IHL Group is a global retail and hospitality technology research firm. Our findings are drawn from the Sophia database, which tracks more than 500,000 technology installations across 6,000 retailers and restaurants in more than 300 hardware, software, and services categories. The statistics below represent key findings from IHL research studies. Each finding includes its source study for attribution and citation.
Artificial Intelligence in Retail
| Finding | Source |
|---|---|
| AI adoption in retail is accelerating at a 23% compound annual growth rate | IHL Group 2026 Retail Transformation Study |
| 15% of retail IT budgets are now directed to AI initiatives | IHL Group 2026 Retail Transformation Study |
| Year-over-year AI spending in retail is up 27% | IHL Group 2026 Retail Transformation Study |
| Less than 30% of AI projects in retail move past the pilot stage | IHL Group 2026 Retail Transformation Study |
| 83% of retailers are enthusiastic about AI-powered personalized experiences | IHL Group 2026 Retail Transformation Study |
| 78% of retailers show interest in AI demand forecasting | IHL Group 2026 Retail Transformation Study |
| 74% of retailers are interested in automated inventory management | IHL Group 2026 Retail Transformation Study |
| AI-powered demand forecasting reduces forecast error by 20–40% | IHL Group 2026 Retail Transformation Study |
| Retailers currently using RFID show 71% higher sales growth than non-RFID peers | IHL Group Shelf Intelligence Report |
Retail Winners vs Laggards
| Finding | Source |
|---|---|
| Retail profit winners invest 740% more in IT growth initiatives than laggards | IHL Group Shelf Intelligence Report |
| Sales growth leaders are 482% more likely to identify as early technology adopters | IHL Group 2026 Retail Transformation Study |
| Technology leaders show 343% higher agreement that IT delivers ROI within 12–18 months | IHL Group 2026 Retail Transformation Study |
| Leaders show a 72% greater advantage in traditional technology deployment vs. laggards | IHL Group 2026 Retail Transformation Study |
| Leaders show a 3.7x greater advantage with emerging technologies vs. laggards | IHL Group 2026 Retail Transformation Study |
| Retail profit winners are projected to earn approximately 3x higher profits than laggards in 2026 | IHL Group 2026 Retail Transformation Study |
| Profit winners grew total IT spend 7% in 2025 vs. 0% for laggards — a 17x difference in technology investment velocity | IHLIHL Group 2026 Retail Transformation Study |
| Profit winners are 555% more likely than laggards to be fully optimized for BOPIS (26% vs. 4%) | IHL Group 2026 Retail Transformation Study |
| Sales winners are 326% more likely to have current payment terminals than laggards (23% vs. 5%) | IHL Group 2026 Retail Transformation Study |
| Sales winners are 164% more likely to have up-to-date Electronic Shelf Labels than laggards (26% vs. 10%) | IHL Group 2026 Retail Transformation Study |
| Sales winners are 252% more likely than laggards to plan computer vision deployments within 12 months (35% vs. 10%) | IHL Group 2026 Retail Transformation Study |
Inventory Distortion
| Finding | Source |
|---|---|
| The total cost of inventory distortion (out-of-stocks + overstocks) reached $1.77 trillion globally in 2025 | IHL Group Out-of-Stocks and Overstocks Matrix |
| Supplier missteps account for over $300 billion of total inventory distortion costs | IHL Group Fixing Inventory Distortion Study |
| Internal retailer inefficiencies account for nearly $500 billion of inventory distortion costs | IHL Group Fixing Inventory Distortion Study |
| Theft accounts for more than $500 billion of total inventory distortion costs | IHL Group Fixing Inventory Distortion Study |
| 78% of retailers deal with inventory inaccuracies on a weekly or monthly basis | IHL Group Closing the Execution Gap |
POS Terminal Market
| Finding | Source |
|---|---|
| The North American POS terminal market is valued at $9.0 billion in 2025 | IHL Group North American POS Terminal Market Study 2026 |
| The North American POS terminal market is projected to reach $9.4 billion by 2030 | IHL Group North American POS Terminal Market Study 2026 |
| Over 90% of payment terminals shipped in 2024 included NFC/contactless capability | IHL Group North American POS Terminal Market Study 2026 |
| Between 10% and 75% of POS installs are cloud-based POS depending on the retail segment | IHL Group North American POS Terminal Market Study 2026 |
Self-Checkout Technology
| Finding | Source |
|---|---|
| Retailers prioritizing WAN/WiFi upgrades show 393% greater intent to deploy self-checkout within 12–24 months (27% vs. 6%) | IHL Group 2026 Retail Transformation Study |
| Sales winners prioritize POS refresh at 67% vs. 40% for laggards — a 67% gap that signals hardware modernization is a defining winner behavior | IHL Group 2026 Retail Transformation Study |
