Andy Demir | Qalbur Perspective | Strategy | 14 minute read
Executive Summary
Strategic thinking can be developed, but the capability now needs to develop differently. Artificial intelligence is making research, comparison, summarisation and first-stage analysis faster and cheaper. That moves executive value towards framing, judgement, imagination, contextual understanding, reflection and responsibility. Experience remains important, although experience that is never questioned can become a constraint rather than an advantage. The task for leaders is therefore not simply to become competent users of AI. It is to use new technology while continuing to develop the human capacity to decide what deserves attention, which assumptions no longer hold and what should be done when several credible answers remain possible.
In This Article
- Strategic Thinking Begins Before Strategy
- A Decade Changed the Conditions for Strategic Thought
- Information Is Becoming Abundant. Judgement Is Not.
- The First Strategic Decision Is Often How We Define the Problem
- Some Things Are Changing Fast. Others Hardly Change at All.
- Experience Creates Judgement, and Sometimes Blindness
- Personal Development Must Move Beyond Accumulating Expertise
- AI Should Increase the Quality of Thought, Not Merely the Speed of Work
- There Is a Serious Counterargument
- What Senior Leaders Should Consider
- Questions for the Boardroom
- Judgement Becomes the Advantage
- References
- About the Author
Strategic Thinking Begins Before Strategy
People are frequently told that they need to become “more strategic” as they progress into senior management. The advice is reasonable. The difficulty is that the expression has become so elastic that it can mean almost anything: thinking longer-term, delegating more operational work, becoming commercially aware, learning strategy frameworks, or simply behaving more like senior management.
I think the distinction is more fundamental.
Strategy concerns choices. Strategic thinking concerns the quality of thought from which those choices emerge.
The difference matters because an organisation can execute an intelligently designed strategy against the wrong interpretation of reality. It can analyse the wrong problem extremely well. It can produce sophisticated forecasts from assumptions that no longer deserve to survive. It can become more efficient at doing something whose strategic value is already declining.
This is one reason strategic thinking cannot be reduced to knowing a collection of frameworks. Models are useful because they impose discipline on complexity. They become dangerous when the model begins determining what the executive is able to see.
A strategist therefore needs a capacity that precedes analysis: the ability to ask what kind of situation he or she is actually looking at.
Is weak growth a sales problem, a proposition problem or evidence that value is moving elsewhere in the market? Is a competitor succeeding because it has a better product, because its economics are structurally different, or because customers have changed what they are willing to pay? Is a new technology genuinely altering the basis of competition, or is management reacting to the intensity of the conversation surrounding it?
The answer determines where capital, management attention and organisational energy will go next.
Julia Sloan's Learning to Think Strategically remains useful because it approaches the subject through the development of the thinker rather than merely the production of strategy. The fifth edition, published in 2024, describes strategic thinking as an advanced conceptual cognitive capability and places particular emphasis on nonlinear learning, reflection and informal development. It also adds a chapter on AI and strategic thinking. Routledge's description of the fifth edition is available here.
What interests me in Sloan's work is not the promise of a method that turns somebody into a strategist. There is no such shortcut. It is the proposition that strategic thinking itself is learnable.
That proposition now deserves reconsideration because the environment in which we learn to think is changing.
A Decade Changed the Conditions for Strategic Thought
Ten years ago, the executive conversation already felt dominated by technological disruption. Companies were responding to cloud computing, mobile platforms, data analytics, digital channels and businesses whose economics looked very different from those of established incumbents.
In 2016, McKinsey observed that companies could mistake a collection of digital initiatives for an actual digital strategy. That was an important distinction. Buying technology, launching projects and digitising existing processes did not necessarily mean that leadership understood how digital technologies were changing competition. The 2016 McKinsey Quarterly discussion remains instructive.
The same temptation exists today with artificial intelligence.
An organisation can have pilots, copilots, AI programmes, automation projects and an impressive inventory of use cases while remaining strategically unclear about what AI changes in its business.
The decade between those two moments has made this distinction more important. The pandemic exposed assumptions about work, supply chains, demand and organisational resilience that had previously appeared dependable. Geopolitical tension returned dependency and strategic autonomy to conversations that had often been dominated by efficiency. Software continued moving into products and services. Data became easier to obtain. Customers became easier to reach and, in many markets, harder to retain. Industry boundaries became less comfortable.
Artificial intelligence now enters this environment differently from many earlier technologies because it reaches directly into knowledge work.
The Stanford AI Index 2026 reports that 88% of surveyed organisations used AI in at least one business function during 2025, while 70% reported generative AI use in at least one function. The precise numbers will continue to move. The strategic significance lies in the direction: technology is increasingly participating in activities through which managers and professionals have historically demonstrated intellectual value.
Research. Comparison. Interpretation. Drafting. Modelling. Coding. Option generation.
