About Exa Capital
Exa Capital is a permanent capital holding company focused on acquiring and building vertical market software businesses. We take a long-term, stewardship-driven approach – buying and holding companies forever, and empowering leaders through a decentralized operating model.
Position Overview
The Director of AI Enablement is a high-visibility, hands-on leadership role responsible for turning AI from strategy into measurable operational that impacts products across Exa’s portfolio of enterprise software companies. This is not a research role, an innovation lab, or an IT delivery position. You will build and lead a small, high-impact AI enablement department or team that identifies the highest-value problems, rapidly prototypes solutions, deploys tools that are actually used, and quantifies the results.
Operating within Exa’s decentralized model, you will partner directly with PortCo CEOs, CTOs, and operating teams to assess AI readiness, design and deliver production-grade pilots, embed AI into day-to-day workflows, and build repeatable playbooks that scale across the portfolio. You will own end-to-end delivery from problem definition through sustained adoption and serve as the decision gate between experimentation and enterprise implementation. You have authority to test, prioritize, iterate, and stop initiatives before committing to scale.
Success in this role means a portfolio of completed AI pilots with multiple solutions in sustained operational use within the first 6-12 months, with clear ROI documented, adoption thresholds met, and playbooks ready for cross-portfolio scaling. You will combine deep technical credibility with business acumen, influence without authority, and the patience of a long-term builder.
Key Responsibilities
1. AI Enablement Strategy & Portfolio Prioritization
· Define and maintain a 12–24-month AI and automation roadmap aligned to Exa’s enterprise priorities and each PortCo’s competitive context
· Conduct AI readiness assessments across portfolio companies, identifying the highest-impact opportunities for operational optimization, product enhancement, and revenue enablement
· Lead a structured intake process for AI and automation opportunities across PortCo functions (engineering, operations, sales, CS, finance), applying clear qualification criteria: business value, technical feasibility, adoption readiness, and risk profile
· Own and manage the enterprise AI use-case backlog and prioritization framework; translate leadership priorities into clear, executable use cases with defined ROI targets
· Monitor the AI technology landscape and make disciplined adopt/test/defer recommendations based on value, feasibility, and risk
· Provide Exa leadership with regular updates on priorities, progress, outcomes, and ROI
2. End-to-End Pilot Delivery & Production Deployment
· Own the full lifecycle of AI pilots: problem definition, solution design, rapid prototyping, iteration, deployment, and sustained adoption
· Design and execute time-boxed AI pilots (typically 30–90 days) with defined success metrics, adoption thresholds, and clear exit criteria
· Drive rapid prototyping and experimentation so pilots produce usable, production-grade solutions – not just demonstrations or proofs-of-concept
· Evaluate pilot results using a standardized evaluation framework and make formal recommendations to scale, iterate, defer, or stop
· Serve as the decision gate between experimentation and enterprise implementation, applying rigorous assessment across business value, technical performance, adoption readiness, and risk
· Ensure solutions are embedded into day-to-day workflows by redesigning processes, balancing efficiency, usability, and appropriate human oversight
· Target a portfolio of 3–5 completed pilots per PortCo annually, with measurable cost savings, productivity gains, or revenue impact
3. Portfolio Company Collaboration & Field Adoption
· Build trusted relationships with PortCo CEOs, CTOs, and product leaders, earning the right to influence through expertise and results rather than mandate
· Design and facilitate structured discovery sessions with PortCo operating teams to validate problem statements, pressure-test workflows, and identify adoption barriers early
· Build and maintain an AI Champion Network across the portfolio to drive engagement, change readiness, and grassroots adoption
· Ensure field feedback directly informs pilot design, iteration, and scale decisions
· Facilitate cross-portfolio AI knowledge sharing, creating communities of practice and enabling reusable patterns across companies
4. Product Competitiveness & Revenue Enablement
· Identify opportunities to integrate AI capabilities into portfolio company products that enhance customer value, competitive differentiation, and expansion revenue
· Lead development of AI-powered features that create defensible moats and expand addressable markets for portfolio companies
· Guide product teams in responsible AI development, including ethical considerations, bias mitigation, and transparency
· Quantify the revenue and retention impact of AI-powered product enhancements
5. Operational Efficiency & Automation
· Assess portfolio company operations to identify processes suitable for AI-driven automation and optimization across customer support, sales enablement, marketing, and back-office functions
· Design and deploy AI solutions that deliver measurable OpEx reduction while maintaining or improving service quality
· Quantify expected benefits including capacity creation, cycle-time reduction, cost avoidance, and scalability impact
· Develop ROI frameworks for AI investments, ensuring disciplined capital allocation, documented payback periods, and measurable returns
· Target 10–20% OpEx reduction in key functional areas through AI-driven workflow redesign
6. Technical Governance, Data Readiness & Responsible AI
· Establish AI development standards, security protocols, and governance frameworks applicable across diverse portfolio companies
· Partner with IT and data teams to assess data readiness and enable responsible access and integration for AI use cases
· Guide build-vs-buy decisions for AI capabilities, evaluating third-party tools against custom development with disciplined cost-benefit analysis
· Establish and enforce responsible AI and data-handling guidelines, including clear governance processes for approvals, risk review, and human-in-the-loop controls
· Ensure AI implementations align with data privacy regulations, security requirements, and compliance obligations
· Maintain documentation to support audit and regulatory readiness
7. Team Building, Change Management & Capability Development
· Build and lead a small, high-impact AI enablement team; coordinate with external specialists and vendors as needed
