AI Operations Enablement Lead

STARK is becoming an AI-native company by redesigning how work gets done. This role focuses on the operational engine: Finance, Product, Inventory, Warehouse, and HR. You will simplify processes, build systems, and reduce manual effort to lower the human work required per order.

About STARK

STARK is an 85-year-old luxury carpet and rug company with a strong reputation in the design community. We operate showrooms nationally, a wholesale division, e-commerce, and a warehouse and fabrication hub in Calhoun, Georgia. Our goal is to feel less like a legacy business and more like a technology company that sells carpet.

Why This Role Exists

Our operating functions carry too much manual work, and our systems do not integrate well. Examples of current challenges include:

• One person manually edits every e-commerce order that flows into our ERP.
• Accounts payable spends three hours a day on manual batch processing.
• Commission reporting involves complex rates, overrides, exceptions, and manual judgment, resulting in roughly 250 individual emails a month.
• $40 million in carpet inventory with no metric to track 10% utilization improvements worth $4 million.
• $8.1 million spent on outbound freight last year with limited vendor or showroom analysis.
• Discrepancies between order-entry systems and ERP, such as an order reading $15,000 in one system and $300,000 in another.

We’ve already built tools like an audit engine, replenishment dashboard, pricing engine, and AP dashboard, but many are fragile and maintained by one person. This role will turn prototypes into dependable systems.

Responsibilities

• Learn how the work actually happens — then make the rules explicit:
  • Sit with Finance, Product, and Warehouse teams to observe workflows.
  • Map workflows, handoffs, duplicated effort, workarounds, and manual processes.
  • Audit existing tools to identify what’s live, fragile, maintained, or broken.
  • Translate legacy processes into clear requirements.
  • Identify opportunities for AI, automation, software, or process redesign.
• Own real, high-value problems:
  • Rebuild commission reporting end-to-end, clarifying rules for automation and reducing Finance’s role to approving exceptions.
  • Unblock active projects in reporting, inventory optimization, warehouse capacity, wholesale planning, and financial operations.
• Help define how an AI-native company operates:
  • Separate the employee experience layer from systems of record.
  • Make judgment calls on AI vs. deterministic logic, recommendations vs. execution, and application layer vs. ERP.
  • Build tools like inventory views, exception dashboards, and order monitoring to reduce disputes over data.
• Use the simplest tool that solves the problem:
  • Not everything requires AI; prefer deterministic rules when they work better.
  • Example: A buying-group workflow may need only a new order type, deposit rules, a portal, and exception monitoring.
• Build AI-native operations tools:
  • Design and launch tools for order-to-cash automation, audit and exception monitoring, finance automation, cross-functional views, and inventory/warehouse tools.
  • Make existing tools durable through documentation, ownership, and monitoring.
• Build through embedded partners:
  • Collaborate with internal subject-matter experts to drive adoption.
  • Work with the AI task force (AI Chief of Staff, AI Sales Enablement Lead) and IT teams.
  • Train teams on practical AI use and build reusable prompts/workflows.

What Success Looks Like

• First 30 days:
  • Join project management cohort and observe active teams.
  • Document workflows, audit tools, identify fragility, and find internal champions.
  • Build small tools to earn credibility.
• By 60 days:
  • Ship a real workflow improvement with measurable hours saved.
  • Stabilize the highest-risk tool the company depends on.
• By 90 days:
  • Improve a second workflow in a different function.
  • Develop a clear view of which tools to invest in, rebuild, or retire.
• At 6 months:
  • Achieve a measurable drop in effort per order across workflows.
  • Replace fragile prototypes with dependable tools.
  • Ensure Operations and Finance work from the same data.
  • Have leaders proactively bring problems worth solving.

Success is defined by less friction, better data, faster decisions, and fewer people doing work a system should handle.

What This Role Is Not

• Not a traditional business analyst, project manager, or pure trainer role.
• Not a people management, data engineering, IT, or consulting engagement.
• We want someone who builds, stays, and owns the outcome.

Qualifications

You might be a fit if you are:

• AI-native but equally comfortable saying, “This does not need AI.”
• A builder with operator’s instincts who ships rather than just specifies.
• Energized by simplifying messy, clunky systems.
• Humble and curious enough to learn a process before trying to fix it.
• Comfortable being challenged by senior leaders and creating your own direction.
• Focused on building systems that remove work rather than absorbing it.
• Excited by helping a 90-year-old company become AI-native.

Skills And Experience

Strong candidates will likely bring some mix of:

• Demonstrated ability to personally build internal tools, automations, or lightweight applications.
• Strong fluency with AI tools, agents, and AI-assisted development.
• Ability to debug when AI tools and automations fail.
• Sound judgment about when not to use AI.
• Data fluency with tools such as SQL, data warehouses, Power BI, Power Automate, or similar.
• Strong stakeholder interviewing and process-mapping skills.
• Good instincts around access control, sensitive data, approval boundaries, and audit trails.
• Experience simplifying complex workflows and translating business problems into usable systems.
• Experience in operations, financial operations, supply chain, or business operations (carpet experience not required).

Why Join Now

Most AI roles today are strategy roles. This is a hands-on opportunity to rebuild how a 90-year-old company operates.