Why you are not ready for AI agents and not even AI — Obstacles to AI adoption and how to overcome them
AI agents are all over our newsfeeds these days. In San Francisco there are even billboards suggesting to stop hiring humans.
As if AI itself wasn’t overwhelming enough.
Most companies are still struggling with basic AI adoption let alone being ready for AI agents. Ironically it’s not the technology that is the problem. It’s people. It’s humans and a lack of human intelligence — and not the mental kind we typically measure in IQ tests and that AI is already beating us at.
“90% of successful AI transformation is about leadership, culture, and execution. Only 10% is about the technology.” — BCG
Obstacles to AI Adoption
Cognitive Overload and Overwhelm
Every day there seem to be new AI tools popping up and the next one might just be the one... Even as someone who has been following AI for decades, and thinking about its impact on how we work, I find myself overwhelmed trying to keep up with the pace of AI development. Unless this is your primary profession and focus, I think it is nearly impossible to truly understand the current market beyond a superficial layer.
As Marshall McLuhan pointed out: “When humans face information overload, they become passive and disengaged.”
Rapid advancements of AI and constant information leave decision-makers overwhelmed, leading to procrastination in AI adoption.
Instead of focusing on AI, focus on your brand promise and pick an area of customer value you can improve with AI.
Lack of Leadership Understanding
The rise of LLMs has also given people a skewed understanding of AI. Most people equate AI with models like chatGPT, Claude, Co-Pilot or Gemini. But AI is far more than that. We will probably see more and more SLMs (small language models) and specific purpose AIs in the future (and, of course, AI agents).
Beyond that misunderstanding, many executives treat AI as a plug-and-play tool rather than a fundamental shift in decision-making and operations.
AI adoption will fundamentally shift how we organize ourselves and what organizations will look like — including the kind of people we will need.
Without clarity on its capabilities and strategic impact, initiatives lack direction and fail to deliver.
Leadership Misalignment
While many executives advocate for AI, and nearly every leader is quick to say that we need to adopt AI, many fail to align budgets, incentives, and teams to support its implementation.
You don’t have to invest billions in your AI strategy, but you do need to carve out budget to specifically support AI experimentation and more so on building a culture of experimentation. This is not just some slush fund spending, it is a fundamental shift in your organizational development stage.
It is a shift from an optimization focus where you assume a stable core business to be continuously made more efficient — while innovation is happening on the sidelines — to a focus on continuous transformation across the organization, where every business model, even every process is seen as going through different stages of development from idea to ultimately sunset.
Leadership gets to focus on creating a culture of large scale experimentation in service to your brand promise and customer value.
Fragmented Pilots
That large scale experimentation has to be managed in a different way than regular business. Organizations in an optimization stage of development were already suffering from silos and redundant work across multiple departments.
With AI experiments in particular, it is key that projects don’t get stuck in departmental silos, or the wrong use cases are chosen, as that leads to minimal traction and impact.
Focusing on your brand promise, and your customer, pilots for AI implementation need to bring together people from across the organization.
Instead of each department playing around with AI, focus on a new overarching vision and narrative that is in service to your brand promise and the value you create for your customer.
Then bring together resources from across the organization that can implement that new way of operating.
Weak Governance
When embarking on new ways of operating, effective governance, especially for AI, is essential. Without clear guidelines and accountability, responsibility for AI outcomes becomes unclear, eroding stakeholder trust and slowing progress.
Poor governance, especially around training data, also perpetuates biases in AI systems, leading to unfair outcomes and transparency issues that further undermine confidence.
Weak governance affects data management, risking breaches, privacy violations, and inaccurate data use that can expose your organization to legal and reputational risks.
For successful AI adoption, focus on creating robust governance frameworks based on the values you stand for in your brand promise, with clear accountability, transparency, and ethical standards.
Resistance and Fear
Talking about ethical standards: it is key to get to a vision beyond headcount reduction as the ROI for AI adoption. People are already afraid that eventually AI will replace them. So why would they support this process?
The easiest way to destroy psychological safety in an organization is to announce layoffs. When banners tout not hiring humans anymore, it seems clear that the resulting fear would stifle any effort to get people to participate in adopting it, and create resistance to change instead.
There are ways to overcome resistance to change, but key with AI adoption is to ensure people that there is a place for them, that AI will not replace but enable them to do more meaningful human work. That they will be part of the future and that you will train them to have the skills and capacities required for this new age.
