Opening & Introduction
60-minute session for MSP CEOs & Executives
What's the "State of AI" in the MSP industry right now — is it hype, reality, or both?
You've been at the intersection of MSP operations and cutting-edge AI development. The "State of AI" in the MSP industry is both hype and reality — understanding the distinction is critical for success.
- Hype vs breakthrough distinction matters
- MSP AI adoption curve positioning varies widely
- Early wins vs long-term transformation balance
- Underestimating AI opportunity cost is common
- Practical vs theoretical applications differ greatly
- Predictive vs reactive service models shift paradigms
What is the mindset shift needed to treat AI as a strategic advantage rather than a threat?
For MSP executives watching AI headlines and feeling a mix of FOMO and fear, a fundamental mindset shift is required.
Key Insight: AI is operational leverage, not replacement. Focus on reallocation vs reduction mindset.
- AI as operational leverage, not employee replacement
- Reallocation vs reduction mindset for resources
- Competitive positioning window is closing
- Strategic investment vs cost center view
- Employee time to learn and experiment is critical
- Crawl-Walk-Run framework for implementation
Business Transformation Attendees
This fireside chat brings together technical leaders and security professionals driving AI implementation within their MSPs.
Technical Leadership & Security
Business Transformation Technical Attendees
Attendee | Organization & Role | Technical Focus & AI Involvement |
|---|---|---|
| Christian Kelly, CISSP | Xantrion - CTO (22+ years) | IT/cybersecurity expert, developed Managed Security program, AI security integration |
| Sean Ernst | centrexIT - VP Technology (18+ years) | US Navy veteran, digital workspace focus, AI-enhanced service delivery |
| Rick Mutzel | Omega Systems - Manager of Technology | Since 2013, Smart Guard MDR operations, AI-augmented security ops |
| Bryan Lukralle | Charter Technology - CSO (15+ years) | Cybersecurity program builder, AI threat detection implementation |
| Andrew Neumann | Omega Systems - VP Technical Services | Leads managed services operations, AI service automation initiatives |
| David Cieslak, PhD | Aunalytics - Chief Data Scientist | Leads Innovation Lab, data science research, AI/ML model development |
N-Able AI & Security Leadership
N-Able Technical & AI Strategy Team
N-Able Leader | Role | Technical AI Initiative Focus |
|---|---|---|
| Nicole Reineke | VP AI Strategy & Vision (PhD Stanford AI/ML) | Owns N-able AI Roadmap 2023-2026, enterprise-scale AI deployment architecture |
| Will Ledesma | Senior Director MDR Cybersecurity (15 years SOC) | AI-augmented MDR, MCP-based SOC playbooks, automated threat response |
| Laura DuBois | Chief Product & Customer Officer (18 years) | Building self-service AI-tool catalog, partner AI enablement platform |
| Rob Wilburn | VP Partner Growth (20 years channel experience) | Piloting Partner-First AI Marketplace, MSP AI GTM strategy |
Topic 1: Internal Operations & Efficiency
What's a practical first AI use case to reduce ticket noise without breaking existing workflows?
Quick Win: Companies report 40-60% of L1 tickets resolved automatically with AI-driven ticket triage & L1 automation.
Practical first AI use cases for reducing ticket noise:
- L1 support automation for common requests
- Conversational AI for password resets
- Account unlock automation
- Intelligent ticket routing and categorization
- After-hours coverage without adding headcount
- First-contact resolution improvements
40-60%
L1 Tickets
Resolved automatically with AI
45%
Volume Drop
Ticket reduction in 3 months
75%
Time Saved
Resolution time: 4h → 1h
4-Step Ticket Management Model
A systematic approach to transforming your help desk:
4-Step Model for Ticket Management
Step | Action | Outcome |
|---|---|---|
| 1 - Eliminate | Disable/raise thresholds on system-generated alerts; route info-only tickets to separate queue | ≥ 30% ticket volume drop |
| 2 - Improve | Pull user & asset data into tickets; standardize onboarding; auto-assign by client/skill | Time-to-First-Touch ≤ 5 min |
| 3 - Automate | Deploy chatbot for routine requests; add RPA scripts for provisioning tasks | FCR ≥ 70%; avg resolution ≤ 30 min |
| 4 - Monitor | Track metrics: volume, FCR, CSAT, automation success. Review weekly, adjust thresholds | Continuous improvement loop |
Target Metrics: Ticket volume ≤ 20/day, % eliminated ≥ 30%, avg. Time-to-First-Touch ≤ 5 min, FCR ≥ 70%, CSAT ≥ 4.5/5, automation success ≥ 85%
How can MSPs make sure their AI solutions understand client environment data — not just give generic chatbot answers?
