Key Factors for Project Managers to Consider When Implementing AI Projects 

Introduction

Artificial intelligence (AI) is revolutionising all industries globally. Project managers are inevitably immersed in implementing these transformative technologies. Integrating AI into project management practices and deliverables presents project managers with unprecedented opportunities and unique challenges. Project managers need to know how to navigate this complex landscape, balancing the potential for increased efficiency and innovation with the need for careful planning, ethical considerations, and change management.  

In this article, I aim to guide project managers through the critical factors they need to take into account when implementing AI technologies in their projects. I will provide key considerations for project managers to ensure successful AI integration in their projects by examining various aspects of AI implementation, from strategic alignment and technical feasibility to ethical implications and workforce impact. 

1. Strategic Alignment and Value Proposition of AI

Before starting any AI implementation project, it's important for project managers to ensure that the AI initiative they're about to implement aligns with the organisation's overall strategy and offers a clear value proposition. I strongly encourage the project managers to engage the relevant Strategy and Enterprise Architecture teams. Why would we waste time spinning our wheels in different directions like Brownian motion (remember high school physics?)? Let's align and deliver value as a team. 

Key Considerations for AI's Value Proposition

• Identify specific business problems or opportunities that AI can address
• Assess the potential return on investment (ROI) of AI implementation
• Ensure alignment with the organisation's long-term strategic goals 

According to a recent McKinsey Global Survey, companies that want to see the highest value from AI align it with their core business strategy. McKinsey's survey found that 66% of respondents from high-performing companies reported that their AI initiatives are aligned with their overall corporate strategy, compared to just 24% of other respondents. As project managers, we need to raise the bar based on the circumstances we find ourselves in.  

Project managers need to work closely with stakeholders to communicate the clear objectives for AI implementation and establish key performance indicators (KPIs) to measure success. The benefits Realisation Framework is a very pragmatic approach to such KPIs and measurements. This strategic alignment will help justify the investment in AI and will continuously support decision-making throughout the project lifecycle.

2. Data Readiness and Quality

To emphasise the importance of data quality in AI projects, a Harvard Business Review study states that "without a strong foundation in data management and governance, AI initiatives are likely to fail."   

We live in a data-driven age, and the success of any AI implementation heavily depends on both the quality and availability of data. A degraded quality and unavailability of data lead to increasingly unreliable LLMs, for instance. Project managers need to include data readiness and quality as prerequisites during their planning exercises before proceeding with the implementation of AI initiatives. While a 100% goal is ideal, we need to be pragmatic and have a realistic threshold in mind, aligned with the business, strategy and EA teams. 

Key Considerations

• Evaluate the quantity, quality, and accessibility of relevant data
• Identify data gaps and develop strategies to address them
• Ensure compliance with data privacy regulations and ethical standards  

3. Technical Feasibility and Infrastructure

As per a Project Management Institute (PMI) report, 37% of project professionals cite the lack of AI infrastructure as a significant barrier to adoption and successful implementation. Enterprises can either set up infrastructure on-premise or, preferably, take advantage of the cloud (and elastic) options of AWS. The recent surge of GPUs may necessitate significant changes to existing IT infrastructure and processes, as well as the CapEx and OpEx budgets required.  

Key Considerations

• Evaluate the compatibility of AI solutions with existing systems
• Assess the need for additional computing power, storage, or networking capabilities
• Consider cloud-based solutions versus on-premises infrastructure  

Project managers need to work closely with IT teams to develop a comprehensive technical roadmap for AI implementation. Our projects may deliver one or many aspects of the short term goal, while we collaborate with the Starategy and EA teams and share the long term scalability view to them, which can be included in their plans for subsequent quarters and years.  

4. Skill Gap Analysis and Team Composition

Successful AI implementation requires a diverse set of skills, including data science, machine learning engineering, and domain expertise. Project managers must assess the existing skill set within their teams and identify any gaps that need to be addressed.  

McKinsey research suggests that organisations successful in AI adoption are more likely to provide AI-related training, with 77% of high performers offering such training compared to 39% of other companies.  

Key Considerations

• Conduct a skill gap analysis within the project team and broader organisation
• Develop strategies for upskilling existing team members
• Identify roles that may require external hiring or partnerships 

5. Ethical Considerations and Governance

As AI technologies become more prevalent, ethical considerations and governance frameworks are increasingly important. Project managers must ensure that AI implementations adhere to ethical standards and comply with relevant regulations. 

Key Considerations

• Develop an AI ethics framework for the project
• Ensure transparency and explainability of AI decision-making processes
• Address potential biases in AI algorithms and training data  

Harvard Business Review emphasises the importance of AI governance, stating that "without proper governance, AI systems can perpetuate or even exacerbate societal (and cultural) biases". Project managers should establish clear guidelines for ethical AI development and use, and involve diverse stakeholders in the decision-making process. 

6. Change Management and Stakeholder Engagement

Implementing AI technologies often requires significant changes to existing processes and workflows. Effective change management (and end-user adoption) and stakeholder engagement are crucial for successful adoption. 

Key Considerations

• Develop a comprehensive change management (and end-user adoption) strategy
• Engage stakeholders early and often throughout the project lifecycle
• Address concerns and resistance to AI adoption proactively  

The PMI's report found that organisations with high AI maturity are more likely to have strong change management practices. Project managers should prioritise clear communication, training, and support to ensure the smooth adoption of AI technologies. 

7. Scalability and Future-Proofing

As AI technologies continue to evolve rapidly, project managers must consider the scalability and future-proofing of their AI implementations. 

Key Considerations

• Design AI solutions with scalability in mind
• Plan for ongoing maintenance and updates of AI systems
• Stay informed about emerging AI trends and technologies  

McKinsey advises organisations to "think big" when it comes to AI implementation, suggesting that "scaling AI across the organisation is key to capturing its full value". Project managers should develop roadmaps for scaling successful AI pilots and establish processes for continuous learning and improvement. Organisations often use Continuous Improvement Continuous Development (CI-CD) as a mechanism. 

8. Risk Management and Contingency Planning

Implementing AI technologies introduces new risks to which project managers must apply the appropriate risk management responses.

Key Considerations

• Conduct a thorough risk assessment for AI implementation
• Develop contingency plans for potential AI failures or unintended consequences
• Establish monitoring and control mechanisms for AI systems  

The PMI's "Pulse of the Profession" report highlights that high-performing organisations are more likely to use risk management practices, with 76% of high performers using these practices compared to 46% of low performers. Project managers should integrate AI-specific risks into their overall risk management framework and develop robust contingency plans. 

Conclusion

Implementing AI technologies in projects presents project managers with a complex set of challenges and opportunities. By carefully considering strategic alignment, data readiness, technical feasibility, skill gaps, ethical implications, change management, scalability, and risk management, project managers can navigate the AI landscape more effectively.  

As AI continues to transform the field of project management, it's crucial for project managers to stay informed about emerging trends and best practices. By embracing a holistic approach to AI implementation that balances technological innovation with human factors and ethical considerations, project managers can drive successful AI adoption and deliver significant value to their organisations.  

The journey of AI implementation is ongoing, and project managers play a pivotal role in shaping how these technologies are integrated into business processes and solutions. By addressing the key considerations outlined in this article, project managers can position themselves and their organisations at the forefront of the AI revolution, driving innovation and competitive advantage in an increasingly AI-driven world.