AI vs Automation: What MSPs Really Need to Know
Everywhere you look, companies claim AI is changing the game – but is it truly making MSPs more money?
Vendors are slapping “AI-powered” onto every product, hoping to grab attention and boost sales. But let’s be honest: how much of this so-called AI is actually useful? And should MSPs even be focusing on AI when many haven’t fully embraced automation yet?
Right now, AI is one of the most overused buzzwords in tech. It’s easy to get caught up in the hype, thinking AI will magically solve all your problems. But in reality, most of what’s being sold as AI today is just glorified automation – fancy search tools and pattern recognition rather than real intelligence. Meanwhile, the one thing that’s already proven to save time, cut costs, and boost efficiency – automation – is still being underused by many MSPs.
Think about it: How many MSPs still struggle with basic process inefficiencies? How many are manually handling tasks that could be automated, like ticket triaging, billing reconciliation, or customer onboarding? Chasing AI before nailing automation is like trying to run before you can walk. The real question isn’t whether AI is the future – it’s how MSPs can make smarter use of both automation and AI to drive real efficiency and profitability.
Instead of getting lost in the AI hype, you should be asking:
- Where can we get immediate value?
- How can we streamline our operations today?
- And what role should AI play once we’ve built a strong automation foundation?
But before you can make informed decisions, you need to understand the real differences between AI and automation – what each one does, where they overlap, and how to use them effectively.
Contents:
What Most MSPs Get Wrong About AI – Find Out Inside
Section 1: Cutting Through the AI Hype
The Problem with AI
- "AI is the new cloud - everyone's just rebranding something they’ve already done."
And he’s right. Everywhere you look, vendors are throwing the AI label onto their products, making them sound like game-changers. But when you dig deeper, a lot of these so-called AI features don’t actually do anything revolutionary – they’re just automation with a fancy name.
For example, take AI-powered triaging in a PSA. It sounds cutting-edge, but in most cases, it’s just recognising patterns in past tickets and suggesting similar resolutions based on keywords. While it might seem helpful, a well-organised knowledge base and smart automation could achieve the same result.
Then there’s the issue of inflated expectations. Many MSPs buy into AI thinking it will solve all their workflow inefficiencies overnight, only to realise that it requires clean, structured data to work properly. Without that, AI can actually add to the confusion rather than reduce it.
That doesn’t mean AI has no value.
When used correctly, it can enhance decision-making and optimise certain processes. But you need to cut through the noise and focus on where AI can genuinely help, rather than getting caught up in trends that sound exciting but don’t deliver real impact.
Instead of asking, “How can we use AI?”, ask yourself, “Where do we actually need improvement?” and only then decide whether AI is the right solution.
The Risk of Investing in AI Too Early
Many are eager to jump on the AI bandwagon, thinking it will give them an edge over competitors. But without a clear strategy, AI can quickly become an expensive distraction rather than a real productivity booster.
- "MSPs buy AI-powered tools before they've automated basic workflows. It's like trying to run before you can walk. AI needs a foundation of structured data and automation to work. If your processes are inconsistent, AI will only amplify the chaos."
This is a common pitfall. MSPs invest in AI expecting instant improvements, but AI isn’t a magic fix – it relies on well-organised data and established workflows. If processes are already messy, AI won’t solve the problem; it will just make the mess more complicated.
Instead of rushing into AI, focus on getting automation right first.
That means streamlining workflows, improving data consistency, and ensuring your team understand how AI fits into their operations. Only once a strong automation foundation is in place should AI be introduced to enhance and refine existing processes.
Section 2: Where AI and Automation Fit in MSPs
AI vs Automation: What’s the Difference?
There’s a lot of confusion around AI and automation, with many unsure where one ends and the other begins. So let’s break it down in simple terms:
- Automation: This follows a set of predefined rules to complete tasks consistently. It’s great for repetitive processes like SLA reminders, billing reconciliation, and workflow steps in a PSA – things that don’t require decision-making, just execution.
- AI: Unlike automation, AI isn’t based on fixed rules. Instead, it learns from patterns in data and tries to make decisions, but its results can be unpredictable.
- "With automation, you tend to have fixed parameters and a fixed outcome as a result of those parameters. AI, on the other hand, is always guessing what the next step is, which makes it much harder to use for systematised processes."
For MSPs looking to improve efficiency and reduce costs, automation is always the smarter starting point. AI can be useful in some areas, but it works best as an enhancement to automation – not a replacement.
Watch the scaleUP Podcast: AI vs Automation: Cutting Through the Fog for MSPs
Where Should MSPs Start?
Many are eager to dive into AI, thinking it’s the next big thing. But here’s the problem: you may not have nailed the basics of automation yet. Before investing in AI tools, you need a clear plan, because AI won’t magically transform your operations.
- "I speak to MSPs who say, ‘I want to use AI!’ My first question is always, ‘What for?’ And most of the time, they don’t know. They just know it’s a buzzword and feel like they should be using it."
Instead of chasing the latest trend, first focus on streamlining existing processes. The best way to do that? Get automation right first.
Here are some high-impact areas to start with:
- Billing reconciliation – Sync RMM, CSP, and distributor data with your PSA to automatically bill based on actual usage.