Many of these activities remain valuable. They are becoming less scarce.
That changes what an executive should be trying to develop.
Information Is Becoming Abundant. Judgement Is Not.
For much of corporate history, access to information created advantage. Senior executives had access to reports others did not see, networks others did not possess, and specialists capable of gathering material that would have been difficult for an individual to obtain independently.
The internet weakened this scarcity. AI reduces it further.
An executive can now enter an unfamiliar subject and obtain a credible overview within minutes. Markets can be compared. Documents can be analysed. Competitor positions can be summarised. Scenarios can be generated. Arguments can be constructed from several points of view.
This is a substantial improvement in executive productivity. But productivity is only part of the strategic consequence.
When the cost of producing an answer falls, merely producing an answer becomes less valuable.
The premium moves somewhere else.
It moves towards deciding which questions deserve attention. Towards recognising whether an attractive answer rests on weak assumptions. Towards connecting information drawn from different domains. Towards knowing which uncertainty matters enough to change a decision and which uncertainty simply has to be carried.
Most importantly, it moves towards judgement.
This is where I think the implications of AI for executive development are still underestimated. The discussion often focuses on which tasks will be automated and which skills people should acquire. Those questions matter, but the deeper shift is in the economics of cognition itself.
If information processing becomes cheaper, interpretation becomes relatively more important. If analysis becomes easier to generate, the ability to recognise poor analysis becomes more valuable. If every executive can produce ten scenarios, choosing which scenario deserves serious consideration becomes the harder problem.
Abundance does not remove the need for strategy. It creates a new form of scarcity.
The First Strategic Decision Is Often How We Define the Problem
Imagine that margins are falling.
One executive asks how costs can be reduced. Another asks whether pricing has weakened. A third asks whether value has shifted within the industry so that the organisation continues carrying a large share of the cost while another participant increasingly captures the economics.
Each interpretation could be reasonable. Each sends management in a different direction.
Strategic thinking begins with recognising that the first question may be wrong.
This sounds obvious when written down. Inside organisations, it is surprisingly difficult.
Problems acquire owners. Investments acquire defenders. Targets create incentives. Success creates reputations. Once an organisation has invested financially and psychologically in an explanation, questioning it can become harder than questioning its execution.
Across technical and commercial environments, I have repeatedly seen an important difference between what looks logical in a presentation and what survives contact with customers, organisations and markets. The problem is not usually a lack of intelligence. It is that reality contains interests, constraints and behaviours that the presentation has simplified away.
Strategic thinking therefore requires more than analytical ability. It requires the willingness to examine the frame itself.
What assumption must remain true for our present strategy to work? Which part of the business are we treating as permanent because it has been profitable for a long time? What behaviour are customers demonstrating that contradicts what they tell us? Which capability looks expensive only because its value appears after the period our financial analysis emphasises?
At senior level, questions of this kind can become politically difficult. They may imply that previous investment was misplaced or that a strategy associated with influential executives needs to be reconsidered.
Strategic thinking is therefore partly intellectual and partly organisational. A company can hire intelligent people and still think badly if its culture makes certain questions feel unsafe to ask.
Some Things Are Changing Fast. Others Hardly Change at All.
Every major technological change creates a temptation to treat the previous world as obsolete.
Some things genuinely are becoming obsolete. Other things change much more slowly.
Customer value still matters. Cash still matters. Capital still has an opportunity cost. Incentives influence behaviour. Organisations contain competing interests. Trust remains slow to build. Authority and expertise do not always sit in the same place. People continue to defend status, familiar practices and professional identities when change threatens them.
International business adds another level of complexity. The same proposal can encounter very different responses depending on attitudes towards hierarchy, institutional authority, direct communication, trust and risk. Technology may make communication instantaneous. It does not make context disappear.
This creates what I regard as one of the central challenges of modern strategic thinking: leaders need to understand several clocks at once.
Technology can change in months. Regulation may move over years. Organisational culture often changes more slowly. Physical capacity can require long investment cycles. Institutional trust may take repeated interactions to establish. Capability that is dismantled quickly can take years to rebuild.
A strategy that assumes these clocks move together can be analytically elegant and operationally naïve.
The pace of AI makes this more visible because technology is accelerating while many of the systems into which it is being introduced remain stubbornly human.
This is also why experience still matters.
Experience Creates Judgement, and Sometimes Blindness
Experienced executives often recognise patterns before they can fully explain them. Something in a proposal feels incomplete. A commercial assumption appears too clean. An apparently small contractual detail attracts attention because similar details have mattered before.
We call some of this intuition. In practice, much of it is compressed experience.
That can be enormously valuable.
It can also be dangerous.
The same experience that allows an executive to recognise patterns can also cause a new situation to be interpreted through the lens of an old one. Previous success makes this particularly difficult because successful assumptions rarely announce when they have expired.
