MSPbots.ai

Sales

• Prospect, qualify, and close new business opportunities to achieve revenue targets.
• Execute sales strategies tailored to the MSP, SaaS and AI products.
• Build and maintain strong relationships with clients and partners to drive account expansion along with the Client Success Manager.
• Conduct product demonstrations and sales presentations to showcase the value of MSPbots.ai solutions.
• Utilize a consultative sales approach to identify customer pain points and align solutions accordingly.
• Collaborate with marketing and product teams to refine messaging and improve sales enablement.
• Represent MSPbots.ai at industry events, trade shows, and networking opportunities to generate leads and build brand awareness.

Regional Sales Manager

Product Manager

• Revenue & Market Strategy (The "Business" Owner)
• Monetization Architecture: Move beyond flat SaaS seats. Design and implement usage-based pricing, credit systems, or "value-based" billing tied to AI-automated outcomes.
• Competitive Intelligence: Maintain a "Battlecard" mentality. Know exactly what the incumbents and startups are shipping, and ensure our AI capabilities (triage, QA, and automation) are 10x better, not 10% faster.
• GTM Execution: Don't just hand off a deck to Sales. Drive the narrative, define the ICP (Ideal Customer Profile), and ensure our positioning clearly articulates the ROI of "AI over Headcount."
• AI Native Product Leadership
• The "AI Trinity" Tradeoff: Ruthlessly manage the balance between Accuracy, Latency, and Unit Cost. You know when to use a frontier model (GPT-4/Claude 3.5) vs. a fine-tuned small language model (SLM) for specific MSP tasks.
• Agentic Workflows: Shift from simple "chatbots" to autonomous agents that can execute ticket resolution, not just summarize it.
• Data as a Moat: Define the data collection strategy to create a proprietary feedback loop (RLHF) that makes our models smarter with every MSP ticket handled.
• Rapid Execution & Experimentation
• 0 → 1 Execution: Lead "Flash Builds"—getting prototypes in front of customers in days, not months.
• Performance Benchmarking: Define what "Good" looks like. Build the evaluation frameworks (Evals) to ensure our AI isn't just "cool" but consistently reliable in a production MSP environment.

AI Product Manager

Chief of Staff

• AI-First Business Operations (The "Automator") Dogfooding & Transformation: Actively replace internal manual processes (Marketing, CS, Ops) with MSPbots’ own AI agents and RPA tools.
• Office of the CEO Automation: Build and maintain AI workflows to filter CEO emails, summarize L10 meetings, and pre-draft responses. Goal: Reduce CEO operational load by 40% within 6 months.
• Governance & Safety: Enforce the AI governance framework. Ensure all internal AI agents have correct permissioning, audit logs, and "human-in-the-loop" fail-safes before scaling.
• Strategy Execution & EOS Management (The "Integrator Proxy") L10 & Rhythm: Owner of the CEO’s L10 meeting. You do not just take notes; you audit the Scorecard data for accuracy before the meeting and force resolution on "Issues."
• Rocks Management: Track progress on quarterly Rocks across departments. Use BI dashboards to validate "Done" status (no verbal updates without data).
• Cross-Department Alignment: act as the "API" between the Visionary (CEO) and the department heads. Translate high-level vision into execution tickets with clear owners and deadlines.
• Special Projects & GTM Experiments Launchpad: Lead rapid-fire GTM experiments for new AI features. Define success metrics (CAC, Adoption %, Churn impact) and kill projects fast if data fails.
• Feedback Loops: Institutionalize the feedback loop between Product and Customer Success. Ensure ticket data is structured and fed back into Product R&D for agent training.

• Lead the strategy, roadmap, and execution for AI-powered apps, including ticket triage, ticket QA, and sentiment analysis, ensuring alignment with company goals and client needs.
• Work closely with engineering, data science, design, marketing, and sales to deliver high-quality products on schedule.
• Engage with MSP clients and internal stakeholders to gather requirements, validate product value, and ensure exceptional user experience.
• Monitor industry trends, competitor offerings, and customer feedback to inform product strategy and feature prioritization.
• Define and track key product metrics, using data-driven insights to guide product improvements and reports on outcomes.
• Ensure clear product documentation and support materials are available for both internal teams and clients.
• Lead rapid prototyping and MVP releases, especially for new AI-driven features and apps, leveraging client feedback for continuous improvement.