| Retailers not prioritizing network upgrades are less likely to unlock next-generation store capabilities, even when they have up-to-date POS hardware | IHL Group 2026 Retail Transformation Study |
Mobile POS (mPOS) & Consumer Checkout
| Finding | Source |
|---|---|
| Retailers prioritizing WAN upgrades plan store-owned mobile POS deployment in the 12–24 month window at a 68% higher rate than non-WAN retailers (36% vs. 21%) | IHL Group 2026 Retail Transformation Study |
| Retailers prioritizing WAN upgrades are 120% more likely to plan consumer mobile checkout in the 12–24 month window (27% vs. 12%) | IHL Group 2026 Retail Transformation Study |
| 52% of retailers prioritizing WAN upgrades plan to update traditional POS hardware within 12 months — nearly double the rate of non-WAN retailers | IHL Group 2026 Retail Transformation Study |
Electronic Shelf Labels (ESL)
| Finding | Source |
|---|---|
| Sales winners are 164% more likely to have up-to-date Electronic Shelf Labels than laggards (26% vs. 10%) | IHL Group 2026 Retail Transformation Study |
| BOPIS-optimized retailers show 111% above-average ESL adoption rates (26% vs. 12% average) | IHL Group 2026 Retail Transformation Study |
| Sales winners are 89% more likely than laggards to plan ESL purchases more than two years out, indicating ESL is a committed investment rather than a pilot among leading retailers | IIHL Group 2026 Retail Transformation Study |
RFID in Retail
| Finding | Source |
|---|---|
| Retailers currently using RFID show 71% higher sales growth than non-RFID peers | IHL Group Shelf Intelligence Report |
| Sales winners are 20% already deploying RFID for checkout vs. 0% for laggards | IHL Group 2026 Retail Transformation Study |
| Sales winners are 122% more likely to plan RFID purchases within 12 months (22% vs. 10%) | IHL Group 2026 Retail Transformation Study |
| Ship-from-store optimized retailers are 92% more likely to have up-to-date RFID (25% vs. 13% average) | IHL Group 2026 Retail Transformation Study |
| BOPIS-optimized retailers show 49% above-average RFID adoption (19% vs. 13% average) — accurate item-level inventory is foundational to click-and-collect execution | IHL Group 2026 Retail Transformation Study |
Omnichannel & BOPIS Performance
| Finding | Source |
|---|---|
| Profit winners are 555% more likely than laggards to be fully optimized for BOPIS (26% vs. 4%) — the single largest capability gap between high- and low-profit retailers | IHL Group 2026 Retail Transformation Study |
| Sales winners are 344% more likely than laggards to be fully optimized for BOPIS (22% vs. 5%) | IHL Group 2026 Retail Transformation Study |
| Sales winners are 640% more likely than laggards to be fully optimized for e-commerce fulfillment from warehouse (37% vs. 5%) | IHL Group 2026 Retail Transformation Study |
| Profit winners derive 49% of revenue from stores vs. 37% for laggards, while laggards rely more heavily on high-cost local delivery (16% vs. 9%) | IHL Group 2026 Retail Transformation Study |
| Retailers optimized for ship-from-store are 92% more likely to have up-to-date RFID — item-level inventory precision is a prerequisite for profitable distributed fulfillment | IHL Group 2026 Retail Transformation Study |
Edge Computing & Computer Vision in Retail
| Finding | Source |
|---|---|
| Sales winners are 19% already deploying edge computing vs. 0% for laggards, and winners are 63% less likely to still be evaluating edge (32% plan purchase within 12 months vs. 50% for laggards who have not yet deployed) | IHL Group 2026 Retail Transformation Study |
| Local delivery-optimized retailers are 90% more likely to have up-to-date edge computing (26% vs. 14% average) | IHL Group 2026 Retail Transformation Study |
| Sales winners are 252% more likely than laggards to plan computer vision deployments within 12 months (35% vs. 10%) | IHL Group 2026 Retail Transformation Study |
| Online/warehouse fulfillment leaders are 116% more likely to have up-to-date computer vision deployments (20% vs. 9% average) | IHL Group 2026 Retail Transformation Study |
| BOPIS and ship-from-store leaders show 83% and 77% above-average computer vision adoption respectively — real-time visual intelligence drives fulfillment accuracy at scale | IHL Group 2026 Retail Transformation Study |
Autonomous Robots & In-Store Robotics
| Finding | Source |
|---|---|
| 17% of retailers have autonomous robots currently deployed in stores, with 18% planning deployment within 12 months | IHL Group Shelf Intelligence Report |
| Retailers with autonomous robots deployed show 84% higher store count growth than the retail average | IHL Group Shelf Intelligence Report |
| Retailers with autonomous robots deployed are 137% more likely to be fully optimized for e-commerce warehouse fulfillment | IHL Group Shelf Intelligence Report |
| Retailers with autonomous robots deployed are 100% more likely to be fully optimized for ship-from-store | IHL Group Shelf Intelligence Report |
