Strategic Centering 2026: How CEOs Build Growth When the Rules No Longer Apply
TL;DR / Executive Summary
The old playbook for corporate strategy is no longer fit for purpose and the data from the world's leading research institutions in 2026 makes that case with unusual force. Columbia Business School professor Rita McGrath, writing in Harvard Business Review, argues that traditional frameworks built around competitive positioning cannot keep pace with an economy where value has shifted from physical assets to data, software, and human capability. The consensus response, layering AI onto existing structures and calling it transformation, is precisely what McKinsey's State of Organizations 2026, drawing on more than 10,000 senior leaders across 15 countries, identifies as the dominant failure mode. CEOs who want to grow in this environment need a different starting point: a single, coherent organizing principle that aligns capital allocation, talent, and innovation around one clear identity rather than a fragmented portfolio of bets.
- 86% of leaders surveyed by McKinsey say their organizations are not prepared to embed AI into day-to-day operations, yet 88% report active deployment, that gap is destroying value, not creating it.
- Organizations that intentionally redesign human-AI interactions are 2.5x more likely to report superior financial results, according to Deloitte's 2026 Global Human Capital Trends, yet only 6% of leaders say they are actually doing it.
- BCG's 19th Annual Most Innovative Companies Study found that top innovators outperformed the broader market by 2.4 percentage points annually over 20 years, with the gap widening most sharply during economic downturns.
1. The Context
The competitive environment that most strategy frameworks were designed for, with stable industry boundaries, predictable profit pools, and measurable cost advantages, has effectively dissolved. Rita McGrath's HBR article, "The Power of Strategic Centering," due in the July-August 2026 issue, argues that in a dematerializing economy where intangibles like data, software, and organizational capability now account for the majority of enterprise value, companies can no longer win by positioning within an industry structure. Industries themselves are dissolving, and the question is no longer "where do we stand in the value chain?" but "what are we fundamentally about, and how does everything we do reinforce that answer?" That is a harder question than it sounds, and most leadership teams are not yet asking it seriously.
Three forces are making this challenge operationally urgent. McKinsey's State of Organizations 2026, covering 10,000 senior executives across 16 industries, identifies them precisely: the acceleration of AI and automation; intensifying economic and geopolitical fragmentation; and the fundamental transformation of workforce expectations. McKinsey's own framing has shifted to reflect this reality: change is no longer episodic but has become a permanent condition and the new baseline for operating. The organization that expects a return to stability after the next disruption cycle is building strategy on a false premise. 72% of leaders told McKinsey that geopolitical uncertainty has already had a notable impact on their operations, while two-thirds say their organizations are overly complex and inefficient as a cumulative result of responding to past crises without a coherent center.
The resolution that is emerging from the research is not the one most boards are currently funding. More AI vendors, another transformation program, or another reorganization is not the answer. Deloitte's 2026 Global Human Capital Trends research, conducted with Oxford Economics across 9,000 business leaders in 89 countries, identifies the decisive structural shift: organizations that redesign work around genuine human-AI collaboration, rather than mere AI adoption, are twice as likely to exceed their return-on-investment expectations for technology. The bottleneck, as Deloitte puts it, is the failure to design the human layer around AI, a gap the research labels cultural debt. 65% of organizations believe their culture needs to change significantly because of AI, and 34% say their culture is currently blocking their AI transformation goals. That is not a technology budget problem; it is a strategic coherence problem.
2. The Evidence
The numbers behind this argument are not marginal. BCG's 19th Annual Most Innovative Companies Study, covering 20 years of data, found that top innovators outperformed the MSCI World Index by 2.4 percentage points per year on average, with the performance gap widening most dramatically during the Great Recession and the COVID-19 pandemic. The implication for boards is direct: innovation capability is not a discretionary expenditure to cut when times are difficult, but rather the mechanism by which companies protect and extend their valuation advantage precisely when competition retreats. Despite this evidence, the share of executives who describe their own companies as innovation leaders fell by 24 percentage points between 2021 and 2024. BCG found no consistent link between raw R&D spend and total shareholder return. What the outperformers share is disciplined focus on where to compete and that is strategic centering operating under a different label.
