Prioritize Human Factors: The Hidden Key to AI Project Success

In the race to adopt artificial intelligence, organizations are learning a hard truth—success isn’t just about the technology. A recent RAND report highlights a startling statistic: 80% of AI projects fail. But why? The answer lies not in the algorithms or the data, but in the human element that underpins these initiatives.

As we dig deeper into the reasons behind these failures, HR leaders will most likely have a critical insight: The success of AI projects hinges on people and the ways in which they work, far more than on technology alone. For example, Mickey McManus, a senior advisor at BCG, tells clients that success is “70% people and ways of working” and only ”30% technology.” This reality places HR at the forefront of AI transformation, with an important role in leading change communication as well as learning and development (L&D) around basic human factors.

The Skills of the Future: A Moving Target

Before we dive into the specific reasons for AI project failures, it’s worthwhile to understand the shifting landscape of skills needed in this new era. The AI revolution is accelerating the pace of change in required workforce skills. According to SHRM’s 2024 Talent Trends report, changing technology is one of the top reasons why organizations are seeking new skills. However, many organizations struggle to find qualified individuals with these emerging skills.

What’s particularly noteworthy is that after technical skills, three of the top five additional skills that organizations are seeking for new technology hires to possess are human factors:

1. Soft skills: In an AI-driven workplace, uniquely human capabilities such as emotional intelligence, creativity, and interpersonal skills become even more crucial. These skills enable employees to navigate complex social situations, foster collaboration, and bring a human touch to technology-driven processes.
2. Management: As AI takes over routine tasks, effective management becomes more important than ever. Managers need to be adept at leading diverse teams, integrating AI solutions into workflows, and balancing technological capabilities with human strengths.
3. Leadership: In a rapidly evolving technological landscape, strong leadership is essential. Leaders must guide their organizations through digital transformation, make strategic decisions about AI implementation, and inspire their teams to embrace change and innovation.

This emphasis on human factors underscores a critical point: Technical skills are necessary, but it’s the uniquely human capabilities that will truly drive success in the AI era. Organizations need leaders and managers who can navigate the complexities of AI integration while fostering the soft skills that enable teams to thrive in this new environment.

This skills gap presents both a challenge and an opportunity for HR. Organizations need to prioritize upskilling and reskilling programs, develop AI/human collaboration skills, and foster a culture of continuous learning. HR professionals are uniquely positioned to lead these initiatives, ensuring that their organizations have the right mix of technical and human skills to thrive in the AI era.

Points of Failure and Human Factors 

Now, let’s explore the five main reasons for AI project failures identified in the RAND report and dig deeper into where, in my opinion, the underlying human factors contributing to them become obvious.  These human factor assessments are my “between-the-lines" reflection on RAND’s analytical and technical assessment of failure.

1. Leadership-Driven Failures: More Than Misaligned Goals

On the surface, leadership-driven failures appear to stem from misunderstanding AI capabilities or selecting the wrong problems for AI to solve. However, digging deeper reveals a more fundamental issue: communication breakdowns and lack of buy-in.

The real problem here is a disconnect between leadership and technical teams. Leaders often fail to involve technical experts sufficiently in clarifying business needs, while technical teams struggle to effectively challenge or communicate with leadership. This results in unrealistic expectations and a lack of shared understanding.

Key human factors:

• Insufficient collaboration between business and technical teams.
• Lack of feedback loops for iterating on goals and expectations.
• Leadership focuses on demonstrating AI adoption rather than solving real problems.

2. Data-Driven Failures: Beyond Quality Issues

Data quality and availability are often cited as technical issues leading to project failures, but the root causes are largely organizational. Data silos, poor documentation, and misuse often result from teams not working together effectively to understand both the domain and the data itself.

Key human factors:

• Poor collaboration between data engineers, data scientists, and business units.
• Lack of shared understanding about data quality and organization.
• Undervaluation of data engineers, leading to turnover and disrupted information flow.

3. Bottom-Up Failures: The Pitfall of Shiny Objects

Technical teams focusing on cutting-edge tools rather than solving real business problems might seem like a simple case of misaligned priorities. However, this issue is fueled by poor communication between teams and incentive structures that reward technical novelty over practical outcomes.

