Director of AI

Director Of Artificial Intelligence

Open to remote - EST/CST required. Dallas, Columbus, Cincinnati and Richmond Think Consulting is a national technology and operations consulting firm partnering with organizations to build the people, process, and technical capability behind their growth. We've been engaged by Our Client — a fast-scaling, multi-operating-company enterprise standing up an enterprise AI capability on a data foundation it has just finished building — to identify a Director of Artificial Intelligence who can close the gap between deployed assistants and governed, production AI.

A modern cloud data platform is live, an enterprise AI assistant is deployed to an initial seat population, a second enterprise-grade AI assistant is scoped for rollout, and an AI Center of Excellence charter is drafted but not yet launched. What does not exist yet is production AI: no governed intake process, no operational use cases beyond individual productivity, and no ML engineering capacity. The AI Leader is hired to close that gap.

This role owns Our Client's AI strategy and its execution — standing up and running the AI Center of Excellence, scaling assistant adoption from pilot to enterprise, shipping the first production use cases in estimating, RFP response, and document intelligence, and building the governance framework that makes AI safe to deploy in a business where bad output has cost and safety consequences. Success is measured in adopted, governed AI that moves named workflows, not in demos.

The role is a builder's role with a clear runway: a funded three-year AI investment plan, an executive mandate, and a target of meaningful productivity uplift in named workflows within three years. It works in close partnership with the data function — data quality is the explicit gate on this roadmap — and with Applications, Integration, and Cybersecurity.

AI Center Of Excellence

• Take the drafted AI CoE charter to launch — operating model, membership, decision rights, funding model, and reporting cadence to IT and executive leadership.
• Build and run the AI intake process: how ideas are submitted, scored for value and risk, prioritized, funded, piloted, and either scaled or killed.
• Establish the reusable delivery pattern for AI work — reference architecture, prompt and model standards, evaluation methodology, testing, and production support expectations.
• Embed and enable business AI champions in each operating company so demand is generated and qualified locally rather than routed through IT alone.
• Own the AI roadmap and portfolio view, sequencing productivity, workflow, and agentic use cases against capability and data readiness.

Use Case Delivery

• Ship the first wave of operational use cases to production — estimating assist, RFP and specification parsing, submittal and contract-clause extraction, and document summarization.
• Progress the portfolio into workflow automation: three-way PO and invoice matching, change-order triage, dispatch and route optimization, project risk scoring, and financial close assistance.
• Build toward agentic use cases with appropriate human approval gates — bid response, field ticket coding, supply chain, tier-one customer service, and continuous project controls reforecasting.
• Define the value case and measurement approach for each use case before build, and report realized benefit against it after deployment.
• Partner with the data function to ensure each use case has governed, quality-gated data with documented lineage before it goes to production.

Assistant Adoption & Enablement

• Own the enterprise assistant program — scaling the primary enterprise AI assistant from its current pilot population to broad white-collar deployment, and deploying a second enterprise-grade AI assistant from scoped to live at scale.
• Drive real adoption rather than seat counts: role-based training, workflow-specific playbooks, champion networks, office hours, and measured usage by function.
• Design and deploy low-code agent tooling for targeted workflows such as HR and onboarding policy Q&A, parts lookup, and field technician guidance.
• Manage license allocation and seat economics against the approved investment plan, reclaiming and redeploying unused capacity.

AI Governance & Risk

• Build Our Client's AI governance framework — acceptable use policy, model and tool approval process, data handling rules, human-in-the-loop requirements, and audit trail.
• Establish model risk management: evaluation and accuracy thresholds, bias and fairness review, hallucination controls, drift monitoring, and defined rollback criteria.
• Use enterprise data governance tooling to govern prompts, model access, and data exposure, in partnership with the cybersecurity function and vCISO.
• Maintain the AI inventory and stay ahead of regulatory, insurance, contractual, and customer requirements affecting AI use in a construction and field-services context.
• Set clear guardrails for shadow AI — what employees may and may not use, and how to bring unsanctioned tools into the sanctioned estate.

Team, Partners & Investment

• Build and lead the AI team, hiring against the approved plan — ML engineering, prompt engineering, AI governance, and MLOps — and developing the AI Architect role as the technical anchor of the CoE.
• Manage strategic vendor relationships across major AI assistant, model, and platform providers, plus implementation and staff-augmentation partners.
• Own the AI budget across software, compute, and services, tracking spend against the multi-year plan and defending incremental investment with realized results.
• Serve as Our Client's internal authority on AI — briefing executives, operating company leadership, and the board or sponsor on capability, risk, and progress in plain business language.

The AI Leader operates on the following stack, and inherits a data platform purpose-built as an AI foundation:

Layer - Platform / Tooling

Assistants - Primary enterprise AI assistant (scaling); second enterprise-grade AI assistant (scoped for deployment)

AI Services - Cloud LLM platform services; low-code agent builder for targeted workflows

Data Foundation - Modern cloud data platform with planned capacity scaling; cloud data lake and warehouse

AI & Data Governance - Enterprise data governance platform — model, prompt, and data governance plus audit

BI & Reporting - Enterprise and self-service business intelligence tooling

Integration - iPaaS integration platform; API-based patterns

ERP - Multiple ERP platforms across operating companies (consolidation in progress)

CRM - Enterprise CRM selection in progress

Field Service & Ops - Field-service systems; enterprise performance/financial planning management system

Candidates with deep Microsoft-stack experience (Copilot, Copilot Studio, Azure OpenAI, Fabric) and familiarity with a leading frontier model provider will be especially well-matched, but the core requirement is production AI leadership depth — not a specific vendor stack.

Required Qualifications

• Bachelor's degree in computer science, engineering, data science, information systems, or equivalent practical experience.
• 10+ years in technology roles with at least 3 years leading AI, data science, machine learning, or advanced analytics teams or programs.
• Demonstrated record of putting AI into production — not pilots — with named business outcomes and measured adoption.
• Hands-on fluency with modern LLM platforms and patterns: prompt engineering, retrieval-augmented generation, evaluation frameworks, fine-tuning trade-offs, and agent design with human approval gates.
• Working knowledge of a major enterprise AI assistant stack and of at least one other frontier model provider.
• Experience establishing AI or model governance where none existed — acceptable use, approval workflow, model risk, and audit.
• Ability to build a business case, defend a budget, and communicate AI capability and risk credibly to executives who are not technologists.
• Track record building and retaining technical teams, and managing vendors and implementation partners to outcomes.

Preferred Qualifications

• Experience launching and operating an AI Center of Excellence or comparable enterprise capability from charter to steady state.
• Experience in construction, mechanical or specialty contracting, manufacturing, distribution, or field services — particularly with estimating, bidding, project controls, or dispatch workflows.
• Experience deploying document intelligence against unstructured project documents such as RFPs, specifications, submittals, and contracts.
• Experience with agentic architectures and orchestration frameworks in a production environment.
• Familiarity with MLOps practice — deployment pipelines, monitoring, model refresh, and cost control on inference spend.
• Experience in a multi-operating-company or acquisitive environment, and comfort operating