Beyond RPA: Why MSPs Are Switching to AI-Powered Automation

AI automation is transforming how Managed Service Providers (MSPs) operate, replacing traditional Robotic Process Automation (RPA) with smarter, more flexible systems. Here’s why MSPs are making the shift:

• Limitations of RPA: RPA struggles with unstructured data, dynamic workflows, and scalability. It requires manual updates for process changes and faces integration and security challenges.

• Advantages of AI Automation:

  • Learns and adapts to changes automatically.

  • Handles both structured and unstructured data.

  • Reduces IT incidents by 25% and boosts productivity by up to 20%.

  • Improves client retention by 42% and automates 80% of routine queries.

• Applications: AI excels at tasks like ticket management, predictive issue prevention, and security monitoring. For example, predictive analytics can cut breakdowns by 70% and reduce costs by 25%.

• Financial Impact: MSPs report higher revenue, reduced costs, and improved margins with AI automation. A $7.5M MSP improved EBITDA margins from 18% to 23% in the first phase of AI adoption.

Quick Comparison: RPA vs AI Automation

Switching to AI automation helps MSPs scale faster, improve service quality, and reduce costs, making it a game-changer for the industry.

RPA vs AI Automation: Main Differences

Fixed Rules vs Learning Systems

RPA, or Robotic Process Automation, sticks to its programming - it executes tasks exactly as instructed. On the other hand, AI automation brings a layer of intelligence, evolving and improving based on experience.

"If RPA imitates what a person does, AI imitates how a person thinks"

This distinction highlights AI's ability to go beyond basic automation, especially when dealing with complex scenarios.

Data Processing Capabilities

When it comes to processing data, AI automation outshines RPA by handling a wider variety of inputs. RPA is great for structured data - think spreadsheets or forms. But AI automation steps up with the ability to process unstructured data, such as emails, chat logs, and alerts.

For example, Telco ICT implemented AI automation to analyze and prioritize service requests using natural language processing, delivering results that traditional RPA couldn’t match.

AI automation’s ability to process diverse data types makes it a powerful tool for more dynamic and unpredictable workflows.

System Growth and Expansion

AI automation doesn’t just handle tasks better - it also scales effortlessly to meet growing demands. For Managed Service Providers (MSPs), this scalability is critical. Expedient Technology Solutions, for instance, successfully scaled their operations with AI automation.

The numbers back this up: 62% of MSPs expanded their AI deployments in Q4 2023, and Canalys predicts an 11% revenue growth for MSPs in 2024, largely fueled by AI adoption. This shows how AI automation is becoming a key driver for growth in the industry.

AI Automation Applications for MSPs

Smart Ticket Management

AI-driven ticket systems simplify handling support requests by automatically analyzing, categorizing, and prioritizing tickets. For instance, one MSP cut ticket assessment time by 40%, while another managed to auto-resolve 30% of tickets. ThrottleNet reported saving $50,000 annually by using AI for ticket categorization and PSA logging, which also improved processing efficiency.

Issue Prevention Systems

AI doesn't just resolve issues - it helps stop them before they happen. Predictive maintenance powered by AI can identify potential problems early, preventing service disruptions. According to Deloitte, predictive maintenance boosts productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%.

One healthcare-focused MSP used predictive analytics to reduce EHR-related tickets by 50% by addressing problems proactively. Typical savings from predictive systems can be seen across different cost areas:

Security Monitoring and Defense

AI also strengthens security operations by offering continuous threat detection and rapid response capabilities. With cyberattacks increasing by 30% annually, AI tools are becoming indispensable for MSPs.

"These recent AI-powered enhancements to the Elements platform represent a milestone achievement in AvePoint's platform offering and channel business growth, now providing MSPs with the fully automated insights needed to enhance security and power success. Artificial Intelligence is a critical tool that enables MSPs to streamline threat and risk detection, ensure compliance, and automate data policy enforcement on a constant basis, allowing them to both better secure client data and efficiently scale their own customer base."
– Scott Sacket, senior vice president of partner strategy, AvePoint

Examples of AI in action include isolating compromised devices, quarantining phishing emails instantly, and blocking malicious network traffic in real time. With MSPs managing an average of 11,000 security alerts daily, AI helps cut down false positives and speeds up threat responses. This is vital, especially with the International Monetary Fund estimating that cybercrime costs could hit $23 trillion by 2027.

Steps to Deploy AI Automation

Identifying Key Automation Targets

To make the most of AI automation, MSPs should focus on high-volume, repetitive tasks that take up significant staff time. These areas often offer the best opportunities to improve efficiency and reduce costs - by as much as 20% in some cases.

