If your AI investments are still in pilot mode, you're falling behind. The latest research data shows 42 percent of retailers have moved AI into production, revenue leaders report 20+ percent lifts, and 97 percent are increasing budgets next year.
The question is no longer whether to scale AI, but whether you can scale it fast enough to maintain a competitive position.
As an advisor to the C-suite, I see retailers and CPG firms shifting from experimentation to scaled deployment, with AI moving from the innovation lab into core P&L ownership.
This latest "State of AI in Retail and CPG" study from NVIDIA reveals a critical inflection point: AI is now a broad-based transformation lever, driving revenue, compressing costs, and reshaping how retailers compete across digital, store, and supply chain operations.
The Adoption Reality Check
Nine in ten companies are either actively using AI or assessing it through pilots, that's up from 82 percent in 2023. But the spread tells the competitive story: 42 percent are using AI in production, while 47 percent remain in assessment phase.
If you're still piloting, you're in the middle of the pack at best.
The performance gap is measurable. Four out of five respondents report AI has increased annual revenue, and a full quarter report revenue gains exceeding 20 percent; a transformational lift in a low-margin sector.
On costs, 94 percent report operational reductions, with over a quarter seeing cuts above 20 percent. These aren't marginal improvements; they're business-model advantages.
What to do this quarter:
- Audit your current AI initiatives: Which are still in pilot after 12+ months? These need a production path or a kill decision.
- Benchmark your revenue impact: If you're not tracking AI contribution to topline growth, you can't manage it.
- Secure your 2026 budget increase: 97 percent of competitors plan to increase AI spending, with over half planning 10+ percent increases. Flat AI budgets signal retreat.
From Strategic Talk to Operational Action
Generative AI (GenAI) has crossed the credibility threshold. Eighty-two percent of retailers are using or assessing it, and more than half have production deployments. About half characterize GenAI as a strategic differentiator, and 89 percent plan to increase investment next year, with 31 percent planning increases above 20 percent.
The use case hierarchy is clear. Marketing and content generation leads at 60 percent adoption; this is merely table stakes.
The next competitive layer combines predictive analytics (44 percent), customer segmentation (41-42 percent), and personalized marketing (42 percent). Digital shopping assistants and copilots, at 40 percent adoption, are emerging as the interface layer that connects these capabilities into customer-facing experiences.
The concern profile has shifted from theory to execution. Data privacy remains the top concern at 60 percent, but the sharpest change is cost: worries about GenAI implementation costs jumped from 25 percent to 57 percent year-over-year.
This isn't skepticism, it's the transition from pilot budgets to production economics. Boards are asking harder questions about total cost of ownership and time to value.
What to do this quarter:
- If you haven't deployed GenAI for marketing content, you're behind; 60 percent have. Get this into production within 90 days.
- Build a business case template that links GenAI investments to specific revenue or cost outcomes, not innovation.
- Challenge your team on cost projections: The jump from 25 percent to 57 percent concern means most initial estimates were wrong. Re-forecast with production-scale assumptions.
AI is no longer a point solution, it's a full-stack capability. Fifty-seven percent are investing in omnichannel digital retail, 50 percent in back-office functions, 45 percent in supply chain, and 31 percent in physical stores. Over half are deploying AI across more than six use cases.
But not all use cases deliver equal returns. When asked which applications generate the greatest ROI, the data provides a clear priority stack:
1. Marketing and advertising content creation (23 percent).
2. Customer analysis and segmentation (19 percent).
3. Hyper-personalized recommendations (18 percent).
4. Demand forecasting and predictive analytics (17 percent each).
This is your investment sequencing guide. Leaders are funding the journey with high-ROI marketing and personalization use cases, then expanding into supply chain and store operations for margin and resilience.
Impact metrics show the operational leverage. Improved insights and decision-making tops the list at 43 percent, but the standout movement is enhanced employee productivity, which jumped from 14 percent in 2023 to 42 percent in 2024.
AI isn't just a customer experience play — it's a labor multiplier.
What to do this quarter:
- Map your AI portfolio against the ROI rankings above. If your investments don't align with the top four categories, you're taking on higher execution risk.
