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AI transformation is not only a technological journey. It is a human one

Writer: pamela woitschach
pamela woitschach
Feb 15
5 min read



November 15, 2025

Pamela Woitschach







“The only certainty is that between here and there will be a lot of change. Where there’s change {and humans}, there’s transition.” (Bridges & Bridges, 2016, P. 154).



Drawing on my experience in clinical psychology, business, and research science, I spent this week exploring a critical question: 


What does AI-driven transformation mean for the people affected by it, both inside and outside organizations, and how can organizations manage the human side of this change effectively?


After reading the Report state of AI in 2025: Agents, innovation, and transformation (Quantum Black AI by McKinsey and Company, 2025) and the Report Advancing Responsible AI innovation: A playbook (World Economic Forum, 2025), I was drawn back to Managing Transitions: Making the Most of Change (Bridges & Bridges, 2016), a book that once helped me understand the nature of change and transitions organizations experience during pivotal stages in their life cycle.


Current state of AI adoption

In the pursuit of survival and competitiveness, organizations are accelerating AI adoption across all levels and functions, with some risking the pitfall of AI washing (World Economic Forum, 2025, p. 11). Yet when we step outside and observe everyday life, we see a different reality. Many workers are still exposed to news about technology they don’t fully understand or have equal access to. Highlighting the gap between the potential (of AI) and employees’ lived experience (McKinsey and Company, 2025, p.7).


The Quantum Black AI Report by McKinsey and Company, 2025, recent survey (p. 4-5) highlights that AI tools are now part of most organizations, but they have not yet been deeply integrated into processes and are still far from being scaled. Only two-thirds of respondents are between experimenting and piloting, one-third is scaling AI solutions, and only a small number of respondents are fully scaled (See Exhibit 1). The MIT 2025 report, The GenAI Divide: State of AI in Business 2025, reflects that: despite “high adoption,” there is still “low transformation.” The report observes that there is a dramatic drop from pilots to production for task-specific GenAI tools, with only 5% successfully implemented. ROI remains difficult to quantify, and in many cases, it is minimal. Enterprise-wide EBIT impact still is limited, and most cost savings occur at the level of individual AI use cases (McKinsey and Company, 2025, p. 12).

 



AI-Driven Transformation: The human impact from inside the organization

High AI-performing organizations have been able to define an AI agenda, support change management and align staff behind a shared vision of opportunity through innovation. They have educated and championed their leaders. Data shows that AI high performers have 3.0x leaders that demonstrate strong ownership and commitment to AI initiatives (McKinsey and Company, 2025, p. 18). They have defined vision and processes to measure success and ensure human input for validation. Redesigning individual workflows is the largest contributor to meaningful business impact (McKinsey and Company, 2025, p. 16).

Strikingly, six out of ten best practices seen in AI high-performing organizations are human-based (see Exhibit 14 from AI in 2025: Agents, innovation, and transformation, McKinsey and Company, 2025, p.20).







While a few organizations excel, most are still in the early stages of piloting AI, with employees often being unprepared or unaware of how these changes will be implemented. Bridges and Bridges (2016) refer to this as the “marathon effect.” In a marathon with thousands of runners, the front group sprints ahead, followed by successive waves. By the time the leaders are far along, those at the back who may not have even heard the starting signal are just beginning to move.


AI-Driven Transformation: The human impact from beyond the organization

Another key ingredient to enable organizations to advance responsible, resilient, and scalable AI efforts is collaboration with stakeholders. The WOF Report (p.4) notes that despite many organizations reporting the benefits from implementing responsible AI efforts, fewer than 1% of organizations have fully operationalized responsible AI. The report emphasizes that:


“This gap in implementation slows progress, undermines trust in AI technologies and limits their transformative potential.” p.4


The WOF report presents a playbook including nine essential plays across three key dimensions of responsible AI for organizations and government leaders that they can adapt based on their AI maturity level. This time, I will only focus on Play 3: Designing resilient, responsible AI processes for business continuity.


While driving transformation, much of what we implement or plan to implement directly involves external stakeholders: those who benefit from our products and services, and those whose buy-in is essential. To successfully create and deploy AI-enabled systems and products in a rapidly evolving tech landscape, we must design resilient solutions. Achieving this requires ongoing engagement with external actors, ensuring a balance between global consistency and local responsiveness (World Economic Forum, 2025, p. 15).


AI Transformation: Managing transitions

John McCarthy coined the term “artificial intelligence” in 1955, and since then, it has evolved from a scientific concept into a reality transforming society and business (Accenture, 2025).


In Imagining AI: How the World Sees Intelligent Machines, Stephen Cave and Kanta Dihal explore the term “AI” and its equivalents in other languages. The authors remind us that “AI” was coined not to name a specific technology, but an aspiration, an idea of intelligence beyond the human (Cave & Dihal, 2023).


To help us be on the same page, let me clarify the concepts of change and transition from Bridges and Bridges’s book (2016), and compare them with transformation. The authors explain that change refers to an external, situational event (action), while transition is an internal, psychological experience people go through when adapting to a change. On the other hand, transformation involves a deeper, structural shift closely tied to identity.


The three stages of transition proposed by Bridges and Bridges (2016) can be a powerful support for organizations navigating change.

1. Letting go: Transition begins with letting go of old ways and identities, confronting losses, and preparing for endings.

2. Neutral zone: This is the in-between space where the old is gone but the new has not yet arrived. Though filled with uncertainty, this stage offers opportunities for creativity, reflection, experimentation, and innovation.

3. New beginning: This emerges naturally when the transition is complete. It brings renewed energy, a sense of purpose, and confirms that the ending was real, even if it still feels challenging at first.


AI represents a once-in-a-generation transformation. But transformation only succeeds when humans feel equipped and supported through the transition process. This is why the heart of AI transformation is profoundly human.

Reference list 


References

Bridges, W., & Bridges, S. (2016). Managing transitions: Making the most of change (4th ed.). Da Capo Press.

Cave, S., & Dihal, K. (2023). Imagining AI: How the world sees intelligent machines. Oxford.

McKinsey & Company. (2025). State of AI in 2025: Agents, innovation, and transformation (Quantum Black AI). McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

MIT. (2025). The GenAI divide: State of AI in business 2025. Massachusetts Institute of Technology. https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf

World Economic Forum. (2025). Advancing responsible AI innovation: A playbook. World Economic Forum. https://www.weforum.org/publications/advancing-responsible-ai-innovation-a-playbook/


Disclaimer: The views expressed in my posts and articles are my own and do not represent the official positions or policies of any institution or organization with which I am or have been affiliated. All content is shared in a personal capacity.

 
 
 

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