Despite this potential, the hype around AI often leads to unrealistic expectations, causing disillusionment when quick returns don’t materialize. Boston Consulting Group notes that only a minority (5%) effectively scale and thrive using AI.

Now, here’s where we start diverging with some experts. When Boston Consulting Group also reports that 62% of companies face a significant talent shortage, it’s possible that they’re conflating an organization’s ability to create and manage tools with its ability to adopt those tools.

Organizations must revisit the People, Process, and Technology paradigm to harness AI’s potential. The predominant technology-first mentality risks overshadowing the need for robust process improvements and, critically, a deeper investment in people. This imbalance spells significant implications, including the underutilization of AI’s capabilities, employee discomfort, and disengagement, ultimately leading to cultural stagnation against the backdrop of technological change.

This changing environment isn’t new, though AI is its current poster child. I love this quote from Mark Savinson, who is writing a guest blog post on Kompetently.AI about Change Management: Has the rate of change increased? “Yes, the pace of change – think about this – it took the telephone 75 years to reach 50 million users, but it took the internet just 4 years. Similarly, smartphones achieved this milestone in a mere 3 years.” To help us transition this conversation, he pointed out, “Thomas Gilbert, a notable figure in the field of performance improvement, posits that organizational factors drive approximately 75% of its ability to change, including systems, processes, and management practices, while 25% is attributable to individual actions and behaviors.”

These change elements represent the external pressures organizations must recognize and address to adapt and thrive. The goal is to understand how changes in the environment—such as technological advancements (AI), shifting markets (economic headwinds), or internal growth—can shape an organization’s needs for leadership.

Let’s look at the elements:

• Volume: Volume refers to the sheer number of environmental changes and the amount of data, tasks, and new information generated by change.
• Velocity: Velocity represents the speed at which these changes occur. Fast-moving technological innovations or shifts in customer expectations can challenge organizations to adapt quickly. Velocity forces organizations to be nimble, making decisions in shorter timeframes to stay competitive.
• Variety: Variety highlights the diversity of changes affecting an organization. Technological advancements rarely occur in isolation—they impact multiple departments, processes, and systems. Organizations must manage various changes simultaneously, which can increase complexity and require cross-functional collaboration.

Successful AI readiness hinges on an organization’s leadership team championing change from the top down. It begins with recognizing and fostering cultural traits conducive to AI integration—agility, resilience, and a shared vision. For instance, leaders at a leading tech firm transformed their AI readiness by prioritizing innate skills and fostering an environment of continuous learning and cultural metamorphosis driven by a leadership commitment to these softer competencies.

The Unsung Heroes: Policies and Steering Committees

While technological capabilities may seem prima facie to readiness, the role of effective processes is the path to the right direction. Governance frameworks, policies, and steering committees are the intermediaries that bridge technology with human capacity. However, this process-centric focus can only succeed if human capital development is in place to overcome inertia and resistance.

Spotlight on People: The Undervalued Element

Technology may be the direction, and process and governance may be the path, but people are the vehicles that propel innovation and are the critical element in adapting to change and achieving adoption.

Beyond training to use tools, softer skills such as psychological readiness, adaptability, and problem-solving are pivotal as organizations try to enact AI adoption. Despite this, many organizations struggle to

Advanced Competency Assessments: A New Paradigm in Readiness Evaluation

Traditional assessments seldom capture the complexity of AI readiness. Kompetently emphasizes the necessity of utilizing advanced, dual-focused assessments that gauge both organizational and individual readiness. These tools should offer dynamic insights aligned with roles and organizational goals, ensuring that the readiness journey evolves as technology does. Organizations can foster a more resilient workforce by deploying continuous feedback mechanisms and behavioral evaluations.

Here are examples of the skills and competencies necessary to adapt to change, in particular, organizational readiness to adopt AI:

Psychological Traits:

• Cognitive Flexibility: Adapting thoughts and behaviors in response to new, changing, or unexpected situations is critical for embracing new technologies.
• Curiosity and Openness to Experience: A strong desire to learn and explore new concepts correlates with the ability to adapt to innovative technologies like AI.
• Critical Thinking: Important for assessing AI outputs and making data-driven decisions.

Analytical Traits:

• Problem-Solving Skills: Effectively identifying, analyzing, and resolving complex problems is essential for interpreting AI insights and applying them to real-world scenarios.
• Data Literacy: Understanding, interpreting, and leveraging data is fundamental for working effectively with AI algorithms and models.
• Systems Thinking: Understanding how different parts of a system interrelate is vital for integrating AI within broader organizational processes.

Emotional Traits:

• Resilience: The ability to withstand setbacks and quickly recover is key for navigating the challenges of AI implementation.
• Emotional Intelligence: Recognizing and managing emotions is crucial for managing change and maintaining strong team dynamics during AI adoption.

Attitudinal Traits:

• Growth Mindset: Believing that effort and learning encourage continuous improvement and new abilities development.
• Innovation Orientation: Embracing new methods and ideas drives the willingness to experiment with AI solutions.
• Collaborative Spirit: Working cooperatively with others facilitates the interdisciplinary collaboration necessary for successful AI integration.