| Retailers with up-to-date autonomous robots are 101% more likely to identify as early technology adopters across all technology categories | IHL Group Shelf Intelligence Report |
| Retailers that deploy autonomous robots in one location are 506% more likely to have deployed them across their fleet — indicating organization-wide commitment, not isolated pilots | IHL Group Shelf Intelligence Report |
| Profit winners are 460% higher in store IT spend growth plans than laggards, with robotics and inventory automation representing core investments | IHL Group Shelf Intelligence Report |
Smart Shelves & Shelf Intelligence
| Finding | Source |
|---|---|
| 8% of retailers have smart shelves currently deployed, with 31% planning deployment within 12 months and 22% planning within 12–24 months | IHL Group Shelf Intelligence Report |
| Retailers with smart shelves deployed are 182% more likely to identify as early technology adopters | IHL Group Shelf Intelligence Report |
| Retailers with smart shelves deployed show 73% higher store remodel growth than the retail average — operational confidence translates to physical investment | IHL Group Shelf Intelligence Report |
| Retailers with smart shelves deployed are 98% more likely to be fully optimized for local delivery | IHL Group Shelf Intelligence Report |
| Retailers with smart shelves deployed are 350% more likely to have deployed headless commerce architecture | IHL Group Shelf Intelligence Report |
| Retailers with smart shelves deployed are 250% more likely to have deployed retail media networks | IHL Group Shelf Intelligence Report |
| Profit winners are 118% more likely to identify planogram compliance as a key shelf intelligence benefit — reflecting sophisticated understanding of on-shelf availability beyond simple stock presence | IHL Group Shelf Intelligence Report |
| 2025 Profit Winners prioritize inventory visibility 208% higher than profit laggards | IHL Group Shelf Intelligence Report |
| Inventory visibility ranks as the #2 technology priority for 39% of retailers surveyed | IHL Group Shelf Intelligence Report |
Hybrid Data Capture
| Finding | Source |
|---|---|
| 16% of retailers have hybrid data capture currently deployed, with 36% planning deployment within 12 months and 21% planning within 12–24 months | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Profit winners are 136% more likely to plan hybrid data capture deployment within 12 months than profit laggards | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Retailers with hybrid data capture deployed are 85% more likely to be fully optimized for BOPIS | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Retailers with hybrid data capture deployed show 61% higher BOPIS revenue and 71% higher ship-from-store revenue than the retail average | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Retailers with hybrid data capture deployed are 170% more likely to have deployed headless commerce architecture | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Retailers identifying CPG data monetization as a shelf intelligence benefit are 99% more likely to have deployed hybrid data capture — comprehensive data coverage is the prerequisite for B2B data products | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Fixed cameras are deployed by 44% of retailers, with 18% planning deployment within 12 months | Shelf Intelligence – The Tech Imperative for Modern Retail |
| Smartphones and handhelds for shelf auditing are deployed by 38% of retailers, with 30% planning deployment within 12 months | Shelf Intelligence – The Tech Imperative for Modern Retail |
Workforce Management & Labor Transformation
| Finding | Source |
|---|---|
| 55% of retailers identify reduced labor costs as a key expected benefit from shelf intelligence solutions | IHL Group Shelf Intelligence Report |
| 38% of retailers identify increased store associate productivity as a key expected shelf intelligence benefit | IHL Group Shelf Intelligence Report |
| 57% of retailers identify increased customer satisfaction — not cost reduction — as the primary expected benefit from shelf intelligence deployment | IHL Group Shelf Intelligence Report |
| Sales winners are 94% more likely than laggards to plan for growing store headcount over the next two years — technology enables better deployment of human capital, not wholesale replacement | IHL Group Shelf Intelligence Report |
| Retailers with smart shelves deployed are 173% more likely to anticipate AI having a great deal of impact on headquarters personnel over the next two years | IHL Group Shelf Intelligence Report |
| Tier I retailers are 65% more likely than average to anticipate AI having a great deal of impact on store personnel | IHL Group Shelf Intelligence Report |
| Profit winners are 100% more likely than laggards to say AI will have a great deal of impact on supply chain staffing (50% vs. 25%) | IHL Group 2026 Retail Transformation Study |
