When technology and people evolve at different speeds, AI adoption slows down.
I help organisations and teams prepare the human and organisational environment before AI systems are deployed.
What Is AI Adoption For?
According to Gartner reports, organisations with stronger change adoption capabilities achieve significantly higher revenue growth rates than their peers.
Consider what happens when an AI system is deployed but never truly adopted by the people who could benefit from it every day.
Features remain unused. Adoption becomes fragmented. Resistance develops beneath the surface. Expected ROI gradually declines.
Many of these issues emerge because AI adoption is addressed too late or left to chance, without a shared process connecting those who develop the system with those who will integrate it into daily work.
Before technology comes expectations, beliefs, language and meaning.
These elements shape how AI is described, understood and ultimately used within an organisation.
Who is responsible for uncovering the everyday semantics of teams, defining a shared taxonomy and building an ontology that genuinely reflects how the organisation operates?
Very few organisations address these questions systematically.
This is why AI.People.Adoption™ was created in 2018 by Federica Tabone, building on more than twenty years of experience as an Organisational Psychologist.
How Does AI.People.Adoption™ Work?
AI.People.Adoption™ acts as a bridge between AI developers and AI users.
The model provides guidance throughout the AI adoption phase, maximising the likelihood that AI solutions become embedded in organisational life and generate measurable impact through:
- faster workflows and operational acceleration
- reduced technical and decision-making times
- automation of repetitive activities
- increased individual and team productivity
- reduced manual work and greater opportunities for upskilling
- improved scalability
As an Organisational Psychologist, I collaborate with organisations and AI development companies on an often-overlooked aspect of implementation: everything that needs to be understood and defined before AI system design begins. AI promises productivity, innovation and speed.
AI.People.Adoption™ focuses on what makes these outcomes genuinely achievable: people, language, expectations and organisational dynamics.
These elements are explored through a structured sequence of four workshops.
Key Stakeholders in Effective AI Adoption
When AI is introduced into an organisation, different stakeholders experience the change through different perspectives.
Management, internal customers, end users and developers often interpret the same process through different languages, expectations, desired outcomes and concerns.
The success of AI adoption is often determined by the gap between those who imagine the solution, those who build it and those who will use it every day.
Organisations adopting the AI.People.Adoption™ model involve all relevant stakeholders:
· organisational sponsors
· process experts
· informal opinion leaders
· end users
· AI designers
· knowledge engineers
· AI developers
These stakeholders participate in four focus groups that go beyond traditional needs analysis.
The objective is to identify what must be activated before deployment so that valuable AI solutions do not remain unused, misunderstood or underutilised.
AI.People.Adoption™ creates dialogue between these perspectives, facilitating mutual understanding, organisational alignment and the effective integration of AI into daily work.
What Does the AI.People.Adoption™ Model Analyse?
To connect today’s organisation with tomorrow’s hybrid-intelligence organisation, multiple stakeholders need to collaborate throughout the entire journey, from system design to deployment. AI.People.Adoption™ supports this process by helping organisations:
- assess readiness for change
- analyse beliefs, resistance and perceptions related to AI
- understand the real needs behind requests and expectations
- create a shared language that aligns different perspectives and brings clarity to the solution being designed
- activate future episodic thinking, ensuring that stakeholders are envisioning the same outcomes and building shared future scenarios
AI projects often involve multiple actors with different perspectives, languages and objectives.
If these elements are not explored and managed, adoption risks remaining superficial, incomplete or merely symbolic.
Would you like to avoid resistance, misunderstandings and fragmented use of new AI tools?
Let’s discuss the most effective and sustainable AI adoption process for your organisation.
Frequently Asked Questions
How Does Visualisation Support AI Adoption Processes?
Only organisations willing to recognise internal misalignments, surface differing interpretations and converge around a shared objective can successfully define the critical elements of a transformation process.
This is where the visual synthesis techniques of the Visual Psychology® Method become a key enabler.
During the workshops, we work on three fundamental dimensions:
· shared semantics
· expected outcomes
· future scenarios
Each dimension is explored from the perspective of every stakeholder involved.
Every contribution matters when defining how processes, decision-making systems and skills will evolve as organisational intelligence becomes increasingly hybrid.
When participants see their perspectives reflected in visual summaries, conversations become more integrated and progressively coherent.
As a result, influential stakeholders become aligned and better equipped to act as ambassadors within informal conversations, Teams chats and day-to-day interactions, helping to reassure and influence colleagues who were not directly involved in the design and implementation process.
The Risks of Poor AI Adoption: Weakly Designed Hybrid Intelligence
When AI is introduced without a genuine adoption process, the impact is reflected not only in ROI but in the functioning of the organisation itself.
Consider a simple question:
If the power went out tomorrow, which decisions would continue to exist and which would disappear with the system?
In other words:
Is it truly clear who decides, who monitors, who validates information and who uses AI-generated outputs?
In hybrid-intelligence organisations, where human and agentic intelligence coexist, unclear decision-making responsibilities can create technological dependency, operational overlap, inefficiencies and a loss of shared accountability.
The Risks of Poor AI Adoption: Strategic Information Remains Scattered
Many of the most important insights about AI usage emerge through everyday conversations that are informal, fragmented and distributed across teams, managers, users and developers.
AI.People.Adoption™ helps identify and organise this information before it remains invisible or lost.
Implicit expectations, hidden resistance, operational challenges and practical needs become valuable input both for the organisation and for AI developers, improving future adoption and feature development.
The Risks of Poor AI Adoption: Valuable Features Remain Unused
When end users do not fully understand the purpose or operation of an AI tool, even technically excellent features may be ignored, partially used or gradually abandoned.
This reduces expected ROI and slows the broader process of digital transformation.
The Risks of Poor AI Adoption: Resistance Emerges Too Late
Many forms of resistance do not appear openly during the initial stages.
Instead, they emerge after deployment through operational slowdowns, fragmented usage, passive resistance or the failure to integrate AI into everyday workflows.
Identifying resistance early significantly reduces the risk of later organisational surprises.
The Risks of Poor AI Adoption: Loss of Trust in Change
When people perceive AI as distant, imposed or difficult to understand, the risk extends far beyond a single project.
Trust in future innovation initiatives can also be damaged.
For this reason, semantics, context, objectives and expectations need to emerge through a structured dialogue that reduces misunderstanding, conflicting interpretations and collaboration difficulties among stakeholders involved in the transformation journey.
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