- Set a threshold: Any AI use case not delivering measurable impact within six months needs restructuring or termination.
- Instrument productivity impact: The 14 to 42 percent shift in productivity benefits means this is now measurable. If you can't quantify productivity gains, your tracking is inadequate.
Turn Pressure into Competitive Advantage
The supply chain remains under stress; 59 percent of executives report increased challenges over the past year. But AI is becoming the primary response mechanism.
The top issues being addressed with AI are operational efficiency and throughput (58 percent), reducing rising costs (45 percent), and meeting customer expectations (42 percent).
Investment momentum is unambiguous: 82 percent of supply chain leaders plan to increase AI spending next year, and zero plan to cut it. Demand forecasting and prediction leads at 82 percent planned investment, followed by warehouse knowledge copilots (35 percent), automated reporting (33 percent), and logistics simulation (27 percent).
Physical AI applications — pick-and-place robotics, smart forklifts, AMRs, loading dock intelligence — are entering the investment mix at 24-29 percent adoption.
The outcomes justify the investment. Sixty-one percent report AI has automated repetitive tasks, 58 percent see improved decision-making, and 55 percent report enhanced customer service.
Four out of five companies report AI has reduced supply chain operational costs, with 25 percent seeing reductions of at least 10 percent. In an environment of volatile demand and margin pressure, this level of impact is strategically significant.
What to do this quarter:
- If demand forecasting isn't your top supply chain AI priority, realign — 82 percent of peers have made this call.
- Quantify your cost reduction opportunity: The bottom quartile is leaving 10+ percent in operational savings on the table.
- Evaluate physical AI readiness: Robotics and AMRs are no longer edge cases. If your facilities aren't designed for automation, this becomes a capital planning issue.
Close the Gap Before It Becomes a Crisis
Here's the hidden risk: 52 percent of respondents say AI governance is very important, but only 46 percent have formal policies aligned with industry standards, 44 percent have frameworks for ongoing improvement, and just 36 percent have established an AI governance panel.
This is a 16-point execution gap between perceived importance and actual structure. As retailers scale GenAI and move toward Agentic AI and autonomous systems, this gap will become a competitive vulnerability and a regulatory exposure.
What to do this quarter:
- If you don't have an AI governance panel, form one before the end of Q1. Representation should include legal, risk, technology, and business leadership.
- Audit your AI policies: 54 percent of retailers lack formal standards. If you're in this group and you're scaling AI, you have unmanaged risk.
- Set explainability requirements: The top challenge cited is the need for more explainable AI tools (33 percent). Build this into procurement and development standards now.
Where You Are Determines What You Do Next
If you're in the assessment or pilot phase (47 percent of retailers):
- Move your top three use cases to production within six months, or kill them.
- Focus on the high-ROI quartet: marketing content, segmentation, personalization, demand forecasting.
- Secure budget for scaled deployment; pilot-level funding won't get you to production
If you're in early production (lower half of the 42 percent):
- Expand from 1-2 use cases to 6+ within 12 months — this is where the operational leverage compounds.
- Build governance structures before you scale; the 16-point gap will become a constraint.
- Instrument ROI tracking: If you can't prove value, you can't secure expansion capital.
If you're in scaled production (top quartile):
- Shift from use case accumulation to platform integration — connect marketing AI, supply chain AI, and store AI into unified customer experiences.
- Lead on governance; this will become a competitive differentiator as regulation tightens.
- Explore agentic AI and autonomous systems — this is where the next performance gap will open.
The Competitive Savvy Retailer Reality
Retailers largely feel they now have "enough technology." Concerns about inadequate tech dropped from 42 percent to 28 percent, and compute bottlenecks fell from 22 percent to 8 percent.
The constraints are no longer technological; they're organizational, financial, and strategic.
The winners in this decade will be retailers that marry aggressive AI adoption with disciplined governance, clear business ownership, and relentless focus on measurable outcomes. The adoption curve has steepened.
The performance gaps are widening. Your competitors are increasing AI budgets by 10-20+ percent next year. The question isn't whether AI matters. It's whether you're moving fast enough.
Reach out to learn more about the most effective retailer strategies.