AI is automating the exact tasks that web agencies once monetized, like routine maintenance and web monitoring.
This shift may seem scary to some, but it is also unlocking new opportunities, creating operational efficiencies, and freeing up skilled website professionals to focus on high-value tasks.
I caught up with Suhaib Zaheer, SVP of Managed Hosting at DigitalOcean and GM of Cloudways, to discuss how AI is redefining how agencies deliver value to their clients. During our conversation, he highlights the opportunities and risks of AI adoption, as well as breaking down exactly how agencies can mitigate those risks.
Historically, agencies would bill clients for ‘maintenance and monitoring’. As AI automates a lot of these tasks, how are agencies’ value propositions changing?
What used to take 30–40 minutes of investigation can now be understood in minutes.
For years, agencies have spent significant amounts of time on reactive operational work like investigating outages, reviewing logs, troubleshooting performance issues, and implementing fixes.
AI is changing that dynamic. What used to take 30–40 minutes of investigation can now be understood in minutes. AI can quickly surface root causes, identify patterns across applications and infrastructure, and in some cases even automate the next action.
Leveraging AI allows agencies to move further up the value chain and focus more of their time on performance optimization, improving SEO visibility, conversion improvements, and helping clients achieve better business outcomes.
This shift matters because the landscape websites operate in is more demanding than it used to be. There’s more automated traffic, more variability, and more pressure on performance and visibility. It’s not enough for a site to just be online anymore. The agencies that will stand out are the ones that use AI to reduce their operational load and reinvest that time into higher-value work that clients are willing to pay for.
Is AI helping smaller, creative-heavy agencies to become competitive with larger technical agencies by lowering the barrier of entry? How are these smaller agencies gaining a competitive edge?
AI is lowering the barrier, but it’s not a complete leveller.
It gives smaller teams access to capabilities that once required deeper technical expertise, like monitoring, diagnostics, and performance optimization. Most of the issues agencies deal with aren’t unique. They follow consistent patterns – inefficient queries, resource limits, or configuration issues that show up across different environments.
AI can identify those patterns quickly and point to the likely cause, which reduces the need for specialist input on every issue.
That allows smaller agencies to stay lean while still operating at a much higher technical level than they could have a few years ago.
Agencies often inherit ‘messy’ legacy websites through the onboarding process. How is AI helping agencies audit and stabilise unoptimized portfolios at scale?
Most legacy websites look messy, but the underlying issues are usually not unique. They tend to follow similar patterns – excessive database usage, ineffective plugins, poor caching configurations, or other application-level bottlenecks.
AI helps agencies audit those portfolios by identifying issues quickly and consistently across multiple sites. Instead of manually reviewing each environment, it can surface the likely root cause in real time, which significantly reduces the effort involved in diagnosing problems.
That makes stabilisation much more scalable. Once those common issues are identified, they can be resolved faster and in a more consistent way across the portfolio, bringing sites into a stable state sooner.
For agencies managing large numbers of inherited sites, that’s the shift. Auditing and stabilising portfolios becomes far less manual and much easier to do at scale.
How is AI changing how agencies approach security? For example, can it help tell the difference between legitimate spikes in high-volume traffic and DDoS attacks?
It’s not a replacement for dedicated security tools.
Distinguishing between normal traffic and malicious activity is getting harder. There’s more automation, more bot traffic, and more variability in how sites behave.
AI has the potential to help by analysing patterns in real time and flagging behaviour that doesn’t look normal – whether that’s an unusual spike or activity that suggests something more aggressive. It gives agencies a clearer starting point when something goes wrong.
It’s not a replacement for dedicated security tools, and it shouldn’t be treated as one. Traditional security layers like WAFs, DDoS protection, rate limiting, malware scanning, and access controls remain foundational. Where AI is useful is in speeding up diagnosis and helping teams understand what they’re dealing with more quickly.
What risks could agencies face when relying too heavily on AI-automated hosting? How can they mitigate these risks?
The biggest risk is over-reliance.
AI is very effective on the problems it was designed for, but it doesn’t cover every scenario. More complex or highly customised issues are still much harder to diagnose consistently.
There’s a tendency to assume AI can handle the full workflow, when in reality it’s strongest as a diagnostic layer. It can surface the likely root cause quickly, but that still needs to be validated and acted on in context.
That’s why it needs to stay grounded in a human-in-the-loop model. In our case, Cloudways Copilot operates in a controlled environment, where it guides investigation but doesn’t have unrestricted control.
The balance is using AI to remove the repetitive part of troubleshooting, while keeping decision-making with the team. That’s how you get the benefit without introducing risk.
What emerging AI-powered hosting technologies do you think will prove to be the biggest opportunities for agencies in the coming years?
The opportunity is less time spent reacting to problems and more time spent improving how sites perform across the portfolio.
The biggest opportunity is the move from reactive troubleshooting to more proactive systems.
Today, most of the value comes from faster diagnosis – identifying issues quickly and reducing the time spent figuring out what’s gone wrong. The next step is shifting earlier in that process, where common infrastructure issues are identified and addressed before they impact performance.
In practice, that means systems that continuously analyse signals across servers and applications, and flag patterns that are likely to cause problems before they escalate.
It won’t remove the need for human input, especially in more complex or application-level scenarios. But if agencies can catch and resolve the most common issues earlier, it has a significant impact at scale.
The opportunity is less time spent reacting to problems and more time spent improving how sites perform across the portfolio.
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