Head of Business Transformation (AI)

VTS•New York, NY

3d•$190,000 - $250,000•Onsite

About The Position

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.

Requirements

6+ years in a cross-functional operator role — Business Operations, Revenue Operations, Chief of Staff, Strategy & Operations, or a comparable function at a SaaS company. You have held a role where you had to change how other people's functions worked, without those people reporting to you.
Demonstrated track record of rolling out AI initiatives that produced measurable business outcomes. Not a pilot that got shelved, not a tool that got purchased but not adopted — a program that changed how a team worked and you can point to the before and after. You should be able to describe at least two or three examples in detail: what the initiative was, how you designed and launched it, what resistance you encountered, how you drove adoption, and what the result was.
Technical fluency without being a pure technologist. You understand how LLMs work at a conceptual level, how AI agents and workflow automation function, what APIs do, and how data needs to be structured for AI to use it effectively. You can evaluate a vendor's technical claims critically, brief an engineer on what you need built, and identify when a proposed AI solution is technically infeasible given the company's data infrastructure — all without needing to write the code yourself.
Deep familiarity with the modern GTM and AI tool landscape. You have hands-on experience with tools like Clay, Gong, Outreach or Salesloft, ZoomInfo, and AI-powered workflow automation platforms. You know which tools are genuinely differentiated and which are overhyped, and you have opinions about where the category is heading.
Organizational credibility and executive presence. You have operated at a level where VPs and C-suite leaders trusted you to tell them what to deprioritize. You can walk into a room with the CRO, CMO, and CFO and hold your own — challenging their assumptions with data, making a clear recommendation, and getting alignment on a difficult tradeoff. You have done this before and you are comfortable doing it again.
Ruthless prioritization. The AI landscape generates more ideas than any organization can execute. You know how to say no, how to build a framework that feels principled rather than arbitrary, and how to keep the portfolio focused on the initiatives that will actually move the needle on revenue or margin rather than the ones that are interesting to work on.
Change management skills that have been tested. You have managed a change program where the initial reaction from the function was resistance — and you got through it. You know how to build coalitions, how to make change feel inevitable rather than imposed, and how to identify and work through the real blockers (which are almost always human, not technical).
Understanding of business data and systems. You are fluent enough in how GTM and financial data is structured — across a CRM, a data warehouse, CS tools, and financial systems — to evaluate technical feasibility, identify data gaps that need to be closed before a use case can go live, and have a credible conversation with a data or engineering team about what it will take to support an AI initiative.

Nice To Haves

Experience with GTM functions – Some of the highest-impact AI opportunities at this stage are in Sales, Marketing, and CS — e.g. outbound automation, conversation intelligence, customer health, proactive churn intervention. Candidates who have lived inside these functions or been deeply embedded in them as operators will have a meaningful advantage.
Experience with AI tools in production at a SaaS company — not just pilots. You have deployed tools like Clay for outbound enrichment and sequencing, Gong for conversation intelligence and coaching, AI-powered CS platforms, or workflow automation built on LLM APIs, and you have managed the full lifecycle including adoption, iteration, and measurement.
Familiarity with AI agent frameworks and workflow automation platforms — tools like Workato, n8n, or Claude-powered workflows. You do not need to build these yourself, but you need to understand how they work and be able to direct a technical resource who does.
Experience managing or evaluating AI vendor contracts and relationships — understands how to structure a pilot agreement, what evaluation criteria matter, and how to avoid getting locked into tools that underdeliver.
Background that spans more than one business function — operators who have touched GTM teams, Product and Finance are significantly more effective in this role than those who have lived in a single function.

Responsibilities

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 processes.
Build organizational AI fluency: Not through generic training sessions, but by embedding AI thinking into how functional teams plan, prioritize, and operate. The measure of success is not how many people attended an AI workshop — it is how many workflows have been permanently changed.