From AI Curiosity to 10% Growth and 10% Cost Savings: A Chicago Retailer’s Playbook Story

In the Midwest, retail is a sport. It’s fast, it’s physical, it’s seasonal, and it’s brutally honest about what works. You can have a beautiful strategy and still lose margin because inventory arrives late, stores run short-staffed, or customer service can’t keep up with the volume spike you knew was coming.

That’s exactly why the leadership team at Lakefront Retail Group (name changed), a $5B US retailer headquartered in Chicago, decided they couldn’t treat AI as a side experiment anymore. They didn’t want a pilot for the sake of a pilot. They wanted something far more practical: a repeatable way to turn AI into measurable business value.

A year later, the numbers were hard to ignore. They attributed roughly 10% growth to a set of AI-enabled changes in availability, service, and digital conversion. In parallel, they captured around 10% in operational cost savings, not through one magical AI project, but through a string of improvements across high-frequency workflows: less rework, faster cycle times, fewer costly mistakes, fewer fire drills.

What changed? Not the existence of AI. They had “AI” everywhere already—tools, experiments, slide decks, vendor demos. What changed was that they built an operating system for value.

This is how they did it.

The moment the excitement turned into urgency

When Maya Patel joined as Head of AI Value Delivery, she walked into a situation that will feel familiar if you’ve spent time in a modern enterprise. There were promising initiatives scattered across departments: a customer service experiment here, a forecasting enhancement there, a few clever automations in finance, and a marketing team trying out generative tools on their own.

Individually, none of it was wrong. But together it wasn’t moving the needle.

In her first week, Maya asked a simple question in a leadership meeting:
“Which KPI will improve because of AI this quarter?”

The room went quiet—not because people didn’t believe, but because nobody could confidently connect the activity to measurable outcomes. The CFO could see spend, but not ROI. The COO could see effort, but not repeatability. The Chief Customer Officer could see potential, but also brand risk.

Maya didn’t respond by proposing a new platform. She proposed a different contract:

“Give me 90 days. We will deliver evidence—measured KPI impact—from one or two AI use cases, with guardrails, adoption plans, and a clear decision gate to scale. Not demos. Evidence.”

That became the turning point. Suddenly, AI wasn’t “innovation.” It was delivery.

Choosing outcomes the business actually cares about

The first move was leadership alignment—because without it, every team builds what they want and calls it strategy.

Maya organized a single 90-minute session with the CEO, CFO, COO, Chief Customer Officer, and heads of merchandising and digital. She asked them to stop thinking about AI for a moment and focus on the outcomes they’d be proud to report.

They chose three. Not ten. Three.

1. Grow revenue by improving availability and relevance across channels
2. Reduce cost-to-serve in customer service and store operations
3. Increase inventory productivity by reducing stockouts and overstocks

These weren’t abstract. Each had baseline KPIs the company already tracked. And that mattered, because it eliminated excuses later.

Then came the part leaders often try to skip: decisions. Maya insisted on three executive decisions up front—because without them, pilots die slowly and expensively.

The executives agreed to:

• Allocate a small, dedicated cross-functional team for 90 days (not “side of desk” work)
• Name a business owner for each use case, accountable for adoption and KPI impact
• Approve a short set of “minimum guardrails” so the teams could move safely without constant escalation

That third decision—guardrails—was the one that unlocked speed. It was also the one that reduced fear. If you’ve ever watched AI projects stall, you’ll recognize the pattern: teams avoid building because they’re not sure what’s allowed, while risk teams resist because nobody is proposing controls early enough. Lakefront broke that loop by deciding the seatbelts before hitting the highway.

The readiness reality: clear ambition, uneven execution

Next, Maya ran a quick readiness pulse. It wasn’t a maturity assessment. It was an awareness check—designed to guide a practical plan.

The results were telling. The organization had decent clarity: leaders could articulate the “why,” and there was no shortage of ideas. But execution readiness was uneven. Some teams had good data and strong operational discipline; others were fragmented, with inconsistent access, unclear ownership, and governance concerns that showed up late.

