Director, AI Product Development

Databook Labs•Palo Alto, CA

About The Position

Databook is hiring a Director of AI Product Development to lead the design, development, testing, and launch of its AI-guided selling product for large enterprise sales organizations. The role focuses on building agentic workflows and a revenue operating system that helps sellers understand accounts, reason over deals and seller context, and act on evidence-backed recommendations. The product builds on Databook’s World Model of companies, people, strategies, and events, and works alongside customers’ existing CRM systems. The Director reports to the CEO and works closely with customers, Applied AI Engineering, Data & Insights, and GTM AI Strategy & Deployment. The role leads a team of Agent Builders who configure agentic workflows and build quality assurance into them. Customer deployments are handled by the AI Strategy & Deployment team; this role focuses on the product those teams deploy. It calls for strong product judgment, enterprise sales domain knowledge, experience shipping AI applications into enterprise production, and the ability to distinguish technically correct output from output that salespeople and executives will use. Time is divided approximately among product (40%), delivery and quality (30%), and customer work (30%). The Director will define the product experience, develop the product’s CRM-layer workflows, collaborate on data coverage and quality, ship and improve AI applications, establish measurable quality standards, engage with enterprise customers, align product capabilities with go-to-market work, and help deliver customer AI Bootcamps. Success in the first 90 days includes customer immersion, a tested product point of view and agreed priorities, quality evaluation and QA standards for new workflows, a clear operating model for the Agent Builder team, and teaching in an AI Bootcamp. Databook provides equal employment opportunities and prohibits workplace harassment on the bases described in its equal employment and anti-harassment policy.

Requirements

6 to 10 years of experience building and shipping software for enterprise customers.
Product leadership responsibility and experience leading or developing people.
Strong enterprise sales domain knowledge, including experience building products used daily by enterprise GTM teams and understanding account planning, complex deal cycles, forecasting, and CRM data.
A record of designing, building, testing, and deploying AI applications—such as LLMs, agents, retrieval, or tool use—in enterprise production, with real users depending on them.
Understanding of AI evaluations, context design, and failure modes of LLM output.
Ability to prototype ideas independently using AI tools; coding is not identified as the primary strength of the role.
Strong product judgment and attention to output quality, workflow friction, and usefulness to CROs and sellers.
Confidence engaging senior customer stakeholders, including CROs and other executives.
Clear, concise communication and the ability to lead sessions that conclude with a clear decision.
High ownership, fast learning, and readiness for a broader leadership role.

Nice To Haves

Experience building on or for sales AI or GTM platforms, or in the Salesforce ecosystem.
Experience with knowledge graphs, entity resolution, or other data products where underlying data quality shapes user experience.
Experience designing or delivering customer training, enablement, or bootcamp programs.
Prior experience at a top-tier consulting firm such as McKinsey, Bain, or BCG, advising CxOs and influencing large-scale transformations.
Experience working with distributed teams across the US and globally.

Responsibilities

Define the product experience and turn product strategy into agentic workflows that sellers and sales leaders use.
Exercise product judgment about what to build, what not to build, and what constitutes a good product; use adoption and business impact to guide priorities.
Design how sellers work in Databook’s CRM layer, including opportunities based on companies in the World Model, capturing actions and outcomes as they happen, and supporting pipeline and forecasting alongside customers’ existing CRMs.
Partner with Data & Insights to reflect data coverage, freshness, and confidence in the product and use customer questions to inform future data collection.
Partner with Applied AI Engineering to take agentic workflows and next-best-action recommendations from concept and prototype through evaluation, testing, production release, and continued improvement.
Lead and develop the Agent Builder team, establish standards for configuring workflows, and build quality assurance into the team’s work.
Define measurable quality standards for AI output, including evaluation sets, acceptance criteria, regression testing, and production monitoring.
Spend time with enterprise customers’ sellers, sales leaders, marketing, customer success, enablement, and RevOps teams; understand where deals are won and lost and incorporate those insights into the product.
Coordinate with the AI Strategy & Deployment team to keep what Databook sells, builds, and delivers aligned.
Turn patterns from customer deployments into reusable product capabilities rather than one-off customizations.
Help design and deliver AI Bootcamps to teach customer sales and enablement teams how to work with AI and use Databook.
Within the first 90 days, immerse in customer workflows, develop and test product concepts with the CEO and customers, agree priorities with partner teams, establish workflow evaluation and QA standards, set an operating model for the Agent Builder team, and teach in an AI Bootcamp.