The organizational data reinforces the same conclusion from a different angle. McKinsey's State of Organizations 2026 found that 88% of organizations report active AI deployment, yet fewer than 20% of those who attempted implementation saw a meaningful financial impact. The gap between deployment and value creation is a strategic coherence problem, not a technology one. When there is no clear center, AI gets deployed to whatever problem is most politically visible rather than whatever problem most directly drives value. Deloitte's research adds a second finding: 88% of employees report using AI at work, but only 5% use it in ways that meaningfully transform how work gets done, and 60% of executives use AI in decision-making while only 5% say they manage its effects well. The adoption rate and the governance rate are not in the same zip code, and that gap is where cultural debt accumulates.
| Metric | Value | Source |
|---|---|---|
| Leaders who say their organization is unprepared to adopt AI in day-to-day operations | 86% | McKinsey State of Organizations 2026 |
| Leaders who recognize the importance of human-AI interaction design versus those actually leading it | 66% recognize it; only 6% are leading it | Deloitte Global Human Capital Trends 2026 |
| Annual outperformance of top BCG innovators vs. MSCI World Index (20-year average) | +2.4 percentage points per year | BCG Most Innovative Companies Study 2025 |
| Organizations more likely to exceed AI ROI expectations when they prioritize human-AI work redesign | 2x more likely | Deloitte Global Human Capital Trends 2026 |
| Organizations where AI deployment produced meaningful financial results | Fewer than 20% of those that deployed | McKinsey State of Organizations 2026 |
| Organizations that say their culture is actively blocking AI transformation goals | 34% | Deloitte Global Human Capital Trends 2026 |
| Leaders reporting geopolitical uncertainty has had a notable operational impact | 72% | McKinsey State of Organizations 2026 |
| Business leaders whose primary competitive strategy is to be fast and nimble over the next three years | 70% | Deloitte Global Human Capital Trends 2026 |
3. MD-Konsult Research View
Consensus position: McKinsey, BCG, and Deloitte agree that the combination of AI acceleration, geopolitical fragmentation, and workforce transformation demands faster organizational adaptation, more agile operating models, and greater AI investment. BCG's CEO Agenda frames this explicitly as a growth imperative, with 72% of CEOs now personally leading their company's AI strategy, a significant escalation in ownership since 2024.
MD-Konsult position: Speed without a center is not agility; it is expensive drift, and the evidence shows that most organizations are already experiencing it at scale.
McKinsey's own data makes this hard to argue against, as 88% of companies are deploying AI, yet fewer than one in five are seeing meaningful financial returns. Deloitte documents that 88% of employees report using AI at work, but only 5% use it in ways that transform how work actually gets done. That gap is structural, not technological: organizations moved fast without building the trust, governance, and role clarity that make human-AI collaboration durable. Forbes and Deloitte call this "cultural debt," defined as the invisible liability that accumulates when AI adoption outpaces the organizational design needed to sustain it. Meanwhile, McGrath's strategic centering framework, which ask the question before any decision is made: what are we, and what will we always be, regardless of what technology or geopolitics does next?", provides the missing structural answer: companies that commit to a clear center gain the internal coherence to decide what to automate, what to protect, and what to stop doing entirely. The three real-world cases below show what that looks like in practice, and the results are not hypothetical.
Being early to this position has asymmetric value, because the companies that build strategic coherence now, before the next AI capability wave and before the next geopolitical shock, will not just manage disruption better; they will use disruption as a growth catalyst, moving decisively while competitors are still deciding where to focus. BCG's 20-year innovation data is unambiguous: the performance gap between focused innovators and the broader market is largest during crises, not during stable periods. The time to establish that center is before the next wave arrives, not after.