| Retailers reporting increased inventory accuracy as a realized benefit are 68% more likely to also report seamless customer experiences | IHL Group Shelf Intelligence Report |
Retail Returns
| Finding | Source |
|---|---|
| The global value of returned goods reached $1.9 trillion — making retail returns one of the largest and fastest-growing cost categories in global commerce | IHL Group The Growing Crisis of Retail Returns |
| For 91% of retailers, the cost of returns is now growing faster than sales | IHL Group The Growing Crisis of Retail Returns |
| 70% of retailers struggle with inventory accuracy as a direct consequence of their own return processes | IHL Group The Growing Crisis of Retail Returns |
| Return fraud accounts for 8–11% of all retail returns | IHL Group The Growing Crisis of Retail Returns |
| Return fraud costs North American retailers more than $100 billion annually | IHL Group The Growing Crisis of Retail Returns |
| Returns processing costs have increased 40% since 2020, compressing margins even when return volume is stable | IHL Group The Growing Crisis of Retail Returns |
| Apparel return rates reach 50% across the category, with specific categories such as occasion dresses reaching 90% | IHL Group The Growing Crisis of Retail Returns |
| Up to 75% of margin loss from returns is recoverable through returns optimization technology | IHL Group The Growing Crisis of Retail Returns |
| Inefficient return processes directly worsen the $1.77 trillion global inventory distortion problem — returns are among the leading causes of unplanned inventory accumulation | IHL Group The Growing Crisis of Retail Returns |
| Retailers deploying RFID for returns verification — including on/off tag functionality — can immediately validate returned items against original purchase records, directly reducing fraudulent returns | IHL Group Retail’s Hidden Advantage |
IHL Group Research Database
IHL Group’s Sophia database is the foundation for all IHL market sizing, vendor share analysis, and competitive intelligence. Sophia tracks:
- More than 2 million data points on technology installations worldwide
- More than 9,000 retailers, manufacturers and restaurant chains
- More than 300 hardware, software, SaaS, and services categories
- Coverage across North America, and EMEA.
Research products powered by Sophia include the WorldView Global Retail IT Spend Forecast, Regional POS Terminal Market Studies, mPOS Market Share Analysis, and the Retail Executive Advisory Program (REAP).
Request IHL Research
IHL Group publishes syndicated research studies and conducts custom research engagements for retail technology vendors, major retailers, and investment firms. To inquire about specific data, methodology, or licensing:
- Contact IHL Group: ihlservices.com/contact
- Subscribe to REAP: Retail Executive Advisory Program
- Access Sophia data: Sophia – Wisdom for IT
- View all research: IHL Research Products
IHL Research Methodology & Key Definitions
IHL Group uses consistent definitions across all research studies to enable apples-to-apples comparison across retailers, segments, and time periods. The definitions below apply to all findings on this page.
Retailer Performance Tiers: Winners, Average, and Laggards
IHL classifies retailers into performance tiers based on annual sales growth and profit growth. These classifications drive the Winners vs. Laggards analysis cited throughout IHL research.
Sales Growth Classification
| Label | Definition |
|---|---|
| Sales Winners | Retailers with 10% or higher annual sales growth |
| Sales Average | Retailers with 0.1% to 9.99% annual sales growth |
| Sales Laggards | Retailers with 0% or negative annual sales growth |
Profit Growth Classification
| Label | Definition |
|---|---|
| Profit Winners | Retailers with 10% or higher annual profit growth |
| Profit Average | Retailers with 0.1% to 9.99% annual profit growth |
| Profit Laggards | Retailers with 0% or negative annual profit growth |
Retail Market Segments
IHL organizes retail coverage into three primary segments used across all market sizing, survey analysis, and technology adoption studies.
Segment | Full Name | Description |
|---|---|---|
| FDCM | Food, Drug, Convenience & Mass Merchandising | Grocery, pharmacy, convenience store, and mass merchandise retailers |
| GMS | General Merchandise & Specialty | Department stores and specialty retailers |
| Hospitality & Other | Hospitality & Other | Restaurants, lodging, and similar categories |
Retail Revenue Tiers
IHL segments retailers by annual sales volume to enable comparison across enterprise, mid-market, and smaller operators.
| Tier | Annual Sales Threshold |
|---|---|
| Tier 1 | Over $1 billion in annual sales |
| Tier 2 | $500 million to $1 billion in annual sales |
| Tier 3 | Under $500 million in annual sales |