Head of Business Transformation (AI)

** Please note that this opportunity is located in New York, NY, and requires this hire to work from our office 4 days a week. **

 
The Head of Business Transformation (AI) is a high-visibility, cross-functional leadership role responsible for driving the company's internal AI program from strategy through execution. Acting as the operational owner of AI at the business, you will partner with functional leaders to identify where AI creates the highest leverage across revenue growth and margin improvement, design and launch pilots with functional owners, and hold the organization accountable to real business outcomes — not just tool deployments. You will translate the rapidly evolving AI landscape into a prioritized, sequenced roadmap that leadership can align behind and the business can actually execute.

This role is designed for a seasoned cross-functional operator with real authority to change workflows and systems — not just advise on them. You will be trusted and respected across GTM, Customer Success, Finance, and Operations, with the C-suite mandate to move fast, make hard prioritization calls, and drive adoption through functions that have competing priorities. Reporting directly to the SVP of Business Operations, you will own the internal AI transformation roadmap and be accountable for the business impact it generates.

We are looking for an operator who understands the business deeply, is technically dangerous enough to evaluate and direct AI solutions, and has the change management skills and organizational credibility to get programs live and make them stick.

As Head of Business Transformation (AI), you can expect to:

• Own the internal AI transformation roadmap: Build and maintain the company's portfolio of AI initiatives — defining the prioritization framework, sequencing the work, and ensuring executive alignment at every stage. You work with functional leaders to decide what gets resourced and what gets deprioritized.
• Partner with functional owners to identify and launch AI use cases: Embedded with GTM, Customer Success, Finance, and Operations, you will work with functional owners to surface the highest-impact opportunities for AI to drive revenue growth or margin improvement, design the pilots, and manage them from hypothesis through adoption. You are not handing off to someone else to implement — you are in the room until it works.
• Drive adoption and change management: Getting an AI tool live is the easy part. Getting an organization to change how it works is the job. You will work with functional owners to design the change management approach for each initiative — the rollout plan, the training model, the accountability structure, and the feedback loop that separates tools that stick from tools that get abandoned after week three.
• Build the operating model for AI at the company: Establish the governance structure, vendor evaluation framework, and decision rights that determine how AI initiatives are approved, funded, and measured. Build the intake process so that good ideas from anywhere in the company have a path to evaluation and execution.
• Measure and report business impact: Every initiative has a hypothesis and a measurement plan before it launches. You will own the ROI framework — working with the data team to define what success looks like, track it rigorously, and report it to the executive team in a way that builds confidence in the program and informs future prioritization.
• Manage vendors and the AI tool landscape: Evaluate, select, and manage AI vendors across the GTM and operational stack — from outbound and enrichment tools (e.g. Clay) to conversation intelligence (Gong) to workflow automation. You know the landscape, have opinions about it, and can direct vendors rather than being directed by them.
• Influence systems, data, and process: You understand how data flows across the business — across Salesforce, our data warehouse, CS tools, and financial systems — and you use that understanding to evaluate what AI solutions are technically feasible, what data gaps need to be closed before a use case can go live, and how existing workflows need to change to support AI-powered proc