Key human factors:

• Isolation of technical teams from broader organizational goals.
• Lack of collaborative feedback loops to align technical efforts with business needs.
• Insufficient engagement of technical teams in understanding business challenges.

4. Underinvestment in Infrastructure: A Communication Gap

The reluctance to invest in the necessary infrastructure for AI projects reflects broader organizational and communication issues. Often, technical teams fail to articulate the importance of infrastructure in a way that resonates with business needs and priorities.

Key human factors:

• Disconnect between leadership and technical teams in setting priorities.
• Gap in how the importance of infrastructure is communicated and understood.
• Short-term focus on quick wins rather than long-term foundational investments.

5. Immature Technology and Overconfidence: Managing Expectations

Issues around immature technology and overconfidence in AI capabilities stem from inflated expectations and poor communication. Leaders may not understand AI’s limitations because technical teams aren’t effectively educating them, leading to unrealistic goals and eventual disappointment.

Key human factors:

• Breakdown in setting realistic goals for AI projects.
• Failure to manage expectations and communicate AI’s probabilistic nature.
• Hype-driven rather than reality-based understanding of AI capabilities.

The Critical Role of HR in AI Transformation

As we unpack these five failure points, it becomes clear that HR has a pivotal role to play in ensuring the success of AI projects. By focusing on the human factors that underpin these challenges, HR can help bridge the gaps between technical teams, business units, and leadership.

HR must take the lead in facilitating clear, ongoing training in how leaders learn to communicate as well as the incentives, explicit and implicit, that are communicated through performance management and culture about how departments or business units collaborate. This L&D action will impact the relationships between all stakeholders involved in AI projects. As the custodian of learning and development, HR is also uniquely positioned to build AI literacy throughout the organization—and it can play a crucial role in breaking down the silos that often hamper AI project success by fostering a culture of collaboration.

As AI reshapes the skills landscape, HR must lead the charge in ensuring the organization has the right talent mix. This involves identifying critical skills gaps, creating career paths that value both technical expertise and business acumen, and implementing retention strategies for key AI talent.

The Long View: AI’s Gradual but Transformative Impact

The current hype around AI might suggest a sudden, disruptive change, but the reality is likely to be more gradual, akin to the adoption of electricity. Certain sectors such as customer service may see rapid changes, but widespread integration across industries will take time as AI improves.  

Currently, enterprises struggle with AI implementation, and significant productivity gains remain elusive. Only a small percentage of U.S. industries are effectively utilizing AI, with adoption particularly low in manufacturing and agriculture. This slow progression stems from the ongoing learning curve in real-world applications, cautious approaches by major economic sectors, and pending scientific breakthroughs needed to accelerate adoption.  

Despite this gradual pace, AI will ultimately reshape our technological landscape and become a permanent, omnipresent force.

Conclusion: The AI/Human Synergy

As we navigate the AI revolution, it’s clear that success lies not in the technology alone, but in how we as humans engage with it. By focusing on human factors—soft skills, management, leadership, and collaboration—organizations can unlock the true potential of AI.

Here’s what HR professionals and organizations must do:

1. Prioritize human factors: Invest heavily in developing soft skills, management capabilities, and leadership acumen. These uniquely human abilities will be your competitive edge in the AI era.
2. Lead the change: Step up and take charge of your organization’s AI transformation. Spearhead change communication initiatives and foster AI literacy across all levels.
3. Bridge the gap: Create programs that facilitate collaboration between technical teams and business units. Break down silos and build a culture of cross-functional innovation.
4. Future-proof your workforce: Develop comprehensive upskilling and reskilling programs. Prepare your employees not just for the AI of today, but for the rapid evolutions to come.
5. Embrace ethical AI: Establish clear guidelines for AI implementation that prioritize transparency, fairness, and human values.

The journey ahead is long, and the challenges are significant. But with a focus on the human element, organizations can navigate the AI revolution successfully, unlocking new possibilities and driving sustainable growth in the AI era.