VP, Data Product & Business Solutions

D.I. Management, LLCDallas, TX

About The Position

Dalfen is seeking a VP, Data Product & Business Solutions to design and deliver an AI-enabled operating system for how the firm works, makes decisions, and scales execution across the business. Reporting to the Chief Technology Officer, the VP will serve as a forward-deployed business solutions leader who partners directly with executives and operators to translate real business workflows into practical AI, data, and automation solutions.

Requirements

  • 10+ years of experience in product management, AI/automation, digital transformation, operations technology, enterprise systems, or business solutions leadership.
  • Proven experience working directly with business leaders to understand workflows, decision-making processes, and operational needs.
  • Demonstrated ability to translate ambiguous business problems into functional solutions, prototypes, and delivery roadmaps.
  • Hands-on experience building or assembling AI-enabled workflows, automations, low-code applications, or internal business tools.
  • Strong understanding of AI technologies, including prompt engineering, agents, retrieval-augmented generation (RAG), workflow automation, and human-in-the-loop systems.
  • Comfort with enterprise tools such as Microsoft Copilot, Power Platform, Azure AI, Microsoft 365, and modern AI platforms like OpenAI and Anthropic Claude.
  • Strong executive communication, stakeholder management, and change management skills.
  • Ability to operate in a fast-paced, entrepreneurial environment requiring both strategic thinking and hands-on execution.

Nice To Haves

  • Experience in commercial real estate, private equity, asset management, or finance preferred.

Responsibilities

  • Lead the Forward-Deployed Business Solutions Model: Work directly with business leaders and operators across Acquisitions, Property Management, Leasing, Investments, Asset Management, Accounting, Capital Markets, Portfolio Management, Legal, and Development. Understand current workflows, pain points, manual work, data gaps, decision points, and adoption barriers. Translate business problems into clear use cases, solution concepts, requirements, prototypes, acceptance criteria, and rollout plans. Own intake and prioritization for AI, automation, workflow improvement, and business solution opportunities. Build a repeatable operating model for moving from problem discovery to prototype, MVP, production handoff, adoption, and value tracking.
  • Build Practical First Versions of Solutions: Personally build or assemble early working solutions using AI, automation, data, low-code, no-code, and modern enterprise tools. Create prototypes for AI assistants, document processing, workflow automations, internal forms, lightweight apps, decision support tools, and business process improvements. Get solutions far enough along that business users can react to something real, not just a requirements document. Partner with Technology and Data Platform teams to productionize durable solutions with the right security, data architecture, support model, and governance. Avoid building shadow systems by handing durable data and platform requirements back into the Data Platform lane.
  • Own AI and Automation Use Cases: Build and manage the AI and automation backlog in partnership with the CTO and business leaders. Prioritize use cases based on value, feasibility, data readiness, risk, adoption, and speed to impact. Develop practical AI-enabled workflows for areas such as lease abstraction, broker package ingestion, IC memo support, due diligence summarization, portfolio Q&A, AR workflows, COI tracking, vendor onboarding, and document-heavy processes. Use appropriate human review, accuracy checks, privacy controls, and responsible AI practices. Manage the AI & Automation Engineer once hired and help shape that role’s backlog and delivery model.
  • Partner with Data Platform and Technology: Work closely with the VP, Data Analytics to ensure business solutions use trusted data and align with the software, the warehouse, reporting, and governance standards. Define what data is needed, what business logic matters, and what outputs are useful to the business. Support software adoption by tying data products, dashboards, and workflows to real business outcomes. Help clarify what should be solved through reporting, what should be solved through workflow automation, what should be solved through AI, and what should be solved through process change. Partner with vendors and internal teams without allowing vendors to own the business roadmap, product judgment, or operating model.
  • Drive Adoption and Measurable Value: Run demos, working sessions, feedback loops, and adoption reviews with business teams. Make business users feel heard and involved while still pushing for standardization and scalable solutions. Create lightweight documentation, training, and operating playbooks for new workflows and tools. Measure adoption, usage, cycle-time improvement, manual work reduction, quality improvement, and business value. Ensure new solutions become part of how the business works, not side tools that sit unused.
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