Here’s a breakdown of key areas to analyze:

For example, Alvarez Technology Group identified service request handling as a key target. By implementing AI-powered tools, they boosted productivity by 15–20%. Pinpointing these areas helps lay the groundwork for smoother integration.

Connecting with Current Systems

Once key targets are identified, the next step is integrating AI into existing systems. Choosing solutions that work well with current PSA and RMM platforms is critical for success. Ameer Karim, general manager at ConnectWise, highlights the importance of compatibility:

"Leveraging OpenAI's advanced language processing capabilities within our Asio platform and ConnectWise's remote monitoring and management tools, partners can quickly and easily write complex scripts, saving them time and resources".

Expedient Technology Solutions also saw improvements by integrating automated bots for process verification. This not only enhanced their reporting but also improved overall operational efficiency.

Staff Training and Adoption

Proper training is essential for a smooth AI rollout. When ABANCA implemented a structured training program, they achieved 60% faster customer response times and regained 1.2 million business hours.

Key training areas to focus on include:

A well-executed training plan ensures teams are ready to embrace AI, unlocking its full potential for streamlined operations.

Tracking AI Automation Results

Performance Metrics

To gauge the success of AI automation, focus on key operational metrics. As ConnectWise's April Taylor notes:

"When looking to grow and scale your managed service provider (MSP) business, it's best to develop a strategy that is guided by data and monitoring trends over time. Data-driven decisions will outperform choices based on intuition or gut instinct".

Here are some important KPIs to track:

Organizations have reported a 68% reduction in resolution time and a 20% decrease in operational costs after implementing AI automation. Beyond operational improvements, client satisfaction offers another layer of validation.

Client Success Indicators

Customer satisfaction is a strong predictor of loyalty - customers with positive experiences are 54% more likely to make repeat purchases.

To measure client success, consider tracking these metrics:

Additionally, 81% of customers believe AI is crucial for delivering customer service. The financial benefits of AI automation further highlight its value.

Financial Impact Analysis

In the first phase of hyperautomation, a $7.5M revenue MSP achieved the following:

• 20% reduction in managed service payroll costs

• 10% reduction in project service costs

• 5% reduction in SG&A costs

• EBITDA margins improved from 18% to 23%

• Net income rose from $1.3M to $1.7M

During Phase 2, results improved even further:

• EBITDA margins increased to 28%

• Net income grew to $2.4M

However, reducing managed service pricing by 30% without further adjustments caused EBITDA margins to drop to 12% and net income to fall to $928K.

"When done right, hyperautomation allows MSPs to reduce payroll costs, deliver services more efficiently, improve the consistency of operations, and reallocate staff to focus on higher‐value work".

Organizations that actively monitor and refine their AI strategies have seen a 13% ROI on AI projects, far exceeding the industry average of 5.9%.

RIP to RPA: How AI Makes Operations Work

Conclusion: AI Automation Outlook

The move from traditional RPA to AI-driven automation is reshaping opportunities for MSPs. Data from Q4 2023 shows that 62% of MSPs expanded their AI deployments, with Canalys forecasting an 11% revenue boost for MSPs in 2024, largely due to AI adoption.

MSPs have reported increases in revenue, better client retention, fewer incidents, and faster resolution times. These advancements highlight how AI automation is improving both efficiency and service quality.

Demand for AI-enhanced services is growing, with 78% of SMBs now expecting their MSPs to offer such capabilities. AI tools have shown real results, cutting false positives by up to 90% and boosting malicious activity detection by 40–50%. This momentum is shaping the future of the industry.

Emerging trends driving MSP strategies include AIaaS for custom applications, AI-based threat detection, and predictive analytics to reduce downtime.

Looking ahead, enterprise spending on generative AI is expected to grow by 50% in 2025, with 65% of enterprises already working with MSPs on AI projects. AI automation's ability to resolve 80% of routine customer queries makes it a key growth area for MSPs.

The integration of RPA and AI has led to advanced solutions like Intelligent Automation (IA) and Agentic Process Automation (APA), enabling autonomous workflows and complex decision-making. By moving beyond traditional RPA, MSPs are tapping into AI's potential to achieve higher levels of operational performance.

Related posts

• AI-Powered Bots Transform MSP Service Delivery

• AI vs RPA for MSPs: Key Differences

• ConnectWise + AI: Unlocking New Efficiencies with zofiQ's Seamless Integration

• ConnectWise Optimization: Leveraging AI to Streamline Your MSP Workflows