Context is everything in support. MSPs need to ensure their AI solutions understand specific client environments.
MCP Protocol: Model Context Protocol provides secure context management across systems. Learn more at cat-mip.org
Context Layer Integration
Context Layer | How AI Uses It |
|---|---|
| MCP for context management | Pulls device inventory, OS version, recent changes |
| Knowledge-base integration | Retrieves up-to-date SOPs; ranks answers by relevance |
| PSA/RMM data | Auto-fills ticket fields, suggests remediation steps |
| Continuous learning | Analyzes resolved tickets to refine intent models |
30%
Higher FCR
vs generic chatbots with context integration
Quick-Start Implementation Checklist
- Export last 90 days of tickets to CSV
- Tag tickets: Eliminate / Improve / Automate
- Disable low-value alerts, create "Info-only" queue
- Standardize onboarding builds per department
- Publish "Birthright Access" list with auto-grant via API
- Connect AD/SSO to ticket view for context
- Build first bot: Password reset automation
- Build RPA script: Auto-close hardware-order tickets
- Pilot on 1 client → measure FCR & CSAT
- Add "Talk to an agent" button in bot flow
- Deploy to all clients → monitor metrics weekly
How do you see AI not just writing SOPs, but continuously keeping them current as systems and processes change?
MSP documentation is notoriously out-of-date. AI provides solutions for both generating and maintaining current documentation.
- Document understanding vs document intelligence
- Automated SOP generation from observed actions
- Change detection and update triggers
- Version control and drift monitoring
- Procedure validation and testing
- Living documentation concept
25%
Fewer Errors
With AI-generated SOPs
20%
Faster Onboarding
New tech productivity
35%
Time Reduction
Onboarding: 4-6 weeks → 2 weeks
What's the smartest way to use AI to onboard new techs and flatten that steep learning curve?
New technicians face a steep learning curve across dozens of client environments. AI can dramatically accelerate onboarding:
- Interactive training agents simulate environments
- Client environment summaries auto-generated
- Context-aware guidance systems
- Progressive knowledge delivery
- Simulated troubleshooting scenarios
How should MSPs deploy AI in monitoring so they get fewer, better alerts — and maybe even proactive resolutions?
AI can generate insights, but it can also generate alert fatigue. The key is strategic deployment for signal vs noise.
Alert Fatigue Solution: AI-enabled clustering and correlation reduces alerts by 50-70% while improving accuracy.
- Signal vs noise filtering
- Pattern recognition for true incidents
- Predictive maintenance timing
- Automated remediation workflows
- Alert correlation and root cause analysis
- Proactive vs reactive service positioning
50-70%
Alert Reduction
Fewer alerts reaching technicians
85%
Prediction Accuracy
Hardware failure forecasting
30%
Auto-Resolved
Incidents handled without human touch
Topic 2: Implementation Metrics & Real ROI
Beyond the hype, here are the real metrics MSPs are achieving with AI implementation. These numbers come from MSP-focused case studies and operational data:
20-35%
OPEX Reduction
Labor cost reduction across operations
40-60%
L1 Auto-Resolution
Tickets resolved without human intervention
30%
Faster Processing
Overall ticket handling speed improvement
L1 Support Automation Performance
Conversational AI and automated L1 support deliver measurable improvements across multiple dimensions:
L1 Automation Impact Metrics
Metric | Improvement | Implementation Note |
|---|---|---|
| AI-Handled Tickets | ≥45% automation rate | Target for full production deployment after pilot |
| L1 Labor Cost | 40% reduction | Cost per L1 ticket drops significantly with automation |
| Customer Satisfaction | Maintain ≥4.5/5 CSAT | AI must maintain or improve CSAT vs human-only support |
| Resolution Time | 4 hours → 1 hour (75% reduction) | Average time to resolve common L1 issues drops dramatically |
| Ticket Volume Drop | 45% reduction in 3 months | Combination of elimination and automation effects |
Cost Structure: Initial fine-tuning $5-10k, ongoing $0.02-$0.05 per request. Pilot Q1 2025, full rollout Q2-Q3 2025.
