Learning by doing, at the state of the art.

Between buying the technology and extracting value from it lies a path most organizations have not yet travelled, and the evidence is hard: the majority of AI pilots never reach scale. This area covers the discipline of turning AI into measurable productivity, with the human being at the centre of the process. It goes beyond adopting tools: identifying and prioritizing the highest-value use cases, redesigning workflows end to end (rather than simply layering AI onto old processes), redefining decision rights and incentives, and confronting the barrier that international studies point to as number one, namely the lack of competency and the change of habits. Every programme can be preceded by an assessment with AI PM Compass™, which establishes the baseline for the group.

High performance has always depended on purpose, well-designed roles, trust and rhythm, and now teams include new members: artificial intelligence agents. International estimates suggest that around three quarters of current roles will require redesign, reskilling or redistribution by 2030 because of agentic AI. Across three combinable fronts, this area prepares the leader for that: building high-performing teams in the age of AI; leading hybrid teams, delegating tasks to agents with clear objectives and guardrails, supervising, and answering for accountability when the machine gets it wrong; and deliberately designing the collaboration, mapping where each role meets the agent, what constitutes human supervision and what remains irreducible judgement. It also addresses the human side that technology threatens: trust, identity and purpose when part of the work moves to the machine. It is the thesis that gives HiLOOP™ its name, Humans in the Loop.

The decision pair of our catalogue, across two fronts that almost never travel together. On one side, the analytical repertoire of engineering, science and strategic consulting: breaking ambiguous problems into workable parts, forming hypotheses, recognizing the biases that distort reasoning, and communicating analysis with the clarity of someone who has to convince a board. On the other, scientific thinking applied to decision-making: assessing the quality of evidence, reading data and research critically, distinguishing correlation from cause, and building a culture that decides on evidence without falling into the opposite extreme, the organization paralysed by analysis. It is a competency almost no management instructor teaches with real grounding, because it is not learned from a management book: it comes from scientific training. Here it is led by someone with a PhD from USP and a post-doctorate from MIT, who spent years structuring high-stakes decisions in strategic consulting.

Complex products and systems frequently fail to meet their objectives and deliver benefits not because of technical problems or managerial problems in isolation, but because of the misalignment between the two, and between the levels of decision that surround them. This area covers the full governance of complex delivery across three fronts that rarely talk to each other: the integration of program management and systems engineering; portfolio management (what to do and, above all, what not to do); and the evolution of the function itself, including the discussion of support functions such as the PMO of the future, powered by AI and automation. It is an area in which Edivandro conducted research at MIT and published a landmark book with Wiley.

For two decades organizations pursued agility by redesigning structures, processes and ways of working. AI is changing that equation: it can be the greatest lever of responsiveness ever available, or simply one more layer of complexity. This area covers the full cycle of agility at business level: formulating strategies that take change as a premise, redesigning structures, governance and decision flows, and developing the culture and behaviours that sustain transformation beyond methods. It also incorporates the discipline of execution, translating strategy into a portfolio, initiatives and clear accountabilities, and using AI to bring speed and transparency to delivery, a subject to which Edivandro devoted years leading transformations and global think tanks.

Project and program management has always repeated the argument that “one size does not fit all”, but the main question has always been how to put it into practice. This area answers that with method, starting from a conviction tested in research: the false binary between agile and predictive has cost organizations far too much, and what separates those who deliver from those who merely follow the fashion is the deliberate design of hybrid models adapted to each business context. The starting point is an assessment of the project environment: degree of uncertainty, pace of change, team maturity, nature of the product and client expectation. From that reading you decide when to use each approach and design a model coherent with the context. Edivandro’s research produced concrete instruments for this: models that combine the flexibility of adaptive discovery with the portfolio governance an organization needs to decide where to invest and what the priorities are, with short replanning cycles that keep the plan alive rather than frozen in a document.

Transformations treated as projects fail, because change at scale demands governance of another order and confronts the hardest and most ignored part: people and culture. International research is consistent: most transformations do not deliver the expected value, and the cause is rarely technical. This area covers the full discipline of transforming organizations: the design and governance of transformation initiatives; the roles of leadership and executive sponsorship; the management of change and behaviours, overcoming resistance and fatigue; and the sustaining of results beyond the initial enthusiasm, installing change capability as routine. It is the repertoire of someone who led large-scale transformations at the head of the Business Agility & Transformation practice of an American multinational.

AI does not just change the executive’s tools; it also changes the work of deciding, and widens the responsibility to see ahead, to discover and create the future. This area covers the leader’s agenda for the next decade: competencies, ethical dilemmas and decision-making augmented by data and AI, with human judgement at the centre. It explores the practice of strategic foresight as an executive competency: the structured capacity to explore multiple plausible futures and translate them into decisions of long-term impact, investment and adaptive capability. Most business leaders believe disruption will hold or accelerate its pace over the next five years, and foresight is the discipline that fills that gap, with methods such as signal scanning, scenario building and backcasting.

Ideas do not defend themselves, and knowledge without a voice does not influence. This area joins the two ends of influence. Communicating with impact: structuring executive narratives, sustaining stage presence and presenting data that decides, from the boardroom to the large auditorium (and, increasingly, to the camera). And the deliberate construction of thought leadership: in an age where influence has become currency and is confused with knowledge, it is the discipline of turning real expertise into recognized authority, through territory, positioning, editorial agenda, publication, stages and media, built with method, for executives and specialists who want to build their voice, and for organizations that want to make deep knowledge a brand asset. Edivandro directed the Thought Leadership function at global scale, and brings deep experience in the subject.

Managing research and development is different from managing conventional projects: uncertainty is high, the scope is discovery and success is rarely linear. And businesses born of science have a nature of their own: real technical risk, long cycles, capital intensity and validation on two fronts, scientific and commercial. This area covers both ends: the management of R&D programs and projects (from the front end to the funnel and the portfolio of bets, with decision points and technical risk management) and the deep tech journey from bench to market. Here come the instruments that give engineers, managers and investors a common language: technology readiness levels (TRL), the crossing of the “valley of death”, and the funding gap between laboratory proof and commercial traction where most good technologies die.

There is a gulf between what science produces and what organizations can absorb, and it is costly to both sides. More than crossing that bridge, this area addresses something larger: how to develop science and translate it into application and use at scale. It covers technology transfer, the academia-industry partnerships that actually work (aligning incentives and horizons that rarely coincide), the translation between two worlds that speak different languages, and the crossing from laboratory to business. Edivandro has lived (and still lives) both sides of that frontier, research from USP to MIT and the market, and built his career precisely upon it.

Innovating in uncertain environments demands a competency that is rarely trained: creating in real time. Improvisation offers powerful principles for this: active listening, accepting and building on someone else’s idea, continuous adaptation. Edivandro’s research with colleagues at MIT, published in MIT Sloan Management Review, found, among other results, that teams operating in certain contexts and with certain characteristics are nine times more likely to show high levels of improvisation. This area translates those results into a practical, experiential framework: the conditions of culture, structure and deliberate practice that help develop this competency.

The Future of Jobs Report 2025 projects that 39% of the core skills required in the market will change by 2030, and that 63% of employers already see the skills gap as the biggest barrier to transformation. Even so, almost no organization treats professional reinvention as a discipline. This area turns the number into method: building the habit of continuous reinvention, deciding what to learn and what to abandon, turning accumulated experience into an asset and sustaining long careers, including those that cross borders. It comes out of a path lived in multiple acts, researcher, global executive, entrepreneur, and is designed for development programmes, corporate universities and leadership journeys.