AI technologies offer unprecedented opportunities for cost transformation in retail
Retail sits at a turning point. Many executive teams have spent the last few years wrestling with inflation, wage increases, and supply chain issues. You’ve likely already squeezed operations for productivity gains. What you haven’t fully done yet, what few have, is unlock the scale of opportunity now emerging through generative AI.
In a recent July 2024 survey by Bain & Company, companies deploying generative AI saw, on average, a 15% improvement in productivity and a 9% improvement to their bottom line, through either higher revenue or lower costs.
Why now? Because we’re no longer in the phase of telling AI to count inventory faster or map delivery routes. Generative and agentic AI can now design custom customer communications, interpret HR policy requests in real time, and reduce legal document reviews from hours to minutes. It allows your teams, whether on the sales floor or in a shared service center, to stop manually solving every problem and focus on the work that drives actual value.
Leaders are starting to realize that the right AI deployment isn’t about replacing talent, it’s about freeing it up. Freeing managers from fielding repetitive policy questions. Freeing support teams from paperwork and process delays. That redistribution of focus scales. It creates leaner org structures with more optionality, lean where it matters, smart where needed.
The tech is already live, and the cost to deploy is accessible. Generative AI is the least expensive talent multiplier you can get right now.
Many retailers underestimate latent opportunities for further cost optimization despite previous initiatives
Here’s a common phrase: “We already ran a cost program.” And? That doesn’t mean there’s nothing left.
A big misconception in retail right now is that once you’ve digitized a few workflows and automated back-office tasks, you’ve hit the limit. That’s wrong. The terrain is still wide open. Digital capabilities are accelerating. The gap between what you thought was optimized and what AI can optimize is growing, fast.
Take support functions, finance, HR, IT. These areas have already seen basic automation. Things like scanning invoices or forecasting have probably been on your roadmap for years. What’s changed is that generative AI now handles more complex, multi-step tasks that used to need entire teams. Drafting employment contracts. Translating recruiting materials. Personalizing onboarding content. These jobs don’t need to be done manually anymore.
Even core retail operations, like answering in-store employee questions, can be automated. One retailer added an AI “copilot” that answered procedural queries instantly. Floor staff no longer needed to stop customer interactions to dig through manuals. Managers, relieved of repetitive training questions, focused on more important work. That’s cost transformation running silently, in real time.
Agentic AI will push this boundary further. We’re not talking about static task automation, we’re talking about autonomous agents that can plan, decide, and execute with minimal supervision. Risk is low, and payoff is massive.
So, no, you haven’t “done AI.” You’ve barely scratched it. This isn’t about optimizing a spreadsheet, it’s about rethinking the actual work. That mindset shift is what lets you see the savings still sitting in plain sight.
Generative AI’s impact on merchandising unlocks significant cost-of-goods savings and operational agility
Merchandising is changing, and it’s changing fast. Retailers relying on old workflows are already behind. Generative AI is making the entire merchandising cycle, from supplier evaluation to promotional planning, faster, sharper, and more efficient.
Let’s look at execution. One retailer using AI dropped the time needed to assess a supplier’s proposal from 45 minutes to just 15. Drafting customized feedback on that offer, once a 60-minute task, now takes only 20. Multiply those gains across thousands of vendors and product lines, and the scale of cost savings and speed starts to matter.
More than time savings, the strategic value is where the real impact sits. By offloading repetitive processes to AI, category managers can shift focus to the suppliers that matter most. You’re no longer stuck managing all vendors equally. You can direct attention to high-margin opportunities, negotiate more effectively, and let automation handle the long tail.
Where the numbers converge, AI enables bottom-line improvements of 1 to 2 percentage points across cost of goods sold (COGS) and merch operating expenses. That’s a solid margin lift in a low-tolerance, high-pressure retail environment. And the teams executing it don’t need to grow, they just need the right toolset.
This efficiency doesn’t replace hard-won merchandising expertise; it amplifies it. The decisions are still human. The execution gets automated.
AI tools are cost-effective and deliver rapid returns, facilitating widespread and scalable adoption
There’s this idea that deploying advanced AI requires massive investment, some multi-year, multi-million-dollar operation. That thinking leads to hesitation, which is unnecessary. The reality: AI technology, especially generative AI, is inexpensive to launch and capable of delivering results fast.
Most deployments can start for tens of thousands of dollars annually. That’s not theoretical, real-world implementations are landing squarely in that range. Bain & Company points out that many of these deliver a positive return on investment within the first year. That’s faster than most retail tech investments you’ve approved in the last five years.
The speed and scalability matter. In a high-cost environment with little room to raise prices, any solution that can cut expense while boosting productivity should be prioritized. Generative AI does both.
It’s not just about saving money. It’s about doing more with the same resources. Front-line staff get more done. Support teams scale without hiring. Managers spend time on value creation, not admin work.
Executives shouldn’t treat AI as a side experiment or isolated pilot, it’s now a core strategic tool. Barriers to entry are lower than ever. The technology has matured, and it meets the pressure of today’s operating conditions. If you’re waiting for a better moment to invest in AI deployment, you’re already late.
