Published September 28, 2026 in Artificial Intelligence • 9 min read • 
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When I was a student at the University of Zurich in the late 1980s and early 1990s, I earned my living as a freelance graphic designer in my own one-man advertising agency. I created flyers, brochures, and posters from copy to layout and pre-print. It was the time when scaling a font by 150% and tilting it by 15 degrees in real time was nothing short of a miracle. I loved Adobe, which for decades was the premier tool for creative arts. PostScript, shipped in 1984, was a major force in the desktop publishing (DTP) revolution. Thanks to Adobe’s technology, I only needed an Apple Macintosh computer and a LaserWriter to produce professional-grade graphic designs.
Over the years, Adobe’s software suite – Adobe Photoshop, Illustrator, InDesign, and, of course, Adobe Acrobat – was a literacy one had to spend years mastering. It created a two-tier divide among graphic designers: those proficient with those tools and those who were not. In the language of marketing strategy, Adobe’s competitive advantage was never really about the product. It was about the customer’s skill. The software was valuable precisely because it was hard to use. The scarcity of the ability to operate it – the customer’s ability, not Adobe’s feature – was the moat.
Generative AI (GenAI) changed this in only three years. The dynamics of this change reveal a critical distinction that too few leaders make: the difference between a product-centric strategy and a customer-centric one.
When GenAI took the world by storm in 2023, the market viewed Adobe as the ultimate beneficiary. If AI could automate the tedious parts of design, surely the Creative Cloud would become an unstoppable super-app. By February 2024, Adobe’s stock had surged from $275 to $630, fueled by the launch of Firefly, Adobe’s family of GenAI tools built into Creative Cloud apps. Firefly allows users to create images, text effects, and designs using text prompts. The logic seemed bulletproof: embed AI into the product, and the product becomes more powerful. This was a textbook product-centric strategy. Adobe looked inward at its own capabilities and asked: “How do we add AI features to our software?” The answer was Firefly – a feature upgrade to an existing product.
But then, the narrative curdled. Competitors – Canva, Midjourney, specialized AI video generators – enabled less-skilled designers to achieve similar results with cheaper AI tools. By September 2026, Adobe’s share price had dropped to $266.
Why did an impressive technological feat fail to protect the stock? None of this is a story about a failing business. In the first quarter of 2026, Adobe reported revenue of $6.40bn, up 12% year on year, non-GAAP earnings per share of $6.06 ahead of consensus, and record first-quarter operating cash flow of $2.96bn. AI-first annual recurring revenue tripled year on year to more than $500m. Yet the stock fell 7.6% that day.
What changed between February 2024, when the share traded at $630, and September, when it was at $266?
Non-GAAP earnings per share (EPS) went from $15.25 in fiscal 2023 to roughly $24 on the fiscal 2026 run rate, up about 60% (note: we use the forward P/E, non-GAAP, the basis Adobe guides on). The share price fell about 58%. Earnings grew and the price fell by nearly the same proportion. The price/earnings multiple did all the work. Investors pay a premium for companies they believe hold a durable advantage. That premium shows up in the price/earnings multiple. Adobe’s forward multiple was roughly 35 in February 2024. By September 2026 it was roughly 10. In other words, the investors concluded that Adobe had lost its moat, despite the launch of integrated GenAI features. A customer-centric view explains why they might be right.
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For years, product managers have lived by the gospel of job-to-be-done (JTBD). The theory, popularized by Clayton Christensen, suggests that customers don’t buy products; they hire them to do a job. A marketing manager doesn’t buy Adobe Illustrator to anchor points on a vector path; they hire it to produce a brand-compliant social media asset.
JTBD is powerful for understanding what outcome customers want. But JTBD alone does not explain what capabilities are required to achieve that outcome. It tells you what the customer wants to get done, but it does not indicate what they need to do it. To understand this critical distinction, we can use the motivation, opportunity, and ability (MOA) framework developed by my IMD colleague Bernie Jaworski (MacInnis and Jaworski (1989) and MacInnis, Moorman, and Jaworski (1991)).
