Navigating the Retail Renaissance: How AI and ‘Radical Agility’ Are Redefining the C-Suite 

As Bain & Company forecasts a decade of tech-driven disruption, leading retailers are ditching blanket price hikes in favor of AI-powered elasticity modeling to survive a volatile market.

For executives in the C-suite, Bain & Company’s recent report, “The Future of Retail: Six Disruptions That Could Shape the Next Decade,” paints a bright picture of the industry’s future while also laying out the formidable steps to get there.

The report outlines a transformative “retail renaissance” that is driven by technological advances and shifting consumer behaviors. The report also presents a challenge because its authors see these tech advances and shifting consumer behavior as rapidly approaching. Amid this evolution, retailers still have to contend with disruptive forces such as tariffs and supply chain hiccups — which are often out of their direct control.

This means retailers need to reexamine what they can control, such as pricing.

The Role of AI

The primary disruption identified in the Bain Co. report is the total integration of AI and automation into core business operations, where algorithms will soon manage pricing, merchandising, and logistics on “autopilot,” the report noted. This shift is expected to commoditize traditional retail skills, forcing incumbents to find new ways to differentiate themselves through human-centric strategy and customer experience while leveraging massive gains in efficiency.

The report also highlights a fundamental shift in how consumers interact with brands, specifically through the rise of AI shopping agents. These autonomous tools are expected to research and purchase products on behalf of consumers, often making brand-neutral decisions based on convenience and logic. This “disintermediation” threatens traditional loyalty models, requiring retailers to rethink how they attract customers — either by building their own ecosystem-based agents or by optimizing their product data specifically for AI interfaces rather than just human search, the report’s authors said, adding that the definition of “value” is also becoming increasingly personalized and contextual, moving beyond simple price points to real-time, data-driven service delivery.

Responding to Disruption in the Short-term

Analysts are describing the retail sector as being in a permanent state of volatility. Between shifting trade policies (specifically, the broad application of tariffs) and a fundamental structural change in consumer behavior, the traditional retail playbook of simply passing costs to the consumer has reached its breaking point.

To maintain and grow profitability, retail leaders must move beyond defensive posturing and embrace, what analysts call, a strategy of radical agility. Retail analysts say the most significant mistake a leader can make in 2026 is applying blanket price increases to offset tariff costs. Today’s consumer is numb to volatility, but highly sensitive to value.

In response, successful retailers are deploying “elasticity-based targeting.” This is where they use AI to identify which specific SKUs can withstand a price move and which must remain anchored to maintain price perception. Here’s how they do it:

Amazon

The industry leader in real-time elasticity modeling. Their system, often referred to as Dynamic Pricing, adjusts prices millions of times per day based on a continuous feedback loop of customer sensitivity.  Amazon identifies “Key Value Items” (KVIs) that have high elasticity (e.g., generic electronics or pet food). They price these aggressively and via the “Buy Box.” For niche items or “long-tail” products where customers are less likely to compare prices (low elasticity), Amazon maintains higher margins.

Walmart

They’ve evolved its “Everyday Low Price” (EDLP) model into a data-driven elasticity engine. You can call it omnichannel elasticity where Walmart uses different elasticity models for its physical stores versus its online marketplace. Walmart also uses localized elasticity data to adjust prices by zip code, recognizing that a customer in a rural area may have different price sensitivity for groceries than one in an urban center.

Starbucks

Using elasticity-based targeting to manage its premium brand status without alienating customers. Rather than a flat 5% increase on all drinks, they use AI to identify which specific modifiers (like oat milk or extra shots) are price-inelastic. As a result, they can raise prices on these add-ons because loyal customers are less likely to change their behavior over a $0.50 increase on a custom latte than they are on the base price of a drip coffee.

Target

Utilizing “Promotion Elasticity.” Their models predict which products will generate the largest basket size if put on sale. The retailer identifies products with high cross-elasticity. For example, if putting diapers on sale (highly elastic) reliably leads to the purchase of full-priced baby wipes and clothes (relatively inelastic), the system triggers a targeted discount on the diapers only. [

Deep Dive: See Target’s “Value Bundles.”

Other pricing strategies include the use of the penny profit shift. Rather than obsessing over margin percentages, retail leaders are accepting lower margins on high-volume, price-sensitive value markers while making up the difference on low-elasticity premium items. Retailers are also bundling slow-moving inventory with high-demand products to create a perceived value that justifies a higher total basket price.