Dries Buytaert

"To misuse a woodworking metaphor, I think we're experiencing a shift from hand tools to power tools. You still need someone who understands the basics to get good results from the tools, but they're not chiseling fine furniture by hand anymore. They're throwing heaps of wood through the tablesaw instead. More productive, but more likely to lose a finger if you're not careful."
– mrmincent, Hacker News comment on Claude Code, via Simon Willison

If, like me, you work in web development, design, or digital strategy, this quote might hit close to home. But it may not go far enough. We are not just moving from chisels to table saws. We are about to hand out warehouse-sized CNC machines and robotic arms.

This is not just an upgrade in tools. The Industrial Revolution didn't just replace handcraft with machines. It upended entire industries.

History does not repeat itself, but it often rhymes. For over two centuries, new tools have changed not just how work gets done, but what we can accomplish.

AI is changing how websites are built and how people find information online. Individual developers are already using AI tools today, but broader organizational adoption will unfold over the years ahead.

It's clear that AI will have a deep impact on the web industry. Over time, this shift will affect those of us who have built our careers in web development, marketing, design, and digital strategy. I am one of them. Most of my career has been rooted in Drupal, which makes this both personal and difficult to write.

But this shift is bigger than any one person or platform. There are more than 175,000 digital agencies worldwide, employing millions of people. Behind those numbers are teams, individuals, and livelihoods built over decades. Our foundation is shifting. It touches all of us, and we all need to adapt.

If you are feeling uncertain about where this is heading, you are not alone.

Why I am writing this

I am not writing this to be an alarmist. I actually feel a mix of emotions. I am excited about the possibilities AI offers, but also concerned about the risks and uneasy about the speed and scale of change.

As the project lead of Drupal, I ask myself: "How can I best guide our community of contributors, agencies, and end users through these changes?".

Like many of you, I am trying to understand what the rise of AI means for our users, teams, partners, contributors, products, and values. I want to help however I can.

I don't claim to have all the answers, but I hope this post sparks discussion, encourages deeper thinking, and helps us move forward together. This is not a roadmap, just a reflection of where my thinking is today.

I do feel confident that we need to keep moving forward, stay open-minded, and engage with the changes AI brings head-on.

Even with all that uncertainty, I feel energized. Some of the hardest challenges the Drupal community has faced, such as improving usability or maintaining documentation, may finally become more manageable. I see ways AI can support Drupal's mission, lower barriers to online publishing, make Drupal more accessible, and help build a stronger, more inclusive Open Web. The future is both exciting and uncertain.

But this post isn't just for the Drupal community. It's for anyone working in or around a digital agency who is asking: "What does AI mean for my team, my clients, and my future?". I will focus more directly on Drupal in my next blog post, so feel free to subscribe.

If you are thinking about how AI is affecting your work, whether in the Drupal ecosystem or elsewhere, I would love to hear from you. The more we share ideas, concerns, and experiments, the better prepared we will all be to shape this next chapter together.

The current downturn is real, but will pass

Before diving into AI, I'd be remiss not to acknowledge the current economic situation. Agencies across all platforms, not just those working with Drupal, are experiencing challenging market conditions, especially in the US and parts of Europe.

While much of the industry is focused on AI, the immediate pain many agencies are feeling is not caused by it. High interest rates, inflation, and global instability have made client organizations more cautious with spending. Budgets are tighter, sales cycles are longer, competition is fiercer, and more work is staying in-house.

As difficult as this is, it is not new. Economic cycles and political uncertainty have always come and gone. What makes this moment different is not the current downturn, but what comes next.

AI will transform the industry at an accelerating pace

AI has not yet reshaped agency work in a meaningful way, but that change is knocking at the door. At the current pace of progress, web development and digital agency work are on the verge of the most significant disruption since the rise of the internet.

One of the most visible areas of change has been content creation. AI generates drafts blog posts, landing pages, social media posts, email campaigns, and more. This speeds up production but also changes the workflow. Human input shifts toward editing, strategy, and brand alignment rather than starting from a blank page.

Code generation tools are also handling more implementation tasks. Senior developers can move faster, while junior developers are taking on responsibilities that once required more experience. As a result, developers are spending more time reviewing and refining AI-generated code than writing everything from scratch.

