July 20, 2026
AI in Retail: Impact and Opportunity
Retailers that overcome barriers to scaling artificial intelligence can enhance the customer experience, improve operational efficiency and unlock new streams of revenue.
CDW Expert
IN THIS ARTICLE
Many retailers are already using artificial intelligence tools to find new efficiencies, deliver better customer experiences and identify opportunities for revenue growth. Industry data shows that retailers are continuing to grow their AI investments. Emerging solutions are helping to deliver personalized promotions and product recommendations to shoppers, while also providing retailers with insights that help them forecast demand and open up new ways to sell targeted advertising to brands through owned channels.
However, retailers cite a number of hurdles to effective AI adoption. In addition to being overwhelmed by the sheer number of AI solutions on the market, many retailers lack the internal expertise, data governance practices and IT infrastructure to make cost-effective investments that will yield positive results. Help from a trusted partner such as CDW can enable retailers to identify opportunities for quick wins, scale AI pilots into production and lay a foundation for sustained success.
Many retailers are already using artificial intelligence tools to find new efficiencies, deliver better customer experiences and identify opportunities for revenue growth. Industry data shows that retailers are continuing to grow their AI investments. Emerging solutions are helping to deliver personalized promotions and product recommendations to shoppers, while also providing retailers with insights that help them forecast demand and open up new ways to sell targeted advertising to brands through owned channels.
However, retailers cite a number of hurdles to effective AI adoption. In addition to being overwhelmed by the sheer number of AI solutions on the market, many retailers lack the internal expertise, data governance practices and IT infrastructure to make cost-effective investments that will yield positive results. Help from a trusted partner such as CDW can enable retailers to identify opportunities for quick wins, scale AI pilots into production and lay a foundation for sustained success.
The State of AI in Retail
In retail, AI has quickly gone from experimental to essential.
“The retail and consumer packaged goods industries have reached a critical inflection point in their artificial intelligence journey,” writes NVIDIA in a 2026 report. “AI is being deployed throughout every line of business, from the back office to the supply chain, in stores and for digital commerce. Most importantly, those AI solutions are having a tangible, measurable impact on the bottom line, helping to streamline operations and boost employee productivity.”
According to the NVIDIA report, 90% of retailers say their AI investments will continue to grow in the year ahead. And nearly half say their organizations are either already using or assessing AI agents to improve operations, customer experiences or enterprise decision-making.
Three macro priorities have emerged for AI implementations in retail. First, companies are using the technology to deliver highly personalized customer experiences. Second, they are using the technology to improve operational efficiency in areas such as inventory, merchandising and loss prevention. And third, retailers are seeking to create new revenue streams through innovations such as retail media networks and AI-enabled commerce models.
Additionally, AI is changing how consumers shop. Many customers, of course, continue to look for individual products in stores or online, but others increasingly expect curated recommendations based on their previous search and buying history. Some are even using AI platforms to directly discover new products. For example, a consumer might use a large language model to help plan an event or family photos, then use that same AI tool to help them make purchasing decisions about clothing and supplies to bring their plans to life.
Still, many retailers are struggling with AI implementation. Although many AI tools are meant to generate insights from enterprise information, much of organizations’ data is often trapped in silos. Also, customer expectations are changing quickly, with many expressing distrust in brands that fail to disclose their use of AI in marketing materials. And AI features and models are changing so quickly that many retail leaders find it difficult to distinguish between meaningful improvements and hype-fueled solutions that fail to solve business problems.
The National Retail Federation reports that retailers are increasing their AI investments for areas such as supply chain operations and marketing, but that leaders also express a number of strategic concerns. More than half of retail leaders surveyed by NRF say that the cost of AI tools and services, AI model accuracy and reliability, and workforce expertise gaps pose significant challenges. And a significant portion express concern about the risk posed by evolving laws and regulations, as well as potential pushback from consumers and social media influencers.
Retailers must strike a careful balance, implementing AI aggressively enough to avoid being outpaced by their competitors, while also ensuring that their investments are guided by careful strategic planning and backed by rigorous governance practices. This means not only laying the IT foundation to support AI applications but also prioritizing business outcomes over technology trends.
89%
The percentage of retail leaders who say AI is helping to increase annual revenue
Source: NVIDIA, “State of AI in Retail and Consumer Package Goods: 2026 Trends,” January 2026
In retail, AI has quickly gone from experimental to essential.
“The retail and consumer packaged goods industries have reached a critical inflection point in their artificial intelligence journey,” writes NVIDIA in a 2026 report. “AI is being deployed throughout every line of business, from the back office to the supply chain, in stores and for digital commerce. Most importantly, those AI solutions are having a tangible, measurable impact on the bottom line, helping to streamline operations and boost employee productivity.”
According to the NVIDIA report, 90% of retailers say their AI investments will continue to grow in the year ahead. And nearly half say their organizations are either already using or assessing AI agents to improve operations, customer experiences or enterprise decision-making.
Three macro priorities have emerged for AI implementations in retail. First, companies are using the technology to deliver highly personalized customer experiences. Second, they are using the technology to improve operational efficiency in areas such as inventory, merchandising and loss prevention. And third, retailers are seeking to create new revenue streams through innovations such as retail media networks and AI-enabled commerce models.
