Navigating the AI Revolution: The Importance of Adaptation

In 1999, Steve Ballmer gave a famous speech in which he said that the “key to industry transformation, the key to success is developers developers developers developers developers developers developers, developers developers developers developers developers developers developers! Yes!”

A similar mantra applies when discussing how to succeed with AI: adaptation, adaptation, adaptation! Artificial intelligence has already begun to transform how we work and live, and the changes AI is bringing to the world will only accelerate.

Businesses rely ever more heavily on software to run and execute their strategies. So, to keep up with competitors, their processes and products must deliver what end-users increasingly expect: speed, ease of use, personalization—and, of course, AI features. Delivering all of these things (and doing so well) requires having the right tech stack and software foundation in place and then successfully executing.

To better understand the challenges organizations adopting AI face, MongoDB and Capgemini recently worked with the research organization TDWI to assess the state of AI readiness across industries.

The road ahead

Based on a survey “representing a diverse mix of industries and company sizes,” TDWI’s “The State of Operational and Data Readiness for AI” contains lots of super interesting findings. One I found particularly compelling is the percentage of companies with AI apps in production: businesses largely recognize the potential AI holds, but only 11% of survey respondents indicated that they had AI applications in production. Still only 11%!

There’s clearly work to be done; data readiness challenges highlighted in the report include managing diverse data types, ensuring accessibility, and providing sufficient compute power. Less than half (39%) of companies surveyed manage newer data formats, and only 41% feel they have enough compute.

The report also shows how much AI has changed the very definition of software, and how software is developed and managed. Specifically, AI applications continuously adapt, and they learn and respond to end-user behavior in real-time; they can also autonomously make decisions and execute tasks.

All of which depends on having a solid, flexible software foundation.

Because the agility and adaptability of software are intrinsically linked to the data infrastructure upon which it’s built, rigid legacy systems cannot keep pace with the demands of AI-driven change. So modern database solutions (like, ahem, MongoDB)—built with change in mind—are an essential part of a successful AI technology stack.

Keeping up with change

The tech stack can be said to comprise three layers: at the “top,” the interface or user experience layer; then the business logic layer; and a data foundation at the bottom.

With AI, the same layers are there, but they’ve evolved: Unlike traditional software applications, AI applications are dynamic. Because AI-enriched software can reason and learn, the demands placed on the stack have changed. For example, AI-powered experiences include natural language interfaces, augmented reality, and those that anticipate user needs by learning from other interactions (and from data). In contrast, traditional software is largely static: it requires inputs or events to execute tasks, and its logic is limited by pre-defined rules.

A database underpinning AI software must, therefore, be flexible and adaptable, and able to handle all types of data; it must enable high-quality data retrieval; it must respond instantly to new information; and it has to deliver the core requirements of all data solutions: security, resilience, scalability, and performance.

So, to take action and generate trustworthy, reliable responses, AI-powered software needs access to up-to-date, context-rich data. Without the right data foundation in place, even the most robust AI strategy will fail.

Figure 1. The frequency of change across eras of technology.

Keeping up with AI can be head-spinning, both because of the many players in the space (the number of AI startups has jumped sharply since 2022, when ChatGPT was first released¹), and because of the accelerating pace of AI capabilities.

Organizations that want to stay ahead must evolve faster than ever. As the figure above dramatically illustrates, this sort of adaptability is essential for survival.

Execution, execution, execution

But AI success requires more than just the right technology: expert execution is critical.

Put another way, the difference between success and failure when adapting to any paradigm shift isn’t just having the right tools; it’s knowing how to wield those tools. So, while others experiment, MongoDB has been delivering real-world successes, helping organizations modernize their architectures for the AI era, and building AI applications with speed and confidence.

For example, MongoDB teamed up with the Swiss bank Lombard Odier to modernize its banking tech systems. We worked with the bank to create customizable generative AI tooling, including scripts and prompts tailored for the bank’s unique tech stack, which accelerated its modernization by automating integration testing and code generation for seamless deployment.

And, after Victoria’s Secret transformed its database architecture with MongoDB Atlas, the company used MongoDB Atlas Vector Search to power an AI-powered visual search system that makes targeted recommendations and helps customers find products.

Another way MongoDB helps organizations succeed with AI is by offering access to both technology partners and professional services expertise. For example, MongoDB has integrations with companies across the AI landscape—including leading tech companies (AWS, Google Cloud, Microsoft), system integrators (Capgemini), and innovators like Anthropic, LangChain, and Together AI.

Adapt (or else)

In the AI era, what organizations need to do is abundantly clear: modernize and adapt, or risk being left behind.

Just look at the history of smartphones, which have had an outsized impact on business and communication. For example, in its Q4 2007 report (which came out a few months after the first iPhone’s release), Apple reported earnings of $6.22 billion, of which iPhone sales comprised less than 2%²; in Q1 2025, the company reported earnings of $124.3 billion, of which 56% was iPhone sales.³ The mobile application market is now estimated to be in the hundreds of billions of dollars, and there are more smartphones than there are people in the world.⁴ The rise of smartphones has also led to a huge increase in the number of people globally who use the internet.⁵

However, saying “you need to adapt!” is much easier said than done. TWDI’s research, therefore, is both important and useful—it offers companies a roadmap for the future, and helps them answer their most pressing questions as they confront the rise of AI.

P.S. ICYMI, here’s Steve Ballmer’s famous “developers!” speech.

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