Traditional user interfaces are becoming less important as AI shifts user interactions toward natural language, voice, and more predictive or adaptive experiences. These still require thoughtful design, but the nature of UI work is changing. AI can now turn visual mockups into functional components and, in some cases, generate complete interfaces with minimal or no human input.

These shifts also challenge the way agencies bill for their work. When AI can do in minutes what once took hours or days, hourly billing becomes harder to justify. If an agency charges $150 an hour for something clients know AI can do faster, those clients will look elsewhere. To stay competitive, agencies will need to focus less on time spent and more on outcomes, expertise, and impact.

AI is also changing how people find and interact with information online. As users turn to AI assistants for answers, the role of the website as a central destination is being disrupted. This shift changes how clients think about content, traffic, and performance, which are core areas of agency work. Traditional strategies like SEO become less effective when users get what they need without ever visiting a site.

Through all of this, human expertise will remain essential. People are needed to set direction, guide priorities, review AI output, and take responsibility for quality and business outcomes. We still rely on individuals who know what to build, why it matters, and how to ensure the results are accurate, reliable, and aligned with real-world needs. When AI gets it wrong, it is still people who are accountable. Someone must own the decisions and stand behind the results.

But taken together, these changes will reshape how agencies operate and compete. To stay viable, agencies need to evolve their service offerings and rethink how they create and deliver value. That shift will also require changes to team structures, pricing models, and delivery methods. This is not just about adopting new tools. It is about reimagining what an agency does and how it works.

The hardest part may not be the technology. It is the human cost. Some people will see their roles change faster than they can adapt. Others may lose their jobs or face pressure to use tools that conflict with their values or standards.

Adding to the challenge, adopting AI requires investment at a moment when many agencies around the world are focused on survival. For teams already stretched thin, transformation may feel out of reach. The good news is that AI's full impact will take years to unfold, giving agencies time to adapt.

Still, moments like this can create major opportunities. In past downturns, technology shifts made room for new players and helped established firms reinvent themselves. The key is recognizing that this is not just about learning new tools. It is about positioning yourself where human judgment, relationships, and accountability for outcomes remain essential, even as AI takes on more of the execution.

The diminishing value of platform expertise alone

For years, CMS-focused agencies have built their businesses on deep platform expertise. Clients relied on them for custom development, performance tuning, security, and infrastructure. This specialized knowledge commanded a premium.

In effect, AI increases the supply of skilled work without a matching rise in demand. By automating tasks that once required significant expertise, it makes technical expertise abundant and much cheaper to produce. And according to the principles of supply and demand, when supply rises and demand stays the same, prices fall.

This is not a new pattern. SaaS website builders already commoditized basic site building, reducing the perceived value of simple implementations and pushing agencies toward more complex, higher-value projects.

Now, AI is accelerating that shift. It is extending the same kind of disruption into complex and enterprise-level work, bringing speed and automation to tasks that once required expensive and experienced teams.

In other words, AI erodes the commercial value of platform expertise by making many technical tasks less scarce. Agencies responded to earlier waves of commoditization by moving up the stack, toward work that was more strategic, more customized, and harder to automate.

AI is raising the bar again. Once more, agencies need to move further up the stack. And they need to do it faster than before.

The pattern of professional survival

This is not the first time professionals have faced a major shift. Throughout history, every significant technological change has required people to adapt.

Today, skilled radiologists interpret complex scans with help from AI systems. Financial analysts use algorithmic tools to process data while focusing on high-level strategy. The professionals who understand their domain deeply find ways to work with new technology instead of competing against it.

Still, not every role survives. Elevator operators disappeared when elevators became automatic. Switchboard operators faded as direct dialing became standard.

At the same time, these shifts unlocked growth. The number of elevators increased, making tall buildings more practical. The telephone became a household staple. As routine work was automated away, new industries and careers emerged.

The same will happen with AI. Some roles will go away. Others will change. Entirely new opportunities will emerge, many in areas we have not yet imagined.

I have lived through multiple waves of technological change. I witnessed the rise of the web, which created entirely new industries and upended existing ones. I experienced the shift from hand-coding to content management systems, which helped build today's thriving agency ecosystem. I saw mobile reshape how people access information, opening up new business models.

