The Foundation for
What’s Next

A Tuesday Morning

Using AI to change the way work gets done

That shows up in three places.

01

Build capability everywhere

You’re trying to put AI-building capability into the hands of every employee, not just a centralized team. The goal is a workforce that can build with AI as a normal part of doing the job, not just use what’s handed to them.

02

Give agents the ability to act

You’re trying to let AI agents actually act inside the business — executing workflows, touching applications, APIs, and data — not just answering questions about them. That’s automation with the ability to take action.

03

Fund it without bloating the cost structure

You’re doing all of this while every dollar spent has to prove operational efficiency. You’ve put a 6% to 7% operating margin target in front of investors and signaled you’re willing to let operating costs grow faster than sales this year to fund the AI build-out.

Build Capability Everywhere

A merchandising analyst, a finance team member, an HR business partner — each one building an internal AI workflow on top of Data Engineering’s foundation, using only pre-approved tools exposed through MCP Server Portals, with authentication, scoping, and logging already in place. No IT ticket. No waiting for a central team. No ungoverned experiment running somewhere nobody can see.

That’s what “every employee builds” looks like when the governance layer exists underneath it. The recent move to bring Data Engineering under the same leadership as AI strategy is exactly what positions the organization to enable this — and what needs governance in place before it scales. The same network underneath every capability in this document — one foundation, not a different one for every use case.

Give Agents the Ability to Act

Dollar General is not just asking AI to answer questions. You are giving agents the ability to act inside the business — executing workflows, touching applications, calling APIs, and reaching into data.

Fund It Without Bloating the Cost Structure

You’re funding the future. You’re still paying a premium for the past.

SG&A (selling, general and administrative expenses — the cost of running the business day to day) has grown nearly every year for years. That part isn’t new. What is new: on your Q1 2026 call, leadership named AI specifically as a reason operating costs will grow faster than sales this year, distinct from the usual drivers like labor, remodels, and incentive comp. That’s the first time AI has been called out by name as the reason for that trade-off in recent disclosure history.

Naming it changes the bar. If AI investment is the reason margin gives a little, every dollar tied to that investment, including what’s spent securing it, needs to actually earn that trade-off.

None of this hands you a check that pays for your AI roadmap outright. What it does is remove a layer of cost and complexity working against the efficiency goals you’ve already set — savings you can choose to redirect toward the initiatives that matter most.

Akamai built its foundation for a different era of the internet — one defined by websites, not ecosystems. Adding capabilities on top of that foundation doesn’t change what’s underneath it. Maintaining it today means paying a premium to protect yesterday’s architecture across multiple disconnected environments, at the exact moment every dollar is being asked to prove it’s earning its place. The cost isn’t just the contract. It’s the opportunity sitting behind it.

Before Q4 2026, you’ll be making a renewal decision on your Akamai contract. That’s not just a procurement moment — it’s a chance to ask whether what you’re renewing is the right foundation for where DG is going.

Consolidating the fragmented security footprint is only half of the cost story. The AI build-out itself needs the same discipline. If 4,500 corporate employees are calling different models through different tools with no spend visibility, AI costs don’t behave like a controllable operating expense. They behave like cloud sprawl.

AI Gateway is what changes that. Spend limits set in actual dollars, not tokens, scoped by team, model, or application, so the AI build-out stays on the right side of the SG&A equation rather than quietly working against it.

Worth noting: AI Gateway can be deployed independently of any other decision in this proposal — you don’t have to consolidate your security footprint or replace any existing vendor to start governing AI spend. It stands on its own.

Protecting the Return on What You’ve Already Committed To

Leadership has accepted higher near-term operating costs specifically to fund the AI build-out — a deliberate trade-off named publicly, separate from the routine capital going toward new stores and remodels. That’s not a budget line. That’s a bet. And every bet needs a foundation that holds.

Cloudflare’s value here isn’t preventing a hypothetical bad day. It’s making sure the capital already committed to the AI operating model gets to do what it was designed to do — on the timeline you’ve already set with the Street.

DG has established a 6% to 7% operating margin target as part of its long-term financial framework while continuing to invest in AI, technology modernization, data engineering, and the systems required to change the way work gets done.

The question is no longer whether AI is worth the investment. DG already made that decision. The question is whether those investments deliver the business outcomes they’re intended to. The ROI.

The larger financial risk is failing to realize the expected business value, not merely the cost of recovering from a security incident.

*Assumptions: ~4,500 AI users, 20 minutes/day, 250 workdays/year, $75/hour loaded labor cost. **1 basis point = 0.01%.

These figures illustrate the financial value dependent on successful AI execution. Delays postpone the return on investments DG has already made. If AI is adopted by approximately 4,500 corporate employees and saves 20 minutes per employee per day, DG stands to recover roughly 375,000 productive hours annually.

Cloudflare’s value to DG goes beyond protecting AI applications and infrastructure. It protects the business outcomes and ROI those investments are intended to deliver. By reducing the financial risk of delays and disruptions associated with security, networking, application delivery, and AI execution, Cloudflare protects the productivity gains, operating efficiencies, and ROI DG expects from the investments it has already made.

On the Q1 2026 earnings call, leadership described the purpose of the AI operating model in explicit terms: lowering SG&A per unit of work over time. That’s a productivity goal, not just a cost-cutting one — getting more output, more workflows completed, more tasks handled, without growing administrative overhead at the same rate.

The $28M productivity figure isn’t a hypothetical. It’s the SG&A-per-unit-of-work goal expressed in dollars — the same objective named publicly, priced out.

You’ve put a 6% to 7% operating margin target in front of investors and accepted operating costs growing faster than sales this year specifically to fund this build-out. Every quarter the AI rollout is slowed by an unstable or ungoverned foundation is a quarter further from both the productivity outcome and the margin target — a direct delay against numbers already shared with the Street.

More Resources & Listening

Selected podcasts, analysis, and primary sources on Cloudflare, AI infrastructure, quantum readiness, and the current threat landscape.