The most dangerous strategy may be the one your company is executing successfully.
Costs are falling. Productivity is improving. Capital is flowing toward businesses with the strongest reported returns. Each decision appears sensible, and quarterly results reinforce management’s confidence. Yet those same decisions can gradually remove the expertise, experimentation, and operating flexibility the company will need when conditions change. A business can become better at delivering today’s results while becoming less capable of producing tomorrow’s.
This tension has been on my mind during my DBA work at Haskayne, particularly while studying Walter Kiechel III’s The Lords of Strategy and our accompanying course material. Kiechel traces how thinkers including Bruce Henderson, Bill Bain, Fred Gluck, and Michael Porter changed how executives understood competition. What struck me was how their frameworks can produce different recommendations about the same business. An activity that looks wasteful through one perspective may be essential through another. Understanding that disagreement has practical consequences.
Consider an experienced employee who spends part of the week helping colleagues resolve unusual customer problems. A cost analysis might identify an expensive role with limited measurable output. A capabilities analysis might identify the person who helps the organization handle work competitors cannot. An organizational assessment might reveal that this informal coaching is how junior employees become productive. The financial cost is visible in every case. The strategic contribution becomes visible only when management asks the right questions.
The experience curve, associated with Henderson and BCG, explained how cumulative production could reduce costs. As organizations gained experience, they could improve methods and strengthen their position relative to competitors. That reasoning remains useful, but its application requires distinguishing between accumulating experience and preserving the ability to learn. A company that standardizes every activity around its current best method may improve efficiency while making it harder for employees to discover a better one. Management needs to examine both the savings and the learning process producing them.
This matters when leaders introduce AI into everyday work. Suppose a company automates routine customer inquiries and reduces junior service positions. The immediate savings may be straightforward to estimate. A less obvious question is how future specialists will develop judgment. If routine work previously served as an apprenticeship, removing it changes the organization’s learning process. Automation may still be appropriate, but its economics should include developing expertise through another route. Otherwise, management risks recording the savings now and discovering the capability gap later.
Amazon provides a timely example: it is reportedly approaching former employees, including laid-off workers, for AI and cloud roles. That does not establish that the earlier cuts were mistakes. It does, however, raise a question about whether workforce decisions adequately account for capabilities a business may need again. Recruitment, training, and rebuilding working relationships belong in that calculation. A temporary reduction in spending can look like a lasting efficiency improvement when the cost of rebuilding expertise falls outside the planning period.
Portfolio strategy can reinforce the same problem. BCG’s growth-share matrix and McKinsey’s nine-box framework helped executives compare businesses and direct resources toward attractive opportunities. Their discipline remains valuable because organizations cannot fund everything. However, businesses often contribute more than their individual financial results reveal. A mature operation may provide customer access, technical knowledge, or infrastructure that newer operations depend on. Reducing its investment may improve reported cash generation while weakening the businesses expected to produce future growth.
The counterintuitive implication is that some apparently underperforming activities deserve more investment. Their value may reside partly in what they enable elsewhere. That argument needs scrutiny; almost any manager can claim strategic importance to defend a budget. The appropriate response is to identify the dependency and test it. Which customers, processes, or growth initiatives would suffer if the activity disappeared? Could its contribution be replaced, at what cost, and over what period? These questions expose consequences a business-unit return calculation may miss.
Better internal economics also do not guarantee a stronger competitive position. Porter’s industry analysis directs attention toward who captures the value a company creates. Imagine a professional services firm using AI to produce standard analysis at substantially lower cost. If competitors make the same improvement and customers negotiate lower fees, much of the benefit may pass to buyers. The investment could be necessary to remain competitive while offering little lasting advantage. Leaders need to examine changes in bargaining power and customer choice alongside productivity.
A transformation can deliver every promised saving and still leave the company easier to replace.
This is where the distinction between benchmarking and strategy becomes consequential. Bain’s emphasis on finding better practices helped companies challenge inefficient ways of working. But adopting practices available to everyone can make competitors increasingly alike. Leadership teams should distinguish between activities where matching the market is sufficient and activities where customers prefer their particular approach. Applying the same standardization agenda to both can eliminate differences worth preserving. Operational improvement must support a deliberate choice about how the company competes. Harvard Business School
The resource-based view and work on core competencies provide a way to investigate those differences. Barney’s VRIN framework asks whether resources are valuable, rare, difficult to imitate, and difficult to substitute. Often, the answer depends on a combination of resources. A customer database may be useful, but its competitive value could depend on employees who understand its limitations, relationships that encourage customers to share information, and processes that translate insight into service. Outsourcing or automating one component can change the value of the whole combination.
The organizational effectiveness tradition adds another complication: those combinations depend on behavior. Peters and Waterman, and the related 7-S framework, brought attention to skills, management practices, and shared values that more analytical approaches could understate. A company may describe collaboration as essential while rewarding managers exclusively for their own unit’s performance. Sharing an expert then becomes a local expense, even when it benefits the company. Before attributing weak execution to employee resistance, leaders should examine whether their incentives make the desired behavior unattractive.
Value-chain analysis and business process reengineering bring these connections into the design of work. They encourage management to examine how activities combine to create value. Their application becomes dangerous when leaders equate improvement with removing every apparent redundancy. A second supplier, spare capacity, or overlapping expertise carries an observable cost. Its benefit may appear only during a disruption. The decision depends on the consequences of interruption and the cost of recovery. Treating every buffer as inefficiency assigns that protection a value of zero without evaluating it.
The adaptation perspective brings these arguments together. Capabilities that once created advantage can become constraints, while marginal activities may support a future business. This does not justify protecting every legacy operation or funding every experiment. It requires examining the conditions under which an investment makes sense and recognizing when they change. The course material’s “horses for courses” principle is useful here: a strategy appropriate to a stable volume business may be poorly suited to a specialist business facing uncertain demand.
My DBA work has prompted me to reconsider how much confidence leaders should place in a single, internally consistent strategic argument. Consistency can emerge because an analysis excludes everything it cannot easily measure. These schools are useful partly because they challenge one another. Cost analysis demands efficiency. Positioning asks whether customers value the difference. Capability analysis examines what the organization must preserve. Adaptation questions how long those answers will remain valid.
Leaders can use that tension through three actions today:
- Reopen one efficiency decision and examine what it removes. Choose an automation, outsourcing, or cost-reduction initiative. Identify any learning, customer knowledge, or operating flexibility embedded in the affected work. Establish how those contributions will be maintained and include that expense in the decision.
- Test one claimed competitive advantage against customer behavior. Ask why customers choose your company and what evidence supports the answer. Examine whether current investments strengthen that reason. Challenge projects that improve an internal metric while weakening something customers value.
- Attach a reconsideration trigger to one major strategic commitment. Identify its critical assumption, assign someone to monitor it, and specify what evidence warrants a review. Give that person access to the leadership team before the next planning cycle, while practical alternatives remain.