Each transition brought real uncertainty. In the moment, the risks felt immediate and the disruption felt personal. But over time, these shifts consistently led to new forms of prosperity, new kinds of work, and new ways to create value.

The great agency unbundling

AI can help agencies streamline how they work today, but when major technology shifts happen, success rarely comes from becoming more efficient at yesterday's model.

The bigger opportunity lies in recognizing when the entire system is being restructured. The real question is not just "How do we use AI to become a more efficient agency?" but "How will the concept of an agency be redefined?".

Most agencies today bundle together strategy, design, development, project management, and ongoing maintenance. This bundle made economic sense when coordination was costly and technical skills were scarce enough to command premium rates.

AI is now unbundling that model. It separates work based on what can be automated, what clients can bring in-house, and what still requires deep expertise.

At the same time, it is rebundling services around different principles, such as speed, specialization, measurable outcomes, accountability, and the value of human judgment.

The accountability gap

As AI automates routine tasks, execution becomes commoditized. But human expertise takes on new dimensions. Strategic vision, domain expertise, and cross-industry insights remain difficult to automate.

More critically, trust and accountability stay fundamentally human. When AI hallucinates or produces unexpected results, organizations need people who can take responsibility and navigate the consequences.

We see this pattern everywhere: airline pilots remain responsible for their passengers despite autopilot handling most of the journey, insurance companies use advanced software to generate quotes but remain liable for the policies they issue, and drivers are accountable for accidents even when following GPS directions.

The tools may be automated, but responsibility for mistakes and results remains human. For agencies, this means that while AI can generate campaigns, write code, and design interfaces, clients still need someone accountable for strategy, quality, and outcomes.

This accountability gap between what AI can produce and what organizations will accept liability for creates lasting space for human expertise.

The rise of orchestration platforms

Beyond human judgment, a new architectural pattern is emerging. Traditional Digital Experience Platforms (DXPs) excel at managing complex content, workflows, and integrations within a unified system. But achieving sophisticated automation often requires significant custom development, long implementation cycles, and deep platform expertise.

Now, visual workflow builders, API orchestration platforms, and the Model Context Protocol are enabling a different approach. Instead of building custom integrations or waiting for platform vendors to add features, teams can wire together AI models, automation tools, CRMs, content systems, and analytics platforms through drag-and-drop interfaces. What once required months of development can often be prototyped in days.

But moving from prototype to production requires deep expertise. It involves architecting event-driven systems, managing state across distributed workflows, implementing proper error handling for AI failures, and ensuring compliance across automated decisions. The tools may be visual, but making them work reliably at scale, maintaining security, ensuring governance, and building systems that can evolve with changing business needs demands sophisticated technical knowledge.

This orchestration capability represents a new technical high ground. Agencies that master this expanded stack can deliver solutions faster while maintaining the reliability and scalability that enterprises require.

Six strategies for how agencies could evolve

Agencies need two types of strategies: ways to compete better in today's model and ways to position for the restructured system that's emerging.

The strategies that follow are not mutually exclusive. Many agencies will combine elements from several based on their strengths, clients, and markets.

Competing in today's market

1. Become AI-augmented, not AI-resistant. To stay competitive, agencies should explore how AI can improve efficiency across their entire operation. Developers should experiment with code assistants, project managers should use AI to draft updates and reports, and sales teams should apply it to lead qualification or proposal writing. The goal is not to replace people, but to become more effective at handling fast-paced, low-cost work while creating more space for strategic, value-added thinking.

2. Focus on outcomes, not effort. As AI reduces delivery time, billing for hours makes less sense. Agencies can shift toward pricing based on value created rather than time spent. Instead of selling a redesign, offer to improve conversion rates. This approach aligns better with client goals and helps justify pricing even as technical work becomes faster.

3. Sell through consultation, not execution. As technology changes faster than most clients can keep up with, agencies have a chance to step into a more consultative role. Instead of just delivering projects, they can help clients understand their problems and shape the right solutions. Agencies that combine technical know-how with business insight can become trusted partners, especially as clients look for clarity and results.

Positioning for what comes next

4. Become the layer between AI and clients. Don't just use AI tools to build websites faster. Position yourself as the essential layer that connects AI capabilities with real client needs. This means building quality control systems that review AI-generated code before deployment and becoming the trusted partner that translates AI possibilities into measurable results. Train your team to become "AI translators" who can explain technical capabilities in business terms and help clients understand what's worth automating versus what requires human judgment.

