AI Strategy Lead

Skaled is a Revenue Performance Agency that empowers B2B companies to scale smarter by aligning strategy with execution, leveraging AI, data, and expertise. Unlike traditional consulting firms, Skaled proactively implements strategies, partnering with revenue teams to achieve measurable outcomes. With a focus on Revenue Operations, Go-to-Market Strategy, Revenue Enablement, and AI-driven solutions, Skaled eliminates silos and modernizes sales processes. Trusted by startups and enterprises alike, the company uses Generative AI and AI Automation to optimize productivity, streamline revenue operations, and drive sustainable growth.

About the Role

The AI Strategy Lead is the chief architect of use cases and AI solution logic within the practice. This person's purpose is to identify, scope, and design intelligent assistants and agents that solve specific business problems, ensuring that each solution has a sound strategy behind it.

What You'll Do Here

This role will translate business objectives and pain points into concrete AI use-case definitions and agent logic, serving as a bridge between business needs and technical execution. The Strategy Lead is the main point of contact for Skaled customers and works closely with the Practice Lead on vision, supports go-to-market (GTM) efforts by crafting compelling AI use-cases, and collaborates with the Program Manager to ensure those use-cases are delivered as envisioned.

Key Responsibilities

• Use-Case Discovery & Scoping: Engage with clients and internal stakeholders to uncover high-impact opportunities for AI assistants or agents. Conduct workshops or interviews to understand business processes (especially in RevOps/GTM areas like sales, marketing, customer success) and identify where AI automation or insights could add value. Define clear use-case statements – for example, an “AI Sales Assistant to automate lead qualification” – including objectives, boundaries, and expected benefits.
• Solution Design & Agent Logic: For each approved use-case, design the AI agent's logic and ROI in detail. Outline how the assistant/agent will function: what triggers its actions, what data it will use, what steps it will perform, and how it will interact with users or systems. This may involve writing flow diagrams, pseudocode or prompt outlines (for LLM interactions), and defining decision rules. The AI Strategy Lead essentially creates a blueprint that the AI Builder can execute – covering both the conversation or task flow for the AI and the integration points it will need.
• Feasibility Assessment: Work closely with technical team members to vet the feasibility of proposed solutions. Ensure that the ideas conceived are realistic given current AI capabilities and the client's tech environment. For instance, if an agent concept requires pulling data from a legacy system, confirm with the AI Builder that an API is accessible. The Strategy Lead balances ambition with practicality, iterating on the design until it's both impactful and achievable.
• Business Case & Value Definition: Articulate the expected business value of the AI solutions in real ROI terms for Skaled clients. Quantify or qualify how the assistant/agent will improve outcomes (e.g., “reduce time spent on data entry by 30%” or “increase lead response rate by 2x”). This often involves creating ROI estimates or business cases that justify the project. These value definitions are used both to prioritize use-cases and later to measure success. The Strategy Lead might create slides or documents summarizing the proposed solution and its projected impact for client buy-in.
• GTM Support & Thought Leadership: Contribute to sales and marketing efforts by providing the strategic vision for AI offerings. This could mean developing example use-cases for proposals, demoing an AI assistant concept to a prospective client, or authoring points-of-view on how AI can transform RevOps. The Strategy Lead ensures that Skaled's AI offerings are communicated compellingly – bridging the gap between technical potential and business value in messaging.
• Owns the client relationship and Collaboration in Delivery: Participate in key design review sessions or whenever the AI Builder needs clarification on “what should the AI do in scenario X.” Review intermediate outputs (like prompt formulations or workflow mockups) to ensure they align with the original vision. Assist the Program Manager and Practice Lead in making decisions about scope adjustments if the client requests changes – providing insight into how tweaks would affect the solution's logic or value.
• Support Testing & Iteration: Once the assistant/agent is built, help define test scenarios and success criteria based on the intended use. During user acceptance testing or pilot phases, gather feedback on the AI's performance relative to the expected logic. If the AI isn't delivering as expected (e.g., responses aren't accurate or a certain automation step fails), work with the team to adjust prompts or logic rules. Ensure the final deployed solution is tuned to achieve the business outcome originally targeted.
• Client Sign-off/Engagement Management: The Strategy Lead is the ultimate owner of the client relationship NOT project commercials. They contribute to scoping and value discussions, but not negotiating contracts, handling client escalations beyond the solution scope, this holistically sits with the Practice Lead or Program Manager.

Success Metrics:

• Effective Use-Case Conversion: Number of identified AI use-cases that successfully move from idea to delivered solution. A high conversion indicates that the Strategy Lead scopes realistic, valuable projects that get approved and executed (as opposed to ideas that languish or get rejected due to impracticality or lack of clarity).
• Business Impact of Solutions: The actual outcomes achieved by the AI assistants/agents relative to the expected benefits. For example, if the Strategy Lead defined a use-case promising a 30% reduction in data entry time, did the delivered solution achieve something close to that? Success can be measured in aggregate ROI or efficiency gains across projects. Positive variance (solutions delivering equal or more value than predicted) would be a strong indicator.
• Client Satisfaction & Recognition: Do clients feel that the AI Strategy Lead understood their needs and crafted a spot-on solution? Testimonials or survey responses that mention the quality of solution vision or logic are a key metric. Additionally, being invited by clients to scope additional problems (showing trust in the Strategy Lead's expertise) is a qualitative success sign.
• Innovation & Reusability: Introduction of novel or improved AI approaches. For instance, creating a new agent logic pattern that can be reused for multiple clients or developing a framework for scoping that becomes a standard. Metrics might include number of reusable templates or patterns the Strategy Lead contributed, or how often their designs are referenced as best practice by the team.
• Collaboration Efficiency: How smoothly projects run from a requirements/design standpoint. If the team experiences minimal thrash or rework due to unclear requirements, that's a success. Metrics might be internal: e.g., the percentage of projects that proceed without major scope pivots or the number of clarification cycles needed during build. Fewer design-related issues indicates the Strategy Lead provided clear, thorough guidance upfront.
• Thought Leadership Output: The extent of involvement in pre-sales and marketing. Metrics could be the number of proposals or sales decks contributed to, win rate of deals where they provided solution input, or content created (blog posts, webinars) that establish Skaled's authority in AI strategy. While not purely delivery, it demonstrates the Strategy Lead's role in elevating the practice's profile.

Qualifications:

• 7+ years of experience in a consulting or solution architecture role (e.g., Solutions Architect, Business Analyst, or AI Strategist), with a proven track record of designing technology solutions to business problems.
• Solid domain knowledge in Revenue Operations (RevOps) or Go-to-Market (GTM) processes, including sales, marketing, and customer success, and their associated metrics.
• Up-to-date, practical knowledge of AI and Large Language Model (LLM) capabilities, including understanding their strengths, limitations, integration, and effective prompting.
• Demonstrated ability as a strong analytical thinker and communicator, skilled at breaking down complex business processes into logical components and articulating concepts clearly to non-technical stakeholders.
• A history of creative problem-solving, with the ability to identify and frame unconventional but effective AI-based solutions for business challenges.