AI Operations Enablement Lead

Help rebuild how a 90-year-old company operates. STARK is becoming an AI-native company — not by adding AI on top of broken processes, but by redesigning how work gets done. We started with one AI Chief of Staff. It worked: internal teams have since eliminated thousands of manual reviews, cut a four-week pricing process to minutes, and returned hours of daily work to Finance. Now we are building out a three-person AI team reporting to the CEO — one person owning the shared data foundation, one focused on our 200-person sales organization, and this role focused on the operational engine: Finance, Product, Inventory, Warehouse, and HR. You will walk into $40 million of inventory, millions in freight spend, thousands of manually reviewed orders, legacy systems, inconsistent rules, and leaders asking for these problems to be solved. Your job is to understand how the work actually happens, simplify it, and build the systems that make the better way stick. Sometimes that is AI. Sometimes it is software. Sometimes it is automation. Sometimes it is forcing us to finally answer a business question we have avoided answering. The measure that matters most is effort per order — how much human work it takes to move one order through this company. Lowering it is how we grow without growing headcount. About STARK STARK is an 85-year-old luxury carpet and rug company with a strong reputation in the design community: showrooms nationally, a wholesale division, e-commerce, and a warehouse and fabrication hub in Calhoun, Georgia. We want to feel less like a legacy business and more like a technology company that sells carpet. Why This Role Exists Our operating functions are carrying too much manual work, and our systems do not talk to each other well enough for anyone to see the whole picture. A few real examples: One person manually edits every e-commerce order that flows into our ERP Our accounts payable team spends three hours a day on manual batch processing Commission reporting involves complex rates, account-level overrides, exceptions, and manual judgment — then goes out as roughly 250 individual emails a month We carry $40 million in carpet inventory; a 10% improvement in utilization is worth about $4 million — and we do not yet have a metric that would tell us whether we achieved it We spent $8.1 million on outbound freight last year with limited analysis by vendor or showroom Our order-entry system and ERP can disagree on price: one order read $15,000 in one system and booked at $300,000 in the other Our people have already proven what is possible: an audit engine that replaced manual review of thousands of orders, a replenishment dashboard, a pricing engine that cut a four-week cycle to minutes, and an AP dashboard that gave a team back three hours a day. But many of these tools are fragile and maintained by one person. Part of this job is turning prototypes into systems the company can depend on. What You’ll Do Learn how the work actually happens — then make the rules explicit Before building anything, you will need to understand how the work really happens. That means: Sitting with Finance, Product, and Warehouse teams and watching the work Mapping workflows, handoffs, duplicated effort, workarounds, and manual processes Auditing the tools already in use — what is live, what is fragile, who maintains it, and what breaks Translating messy legacy ways of working into clear requirements Identifying where AI, automation, software, or process redesign can create the most value Some of our hardest problems are not technical. The rule may live in someone’s head, an old spreadsheet, or a manual override nobody can explain. Automation forces clarity: What is the actual rule? Why does this exception exist? Should it still exist? We do not want someone who automates the mess. Help us simplify the mess, then build the system. Own real, high-value problems One project you could own is rebuilding commission reporting end to end. Today that includes rates by product type, customer and salesperson overrides, date-based rules, manual judgment, and exceptions — some documented, some not. The goal is not to automate the spreadsheet. The goal is to help Finance and Sales define the rules clearly enough to automate them, build an auditable calculation people can challenge, and reduce Finance’s role to approving genuine exceptions. Unblock the work we have already committed to Leadership has active projects across reporting, inventory optimization, warehouse capacity, wholesale planning, partner-location rollout, and financial operations. Several are waiting on technical work nobody currently owns. You will help move forward: Reporting and dashboards for Product, Finance, and order fulfillment Measurement design for efforts that currently have no baseline or metric Forecast modeling to shift wholesale reordering from historical to forecast-based Integration fixes and cash forecasting on the Finance side Exception monitoring and data-integrity checks that surface problems in real time instead of at month-end Your first job is to make these move. Help define how an AI-native company actually operates We are deliberately separating the employee experience layer from our systems of record. Employees should work through simple, role-specific applications on a shared data foundation, while governed financial transactions remain in core systems. That creates judgment calls every week: AI or deterministic logic? Recommend or execute? Application