An executive shaped by an era of information scarcity may still equate knowledge with authority even as knowledge has become widely accessible. A company that built its advantage through efficiency may continue optimising a model after resilience or speed becomes more valuable. A leader who has learned that customers behave in one way may continue to treat a change in pattern as temporary noise.
Experience therefore becomes strategic capability only when accompanied by reflection.
After an important decision, I find that the useful questions go beyond whether the result was good or bad. What did we expect to happen? Which assumptions proved sound? Which did not? What signal was available but discounted? Did a positive result validate the reasoning, or were we fortunate?
This last question is uncomfortable because organisations naturally reward outcomes.
Yet a poor decision can produce a good outcome. A sound decision can encounter an unfavourable event. If leaders learn only from results, they will sometimes reinforce the wrong behaviour.
Reflection is what converts experience into judgement rather than biography.
Personal Development Must Move Beyond Accumulating Expertise
Most careers begin by rewarding knowledge. We become useful because we understand a product, technology, customer, market or function better than others. Expertise creates confidence and often becomes part of identity.
Executive work gradually reverses the equation.
The more senior the responsibility, the more decisions arrive in areas where the executive cannot be the deepest specialist. Finance, people, technology, markets, regulation and operations intersect. Experts disagree. Information remains incomplete. Time imposes limits.
The executive has to decide, understanding that someone else in the organisation almost certainly knows more about part of the problem.
This requires a different form of confidence.
The important question changes from “Do I know enough to be the expert?” to “Do I understand the system sufficiently to ask the experts the right questions, recognise the trade-offs and make a responsible decision?”
AI accelerates this transition. It increasingly weakens the professional advantage of simply knowing more facts than the people around us.
That should influence personal development.
Executives and those preparing for executive roles should look beyond their own function. Economics changes the way market behaviour is interpreted. History helps distinguish genuinely new phenomena from old institutional problems wearing new technological clothing. Technology literacy helps separate what is technically possible from what is economically or organisationally useful. Exposure to different markets and cultures challenges assumptions that otherwise feel universal.
The arts, which Sloan has long included in her discussion of strategic development, are interesting for a different reason. Their value is not that looking at a painting somehow creates better corporate strategy. It is that disciplines which demand interpretation without a single correct answer exercise a different form of attention. They make ambiguity less threatening.
That matters because strategic decisions rarely arrive with one uncontested interpretation.
The World Economic Forum's Future of Jobs Report 2025 is revealing in this respect. Analytical thinking remains employers' most cited core skill. At the same time, AI and big data, technological literacy, creative thinking, resilience, curiosity and lifelong learning are all expected to grow in importance.
The combination matters more than the ranking.
The executive capability of the next decade is unlikely to be purely technological or purely human. It will depend on the quality of the relationship between the two.
AI Should Increase the Quality of Thought, Not Merely the Speed of Work
The easiest way to use AI is to ask it for an answer.
That is often entirely appropriate. Nobody should preserve unnecessary work merely because the work used to require human effort.
The problem begins when convenience silently becomes dependency.
A Microsoft Research study published at CHI 2025 surveyed 319 knowledge workers and collected 936 examples of generative AI use. Higher confidence in AI was associated with less reported critical-thinking effort, while higher confidence in one's own ability was associated with more. The researchers also found that critical thinking did not simply disappear; it shifted towards activities such as verification, integration and stewardship.
This deserves a careful interpretation.
The study does not demonstrate that AI makes people intellectually weaker. It does suggest that the structure of cognitive effort changes when technology performs more of the initial work.
That raises a development problem.
If AI always produces the first interpretation, when does the manager practise forming one? If it always creates the options, when does the executive exercise imagination? If every complex report is immediately reduced to a summary, how often do we remain inside difficult material long enough to notice the contradiction that the summary has smoothed away?
Efficiency is valuable. So is cognitive fitness.
I therefore find the idea of AI as a thinking partner more useful than AI as an answer engine. Microsoft researchers themselves have described work in this area as developing “tools for thought”.
For strategic work, that changes how I would use the technology.
Form an initial position before asking AI for one. Then ask it to attack the reasoning.
Ask for the strongest argument against your preferred conclusion. Ask which stakeholder is missing. Ask what evidence would make your assumption false. Ask whether you are treating correlation as causality. Ask it to construct a scenario in which the strategy fails despite management executing exactly as planned.
Then return to the underlying evidence.
Used this way, AI creates intellectual resistance rather than removing it.
That distinction is important. One use saves cognitive effort. The other can strengthen judgement.
There Is a Serious Counterargument
It would be appropriate to end the discussion by declaring judgement, imagination, and strategic thinking as permanently human advantages.
I am not convinced we should.