Structural process flaws and poor data practices limit the full potential of AI-powered transformation
Most companies don’t have a technology problem, they have a process problem. Generative AI delivers real value, but only when the underlying systems are clean, synchronized, and well-structured. If your workflows are broken and your data is unreliable, AI just amplifies the noise.
Let’s take forecasting as an example. In one retail finance team, time was routinely wasted every week just reconciling and standardizing raw data from across departments. That inefficiency used to be tolerable, maybe a few hours here and there. Today, it’s a liability. That same disorganized input prevents AI from delivering accurate, automated forecasts that can actually support faster decision-making.
This matters if you’re serious about scale. AI needs clean, consistent, and accessible data to operate at full strength. That means your executive team must prioritize fixing what’s underneath, process fragmentation, conflicting tech stacks, and data governance gaps, before attempting to fully reap the benefits of AI.
Senior leaders should stop treating data integrity as an IT problem. It’s a business risk issue. Flawed processes drain resources and block tools from delivering on promised ROI. For AI adoption to work long-term, a reset is required, starting with how your teams collect, structure, and share operational data on a weekly basis.
That’s not hard, you just need to look at your workflows and ask what still relies on human triage. If you’re doing manual patchwork to keep systems talking to each other, you’re slowing down everything AI can accelerate.
Five core principles drive successful tech-enabled cost transformations in retail
Not every AI deployment leads to real savings. Tools don’t deliver value if your execution is weak. Success in tech-led cost transformation comes from disciplined thinking, not surface-level automation.
There are five principles you need to follow if you’re serious about making this work.
First, set a bold ambition. Don’t just aim for incremental improvements. Ask whether an existing process can be done with 30%, 40%, or even 50% less cost while still increasing effectiveness. Don’t benchmark against current performance, design for what’s truly possible.
Second, adopt zero-based redesign. This means rethinking workflows from scratch instead of modifying legacy systems. Cut steps, simplify paths, and eliminate redundant effort. AI works best in streamlined workflows, not ones patched together from years of workaround fixes.
Third, focus on extracting more from your data. That means improving collection, enforcing accuracy, and structuring it for use across tools, not just inside siloed systems. Clean data is a multiplier, it forces clarity, increases output, and reduces friction across departments.
Fourth, encourage cross-functional collaboration. Most cost opportunities now sit between departments, not inside them. Teams need to solve workflows end-to-end. That includes working with external technology partners where needed. Functional isolation blocks these gains from materializing.
Fifth, manage change correctly. None of this sticks unless your people use the tools. That requires leadership showing up, clear incentives, no-nonsense training, and frequent communication. If change is just pushed through a project team with no buy-in, initiatives stall. If it has executive ownership, adoption follows.
These aren’t abstract principles. They’re what separate companies making a 2% gain from ones pulling 10%+ cost transformation across their structure. That’s what the market rewards.
Now is a critical moment for retail to capture next-level efficiency gains by integrating new technology with process innovation
Retail isn’t short on complexity right now. You’re operating within thin margins and tighter labor pools, while still expected to deliver performance. If your organization is going to hold or gain ground in this environment, cost control can’t be incremental, it needs a reset. And that reset is available now, with the right combination of generative AI and structural process reform.
Most retailers have already been through traditional cost programs. You’ve automated basic workflows, centralized functions, maybe even cut headcount or renegotiated vendor terms. But those moves aren’t keeping pace with the new rate of technological acceleration. Generative AI and adjacent tools like agentic AI now offer deeper automation, faster cycle times, and less need for repetitive human intervention. But to extract value from this shift, your systems and processes must be designed to integrate with the technology, not resist it.
Retail leaders who act on this now will move faster, operate leaner, and adapt better to volatility in demand or sourcing. The opportunity isn’t limited to cost cuts. You’ll see higher accuracy in forecasting, tighter execution, and increased organizational speed. When AI enables better prioritization and less manual decision-making, your teams stop reacting and start optimizing, in real time.
The organizations that take this seriously aren’t just adding tools. They’re aligning systems, breaking down legacy process debt, and redesigning key workflows to create margin flexibility and stronger competitiveness from the inside out.
This is the inflection point. Wait too long, and the gains are harvested by someone else. Move now, and you reshape how value gets created inside your business. Everything else follows from that.
Concluding thoughts
If you’re leading a retail organization right now, you don’t need more pressure, you need more leverage. Generative AI gives you that. Not just as a tool, but as a catalyst to rethink how your teams work, how your processes run, and how you manage costs at scale. You don’t need to wait for another business case. The tech is proven. The use cases are clear. The ROI is fast. Execution is what separates leadership from lag.
This is the moment to get aggressive. Not with hiring. Not with pricing. With operational redesign. Fix the broken processes. Clean the data. Shift your teams from manual work to higher-value activities. Treat AI as infrastructure, not a side project.
The companies that act now will run lighter, adapt faster, and compete harder. And the ones that hesitate? They’ll spend the next cycle trying to catch up.
