De‑risking AI innovation in retail: What the research reveals

Retailers face relentless pressure to innovate — driven by evolving customer expectations, rising competition and ongoing margin pressure, making retail innovation a strategic imperative. But moving quickly without clear proof of impact creates real risk, particularly as retailers invest in more complex, data‑ and AI‑driven initiatives.

To understand how organizations are navigating this tension, Mastercard commissioned Forrester Consulting to study innovation, risk management and AI adoption across industries. The 2026 study, “The experimentation advantage: Research on de-risking innovation,” is based on responses from 324 global directors and above with responsibility for innovation, strategy, data and analytics, marketing or product/portfolio management decisions. This post highlights the most important findings for retail leaders from the 2026 study, and what the research reveals about how retailers can innovate with greater confidence.

The findings show that retail is one of the most innovation‑forward industries. Twenty‑eight percent of retailers say they pursue innovation proactively, compared to just 14% of financial institutions and 16% of restaurants.  

This early‑mover posture helps retailers stay competitive, but it also introduces risk. Retailers often pilot new technologies and experiences before customer demand, internal capabilities or measurement frameworks are fully mature. 

That reality shows up in performance outcomes. Retailers are more likely than other industries to say innovation initiatives have not met expectations, reflecting the higher cost of being early without clear validation. Being proactive expands opportunity, while making it more important to prove what works before scaling.

Given thin margins and operational complexity, it’s not surprising that risk looms large in retail innovation decisions. The research found that 74% of retailers say minimizing risk is critical to innovation success, yet 54% say it is challenging and 89% struggle to balance risk management with fostering innovation. 

In practice, this tension often leads to hesitation. When confidence in outcomes is low, organizations slow down scaling decisions, extend pilot phases or delay investment altogether. Innovation doesn’t stall because of a lack of ideas — it stalls because leaders lack clear evidence that AI-driven innovation initiatives will deliver incremental impact. 

As a result, many promising pilots never progress beyond early testing.

AI is a clear priority for retail innovation, with leaders evaluating its potential across both customer‑facing experiences and internal operations. But the findings reveal a significant execution gap.

Only 12% of retailers currently leverage AI across innovation initiatives wherever possible, while 74% report difficulty implementing AI effectively.  

One reason is that AI raises the stakes of innovation decisions. AI initiatives often require greater investment, deeper data integration and more cross‑functional coordination. Without clear validation frameworks, it becomes difficult to distinguish true incremental impact from seasonality, market shifts or existing customer behavior. 

In short, AI raises the stakes, so teams want clearer evidence before rolling something out more broadly. 

The research points to a persistent challenge for retailers: 86% say difficulty testing pilots on a small scale is hurting their ability to innovate, and 85% report they could place greater focus on customer needs. 

These gaps make it harder to determine which initiatives are ready to scale, which need refinement and which should be deprioritized. Experimentation helps close that gap by providing a structured test and learn approach to validate initiatives in real‑world conditions before making high‑stakes decisions. 

In fact, 88% of retailers say measuring true business impact through experimentation is critical to accelerating innovation while minimizing risk, and 76% report large or transformational improvement when applying data and analytics best practices. 

Rather than slowing innovation, experimentation gives leaders the confidence to move forward.

Consider a retailer exploring how utilizing AI in retail could reshape its health and wellness assortment. Using an AI‑powered optimization model, the retailer redesigns shelf layouts and product placement to create a more differentiated offering.

Before rolling the strategy out broadly, the retailer tests the changes across a limited set of locations and compares performance against matched control stores. This approach allows leaders to measure whether the AI‑driven changes deliver incremental lift, rather than simply shifting existing demand. 

By isolating true impact early through business experimentation, the retailer can determine whether the initiative is ready to scale, requires refinement or should be deprioritized — before risk compounds across the full store network.

For retail leaders, the challenge is no longer whether to innovate — it’s how to do so with confidence. Across the study, retailers emerge as early innovators, operating under tight constraints, while increasingly investing in AI‑driven initiatives where the cost of missteps is high.

Experimentation offers a practical way to resolve these tensions. By grounding decisions in real‑world evidence, retailers can move faster, reduce risk and scale what works.

To explore the full findings, download The experimentation advantage: Research on de‑risking innovation, featuring insights from global senior leaders on how structured experimentation and data‑driven best practices help organizations scale innovation with confidence.

Ready to continue your retail innovation journey? Explore how Mastercard Test & Learn helps data, analytics and strategy leaders make smarter, faster decisions.