5. Package repeatable solutions. When custom work becomes commoditized, agencies need ways to stand out. Turn internal knowledge into named, repeatable offerings. This might look like a "membership toolkit for nonprofits" or a "lead gen system for B2B SaaS". These templated solutions are easier to explain, sell, and scale. AI lowers the cost of building and maintaining them, making this model more realistic than it was in the past. This gives agencies a way to differentiate based on expertise and value, not just technical execution.

6. Build systems that manage complex digital workflows. Stop thinking in terms of one-off websites. Start building systems that manage complex, ongoing digital workflows. Agencies should focus on orchestrating tools, data, and AI agents in real time to solve business problems and drive automation.

For example, a website might automatically generate social media posts from new blog content, update landing pages based on campaign performance, or adjust calls to action during a product launch. All of this can happen with minimal human involvement, but these systems are still non-trivial to build and require oversight and accountability.

This opportunity feels significant. As marketing stacks grow more complex and AI capabilities expand, someone needs to coordinate how these systems work together in a structured and intelligent way. This is not just about connecting APIs. It is about designing responsive, event-driven systems using low-code orchestration tools, automation platforms, and AI agents.

Open Source needs agencies, proprietary platforms don't

Every AI feature a technology platform adds potentially takes work off the agency's plate. Whether the platform is open source or proprietary, each new capability reduces the need for custom development.

But open source and proprietary platforms are driven by very different incentives.

Proprietary platforms sell directly to end clients. For them, replacing agency services is a growth strategy. The more they automate, the more revenue they keep.

This is already happening. Squarespace builds entire websites from prompts. Shopify Magic writes product descriptions and designs storefronts.

Open source platforms are adding AI features as well, but operate under different incentives. Drupal doesn't monetize end users. Drupal's success depends on a healthy ecosystem where agencies contribute improvements that keep the platform competitive. Replacing agencies doesn't help Drupal; it weakens the very ecosystem that sustains it.

As the Project Lead of Drupal, I think constantly about how Drupal the product and its ecosystem of digital agencies can evolve together. They need to move in step to navigate change and help shape what comes next.

This creates a fundamental difference in how platforms may evolve. Proprietary platforms are incentivized to automate and sell directly. Open source platforms thrive by leaving meaningful work for agencies, who in turn strengthen the platform through contributions and market presence.

For digital agencies, one key question stands out: do you want to work with platforms that grow by replacing you, or with platforms that grow by supporting you?

Looking ahead

Digital agencies face a challenging but exciting transition. While some platform expertise is becoming commoditized, entirely new categories of value are emerging.

The long-term opportunity isn't just about getting better at being an agency using AI tools. It's about positioning yourself to capture value as digital experiences evolve around intelligent systems.

Agencies that wait for perfect tools, continue billing by the hour for custom development, try to serve all industries, or rely on platform knowledge will be fighting yesterday's battles. They're likely to struggle.

But agencies that move early, experiment with purpose, and position themselves as the essential layer between AI capabilities and real client needs are building tomorrow's competitive advantages.

Success comes from recognizing that this transition creates the biggest opportunity for differentiation that agencies have seen in years.

For those working with Drupal, the open source foundation creates a fundamental advantage. Unlike agencies dependent on proprietary platforms that might eventually compete with them, Drupal agencies can help shape the platform's AI evolution to support their success rather than replace them.

We are shifting from hand tools to power tools. The craft remains, but both how we work and what we work on are changing. We are not just upgrading our tools; we are entering a world of CNC machines and robotic arms that automate tasks once done by hand. Those who learn to use these new capabilities, combining the efficiency of automation with human judgment, will create things that were not possible before.

In the next post, I'll share why I believe Drupal is especially well positioned to lead in this new era of AI-powered digital experience.

I've rewritten this blog post at least three times. Throughout the process, I received valuable feedback from several Drupal agency leaders and contributors, whose insights helped shape the final version. In alphabetical order by last name: Jamie Abrahams, Christoph Breidert, Seth Brown, Dominique De Cooman, George DeMet, Alex Dergachev, Justin Emond, John Faber, Seth Gregory, and Michael Meyers.