The previous article in this series made a claim that many executives recognise the moment they hear it: most organisations do not have a strategy problem. They have a delivery problem. The strategies exist. The investments are approved. The priorities are set. What fails is the capability to govern, execute, adapt, and realise value across a portfolio of change. That argument closed by asking what happens next, as a force even larger than organisational complexity enters the picture. That force is artificial intelligence, and it changes the economics of leadership in a way that makes the delivery capability problem not smaller but more consequential.
For a long time, a meaningful share of competitive advantage was transactional. Organisations competed on the ability to do technical work well: to process, analyse, draft, code, and optimise faster and more reliably than their rivals. That advantage is now being dismantled. AI is exceptionally capable at precisely the work that once conferred transactional advantage: pattern recognition, rule-governed activity, repeatable process, data-intensive analysis, draft generation, and process optimisation. Dave Ulrich, writing on AI and the future of organisations, observes that this is the very work through which people once entered and progressed in organisations. Erik Brynjolfsson and colleagues at Stanford’s Digital Economy Lab have quantified the effect, finding that early-career workers in the most AI-exposed occupations have seen a relative decline in employment of roughly 13 percent since generative AI became widely available, while experienced workers in the same fields held steady or grew.
The strategic implication is larger than a hiring trend. When a capability becomes universally and cheaply available, it stops being a source of advantage. A Harvard Business School field experiment led by Karim Lakhani and colleagues, conducted with 758 Boston Consulting Group consultants, found that for tasks within AI’s reach, access to GPT-4 raised the quality of their work by more than 40 percent and their speed by more than a quarter, and that it lifted the least experienced the most, narrowing the gap between stronger and weaker performers. If every organisation can summon the same technical competence on demand, technical competence no longer separates the winners from the rest. The floor is rising for everyone at once.
The floor is rising for everyone at once. What an organisation can do is being levelled. The question that now separates organisations is how well their leaders can lead change.
This forces a question many leadership teams have not yet confronted honestly. If the transactional layer is no longer scarce, what is? The answer is the one thing AI does not supply. AI can inform a decision, but it does not own the consequences of it. It can model a risk, but it does not carry the responsibility for it. It can draft a plan, but it cannot align a divided executive team behind it, govern the trade-offs as conditions change, or hold an organisation to the realisation of value long after early enthusiasm has faded. That work is leadership.
The differentiator is becoming human leadership capability: judgment, ethics, governance, the definition and protection of value, change leadership, and the ability to translate strategic intention into measurable outcomes. Antonio Nieto-Rodriguez has shown that in the Project Economy, value is now created through change rather than through routine operations. Leading change is precisely where advantage concentrates. Pierre Le Manh , who leads the Project Management Institute, has placed that same capability at the forefront of the AI era, on the conviction that the people who turn ideas into reality are the ones who carry transformation forward.
It is worth being precise about the kind of leadership this demands. It is not the steady administration of a stable function. It is adaptive leadership: the capacity to move through diagnosis, prioritisation, delivery, governance, and adaptation without losing clarity of purpose or accountability for outcomes. The work of change does not stay still, and neither can the leader who carries it. Judgment exercised under genuine ambiguity, accountability that persists long after the project is launched, the discipline to stop investment that is no longer creating value: these are the capabilities that AI cannot supply and that organisations increasingly cannot afford to leave to chance.
If leadership capability is now the competitive advantage, the uncomfortable question is whether organisations actually possess it, or whether the Project Economy has quietly opened a shortfall that years of technical training were never designed to close.
The basis of competition is quietly shifting. The question that used to separate organisations was what they could do, and AI is steadily answering that for everyone. The question that now separates them is how well their leaders can lead change. That question has no automated answer. The advantage that remains, as AI raises the technical floor, is the human capability to lead change well, built deliberately rather than assumed. That is a very different thing from adopting the latest tool, which every competitor will adopt in the same quarter.
Accepting that, though, only sharpens the next question. If leadership capability is the competitive advantage, the uncomfortable matter is whether organisations actually possess it, or whether the Project Economy has quietly opened a shortfall that years of technical training and tooling were never designed to close. Naming that shortfall precisely is where this series goes next. Not as an abstraction, but as a specific gap with a specific cause and a specific consequence for every organisation that believes its leadership development programme is keeping pace with the work its leaders are now being asked to do.
A question worth sitting with. In your organisation, as AI levels the technical work, are you investing in the one advantage it cannot replicate, or still competing on the one it is steadily erasing?
If these questions are live in your organisation, the conversation is worth having. You can book a complimentary strategy conversation with Samia at snsccs.com.
About the author
Samia N. Waqar is the Founder and CEO of S&S Coaching Consulting Services and the creator of Role Agnostic Project Leadership™, a 50-hour executive development program designed for C-suite and senior leaders. She is a PMI Authorized Instructor and holds nine globally recognised credentials spanning the full capability framework she teaches. Connect with Samia at snsccs.com or on LinkedIn.
Sources
Ulrich, D. (2026) ‘AI, Organization Form, and Personalized Career: The Chameleon Employee’, LinkedIn Pulse, 23 June.
Brynjolfsson, E., Chandar, B. and Chen, R. (2025) Canaries in the Coal Mine? Six Facts About the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab. Available at: https://digitaleconomy.stanford.edu/publications/canaries-in-the-coal-mine/
Dell’Acqua, F., McFowland III, E., Mollick, E.R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F. and Lakhani, K.R. (2023) Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper No. 24-013. Available at: https://www.hbs.edu/faculty/Pages/item.aspx?num=64700
Nieto-Rodriguez, A. (2021) ‘The Project Economy Has Arrived’, Harvard Business Review, November to December 2021. Available at: https://hbr.org/2021/11/the-project-economy-has-arrived
Le Manh, P. (2024) ‘The Human Edge: Why Leadership Matters More in the Age of AI’, Project Management Institute. Available at: https://www.pmi.org/