4. Three Real-World Cases That Prove the Point
Case 1: Fujifilm — From Film to a JPY 3 Trillion Healthcare Powerhouse
When digital photography destroyed Fujifilm's core business in the mid-2000s, the same shift effectively ended Kodak. Fujifilm's survival was not luck; it was the result of a deliberate strategic center built on a systematic audit of the transferable technologies the company had developed during its decades in film manufacturing. These included precision coating, collagen chemistry, anti-oxidation compounds, and imaging optics, and Fujifilm made a disciplined decision to compete only in markets where those capabilities created genuine advantage.
The results are empirically documented. Fujifilm committed $11 billion across a three-year strategic plan to make healthcare its largest segment, applying X-ray film expertise to medical imaging and collagen chemistry to skincare products. By its fiscal third quarter of 2025, Fujifilm posted record quarterly revenue and profits, with the Healthcare segment reaching JPY 266.1 billion for the quarter, up 7.7% year over year, and Bio CDMO revenues surging 18% following the launch of new manufacturing facilities in Denmark. Annual revenue now exceeds JPY 3.0 trillion, and healthcare has become the company's primary growth engine. The center was imaging technology and materials science, not cameras or film, and that distinction is what allowed the company to survive and ultimately thrive.
Case 2: Novartis — Pruning the Portfolio to Accelerate the Core
In 2022, Novartis CEO Vas Narasimhan made a decision that many observers questioned at the time: spinning off Sandoz, the company's generics and biosimilars division, to create what he described as "a more focused innovative medicines company." That Sandoz had produced $2.3 billion in net sales in a single quarter only sharpens the point: exiting a profitable business to concentrate on a narrower identity is a textbook strategic centering move, and the financial record since then makes the case plainly.
After the spin-off completed in 2023, Novartis upgraded its mid-term sales guidance twice. By November 2024, the company upgraded its mid-term sales guidance to a 6% CAGR from 2023 to 2028, up from the prior 5% target, driven by eight in-market brands with peak sales potential of $3 billion to $8 billion each. Priority brands including Kisqali (+55% constant currency in Q1 2026), Scemblix (+79% cc), and Leqvio (+69% cc) are growing at rates that would have been diluted under a broader portfolio structure. Full-year 2026 guidance was reaffirmed in April 2026, with a core operating income margin of 37.3%. The Sandoz spin-off was not a divestiture of a failing asset; it was a declaration of strategic center.
Case 3: Toss — Centering on Frictionless Finance to Reach 60% of a Country
Toss, the South Korean fintech founded in 2014 by former dentist Lee Seung-gun, built everything around a single organizing principle: make financial transactions so frictionless that the experience itself becomes the competitive advantage. While incumbent Korean banks competed on product breadth, Toss competed on eliminating friction at every point of contact, and that center informed every product decision, from peer-to-peer transfers to banking, securities, insurance, and payments infrastructure.
The business results bear this out. In Q2 2025, Toss surpassed KRW 668 billion (USD 493 million) in consolidated quarterly revenue, a 41% year-over-year increase. By July 2025, Toss surpassed 30 million registered users, reaching approximately 60% of the South Korean population, with enrollment rates of 95% among people in their 20s and 87% among people in their 30s. The company reported $1.4 billion in full-year 2024 revenue, a 43% year-over-year jump, and is preparing a US IPO at a target valuation of over $10 billion, potentially reaching $15 billion. The center was never financial products; it was the removal of friction, and that distinction is what scaled.
5. Practitioner Perspective
-- Chief Strategy Officer, Global Industrial Conglomerate (Fortune 500)
This view is grounded in patterns that Deloitte's 2026 Global Human Capital Trends research quantifies: organizations that lead on intentional human-AI interaction design are 2.5 times more likely to report superior financial results and twice as likely to say they provide meaningful work for their people. The organizations that are succeeding are not faster AI adopters; they are clearer about what they are asking AI to support, and that clarity comes from strategic identity, not from a technology strategy.
