Fitness Beats Forecasts (4 of 4)
In an unpredictable future, adaptability matters more than prediction.
This post is part of a four-part series: Thinking Clearly About AI. The series doesn’t try to predict the future. It looks at patterns from past disruptions and asks better questions about the present. Each post explores one idea.
The Comfort of Predictions
Executives love forecasts. Slide decks bristle with growth curves, market penetration estimates and ROI models. Forecasts make uncertainty feel controllable.
The problem is: forecasts are almost always wrong.
COVID-19 is a reminder. Few firms predicted a global supply chain freeze, a collapse in travel or a vaccine developed in under a year. Yet some firms thrived. Not because they predicted the shock but because they were fit enough to adapt when it hit.
AI will be no different. The future of large language models, regulation and industry structures is unknowable. What matters is not foresight but fitness: the capacity to adapt when reality diverges from predictions.
What Fitness Means
Fitness is not about muscles. It’s about capacity. An organisation with fitness can:
Sense change early – picking up weak signals before competitors.
Experiment cheaply – running tests without betting the farm.
Learn fast – capturing feedback and embedding it quickly.
Adapt structures – changing processes, pricing, and governance when needed.
Companies that built fitness have outperformed those that built forecasts.
Case Study: Shopify
In 2004, Tobias Lütke tried to open an online snowboard shop. Existing e-commerce tools were clunky, so he built his own software. Within two years, he abandoned the snowboard idea and focused on selling the platform. Shopify was born.
Shopify didn’t predict the rise of “direct-to-consumer” brands, the explosion of small online stores, or the pandemic-driven e-commerce surge. What it did build was fitness:
A modular, API-driven platform that could expand rapidly.
A culture willing to pivot from product to platform.
The ability to integrate payments, fulfilment, and logistics as customer needs changed.
By 2020, Shopify became the backbone of millions of merchants, facilitating $197 billion in GMV. Its market cap briefly surpassed Royal Bank of Canada.
Shopify didn’t forecast the future of retail. It built adaptability into its DNA—pivoting when snowboard sales failed, scaling when merchant demand surged, and absorbing shocks like COVID-19.
Case Study: Zara (Inditex)
Fashion is notoriously unpredictable. Trend cycles shift rapidly, seasons vary, consumer sentiment is fickle.
Zara’s parent, Inditex, didn’t try to predict fashion perfectly. Instead, it built an organisation designed to adapt faster than anyone else.
Stores feed real-time data back to design teams.
Designers work in small batches, releasing new items within 3–4 weeks (vs. industry average of 6–12 months).
Production is deliberately decentralised: 50% of goods are manufactured close to HQ in Spain, allowing rapid reallocation.
This adaptability makes Zara resilient to shocks. When a line fails, losses are small. When a trend emerges, they double down within weeks.
In 2022, despite inflationary pressures and supply chain snarls, Inditex grew revenue by 17% and profits by 54%. Fitness (speed, data loops, flexible production) trumped forecasts about consumer demand.
Zara doesn’t survive by predicting trends more accurately. It survives by being able to respond when trends appear.
Case Study: Pfizer/BioNTech
In early 2020, most pharmaceutical firms had multi-year timelines for vaccine development. Forecasts suggested 4–5 years for deployment.
BioNTech, a relatively small German biotech, and Pfizer, a global pharma giant, partnered to adapt mRNA technology (originally researched for cancer) into a COVID-19 vaccine.
The pivot worked. Within 11 months, Pfizer/BioNTech delivered the first authorised vaccine, saving millions of lives and generating $36.7 billion in sales in 2021.
Why did they succeed? Not because they forecast the pandemic better than rivals. Moderna, AstraZeneca, Johnson & Johnson all worked on vaccines too. The differentiator was:
mRNA platform adaptability: a modular technology ready to be repurposed.
Agile collaboration: Pfizer scaled BioNTech’s science with global manufacturing and trials.
Regulatory fitness: ability to work with regulators in accelerated frameworks.
The success wasn’t foresight. It was fitness: the ability to pivot existing capabilities quickly and execute at scale under pressure.
Case Study: Adobe
In the 2000s, Adobe made its money selling boxed software like Photoshop and Illustrator, updated every few years. Forecasts suggested stable growth.
Instead of clinging to forecasts, Adobe disrupted itself. In 2012, it shifted to a subscription model: Creative Cloud. The decision was unpopular at first; revenue dipped and analysts doubted the model. But subscriptions created recurring revenue, predictable cash flows and stronger customer lock-in.
By 2020, Adobe’s market cap had grown 6x, riding SaaS economics.
Then came generative AI. Rather than fight startups like Midjourney or OpenAI head-on, Adobe embedded Firefly, its generative AI model, directly into Creative Cloud. By 2024, Firefly had generated over 6.5 billion images and Adobe could defend its moat while monetising AI safely within its ecosystem.
Adobe didn’t predict SaaS dominance or generative AI. It built the fitness to reinvent its model twice in a decade: first through subscriptions, then by embedding AI inside its moat.
Case Study: Maersk
Shipping is an old industry. For decades, Maersk was the world’s largest container shipping line, competing on scale and cost. Forecasts suggested steady demand growth.
But Maersk recognised that forecasts of freight cycles were unreliable. Instead, it invested in adaptability, transforming itself from a shipping company to an integrated logistics provider.
Between 2016 and 2022, Maersk acquired customs, warehousing and last-mile logistics businesses. It invested heavily in digital platforms, allowing customers to book, track and manage supply chains end-to-end.
When COVID-19 hit, global supply chains collapsed. While competitors scrambled, Maersk captured market share by offering integrated solutions. Revenue doubled between 2019 and 2022, peaking at $81.5 billion.
Maersk didn’t predict a pandemic or port congestion. It built fitness: diversified services, digital platforms and end-to-end capabilities that allowed it to thrive when disruption hit.
Why Forecasts Fail, Fitness Wins
Across these stories, the same pattern appears:
Shopify didn’t forecast the rise of D2C. It pivoted.
Zara didn’t predict trends. It shortened cycle times.
Pfizer didn’t foresee COVID. It leveraged adaptable science.
Adobe didn’t guess SaaS or GenAI. It reinvented its model.
Maersk didn’t predict global chaos. It built integration capacity.
Predictions give the illusion of control. Fitness gives you the ability to survive reality.
How to Build Fitness
So what does organisational fitness look like in the AI era?
Governance that tolerates experiments
Treat initiatives as portfolios, not binary projects. Amazon’s “two-pizza teams” exist to run experiments cheaply.
Funding models that shift capital quickly
Traditional budgets are too rigid. Envelope funding, venture-style allocation, or rolling portfolios allow faster redeployment.
Cultures that reward iteration
Success in AI will come from teams that try, learn, and adjust, not from those who wait for certainty.
Structures that balance core and explore
Tushman & O’Reilly’s ambidexterity still matters: protect the core while exploring the edge.
McKinsey’s 2023 resilience research found that companies with “dynamic resource allocation” and “rapid decision rights” outperformed peers by 2.4x TSR during disruptions.
The Question for You
Is your organisation trying to forecast its way to safety or building the fitness to adapt when forecasts fail?
Because AI’s trajectory, regulation, and societal impact are unknowable. The only certainty is that forecasts will be wrong.
The winners won’t be the ones who guessed right. They’ll be the ones who had the adaptability to climb whatever hill appeared.
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