Process Automation & Efficiency Gains
Process automation across onboarding, ticketing, and reporting delivers consistent efficiency improvements:
Process Automation Performance Metrics
Process Area | Improvement Achieved | Timeline |
|---|---|---|
| Ticket Handling Speed | ↓30% processing time | Q2-Q3 2025 |
| First Contact Resolution (FCR) | ↑20% improvement | Q2-Q3 2025 |
| Labor Cost Per Ticket | ↓25% cost reduction | Q3-Q4 2025 |
| Overall Labor OPEX | 20-35% reduction | Q1-Q4 2025 phased rollout |
| Ticket Volume | ≤20 tickets/day target | After full implementation |
| Time-to-First-Touch | ≤5 minutes average | Q2 2025 |
| Automation Success Rate | ≥85% successful execution | Target for production workflows |
Investment: Initial setup $10-20k, ongoing $1-2k/month maintenance. Q1-Q4 2025 rollout timeline.
Onboarding & Documentation Improvements
AI-generated SOPs and interactive training accelerate new technician productivity:
35%
Faster Onboarding
4-6 weeks reduced to 2 weeks
25%
Fewer Errors
With AI-generated SOPs
20%
Productivity Boost
New tech ramp-up speed
- Interactive training agents simulate client environments
- Auto-generated environment summaries for each client
- Context-aware guidance during actual troubleshooting
- Progressive knowledge delivery based on tech experience
- Real-time SOP updates as processes change
Comprehensive ROI Analysis
Putting it all together: the complete financial picture for mid-size MSP AI implementation:
Complete AI Implementation ROI (Mid-Size MSP)
Financial Metric | Value | Notes |
|---|---|---|
| First-Year Investment | $50,000 - $150,000 | Platform setup, agent development, integrations, pilots |
| Annual Maintenance | $70,000/year | Licensing, API usage, continuous improvement |
| First-Year ROI | >300% | Return on initial investment within 12 months |
| Payback Period | ≤6 months | Time to recover initial investment from savings |
| Net Profit Growth | ↑20% YoY | Year-over-year profit increase from efficiency gains |
| Ticket Resolution Time | ↓30% average | Faster resolution across all ticket types |
Bottom Line: MSPs investing $50-150k see >300% ROI in Year 1 with ≤6 month payback, achieving 20-35% labor cost reduction and 20% profit growth.
Topic 3: Practical Implementation & MCP Integration
If you had to recommend one AI agent every MSP should deploy this year, what would it be — and why?
Getting started with AI agents requires choosing the right first project for maximum ROI and learning value.
Recommended First Agent: Ticket triage and routing agent - highest ROI, fastest implementation, immediate impact.
- Ticket triage and routing agent (RECOMMENDED)
- Documentation assistant for SOPs
- Monitoring analysis agent for alerts
- Client communication assistant
What new opportunities do MCP servers unlock for MSPs that weren't possible before?
You've been a big proponent of MCP servers for orchestrating AI across systems. Model Context Protocol unlocks significant new capabilities:
- Context aggregation across multiple systems
- Modular and reusable integrations
- Custom controls and security boundaries
- Private data leverage without exposure
- Multi-agent coordination and orchestration
- Alternative to converting every API
- Fine-grained access control (RBAC)
MCP Quick-Start Checklist
MCP Implementation Verification
Item | Verification |
|---|---|
| MCP server reachable | JSON list returned from /resource://knowledgebases |
| Model can invoke tool | Prompt triggers QueryKnowledgeBases with valid response |
| IAM role & SigV4 auth | aws sts get-caller-identity shows expected role |
| TLS & logging | HTTPS endpoint returns spec; CloudWatch logs show session_id |
| Metrics visible | Alarms set for mcp.requests, mcp.errors, latency |
MCP Best Practices & Implementation Guide
Model Context Protocol (MCP) represents a fundamental shift in how AI systems integrate with enterprise tools and data. Understanding best practices is critical for successful implementation.
MCP Core Value: Context is king. MCP provides modular, reusable integrations with custom controls and security, enabling AI to leverage private data without exposure.
Understanding MCP Value & Reputation
MCP has evolved significantly, and it's important to understand both its history and current value:
MCP Evolution: Reputation vs Reality
Aspect | The Challenges | The Real Value |
|---|---|---|
| Early Reputation | Unstable early releases, especially local servers. Basic specification raised questions about necessity. | Latest evolution: remote cloud-hosted MCP servers provide stability and scale |
| Context Management | Generic APIs lack environmental awareness and client-specific context | Context is king: Aggregates data across multiple systems with client awareness |
| Integration Approach | Point-to-point integrations create maintenance burden and duplication | Modularity: Reusable, plug-and-play integrations reduce development time |
| Security & Control | Direct API access lacks fine-grained permissions and audit trails | Custom controls: Security boundaries, RBAC, and private data leverage without exposure |
Critical MCP Implementation Guidelines
Key MCP Don'ts: Avoid these common mistakes that reduce MCP effectiveness and create technical debt.
