project knowledge. While this approach may seem efficient—allowing senior team members to refine AI-generated drafts rather than starting from scratch—it poses serious risks to sustaining the organisation. Traditional career progression relies on junior team members learning through hands-on experience, creating initial work products, and developing expertise through feedback from senior colleagues. This natural mentorship cycle builds the skills and judgement necessary for advancement to senior roles.

This challenge also presents an opportunity to develop new competencies within junior staff. Learning to effectively interact with, direct, and supervise AI systems is a critical skill set for the modern workplace. Junior team members can develop expertise in prompt engineering, output validation, and AI workflow optimisation—capabilities that will become increasingly valuable as AI integration expands across industries. By involving junior staff in AI supervision and collaboration, organisations can simultaneously preserve traditional learning pathways while building next-generation skills that will define future professional success and assure the skills for the organisation's long-term success.

Organisations must strike a careful balance when deploying AI team members. While AI requires supervision from experienced professionals, companies should preserve opportunities for junior staff development rather than allowing AI to completely replace entry-level contributions. Failing to maintain this balance could result in a future shortage of skilled senior professionals, as the traditional pathway for developing expertise becomes disrupted by over-reliance on AI tools. Relying on other organisations to take the burden of training junior staff will result in an expensive market for mid career talent, especially with the skills and experience that your organisation requires.

Implementing AI-Enhanced Project Capabilities

Integrating AI capabilities within project teams and delivery organisations represents a substantial organisational initiative that demands strategic planning and meticulous execution. Success depends on thoughtful preparation and systematic implementation to ensure effective adoption across all team levels.

Organisations should anticipate that AI capabilities will become standard components of teams provided by capability centres or project management offices. These AI enhancement initiatives are typically driven by capability owners and project management offices who recognise the strategic value of AI integration across the organisation. However, implementing these capabilities requires treating them as formal projects in their own right, complete with dedicated resources, defined timelines, and clear success criteria. This approach ensures that AI integration receives the attention and rigour necessary for successful adoption, rather than being handled as an informal or secondary activity. The capability owners and PMO must champion these initiatives while providing the project management discipline needed to deliver sustainable AI-enhanced capabilities across the organisation.

Successful implementation requires comprehensive team preparation and understanding. Project teams must develop familiarity with available AI tools, learn how these capabilities will enhance their specific workflows, and understand the integration processes that will incorporate AI into their daily operations. This preparation involves both technical training and change management to ensure team members can effectively leverage AI capabilities while maintaining project quality and delivery standards.

Projects should have a clear understanding of the level of AI integration of their delivery and project management teams so that the project can plan for the contributions (and risks) of AI. AI adoption should not a side effect of a project; adoption activities need to be managed as a separate project by the capabilities.