AI systems are improving quickly. It is reasonable to expect them to become much better at scenario construction, pattern recognition, simulation and even aspects of strategic reasoning. In some analytical tasks, they may already outperform many human decision-makers.
Executives should not defend human activity simply because people used to perform it.
If a machine produces a more accurate forecast, use it. If it finds a relationship the management team missed, investigate it. If it can explore thousands of scenarios more rigorously than a planning team, refusing the capability would be sentiment rather than leadership.
This means the enduring boundary may not lie around specific cognitive tasks.
It lies closer to responsibility.
Strategy takes place inside organisations that have purposes, obligations and consequences. Somebody decides which objective matters. Somebody chooses which risks are acceptable. Somebody determines whether an efficiency gain justifies a new dependency, whether automation should override discretion, whether short-term returns justify weakening capability that may be needed later.
AI can inform these choices increasingly well.
The organisation still has to own them.
As machine intelligence becomes more capable, accountability does not become obsolete. If anything, it becomes easier for human beings to obscure where accountability actually sits.
That is as much a leadership problem as a technology problem.
What Senior Leaders Should Consider
The development of strategic thinking is not solely an individual responsibility. Organisations create the conditions in which thinking either develops or deteriorates.
A company can say it wants strategic leaders while filling senior calendars with operational escalation. It can claim to value challenge while promoting people who rarely disagree with authority. It can invest heavily in AI productivity while paying little attention to which capabilities employees still need to practise themselves.
The last issue deserves particular attention.
Many apparently inefficient activities historically carried a hidden developmental function. Junior professionals learned through building analyses, writing first drafts, investigating discrepancies and defending recommendations. If technology removes those activities, the productivity improvement may be real, while the underlying learning pathway disappears.
The answer is not to protect obsolete work.
It is to redesign development deliberately.
If AI performs the initial analysis, managers may need to take on more responsibility for challenging it. If first drafts become automated, younger professionals may need more opportunities to defend arguments orally. If information retrieval becomes effortless, development may need to concentrate more heavily on source quality, causality, systems thinking and judgement under uncertainty.
There is an economic trade-off here.
Organisations quite reasonably want the productivity gains from automation. They also need future executives capable of supervising systems whose outputs they did not learn to produce manually.
Efficiency today can quietly consume capability tomorrow.
That does not mean every old skill should be preserved. It means senior leadership should know which capabilities it is allowing to disappear and make that decision consciously.
Questions for the Boardroom
- Which assumptions beneath our current strategy have become difficult to question because too much investment, status or organisational identity depends on them?
- Where is AI improving the quality of our decisions, and where is it merely increasing the speed at which existing thinking is produced?
- Which capabilities do future executives still need to practise personally even when technology can perform the underlying task faster?
- What are we optimising because it is measurable, while a less measurable source of long-term capability is quietly deteriorating?
- Which major decision would look different if we expected to live with its organisational consequences for ten years?
Judgement Becomes the Advantage
Strategic thinking can be developed. I still consider that the most valuable idea in Sloan's work.
Yet the meaning of development is changing.
For a long time, becoming a stronger executive often meant accumulating more knowledge, more experience and more access to information. Those advantages still matter, but technology is reducing the scarcity of some of them.
AI can already retrieve more than we can remember, compare more alternatives than we can examine manually and produce coherent analysis faster than most management teams could have imagined a decade ago.
This does not make strategic thinking less important.
It reveals what was valuable about strategic thinking all along.
Can we recognise that the problem itself has changed? Can we question an assumption that once contributed to our success? Can we distinguish structural change from fashionable noise? Can we understand several time horizons at once? Can we make a decision before certainty arrives and remain intellectually honest enough to revise it afterwards?
And perhaps most importantly now: can we work with increasingly capable machines without allowing convenience to determine the boundaries of our own thinking?
The most useful response to AI is therefore not simply to become faster at using it.
It is to become more deliberate about the mind we develop as we use it.
Technology will continue to change quickly. Many of today's tools will eventually look primitive. The responsibility to interpret, choose and live with consequences will move much more slowly.
That is why judgement becomes the advantage.
References
- Lee, H-P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3706598.3713778
- McKinsey & Company. (2016). McKinsey Quarterly 2016 Number 2. View source.
- Sloan, J. (2024). Learning to Think Strategically (5th ed.). Routledge. Publisher page.
- Stanford Institute for Human-Centered Artificial Intelligence. (2026). AI Index Report 2026. View report.
- World Economic Forum. (2025). The Future of Jobs Report 2025. View report.
About the Author
Andy Demir is an international commercial and business leader with more than two decades of experience across complex B2B and industrial markets. He writes about executive leadership, strategy, organisational transformation, commercial performance, international business and the changing world of work, combining research with lessons drawn from operating across industries, markets and cultures.
