An operator's read on the public signals, and the operating system I'd build under the CIDO to turn AI activity into governed, measurable enterprise capability.
This is a work sample, not a pitch. It is built from what I can see publicly about BJ's today: the Head of AI Strategy & Governance posting, the two adjacent roles (AI Opportunity Development and AI Adoption & Enablement), the Simbe Tally rollout across clubs, the digital and IT modernization agenda visible in the broader transformation strategy, and the club's investments in retail media, drop-ship, and digital commerce. I have made hypotheses where I had to. I have flagged them as hypotheses.
I do not work for BJ's, and nothing here is insider knowledge. The point is to show how I read the situation, what I would build, and how I would make the tradeoffs.
BJ's is not hiring an AI evangelist. It is formalizing the operating layer between AI demand, business value, governance, adoption, and delivery.
The reason I frame the work this way is because I have already had to build it: an AI council, tool registry, policy stack, training program, internal hub, and executive reporting model inside a services organization supporting regulated healthcare, insurance, and retail clients.
Three roles posted at once is not a coincidence. Head of AI Strategy & Governance, Lead AI Opportunity Development, and AI Adoption & Enablement Lead are the structure of an enterprise AI function, not a single hire. Read together they describe a small team being formalized under the CIDO to do four things at once: shape strategy, govern usage, find the use cases that matter, and get the workforce to adopt them.
The pattern suggests BJ's is not hiring one "AI person." It is formalizing a centralized AI Strategy & Enablement function — one lane for strategy and governance, one for opportunity development, one for adoption and enablement. The senior mandate is to make those lanes operate as one system.
The role I'm applying to. Owns the AI operating system — strategy, council, standards, roadmap, measurement, executive and board reporting.
Feeds the system with disciplined use cases. Intake, scoring, business cases, hand-off to delivery teams, value realization.
Drives the workforce side. Training, AI Champion network, productivity tools, communities of practice, adoption depth.
Product, Data, Engineering. They actually build and run the AI-enabled solutions; the function does not.
Merchandising, Supply Chain, Club Operations, Digital, Corporate functions. They bring the problems and adopt the outcomes.
Set the bar, fund the work, and hold the function accountable for AI maturity and business impact.
A hypothesis based on public signals, not an audit. These are the components any enterprise needs to coordinate AI activity into a system — whether each is already in flight at BJ's is a question for the first 30 days.
A hypothesis, not a verdict: the role exists to make the three lanes — strategy, opportunity development, adoption — operate as one governed enterprise system, in a way that is consistent with how BJ's already runs: disciplined, member-focused, and unforgiving on margin. Not "be the person who knows AI." The job is to build and run the operating layer underneath.
Five components. Each one has a published artifact, an owner, and a measurable. The shape below is a proposal — refined and re-prioritized with the existing team in the first 30 days, not imposed.
The Council is the decision body, not a discussion forum. Each workstream has a named owner from the existing organization, a documented charter, and a quarterly outcome target. The Opportunity Development and Adoption & Enablement leads work across these workstreams; I run the Council operationally and report up to the CIDO.
Standards, release gates, synthetic media, IP, member-data rules.
Tool registry, vendor reviews, integrations, infrastructure readiness.
Inventory, pricing, demand, club ops, supply chain.
Personalization, search, retail media, e-commerce, member service.
Adoption, AI Champion network, role-based training, productivity tools.
Intake, prioritization, approvals, decision documentation, board reporting.
One front door. One process. Same path whether the request is from a business team, a vendor, or a leader.
One destination. Approved tools by discipline, current policy stack, council decisions, training, and the status of in-flight pilots. Usage analytics feed back into what the Hub surfaces next. Treated as a product, not a SharePoint.
Five short documents, each owned by a named lane, refreshed quarterly. None of them lives in a vacuum — every staff role is taught the parts that affect them.
Every initiative gets translated into the language a CFO will recognize: hours recovered, decisions accelerated, lift delivered, risk avoided. We do not report "engagement with AI." We report what changed in the business.
Ten candidate use cases scored against three dimensions: value (how much it moves the P&L), feasibility (how realistic in a 6–12 month window), and tight-margin fit (does it earn its keep in a club retailer's cost discipline). 1–5 scale. Top three flagged for first funding.
