Faculty Lead
Paolo A. Catti
Executive Director, AI-Driven Business Models (AIDBM) Executive Master, Università Cattolica del Sacro Cuore.
About this AI Insight
During summer 2026, 50 open-ended responses were collected from managers and professionals through LinkedIn to explore a simple but strategically relevant question: how are organisations moving from enthusiasm for AI to conscious governance and sustainable competitive advantage?
Three findings cut across almost all responses: AI adoption is moving faster than organisational maturity; people, skills and managerial capabilities are both the strongest enablers of transformation and one of its main constraints; and sustainable value does not come from access to AI technology itself, but from the way it is integrated into processes, decisions, data and organisational knowledge.
Beneath this broad consensus, however, lies substantial conceptual fragmentation. “Governance” is used with at least four distinct meanings, while “competitive advantage” emerges in seven different interpretations. The implication is strategically relevant: executives may believe they are discussing the same AI transformation while, in practice, referring to different objectives, responsibilities and decision models.
The study is qualitative and exploratory by design. It does not offer a statistical representation of the Italian market, but a snapshot of the managerial perspectives currently shaping AI adoption. It also proposes a managerial vocabulary for the AI transition, collecting the expressions emerging from the respondents’ own words – from governance literacy to decision perimeter, from AI theatre to imaginative advantages.
Inside this Insight
What 50 managers were asked
During summer 2026 Catti sent the same open-ended question to managers and professionals in his LinkedIn network and, in some cases, to second-degree contacts: he observed enthusiasm and high expectations around AI, rarely supported by conscious governance or by thinking aimed at long-term competitive advantage, and asked how their organisation or team approaches AI. The question was deliberately not neutral, and the paper says so: its framing influenced the answers, with some respondents agreeing and others explicitly distancing themselves. Fifty unique responses were analysed as of 1 September 2026, coded by functional family – Consulting & Advisory, Business & Commercial, Tech / Engineering / Operations – by level of detail and by dominant perspective. The study is qualitative and exploratory: no statistical representativeness is claimed.
Three findings that cut across the sample
First, AI adoption is moving faster than organisational maturity. The wording changes – “hype”, “reactive adoption”, “AI theatre”, “emotion before substance” – but the gap between what is said about AI and what organisations can genuinely govern recurs throughout. Second, value does not lie in the technology but in its integration with systems, processes, data and organisational knowledge; as access to general-purpose tools becomes commoditised, differentiation moves upstream and downstream. Third, people and skills are the decisive factor, from technical training to the manager as translator between technology, business and people. Several respondents are critical of “human in the loop”: adding a person to the process is not enough to make it safe, and the model holds only if that person has the cognitive skills to make rapid decisions.
“Governance” has at least four meanings
The analysis identifies four distinct uses of the term, each implying a different managerial agenda: governance as compliance (regulatory oversight under the EU AI Act, GDPR, NIS2 and Responsible AI frameworks); as strategic direction, making conscious decisions over time about data, risks and accountability; as a value-creation framework, a system of levers connecting AI strategy to value generation, especially where investment funds are present; and as a decision perimeter – an explicit definition of which decisions remain human, which are delegated to machines, and who is accountable when an output is plausible but wrong. The last is the most demanding operational interpretation found in the responses.
“Competitive advantage” has seven
The responses reveal seven recurring, non-exclusive interpretations: conscious integration into processes and decisions; proprietary data, proprietary questions and speed from insight to decision; organisational culture and calibrated trust in tools; modular technical architecture, with APIs as the real prerequisite for action; measurable KPIs and payback; “imaginative” advantages, which consist of rethinking what can be done rather than doing better what is already done; and a hybrid on-premise/outsourcing approach distinguishing what is core from what is commodity. The common denominator is notable: none of these advantages derives simply from access to an AI model. In every interpretation, advantage emerges from complementary organisational assets.
Where the debate openly divides
Four areas show explicitly opposing positions: whether governance is compliance or strategy; the urgency of adoption, with some comparing AI to the internet in the early 2000s and others – especially budget-constrained smaller organisations – openly postponing investment; where the real gap lies, variously located in technical skills, in managers’ decision-making skills, or in the maturity of change management; and the role of AI in creative work. Taken together, the four meanings of governance and the seven interpretations of advantage point to a deeper issue: the managerial debate does not yet operate with a shared strategic vocabulary, and executives sitting at the same table often imagine different futures while using the same words. The paper closes with a managerial vocabulary for the AI transition, collecting expressions coined by respondents – among them governance literacy, decision perimeter, earned autonomy, AI theatre, orchestrators of hybrid human-machine systems and imaginative advantages.
About the Author
Paolo A. Catti works at the intersection of digital transformation, B2B innovation and Artificial Intelligence, combining academic research, consulting and executive education to help organisations translate technological innovation into tangible business value.
As Executive Director of the AI-Driven Business Models (AIDBM) Executive Master at Università Cattolica del Sacro Cuore, he works with executives and professionals to understand and apply Artificial Intelligence in real business contexts. His areas of interest include digital strategy, business model innovation, process transformation and the adoption of emerging technologies to drive organisational change and competitive advantage.
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