- SLA management – Set up automated alerts and escalations to ensure response times stay on track.
- Customer self-service – Use portals to let customers log requests, reset passwords, and handle common tasks without technician involvement.
- Automated triaging – Use predefined rules to categorise tickets before relying on AI for complex cases.
- Proactive maintenance – Automate system health checks and alerts before small issues turn into big problems.
- Automated project workflows – Standardise onboarding, change requests, and approvals to eliminate manual bottlenecks.
- Compliance automation – Stay on top of regulatory requirements by automating audits, security checks, and reporting.
- Patch management – Automatically roll out security updates and patches across client systems with minimal disruption.
Getting automation in place first delivers immediate ROI – reducing manual workloads, improving efficiency, and ensuring consistency. Once MSPs have mastered automation, then they can explore where AI might add additional value.
The Challenge of AI in MSPs
AI is only as good as the data it learns from – and that’s where many MSPs run into trouble. Even when AI is implemented, its effectiveness is often crippled by poor data quality.
Many MSPs lack structured, well-documented ticket resolutions, making it almost impossible for AI to generate meaningful insights.
- "When I started looking at AI for triaging, the first thing I found was that no one was documenting proper resolutions. Most tickets just said ‘problem fixed’ or ‘closed on call.’ AI can’t learn from that."
This is a huge issue because AI depends on patterns and historical data to function properly. If MSPs aren’t maintaining clean, consistent, and detailed records, AI has nothing reliable to work with – it’s like trying to train a salesperson with incomplete customer history.
To make AI useful, you need to get your data in order first:
- Standardise ticket resolutions – Instead of vague notes like “issue fixed,” use structured responses detailing the problem, steps taken, and resolution.
- Use automation to clean data – Implement workflows that require technicians to enter proper resolution details before closing tickets.
- Ensure consistency across teams – Create templates and processes so that all technicians document issues and fixes in a uniform way.
The Future: Where is AI Really Headed?
AI might not be the game-changer some claim right now, but its potential for the future is huge. As AI models continue to evolve, they’ll go beyond simple data processing and start playing a much bigger role in how MSPs operate.
One of the biggest shifts we’re likely to see is AI acting as a central intelligence layer that manages and optimises an MSP’s entire stack. Instead of being locked inside individual tools, AI could oversee everything – coordinating integrations, optimising processes, and providing real-time insights across platforms.
- "I imagine AI won’t just sit within individual tools - it will sit above them, coordinating integrations, optimising processes, and offering real-time insights across all platforms."
That said, AI won’t replace human interaction, especially in customer service. MSPs that stand out in the future will be the ones that combine AI efficiency with a personal touch. MSPs that differentiate themselves in the future will be the ones that continue to provide a human touch. AI will enhance efficiency, but great customer relationships will always be the core of a successful MSP.
Another critical area is AI security. As AI becomes more embedded in business operations, MSPs will need new frameworks to ensure security, compliance, and data integrity.
- "We’re going to see a whole new discipline emerge around AI security and compliance. With AI integrating deeper into business operations, frameworks will need to be put in place to ensure data integrity and security."
The future of AI in MSPs isn’t just about smarter tools – it’s about smarter strategies, balancing AI-driven efficiencies with strong automation and a human-first approach.
Final Takeaways - Be Intentional About AI and Automation
AI has the potential to transform MSP operations, but only when used strategically.
Too many jump on the AI hype without a clear plan, leading to wasted investments and inefficient processes. The key takeaway? AI should enhance, not replace, a well-structured automation strategy.
Instead of chasing trends, focus on getting foundations right first. Here’s how to make AI and automation work for you:
- Prioritise Automation Before AI – Before diving into AI, ensure your automation is solid. AI is unpredictable, but automation is reliable – it reduces manual tasks, improves consistency, and delivers immediate ROI.
- Get Your Data in Order – AI is only as good as the data it learns from. Poorly documented tickets, inconsistent workflows, and messy records will undermine AI’s effectiveness. To set AI up for success:
- Keep AI Focused on Practical Value – AI should solve real problems, not just sound impressive. Before investing, ask:
- Does this AI tool improve efficiency or customer experience?
- Can we achieve the same outcome with automation?
- Do we have the right data structure for AI to work effectively?
- Balance AI with a Human-First Approach – AI can streamline operations, but great customer relationships still require a human touch. MSPs that succeed will be those that use AI to enhance service, not replace it.
- Stay Selective and Future-Ready – AI is evolving, but not every AI feature is worth investing in. Ensure you:
- Vet AI solutions carefully – Don’t buy into hype; test whether AI features truly deliver.
- Monitor industry trends – Stay informed on where AI is heading but avoid being an early adopter without clear value.
- Build for adaptability – Ensure that automation and AI integrations remain flexible as technology improves.
- As Darren Strong puts it: “AI is just another tool. It’s not a magic solution to all your problems. Use it where it makes sense, but don’t let it distract you from the fundamentals of running a great MSP.”
Watch the full podcast here
Thinking About AI?
Read This First
This guide helps MSPs avoid costly mistakes by cutting through the hype and showing where AI really adds value — and where it doesn’t.
Learn how to build a strong automation foundation, clean up your data, and make smarter decisions about where (and when) to invest in AI.