· Drive adoption through structured change management, training, and communications alongside solution delivery
· Build repeatable AI playbooks, frameworks, and documentation that enable portfolio company self-sufficiency over time
· Develop talent assessment frameworks to help portfolio companies build and retain AI/ML capabilities
· Reduce external consulting spend on AI initiatives through internal capability development
8. M&A Support & Strategic Advisory
· Serve as Exa’s primary advisor on AI-related M&A considerations, evaluating target companies’ AI capabilities, data assets, and integration opportunities
· Lead post-acquisition AI integration and transformation, accelerating new PortCos onto Exa’s AI standards and playbooks
· Contribute to Exa’s investment thesis by identifying AI-driven market opportunities and threats
Required Experience
· 10+ years of progressive experience in AI, technology, or digital transformation roles, with at least 3 years in leadership positions overseeing AI strategy and hands-on implementation
· A proven track record delivering practical, production-grade AI solutions that drove measurable operational or business impact in enterprise environments – not just research or proofs-of-concept
· Meaningful recent experience implementing generative AI in enterprise settings, including taking pilots into sustained production use with documented adoption, reliability, and ROI
· Hands-on technical credibility: building and deploying genAI solutions, rapid prototyping (e.g., Python), and ability to evaluate build-vs-buy tradeoffs with depth
· Working knowledge of modern genAI patterns (retrieval-augmented generation, agent-style workflows, prompt engineering, fine-tuning, document intelligence) and how to apply them responsibly
· Demonstrated experience developing and applying AI evaluation frameworks including: business case validation (ROI, capacity impact), technical feasibility assessment (data readiness, accuracy thresholds), adoption risk evaluation (change impact, training requirements), and compliance/risk assessment
· Strong process orientation with demonstrated experience redesigning workflows so AI improves how work actually gets done – not just what tools are available
· Experience working across multiple business units or companies, demonstrating ability to influence diverse stakeholders without direct authority
· Deep understanding of enterprise software business models, SaaS metrics, product development cycles, and competitive dynamics
· Strong communication skills: ability to explain technical tradeoffs to executives and translate business goals into executable delivery plans
· Experience assessing and managing AI-related risks including bias, security, privacy, and regulatory compliance
Strongly Preferred Experience
· Private equity, venture capital, or portfolio company operating role experience
· Track record working in decentralized or matrixed organizations where influence must be earned
· Experience building internal AI accelerator or center-of-excellence functions from the ground up
· History of moving AI from experimentation to production use with documented adoption and ROI, including formal scale/stop decision frameworks
· Experience building or scaling AI-powered solutions using Claude, OpenAI, Microsoft Copilot, or similar enterprise AI platforms
· Background in change management and technology adoption within established organizations, including building AI champion networks or adoption programs
· Experience managing both direct reports and external partners/vendors to deliver outcomes on time and with accountability
· Consulting or advisory experience serving multiple clients or business units simultaneously
· Experience building and mentoring AI/ML teams
Education
· Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related technical field strongly preferred
· Undergraduate degree in Computer Science, Engineering, Mathematics, or related quantitative field required
· Ongoing professional development in AI through research, certifications, or advanced coursework valued
What You’ll Learn & Gain
· Ownership of AI enablement strategy and execution across multiple enterprise software businesses, each with real customers, revenue, and data – not labs or side projects
· The opportunity to build an AI enablement function from the ground up with clear executive sponsorship and the mandate to shape both strategy and execution
· Deep experience deploying production AI in a permanent-capital environment, where decisions are optimized for long-term value, resilience, and compounding – not fund timelines
· Direct partnership with CEOs, CTOs, and product leaders, influencing roadmaps and architecture without bureaucracy
· Hands-on involvement in AI diligence and post-acquisition transformation as part of Exa’s M&A strategy
· The chance to define portfolio-wide AI standards, evaluation frameworks, and playbooks that scale across companies and years
· A rare seat to build AI systems that matter over decades, with tangible impact you can measure every quarter
Who You Are
· A senior AI leader who moves fluidly between strategy and hands-on problem solving – you ship, not just advise
· Credible with engineers and executives alike; you earn influence through expertise and demonstrated results
· Experienced turning AI pilots into sustained production solutions with real user adoption, not just demos
· A process thinker who redesigns workflows, not just deploys tools
· Disciplined about evaluation – you build frameworks, set thresholds, and make clear scale/stop decisions based on evidence
· Comfortable operating across multiple companies at once, balancing standardization with context-specific solutions
· Long-term oriented by default; you care about durability, stewardship, and compounding advantage
· Clear-thinking, direct, and pragmatic – able to cut through noise and make disciplined tradeoffs
· A change leader who builds momentum for adoption through trust, champion networks, training, and visible wins
· Grounded in responsible AI, bringing maturity around risk, ethics, privacy, and security
· Energized by the idea of building something that compounds for decades
Why Exa
· Permanent capital: build AI capabilities designed to last decades, not optimized for exits or fund timelines
· Decentralized model: portfolio CEOs own outcomes – you act as a strategic force-multiplier, not a control layer
· Direct access to the CEO on AI strategy, acquisitions, and portfolio priorities
· The opportunity to shape what “great AI enablement” looks like across an entire software portfolio
· A culture of high standards, low ego, discipline, and intellectual honesty
· Visible, tangible impact: your work will influence products, margins, and competitiveness in real time
· You will help build a new kind of software holding company, with AI as a core advantage