COMMENTARY: AI is more than a trend — it’s a strategic imperative that’s improving efficiencies, driving revenue, increasing profitability, and building advantage for MSPs.
Use cases for AI-enabled tools abound, starting with automating routine tasks and monitoring IT systems for optimized productivity. For resource planning, AI facilitates demand forecasting, capacity assessment, and utilization optimization.
The technology plays a crucial role in addressing security threats through faster detection and remediation. Moreover, always-on compliance monitoring reduces risk while automating audits and policy enforcement.
Overall, you can expect favorable ROI from AI investments based on reduced costs, productivity gains, risk mitigation, scalability, and client retention.
Optimize Productivity
MSPs are tapping AI-enabled capabilities to improve productivity, such as natural language processing(NLP), machine learning (ML), predictive analytics, and robotic process automation (RPA).
Applying NLP, you can automate ticket triage. The technology determines the category (e.g., network issue or software bug), and assigns the ticket to the appropriate team. NLP determines urgency based on keywords, SLAs, and client history to optimize prioritization.
Using ML, you can systemize predictive maintenance by analyzing equipment data to discern maintenance needs. The model continuously monitors anomalies and the remaining life for components and systems.
Predictive analytics prevent capacity issues, delays, and data loss. The technology collects storage metrics and application data (e.g., log generation). Using this data, you can automate storage expansion, data archiving, and policy enforcement.
Some MSPs use RPA for software deployment, such as a CRM system for a client. The bot scans accounts for compatibility and downloads the CRM installer while simultaneously applying role-based configurations. Upon completion, the bot activates licenses, verifies installations, and generates deployment reports.
Improve Capacity Planning and Resource Allocation
Capacity planning can be challenging if you lack visibility into your client base and usage patterns — this leads to increased costs for over-provisioning and service degradation for under-provisioning.
Predictive analytics improves the accuracy of demand forecasting, staffing needs, and resource allocations. The technology analyzes historical data, trends, and external factors to predict and scale future resource needs (e.g., computing power or staffed skill sets) while monitoring utilization in real time — resources are dynamically allocated based on demand for things like virtual machines and load balancers.
For context, say you’re supporting a growing client base, offering services like infrastructure management and application support. Applying AI, you can manage service portfolios, client needs, and disruptions without scrambling to prevent overages and bottlenecks.
Enhance Security and Compliance Protocols
AI-enabled tools apply threat intelligence to detect security and compliance vulnerabilities faster, improving incident response while mitigating risk and automating data protection. The technology analyzes data from client environments to detect suspicious activity in real time, as well as false positives based on ML models.
Proactive compliance monitoring streamlines enforcement of user permissions, and consistent configuration ensures systems adhere to compliance requirements (e.g., GDPR and HIPPA) by automating baseline configurations. If warranted, AI-enabled tools isolate affected systems, revoke compromised credentials, and flag accounts for review.
Make the Case for AI Investment
When considering AI investments, assess your readiness and prioritize high-impact initiatives.
Calculate costs, such as hardware, technology acquisition, training, and software. You’ll need to redesign processes to integrate solutions (e.g., automation workflows). Present your recommendation in the context of improved efficiencies, productivity gains, and the savings AI will bring to your business.
Executing pilot programs, phased implementation, and ROI measurement requires thoughtful planning and execution.
For your pilot, you need to validate the solution — assemble a team, gather baseline data, and select a use case. Then, test with a small group and gather metrics and feedback from the team. Focus on efficiencies and financial impact, but also remember AI can impact user experiences.
Phased implementation relies on a staged rollout to a limited group. This allows you to scale gradually while monitoring performance and addressing issues — when you’re ready, you can fully deploy. Set ROI measurement parameters by identifying metrics, establishing intervals, and monitoring benchmarks.
Evaluate and Integrate AI Tools
Start your process by defining objectives, and set clear goals (e.g., 20% reduction in manual workload). Then, create a shortlist, assess features, evaluate compatibility, and request demos.
Once you’ve selected tools, plan and execute your integration and phased rollout — iterate based on feedback and look for opportunities to expand use cases. Whenever possible, use AI to amplify the impact of your employees by focusing their efforts on high-value, interactions.
When it comes to security, AI tools can introduce new vulnerabilities (e.g., API exposure or misconfigured software) — more systems mean more endpoints. You can prevent attacks on AI systems (e.g. malicious inputs) by implementing encryption, access, controls, and regular testing.
AI’s transformative nature will continue to enable enhanced capabilities at rapid speed. Be on the lookout for expansion of automation to encompass end-to-end processes, democratized access to AI tools for non-technical users, no-code and low-code platforms, and the integration of AI and IoT — the future is bright.
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