6. Strategic Implications by Stakeholder
| Stakeholder | What to Do Now | Risk to Manage |
|---|---|---|
| CTO / CIO | Audit every active AI initiative against the organization's stated strategic center. Pause or kill anything that cannot be directly mapped to that identity, and redirect freed budget toward intentional human-AI workflow redesign rather than more infrastructure. As the Fujifilm and Toss cases demonstrate, the technology advantage comes from applying existing capability to a clear purpose, not from owning more technology. | Cultural debt accumulates silently: 65% of organizations already say their culture needs to change significantly because of AI, and 34% say culture is actively blocking their transformation goals. If the workforce does not trust or understand AI's role in their work, adoption will stall regardless of spend, which means trust frameworks and accountability structures need to be built alongside every deployment, not added as an afterthought. |
| COO / Operations | Redefine productivity metrics around outcomes that reflect the strategic center, not just efficiency ratios. Two-thirds of leaders already describe their organizations as overly complex, and nearly 40% say redefining process flows is the biggest productivity unlock over the next one to two years, according to McKinsey. Simplify workflows before automating them — the sequence matters significantly. | Adding AI to broken processes accelerates failure rather than improvement. The Toss model is instructive here: frictionless outcomes required eliminating steps, not automating them. Treat geopolitical fragmentation as a permanent operating constraint, and embed scenario planning into the quarterly operating rhythm rather than treating it as a crisis management tool activated only during acute disruptions. |
| CFO / Board | Require every major capital allocation decision, including AI investment, to pass a strategic centering test: does this reinforce what we are fundamentally about, or does it dilute our identity? The Novartis Sandoz spin-off is the clearest recent example of a board willing to exit a profitable business to concentrate the capital and leadership attention that a clear center requires. BCG's 20-year innovation data consistently links disciplined focus, not R&D spend volume, to superior shareholder returns. | The board's most significant near-term risk is approving a large AI program without a clear answer to McGrath's foundational question: what is our strategic center? Without that answer, the program will produce activity but not competitive advantage. Deloitte's research shows that 56% of leaders design AI implementations solely around business outcomes like cost and speed, with no accounting for human outcomes like trust, fairness, or skills development — and that imbalance has a measurable financial consequence over time. |
7. What the Critics Get Wrong
The most serious challenge to the strategic centering argument comes from portfolio diversification advocates who argue that in a volatile, multi-polar world, committing to a single organizing principle creates dangerous concentration risk. BCG itself, in its CEO Growth Agenda, acknowledges that diversified innovation portfolios and continuous M&A capability are essential tools for navigating uncertainty. The argument has real merit: companies with diversified revenue streams often redirect resources faster during crises, and if a single center becomes brittle when the environment shifts sharply, the prescription may be worse than the disease it addresses.
That critique confuses strategic coherence with strategic rigidity, however. McGrath's framework is explicit: a center is not a fixed product or a segment definition; it is a dimension of competition along which a company pursues coherent opportunity sets as markets evolve. Fujifilm's center in imaging technology and materials science survived the complete destruction of the film industry and allowed the company to generate record revenue in healthcare, semiconductors, and life sciences without losing strategic identity. Deloitte's 2026 research shows that 70% of leaders identify speed and nimbleness as their primary competitive strategy over the next three years, and clarity of center is precisely what makes that speed possible rather than what prevents it. The organizations that pivot fastest are not the most diversified ones; they are the ones that know precisely what they are preserving while everything else changes.
8. Frequently Asked Questions
What is strategic centering, and how is it different from a mission statement?
