AI Transformation Lead

Twin Valley•Overland Park, KS

1d•Hybrid

About The Position

As the AI Transformation Lead, you are responsible for orchestrating and translating AI investments into positive EBITDA impact across the Twin Valley Family of Companies. This role will help move the company from experimentation and reliance on outside consultants, to a company that has proven internal expertise to use AI as a differentiator for the clients we serve. This role will proactively identify, prioritize, and drive AI-enabled opportunities aligned with the business strategy and vision. It supports the business by translating rapidly evolving AI capabilities into practical, business-relevant initiatives that improve processes, decision-making, efficiency, automation, clear outcomes for customers, and customer credibility, while ensuring efforts are tightly coupled with data quality, reporting mechanisms, and clear guardrails for acceptable use, security, access controls, and data freshness. The role has enterprise-wide impact, developing an actionable roadmap, guiding prioritization, informing build-vs-buy and vendor evaluations, and enabling demonstrable progress within defined time horizons. You will create and direct a small ecosystem of resources rather than personally executing every technical task, but you are technical enough to prototype yourself where speed demands it. While this role is not the architect of our long-term AI platform, the early pilots and architectural patterns established under this leader will form the foundation for a future platform we intend to productize. Decisions made now about agent design, data access patterns, tooling selection, and software-enforced guardrails should be made with that long-term strategy in mind. This role will possess the ability to communicate and work with all areas of the business, while partnering internal teams for requirements, leadership, the internal IT team, and external partners to coordinate resources and execute prioritized initiatives.

Requirements

Practical experience applying AI and data strategies in real business environments, with the ability to show what was done, how it was done, and what results were achieved.
Strong preference for demonstrated results in both internal use settings and external/client outcomes.
3-5 years experience in a solutions architect or product management roles preferred.
Experience leading and implementing AI transformation initiatives.
Has personally led the deployment of at least one AI/ML system into production at a company that uses it daily.
Bachelor’s degree in artificial intelligence / machine learning, computer science, data science, software engineering, or related technology field required.
Strong understanding of how data quality, structure, governance, and reporting mechanisms impact AI outcomes; ability to help define what “good data” looks like and how it is maintained over time.
Technical fluency to read architecture diagrams, evaluate model and tooling choices, and prototype in code when needed.
Track record of orchestrating external partners and vendors, negotiating scope, holding them accountable to outcomes and transferring knowledge in-house.
Strategic mindset with the ability to connect AI initiatives to business strategy, long-term competitiveness, and measurable business impact.
Demonstrated experience in product management, with a track record of translating customer needs and market insights into scalable, technology-enabled solutions that drive business outcomes.
Security focused individual with experience developing and implementing AI governance policies, processes, and systems.
Ability to simplify complex AI concepts for non-experts and communicate effectively with executives, Boards, managers, and frontline teams.
Strong question-asking, listening, and facilitation skills; collaborative approach and ability to influence without direct authority.
Experience engaging with industry forums, conferences, or executive-level discussions as a presenter or thought leader.
Experience developing and presenting business cases.
Experience in office productivity software (email, calendar, word processing, spreadsheets, presentation software, etc.).
Hold and maintain a valid driver’s license; driving record must meet the criteria of the company’s insurance carrier for acceptance of coverage.

Nice To Haves

Master’s degree preferred.
Knowledge of Exponential Organizations concepts preferred.

Responsibilities

Develop and maintain AI use cases and deployment roadmap
Support the security team in setting organizational security guardrails and governance policies in support of AI strategy and ensure compliance
Develop and maintain data strategy in support of AI opportunities
Prioritize, project manage, and deliver AI supported outcomes from ideation through practical use
Source, develop and lead resources needed to deliver on AI deployments (internal and external partners)
Provide structured monthly, quarterly, and annual executive and Board level reporting on AI initiatives and roadmap
Evaluate where AI can meaningfully improve business processes, customer service and outcomes, service delivery workflows, decision-making, and efficiency.
Orchestrate the delivery of AI pilots and for pilots that demonstrate measurable value, determine resourcing needed to go-to-market.
Personally validate partner-delivered work (architecture reviews, model choices, retrieval performance) rather than relying on partner self-reporting
Prototyping or piloting use cases directly when speed or learning demands it
Evaluating AI tooling and platforms hands-on, not just through vendor demos
Lead a structured, collaborative approach to opportunity identification, including discovery with leaders and teams to surface pain points and requirements.
Define a consistent methodology for assessing, framing, and prioritizing AI use cases.
Clarify what success and failure look like, learn quickly, and “fail fast” where appropriate.
Develop clear reporting on AI initiatives measurement criteria.
Develop and articulate AI strategy that connects business objectives, data strategy, and technology capabilities.
Sequence investments across data, security, hardware/software, people, partners, reporting, and automation layers.
Apply enterprise AI architecture principles to early pilots, including scoped agents with defined tools, governed retrieval, and software-enforced access controls, recognizing that early design choices establish patterns that the long-term AI platform will inherit.
Guide prioritization of AI and automation initiatives across teams without personally executing all technical work.
Prevent scope creep and fragmented experimentation.
Coordinate across disparate systems and data sources to enable effective internal AI use.
Support the security team in defining and enforcing governance and security guardrails for internal AI use, including acceptable-use practices and protections against sharing sensitive or competitive data.
For every AI capability we need, lead the decision on the right path: build it ourselves, buy it off the shelf, leverage AI tooling already available through our partner ecosystem, or bring the capability in-house through hiring.
Engage and manage external vendors and partners: ask informed questions about product roadmaps, evaluate solutions, and coordinate internal/external resources to accelerate execution.
Participate in client conversations to understand and identify AI supported solutions to meet client and market needs.
Educate and communicate AI concepts in practical, digestible terms—meeting leaders and teams where they are—and strengthen organizational capability and confidence in using AI tools.
Enable all levels of employees to develop AI as a core competency with a structured, scalable plan.
Continuously scan the external landscape for disruptive threats and emerging opportunities, helping leadership think like a startup or new market entrant.
Support the company by completing other duties as assigned.