Create a vision where people and AI together serve your customer, where AI addresses what it is good at, while supporting humans to do human work, with more autonomy, increasing competence and ultimately a sense of relatedness and belonging.
Relational Intelligence Deficit
Those three factors of autonomy, competence and relatedness are key to intrinsic motivation according to self determination theory. But self-determination ultimately requires self-authoring people.
Most adults are still not at this stage of adult development. For your people to handle AI they need to first grow in their capacity to relate: to themselves, to each other, to your customers, and then to AI.
Especially agentic AI will require clear agreements and solid instructions to perform as desired. People who are externally driven and lack relational intelligence will be guided by AI instead of the other way around.
This requires more than upskilling. Effectively using AI requires you to uplevel your people to a new stage of development.
Upleveling People Over Upskilling
Beyond technical skills, employees need to grow in their capacities, they need to become intrapreneurial in their mindsets and behaviors to embrace what AI can do for your organization.
Apart from resilience, emotional self-regulation, adaptability and relational intelligence, capacities required to deal with all the uncertainty this liminal space of transformation brings, they will need the hallmark capacities of intrapreneurs:
- Self-Reliance — Understanding oneself as a the source of impact, culture and results, independent of position within an organization. This is the foundation of authenticity and relational intelligence.
- Self-Expression — Learning to honor one’s own voice, and the voice of others, so we can use everyone’s genius in co-creation and human-AI collaboration.
- Self-Management — Being accountable and committed, and having the ability to adapt actions toward success. Beyond self-regulation, this requires the ability to consciously relate to oneself, and to take in feedback as a tool for growth.
- Self-Organization — The ability to take an idea from first signal to launching a new venture, and being able to enroll support and resources. AI enabled organizations will look quite different, more akin to holocratic models than traditional hierarchies. This requires innovation everywhere and people who can self-organize around customer value creation.
You will not be able to reach all your people immediately. Here too, the innovation adoption curve applies.
Therefore focus on your innovators and let them build your AI vision, your systems of governance and your cross-silo community.
They are already in your organization. They are the ones, willing to go first. Identify them, then train and empower them to become transformational catalysts for your early adopters, who in turn will bring the rest of the organization along.
4 Types of Transformation Catalysts
You will ultimately require four types of transformation catalysts, which are generally required in any transformation, not just AI. Each addresses different obstacles mentioned above:
- Navigators — they are strategists that provide a north star and decision paths. They create guidelines and policies and manage operational changes. They are focused on a repeatable structured approach and frameworks, and coordination across the organization. They bring clarity, educate and align leadership.
- Guides —they are integrators supporting the emotional processing any transformation requires. Using a psychologically savvy approach, they enable openness and adaptability first and foremost by listening and creating safe spaces for processing anxieties and ambivalences, and as a result foster resilience, empathy, and trust. They reduce overwhelm, resistance and fear and increase relational intelligence.
- Intrapreneurs — as already mentioned, they are the ones actually experimenting with AI, building new prototypes and POCs with your customers. They are customer advocates and rapid learners, surface new opportunities for value creation, and together with your technical experts build future iterations of your business. They build and align your prototypes and create early successes.
- Stewards — Finally, you get to find the guardians of metrics and accountability. They are the custodians of operational integrity focused on transparency and accountability for informed, responsible decision making. They ensure adherence to governance and focus on maintaining integrity and compliance, while integrating AI into the fabric of your business. They ensure adherence to your governance and that your don’t “break things while going fast”.
Adoption of AI and even AI agents is all about people
All the obstacles listed above: overwhelm, lack of leadership understanding or alignment, fragmented pilots, weak governance, fear and resistance and a lack of relational intelligence, are ultimately human issues.
As the BCG quote above said, AI adoption is not about the technology, it’s about leadership and culture. It’s about people.
Technical upskilling is required, no question. But it has to be done while focusing on human development and cognitive well-being.
Skills are best acquired when people solve real world problems. And capacities are best built while doing that — within a proper space of protection and reflection for growth.
At LUMAN we create strategies for transformation and run cohort based trainings, where we develop people while getting work done. We found that creating an engaging narrative, gathering your transformation catalysts, and then getting them to create the future together had lasting impact not just on the participants, but on their home departments, spreading transformation organically throughout the organization. One of our clients found that one participant in average affected ten people around them in how they work.
So, before hiring AI agents, work with the people you have to adopt AI in your organization — but make sure to develop and uplevel them in the process.
Let’s transform your organization toward a new operating system powered by AI.
Connect with me at https://philiphorvath.com or through LUMAN at https://luman.io
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