Combining JTBD with MOA helps distinguish between what customers want and what they actually need to succeed.
In the MOA model, a customer behavior – such as choosing to use Adobe Illustrator – is driven by the three factors:
Combining JTBD with MOA helps distinguish between what customers want and what they actually need to succeed. This is the heart of customer-centric strategy: it shifts the analytical lens from the product’s features to the customer’s resources – their motivation, their context, and, critically, their skills.
Historically, Adobe’s business model was a bet on the ability gap. Professional design was a high-friction activity. Because the ability required of the customer was so high, Adobe could charge a premium. It owned the pro segment because only professionals had the competence to navigate their complex user interface.
Now, by making “professional-looking design” accessible via a text prompt, it helped its users. But other companies can now compete with Adobe thanks to GenAI. This creates a causal link that analysts missed in 2023: as the ability requirement for a job drops toward zero, the value of the specialized tool hosting that ability also drops.
Consider the “prosumer” segment, which both produces and consumes goods and services. In 2022, a small business owner had the motivation to create an ad but lacked the ability to use Photoshop. They had to hire an Adobe-equipped freelancer. Today, tools like Canva, Midjourney, and specialized AI video generators provide that ability natively – not by being better products, but by requiring less of the customer.
In the old world, Adobe sold a brush that only masters could wield. In the new world, it is selling a brush that paints itself - but so does everyone else.
The causal chain, seen through a customer-centric lens, looks like this:
In the old world, Adobe sold a brush that only masters could wield. In the new world, it is selling a brush that paints itself – but so does everyone else. When the ability moves from the customer’s brain to the software’s “brain,” the software becomes a commodity.
Adobe’s Firefly story illustrates a fundamental strategic distinction. A product-centric strategy focuses on attributes and features: Adobe Firefly integrates GenAI capabilities into Adobe’s software. It asks: “How do we make our product better?” A customer-centric strategy focuses on what customers want (job-to-be-done) and what they need (motivation, opportunity, ability). It asks: “How are our customers’ skills, resources, and contexts changing – and what does that mean for the value we provide?”
If investors had looked at Adobe through a customer-centric lens in 2023, the strategic question would not have been “How do we put AI into Photoshop?” but rather “If AI gives everyone the ability to design, what new scarcity do we create for our customers?”
In five years, textbooks will cite Adobe as the case that separates a strategic error from a strategic squeeze.
In five years, textbooks will cite Adobe as the case that separates a strategic error from a strategic squeeze. Firefly was not a mistake. Adobe faced a choice between commoditizing itself and being commoditized by others, and both roads led to the same place. The error was building Firefly as a feature and treating that as a strategy.
Adobe is currently a company seeking a new moat. It still has enterprise workflows and the opportunity to be the industry standard for large corporations. But as the MOA framework teaches us, if the ability to do the work becomes universal, the price you can charge for it eventually trends toward zero.
For leaders, the implication is not to abandon frameworks like job-to-be-done, but to pair them with a forward-looking MOA lens, and to do so through a customer-centric, rather than product-centric, posture. Executives need to ask not only what job the customer wants done, but how motivation, opportunity, and ability are shifting as AI collapses skills, reduces friction, and redraws cost curves. Where motivation intensifies and ability becomes universal, value will migrate. Where opportunity is constrained – by regulation, workflow integration, trust, or scale – new moats can still be built.
Competitive advantage in the AI era will not come from embedding the latest feature into an existing product. It will come from understanding the customer’s evolving resources – their skills, constraints, and context – and deliberately positioning the firm at future intersections of motivation, opportunity, and ability where scarcity persists. Leaders who default to product-centric thinking by asking “What feature do we add?” instead of “What does our customer now need?” may continue to serve the right jobs-to-be-done, only to discover that someone else is being paid for them.
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