At HighESt Lab, we understand, interrogate, and improve the models underlying Artificial Intelligence. Social and theoretical research is essential for developing more effective, transparent systems capable of tackling complex problems in original ways. We experiment with new architectures, training techniques, and human-machine interaction methods that foster not only efficiency, but also creativity and the ability to ask relevant questions.
For this reason, the lab collaborates directly with companies and university students to provide multiple perspectives on the challenges ahead. Below are our ongoing projects.
Our approach, developed from studies conducted in collaboration with Harvard Business School – The Digital, Data, and Design (D³) Institute, views artificial intelligence as a true medicine entering the economy, organizations, and everyday life.
AI is an extremely powerful and promising technology, yet still not fully understood. We do not yet know the optimal dosage, its real effectiveness, its potential side effects, nor the conditions required for a safe, sustainable, and high-impact adoption.
For this reason, we conduct organisational “clinical trials” and longitudinal studies within companies, observing how organizations operate with AI systems under controlled and differentiated conditions (such as different team configurations, roles, skills, and levels of AI integration). This approach allows us to systematically assess the impact of AI on key variables, including innovation capacity, skills development, productivity, and work experience.
A new way to observe AI in organizations. To understand which new organisational configurations companies need to develop, which new skills must be acquired, what the future “AI-augmented employee” will look like, and which governance mechanisms and safeguards are required, it is essential to observe, monitor, and analyze the first real-world uses of AI inside organizations.
Partner: Accenture Italia
Tecnology: Amethyst Studio & Copilot Accenture 365
Duration: 8 months State: Ongoing
The project is grounded in a core assumption: the value of AI does not lie in replacing people, but in collaboration between humans, organizations, and AI systems. Because humans and AI tend to make different kinds of mistakes, identifying the most effective forms of collaboration helps improve outcomes, increase productivity, and expand human cognitive and decision-making capabilities.
What we observe: Data analysis enables the identification of emerging and unexpected patterns, including:
The disruption of traditional organizational models based on specialization and rigid functional boundaries, as Generative AI enables more cross-functional and holistic reasoning;
The emergence of positive psychological and experiential effects associated with interaction with conversational AI systems, in line with the most recent academic literature;
The redefinition of optimal team size and composition, moving toward more flexible, adaptive, and efficient organizational structures.
Project outcomes: The project delivers:
a strategic report supporting companies in identifying new organizational models and competency development trajectories, enabling a more effective use of AI;
the development of a scientific publication, co-authored with the participating organizations;
a results-sharing event, where the organization presents the outcomes of the trial and outlines next steps.
Keywords: organizations, evidence-based, AI
Do you want to launch a clinical trial in your company? If you are an organization interested in experimenting with AI through a scientific, controlled, and evidence-based approach
Partner: Accenture
Duration: 12 months State: Ongoing
Italian startups, Artificial Intelligence, and the transformation of business and organizational models.
The objective of the project is to systematically analyze the adoption and use of artificial intelligence in startups through an innovative analytical tool: the AI Adoption Index.
The AI Adoption Index is a framework designed to assess the level of AI adoption specifically within startups, across five key dimensions of the business model: strategy and governance, infrastructure and data, skills and culture, operational processes, and integration of AI into the value proposition.
The project starts with an in-depth analysis of startups aimed at objectively identifying AI-native startups, moving beyond generic or self-declared classifications. Building on this classification, the project investigates whether being an AI-native startup is associated with a greater organizational, competitive, and industry-level impact.
A further objective is to explore whether, within highly agile and innovation-driven organizations such as startups, it is possible to identify new organizational models, new ways of organizing work, and new skill development trajectories that could also be relevant and transferable to large companies.
If you are a startup and would like to take part in the research, click here.
Keywords: startups, business models, ai adoption