MCP Implementation: Do's and Don'ts
Don't Do This | Why It Fails | Do This Instead |
|---|---|---|
| Convert every API to MCP server | Creates unnecessary abstraction layer, adds latency, increases complexity | Use MCP for context aggregation and orchestration, not simple API wrappers |
| Overload MCP servers with too many tools | Context bloat reduces model performance, increases costs, degrades response quality | Create focused MCP servers with 5-10 related tools max; use multiple servers for different domains |
| Skip RBAC and access controls | Security vulnerabilities, compliance failures, unauthorized data access | Implement fine-grained RBAC from day one; use IAM roles and security boundaries |
| Deploy without monitoring/logging | No visibility into usage, errors, or performance; difficult to debug issues | Enable CloudWatch logs with session_id tracking; set up alarms for errors and latency |
Agent Infrastructure Stack Components
Building a production-ready AI agent infrastructure requires multiple layers of technology:
- AI Gateways and Proxies: Route requests, manage quotas, provide fallbacks
- MCP Gateways and Portals: Centralized access to MCP servers with authentication
- Observability: Real-time monitoring, usage analytics, performance tracking
- Guardrails: Content filtering, prompt injection detection, output validation
- Logging and Analytics: Session tracking, audit trails, compliance reporting
- Containers and Sandboxes: Isolated execution environments for agent actions
- Actions and Workflows: Orchestration layer for multi-step agent operations
Architecture Tip: Start with AI Gateway + MCP + Observability, then add Guardrails and Sandboxes as you scale.
Modes of Agent Use in MSP Operations
AI agents can be deployed in multiple modes depending on use case and integration requirements:
Agent Deployment Modes
Mode | Use Case | Example |
|---|---|---|
| Chatbot | Interactive Q&A, support automation | L1 support bot for password resets and common requests |
| Computer Use | GUI automation, legacy system interaction | Automating tasks in older management consoles without APIs |
| Browser Use | Web-based workflows, data extraction | Automated client portal updates, web-based reporting |
| CLI (Command Line) | DevOps automation, infrastructure tasks | Claude Code, Gemini CLI for automated deployments |
| Agents as MCP | Agent-to-agent communication | Specialized agents exposing capabilities via MCP protocol |
| Vibe Coding Platforms | Low-code/no-code AI development | Rapid prototyping of custom AI workflows for specific clients |
MCP Cost & Operations Considerations
Managing costs and operations effectively is critical for sustainable AI deployment:
- Time-based usage billing: Track API calls and context usage per client
- Controlling context: Limit context window size to reduce token costs
- Scheduled jobs and batching: Run non-urgent tasks during off-peak hours
- Caching strategies: Cache common responses to reduce API calls
- Model selection: Use appropriate model sizes for different tasks
- Request throttling: Implement rate limits to prevent cost spikes
Cost Management: Monitor token usage per client, implement caching, use scheduled batching for non-urgent tasks, and select appropriate model tiers.
Avoiding Common Pitfalls
What's the most common mistake MSPs make on their first AI project — and how do they avoid "pilot purgatory"?
Pilot Purgatory: Projects without clear success metrics and deployment timelines often stall indefinitely.
- Scope creep and perfectionism delays
- Lack of clear success metrics
- Over-complicated first projects
- Insufficient stakeholder buy-in
- No concrete deployment timeline
- Converting APIs to MCP unnecessarily
- Overloading context with too many tools
How should MSPs think about guardrails and governance when deploying AI across multiple client environments?
Security and compliance are top concerns. Here's how to implement proper guardrails:
- Data residency and privacy requirements mapped
- Client data segregation enforced
- Access control frameworks implemented
- Audit logging and monitoring enabled
- Compliance mapping (GDPR, CCPA) completed
- Hallucination detection mechanisms in place
- Escalation triggers and human oversight defined
TL;DR - Key Takeaways
Implementation Summary: Follow the structured approach below to evolve from reactive to proactive AI-augmented operations.
- Start with noise elimination → raise alert thresholds, create info-only queues
- Improve processes → standard onboarding, birth-right access, AD context injection
- Automate only after validation → check if repetitive, no judgement required, stable, data-ready
- Use MCP as secure translator → between LLMs and internal data/tools, scale with containers and IAM
- Always provide human fallback → bots without escalation paths hurt CSAT/NPS
Next Steps: Follow the checklists and metric targets to evolve your service desk into a proactive, AI-augmented operation that resolves most tickets automatically and frees analysts for high-value work.