Strategic centering, as defined by Columbia Business School's Rita McGrath in her July-August 2026 HBR article, is an organizing principle that guides resource allocation, opportunity selection, and organizational identity simultaneously. A mission statement tells stakeholders what the company believes, whereas a strategic center tells the leadership team what to fund, what to kill, and what to stop doing entirely. The difference is operational: companies with a clear center make faster, more consistent decisions because every significant choice can be tested against the same criterion, and the Novartis case demonstrates this precisely — the decision to spin off Sandoz was defensible not because the business was failing, but because it did not fit the center.
Why are so many AI investments failing to deliver financial returns in 2026?
McKinsey's State of Organizations 2026 found that while 88% of organizations report active AI deployment, fewer than 20% have seen meaningful financial impact. The core reason is strategic incoherence rather than technology failure. When organizations lack a clear center, AI gets applied to politically visible problems rather than the ones that actually drive value. Deloitte's research confirms the second layer: 88% of employees use AI at work, but only 5% use it in ways that materially change how work is done, which means the bottleneck is the failure to design the human layer around AI, not the failure to acquire the technology itself.
How do CEOs drive growth through innovation during periods of uncertainty?
The answer from BCG's CEO Agenda 2026 is counterintuitive but supported by two decades of data: the best time to invest in innovation capability is during periods of volatility, because that is when competitors retreat and market position becomes acquirable at lower cost. BCG's study found that the most innovative companies widened their performance advantage over the market most sharply during the Great Recession and the COVID-19 pandemic, not during growth cycles. The practical implication is that executives who treat innovation as a governed growth system with clear accountability and cadence will consistently outperform those who treat it as a portfolio of individual bets that gets suspended when boards get cautious.
What are the three tectonic forces McKinsey identifies, and why do they matter together?
McKinsey's State of Organizations 2026 identifies AI and technology acceleration, economic and geopolitical fragmentation, and the transformation of workforce expectations as three mutually reinforcing forces rather than independent challenges. They matter together because the organizational response to each force, whether faster AI deployment, supply chain restructuring, or new talent models, can undermine the other two if they are not coordinated from a common strategic center. McKinsey's nine organizational shifts are designed as an integrated response, not a sequential checklist, and organizations that address them as such are four times more likely to sustain top-tier financial performance over the following decade.
What does "human times machine" mean in practice, and how do organizations build it?
Deloitte's 2026 Global Human Capital Trends defines the shift from human-plus-machine (additive) to human-times-machine (multiplicative) as the central value creation challenge of the decade. In practice, that shift means replacing the question "what can AI automate?" with "how does AI amplify what humans do best?" This reframing drives fundamentally different design choices, because rather than substituting a human task with an algorithm, organizations redesign the entire workflow so that people use AI to think faster, see further, and decide more reliably, while retaining the judgment and accountability that machines cannot replicate. Deloitte's research shows that organizations making this shift are 2.5 times more likely to report superior financial performance, and building it requires explicit investment in workflow redesign, trust infrastructure, and governance — not just AI licensing.
How does strategic centering apply to M&A and portfolio decisions?
Strategic centering provides the most practical filter available for M&A and portfolio decisions. When a company has a defined center, the question of whether an acquisition reinforces that identity becomes answerable, and the discipline to decline deals that dilute the center becomes defensible to shareholders. BCG's 2026 CEO research notes that the strongest performers treat M&A as a continuous capability rather than a periodic event, and that capability only functions sustainably when there is a clear center to integrate toward. Without that center, serial acquisitions generate the organizational complexity that McKinsey documents as one of the primary destroyers of long-term performance, given that two-thirds of leaders already describe their organizations as overly complex.
9. Related MD-Konsult Reading
- What is a Business Model and How to Write One — MD-Konsult Primers
- What is a Business Plan and How to Write It — MD-Konsult Primers
- What is a Business Model Canvas and Its Purpose — MD-Konsult Primers
- How to Prioritize Customer Requirements Using MoSCoW — MD-Konsult Primers
- MD-Konsult Research — Strategy Intelligence for Executive Teams