layer or ERP? What approval should a human retain? Is the data trustworthy enough to automate the decision? Finance, Operations, ownership, and frontline teams should not need different spreadsheets to understand the same business. You will turn that shared foundation into tools people actually use — inventory views, exception dashboards, Finance reporting, order monitoring, and scorecards. The goal is not more dashboards. It is fewer arguments about whose number is right. Use the simplest tool that solves the problem Not everything you build will use AI. That is intentional. One current project — a new buying-group workflow — may need nothing more than a new order type, clear deposit rules, a portal, and exception monitoring. If a deterministic rule beats an LLM, use the rule. Technology serves the process, not the other way around. Build AI-native operations tools You will design and launch practical internal tools that improve how our operating teams work. Focus areas include: Order-to-cash automation — quote through order, fulfillment, billing, and payment Audit and exception monitoring at scale Finance automation across payables, receivables, commissions, cash application, and forecasting Cross-functional views so Operations and Finance stop working from different numbers Inventory and warehouse tools that improve visibility and decision-making Reporting and monitoring that identify issues before they become month-end problems You will also make existing tools durable: documented, owned, monitored, and no longer dependent on one person. Build through embedded partners, not around them Each function is elevating internal subject-matter experts as your counterparts. They bring deep domain knowledge and local credibility to drive adoption inside their teams. Build through them, not just for them. Adoption is the deliverable. Work with the AI task force You will report to the CEO and work as a peer to our AI Chief of Staff and AI Sales Enablement Lead — together forming an internal AI task force redesigning how STARK works. You will collaborate closely with: AI Chief of Staff — owns the shared data warehouse layer, deployment, security, and permissions; you own the operations application layer built on top of it AI Sales Enablement Lead — owns the salesperson and sales-manager experience; where workflows overlap, Sales owns the seller experience and you own the operational engine behind it Technology and IT — own security, governance, system-of-record APIs, and core infrastructure; you build on that foundation rather than replacing it Most operations pain points sit on the line between fast-moving application experiences and governed core systems. Knowing which side you are on — and when a human approval should remain — is a major part of this job. You will also train Finance, Product, and Warehouse teams on practical AI use, build reusable prompts and workflows they adopt, and support our internal AI champions program. Where You’ll Focus Primary focus: Finance, including Accounting Product Warehouse Secondary focus: People Operations, including headcount data, payroll‑adjacent analysis, and onboarding automation Within those functions, go where the opportunity is largest — the work that saves real money, adds capacity, or lower the effort required per order for the people who are not selling. That is the filter. Not which department it belongs to, and not whether the solution happens to use AI. What Success Looks Like In the first 30 days Sit in on active project teams Spend time with teams across Finance, Product, and Warehouse Document how the work actually happens Audit the tools currently in use Identify what is fragile Find internal champions Build small things quickly to earn credibility By 60 days Ship something real Choose one workflow with a defensible hours‑saved number Build it, pilot it with a small group, and iterate Stabilize the highest‑risk tool we currently depend on By 90 days Prove that the pattern repeats by improving a second workflow in a different function Develop a clear view of which tools we should invest in, rebuild, or retire Identify what you want to tackle next At 6 months Success means: A measurable drop in effort per order across the workflows you touched Dependable tools instead of fragile prototypes Operations and Finance working from the same numbers Leaders coming to you unprompted with problems worth solving Success is not the number of AI tools shipped. It is less friction, better data, faster decisions, and fewer people doing work a system should do. What This Role Is Not This role is not : A traditional business analyst role A pure trainer or facilitator role An IT role We want someone who builds, stays, and owns the outcome. Who You Are You might be a fit if you are: AI-native while being equally comfortable saying, “This does not need AI” A builder with an 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 Comfortable creating your own direction Focused on building systems that remove work rather than personally absorbing the work Excited by helping a 90-year-old company become AI-native Skills and Experience We care about demonstrated ability to build and solve problems. Strong candidates will likely bring some mix of: Demonstrated ability to personally build internal tools, automations, or lightweight applications — not just write requirements or manage developers Strong fluency with AI tools, agents, and AI‑assisted development Ability to debug when #J-18808-Ljbffr Stark Carpet