The takeaway wasn’t “we’re not ready.” It was “we must choose use cases that fit our readiness today, while improving the foundations for the next wave.”

That one insight shaped everything that followed.

Picking the use cases: value, feasibility, risk

Maya’s next step was a workshop with leaders from store operations, e-commerce, merchandising, supply chain, and customer service. The instruction was simple: no “AI ideas.” Only use cases tied to workflows and KPIs.

They used a scoring method that balanced reality with ambition: Value × Feasibility × Risk. Not perfect, but disciplined enough to keep the room honest.

After a few hours, a shortlist emerged. It wasn’t glamorous. It was better: it was practical.

The first wave of use cases looked like this:

• A Customer Service Resolution Copilot to reduce handling time and improve consistency
• A Demand & Inventory Signal Upgrade to reduce stockouts and markdowns
• A Store Operations Shift Planner Assistant to reduce overtime and improve labor efficiency

Three different areas. Three different AI types. Three different paths to measurable value.

And crucially: each had a clear owner, a KPI baseline, and a defined pilot scope.

Solving the concerns before they became roadblocks

Before building anything, Maya invited security, legal, compliance, and brand leadership into the room. Not as gatekeepers. As partners.

She started with an acknowledgement:
“You’re right to worry. AI can leak data, make things up, and cause reputational harm. But the answer isn’t ‘no.’ The answer is controlled execution.”

They adopted a simple risk-tiering approach. Low-risk work could move fast. Higher-risk work required stronger controls.

Then they agreed on “minimum guardrails” for the pilots. The language was intentionally plain, not legalistic:

• Sensitive data was prohibited unless explicitly approved
• Customer-facing outputs required human review (at least initially)
• Approved knowledge sources only—no uncontrolled web browsing
• Logging and feedback loops were mandatory
• Escalation paths were defined: if the AI was uncertain, it routed to a human

It wasn’t a perfect system. It was a usable system. And usability is what creates momentum.

Pilot #1: Customer service, where value meets trust

Customer service was the first proving ground because the pain was clear and the volume was high. Agents spent an enormous amount of time reading long case histories, rewriting summaries, searching for policies, and documenting outcomes. The work was repetitive, mentally taxing, and prone to inconsistency—especially under peak demand.

The pilot didn’t aim to replace agents. It aimed to remove friction.

The “copilot” did three things well:

1. It summarized the case history into a consistent format
2. It suggested a draft response using approved knowledge and templates
3. It produced clean case notes for the CRM so agents didn’t have to rewrite what they already knew

The team measured it like a business product, not a demo. They tracked:

• the impact on handling time and rework,
• the quality of outputs (including how often agents corrected them),
• adoption (who used it, how often, and why not),
• and basic economics (cost per assisted case).

In week two, the team found a predictable failure: the copilot performed well on common issues and struggled with edge cases. The fix wasn’t “change the model.” The fix was operational: restrict the scope to the cases it could reliably handle, improve the knowledge sources, and add clear “uncertainty” behavior—when unsure, it asked clarifying questions or routed to a human.

That decision alone increased trust. Agents don’t need perfection. They need predictability.

By week six, adoption was strong in the teams that had been involved early in pilot design. That wasn’t an accident. Managers had adjusted workflows, set expectations, and made space for learning. The teams that treated it as “one more tool” had slower uptake. It was a reminder that adoption is not a side effect. It’s a design requirement.

Pilot #2: Demand and inventory, where “boring” becomes powerful

If customer service was the emotional pilot, forecasting was the financial pilot.

Lakefront already had forecasting processes. The issue wasn’t the lack of a forecast. The issue was trust and responsiveness. Forecasts didn’t absorb certain signals quickly enough, planners overrode outputs frequently, and the organization paid for it through stockouts in key moments and overstocks that turned into markdowns.

Maya reframed the effort: “We’re not ‘adding AI.’ We’re upgrading the signal and the operating rhythm.”

The pilot was deliberately narrow—one category, a handful of regions, clear measurement. They improved the inputs, tightened the cadence, and focused on the decisions that matter: what to buy, where to allocate, how to respond when demand shifted.