This is illustrative, not prescriptive — built from the public picture of BJ's. The real list would be re-scored with you in the first 30 days.
| Use case | Value | Feasibility | Tight-margin fit | Note |
|---|---|---|---|---|
| Retail media targeting & measurement Fund first | 5 | 4 | 5 | Revenue-positive, brand-funded, measurable. Lowest-risk early win. |
| Inventory & shelf intelligence (Tally signal expansion) Fund first | 5 | 3 | 5 | Data already produced by Tally. Governance/data-pipeline problem first, model problem second. |
| Pricing & promotion optimization Fund first | 5 | 3 | 5 | Direct margin lever. Needs guardrails around member-trust and promo-rule integrity. |
| Demand forecasting (DC and club level) | 4 | 3 | 4 | Likely already in motion in pockets. Council role is to coordinate, not duplicate. |
| Member personalization (digital, app, email) | 4 | 3 | 4 | Retail-media-adjacent. Needs strong member-data rules and a clear opt-out posture. |
| Private-brand content automation (Berkley & Jensen, Wellsley Farms) | 3 | 4 | 4 | Direct adjacency to work I've done at CVS Health and Staples. Fast ROI on cost-per-asset. |
| Member service triage & deflection | 3 | 4 | 3 | Useful, but member-trust risk is real. Phase carefully. |
| Workforce productivity (governed ChatGPT / Copilot / Claude) | 3 | 5 | 3 | Useful, easy. Risk: overspend on tools without measurable lift. Cap and measure tightly. |
| Finance & procurement automation | 3 | 3 | 3 | Slow but compounding. Fold under existing Finance modernization, not a standalone AI bet. |
| Labor planning / club-staffing optimization | 4 | 2 | 4 | High value, high sensitivity. Needs HR, legal, and culture alignment before pilot. |
Three phases. Each ends with a published artifact and a measurable. Illustrative — the actual sequencing would be re-shaped against what is already in flight at BJ's once I have the inside read.
Deliverable: AI Maturity Baseline memo for the CIDO and the AI Council.
Deliverable: working Council, working front door, published policy stack v1.
Deliverable: three pilots in flight with documented kill criteria. First measurable already in motion.
Most enterprise AI conversation treats adoption as a software rollout. The harder, more valuable work is workforce architecture — deciding how roles get reshaped, which ones compound with AI, which ones expand, and which new ones appear. The BJ's JD asks for "a talent and organizational impact plan for agentic AI adoption, including identifying skills gaps, workforce changes, and potential new roles." That is the workforce-architecture question. Here is the lens I use.
The synthesis: AI-native companies are not just adopting tools — they are redesigning work around new role types. The executive question for BJ's is not "who gets ChatGPT?" It is "which functions should become builders, where do we grow agent managers, which roles stay front-liners, and who stewards the whole thing?" That mapping decides where AI compounds value and where it just generates risk.
In a retailer, the agent-manager category — the corporate and operational middle layer running merchandising, marketing ops, finance, HR, legal, and club operations — is where much of the near-term value sits. It is also where adoption will need the most care, because these roles are closest to the work AI will reshape. Underestimating either side stalls the program.
Sixteen years of cross-functional leadership through ambiguous, high-stakes change in U.S. Army Civil Affairs is where I learned how to communicate disciplined change without false comfort and without fear. The same posture applies here — and it matters more in a 30,000-person club retailer than in any single creative-services organization.
AI adoption is emotional before it is technical. In a recent five-hour AI enablement session I led for cross-functional agency teams, the strongest feedback was not simply that people liked the session. It was that people who were already using AI every day still needed the bigger picture made clear.
One client-facing leader wrote that the session made AI feel "exciting instead of daunting" and helped the team move from "dancing around" client AI questions to feeling ready to lead the conversation. Another senior leader noted that the room stayed "engaged and energized" for the full five hours.
That is the work of enterprise AI adoption: not just giving people access to tools, but giving them the confidence, governance, examples, and language to use AI responsibly in the flow of business.
One page. Standardized. Forced to declare success criteria and kill criteria up front. This is the document that turns an exciting idea into a fundable initiative, or correctly kills it.
| Pilot | Retail media targeting model v1 |
|---|---|
| Sponsor | Member & Digital workstream lead |
| Business problem | Brand partners need stronger targeting and measurement to renew at higher CPMs. |
| Data class | Member behavioral + transactional. Confidential. No PII to third-party tools. |
| Success criteria | ≥ 12% lift in measured campaign conversion vs. control over 8 weeks; ≥ 2 brand partners agree to expand. |
| Kill criteria | < 5% lift at week 6; OR any member-data exception flagged by Privacy; OR cost-per-incremental-conversion exceeds existing baseline. |
| Review cadence | Weekly with sponsor; biweekly Council update; full readout at week 8. |
| Decision rights | Council recommends; CIDO and CMO co-approve scale, pivot, or shutdown. |
The pilot scorecard is one of seven artifacts in the policy stack. Full set available on request: intake form, council decision template, tool-approval workflow, data-classification matrix, release gate, synthetic-media review, and the staff quick guide.
Chief Creative Technology Officer at (add)ventures and Door G. Author of AI Ops, the policy stack and operating model the agency uses to govern AI for healthcare, insurance, and retail clients — including 15+ years on CVS Health across Private Brand, retail pharmacy, in-store, digital, and corporate communications.
30+ years leading creative, content, and emerging-technology programs at enterprise scale. 7× Emmy. 16 years U.S. Army Civil Affairs.
scottmaiocchi@gmail.com · 401-301-3778 · linkedin.com/in/scottmaiocchi