The impact wasn’t a single headline number. It showed up where retail value always shows up: fewer expensive surprises. Better availability on high-demand items. Less emergency logistics. Fewer markdowns from overbuying. The kind of changes that make a CFO lean forward.

Pilot #3: Store operations, where adoption decides everything

The store operations pilot taught the biggest lesson of the year.

The first version of the shift planner assistant looked good. It recommended staffing levels based on demand forecasts and store patterns. It had sensible logic. On paper, it should have reduced overtime and improved efficiency.

Store managers ignored it.

Not because they were anti-AI. Because it didn’t fit how scheduling actually happens. It introduced extra steps and felt disconnected from real constraints. So Maya’s team went back to the field, watched the workflow, and redesigned the experience around existing habits.

They embedded suggestions into the scheduling flow managers already used. They reduced the number of clicks. They added simple “why” explanations (“Friday demand spike expected; recommendation increases staffing by X”). They introduced a lightweight feedback loop so managers could annotate why they accepted or rejected suggestions.

When the assistant respected reality, adoption moved. When adoption moved, overtime began to fall.

The difference-maker: decision gates that prevent endless pilots

At the end of each pilot, Maya required a decision memo. Not a celebratory readout. A decision memo.

It answered five questions:

1. What did we build?
2. What KPI moved versus baseline?
3. What quality and risk issues did we discover—and how did we mitigate them?
4. What did adoption look like?
5. What do we do next: scale, iterate, or stop?

This discipline did something important: it made the organization comfortable with stopping work that didn’t justify scaling. That’s a sign of maturity in leadership, not failure in delivery.

One pilot scaled quickly. One required iteration before expansion. One expanded in a second wave. The portfolio stayed alive because it was managed like a pipeline, not a set of experiments.

Scaling without making it “a tech program”

Scaling required some enterprise basics—nothing exotic, but non-negotiable:

• clear ownership and support,
• monitoring for quality, cost, and performance,
• access controls and logging aligned to risk,
• training and workflow updates,
• a cadence to review value and decide next steps.

Maya formed a lightweight monthly “AI Value Board.” It wasn’t bureaucracy. It was a decision engine: approve pilots, review results, scale winners, stop distractions.

That board kept the program aligned with business priorities and prevented the “every team buys a different tool” problem. It also kept leaders engaged, because they could see progress in KPI language, not technical updates.

The cultural shift: the invisible advantage

Ask Maya what she was proudest of and she didn’t lead with a model. She led with behavior.

Within a year, the organization changed how it talked about AI:

• Teams stopped pitching “AI ideas” and started pitching use cases with KPI baselines
• Risk leaders stopped being late-stage blockers because they were involved from day one
• Business leaders owned adoption rather than outsourcing it to IT
• People began to see AI as a capability—repeatable, not exceptional

Maya summarized it with a phrase that stuck:

“We didn’t adopt AI. We adopted a system for value.”

That system is why the results were believable. The 10% growth and 10% cost savings didn’t come from one heroic project. They came from compounding improvements across workflows that happen thousands of times a day.

What you can copy in your own organization

If you want similar outcomes, you don’t need Chicago or retail. You need the moves. These were the ones that mattered most:

• Start with two or three outcomes and commit to KPI language.
• Choose use cases using Value × Feasibility × Risk, not enthusiasm.
• Define minimum guardrails early so you can move safely and quickly.
• Run 4–8 week pilots designed to produce evidence, not demos.
• End every pilot with a decision: scale, iterate, or stop.
• Treat adoption as part of delivery: workflow change, training, measurement.
• Operate a portfolio cadence so value becomes repeatable.

And then, do the simplest thing that most organizations avoid: make three executive decisions in the first 30 days. Budget/capacity, ownership/operating model, and guardrails.

That’s the difference between AI as an idea and AI as a business capability.

A 90-day challenge (if you want to start now)

If you want a practical starting point, borrow Maya’s contract:

Pick three use cases tied to KPIs. Make three executive decisions. Run one pilot that ends with a decision memo. Measure adoption and value weekly.

In ninety days, you won’t just have an AI story. You’ll have a value story.

And in today’s market, that’s the story that matters.