Additionally, AI is changing how consumers shop. Many customers, of course, continue to look for individual products in stores or online, but others increasingly expect curated recommendations based on their previous search and buying history. Some are even using AI platforms to directly discover new products. For example, a consumer might use a large language model to help plan an event or family photos, then use that same AI tool to help them make purchasing decisions about clothing and supplies to bring their plans to life.
Still, many retailers are struggling with AI implementation. Although many AI tools are meant to generate insights from enterprise information, much of organizations’ data is often trapped in silos. Also, customer expectations are changing quickly, with many expressing distrust in brands that fail to disclose their use of AI in marketing materials. And AI features and models are changing so quickly that many retail leaders find it difficult to distinguish between meaningful improvements and hype-fueled solutions that fail to solve business problems.
The National Retail Federation reports that retailers are increasing their AI investments for areas such as supply chain operations and marketing, but that leaders also express a number of strategic concerns. More than half of retail leaders surveyed by NRF say that the cost of AI tools and services, AI model accuracy and reliability, and workforce expertise gaps pose significant challenges. And a significant portion express concern about the risk posed by evolving laws and regulations, as well as potential pushback from consumers and social media influencers.
Retailers must strike a careful balance, implementing AI aggressively enough to avoid being outpaced by their competitors, while also ensuring that their investments are guided by careful strategic planning and backed by rigorous governance practices. This means not only laying the IT foundation to support AI applications but also prioritizing business outcomes over technology trends.
AI in Retail: By the Numbers
51%
The percentage of retail executives who say they are using AI for personalized promotions and offers based on customer data
Source: Adobe, “AI and Digital Trends: Retail,” September 2025
41%
The percentage of shoppers who use AI assistants to research products
Source: IBM, “Own the Agentic Commerce Experience,” January 2026
75%
The percentage of retailers using AI tools for IT coding and app development; 73% are using the technology for office productivity, and 66% are using it for cybersecurity and fraud detection
Source: National Retail Federation, “Retail AI Trends: A Survey Report,” December 2025
AI in Retail: By the Numbers
51%
The percentage of retail executives who say they are using AI for personalized promotions and offers based on customer data
Source: Adobe, “AI and Digital Trends: Retail,” September 2025
41%
The percentage of shoppers who use AI assistants to research products
Source: IBM, “Own the Agentic Commerce Experience,” January 2026
75%
The percentage of retailers using AI tools for IT coding and app development; 73% are using the technology for office productivity, and 66% are using it for cybersecurity and fraud detection
Source: National Retail Federation, “Retail AI Trends: A Survey Report,” December 2025
- KEY RETAIL AI USE CASES
- BARRIERS TO ADOPTION
- SCALING AI IN RETAIL
Conversations about AI in business often get flattened into discussions about model power and token consumption. But in truth, AI in retail is not about implementing a single technological capability. Instead, retailers are finding success with a wide range of practical applications. When retail leaders are asked about real-world AI use cases, they consistently talk about tools that help them to enhance the customer experience, improve operational efficiency and create new revenue streams. For each of these pillars, success depends on retailers’ ability to connect and govern their data across physical store locations, digital channels and enterprise IT systems.
CUSTOMER EXPERIENCE: Retailers have long tried to find ways to target individual shoppers with tailored promotions and product recommendations. AI tools make this goal more achievable than ever. Through the use of loyalty program information, browsing history and buying patterns, these tools can suggest relevant products or upsells, as well as alter homepage and app experiences based on shoppers’ identities.
AI tools can also help consumers with product discovery. For example, instead of searching for “formal dresses,” a shopper might ask an AI assistant for clothing options under $200 for an outdoor fall wedding. A superior customer experience has the potential to dramatically increase customer retention and lifetime value, but many retailers struggle to deliver these experiences, despite significant investment in personalization and omnichannel capabilities. According to Adobe, only 15% of retail brands feel confident that their digital experiences delight customers, and shoppers themselves rate only 16% of brand experiences as excellent.
OPERATIONAL EFFICIENCY: In a high-overhead industry such as retail, any technology that promises to reduce expenses is certain to command attention. Stores are using AI features such as computer vision and video analytics to catch theft and unscanned items in real time, helping to reduce shrinkage rates. By giving employees AI-enabled tools, retailers are enabling store associates to find product information more quickly and make on-the-spot, personalized recommendations to customers.
At the store and regional manager level, leaders are using AI tools to forecast demand and identify trends, helping to prevent common problems such as stockouts and excess inventory. Retailer leaders should also consider the potential impact of AI tools on back-office productivity. Like organizations in many knowledge work sectors, retailers can leverage AI to streamline reporting and financial tasks, offer immediate answers to natural language queries and automate routine workflows in departments such as marketing and human resources.
REVENUE GENERATION: For a time, many large companies were content to spend millions of dollars on AI experiments, with no real promise of ROI. Increasingly, though, business leaders are demanding that AI investments pay for themselves in the form of both improved efficiency and new revenue. In retail, organizations are looking to emerging models such as agentic commerce and retail media networks to help them create new revenue streams.
In an agentic commerce model, AI agents can help customers discover new products and compare options. Retail media networks, meanwhile, allow retailers to monetize their owned channels by selling targeted advertising and precise campaign measurement to brands. Other potential revenue opportunities include dynamic pricing (where stores use AI to adjust prices based on real-time supply and demand) and subscription offerings that use AI-enabled replenishment.
Click Below To Continue Reading
Employees are essential to implementing an effective AI strategy, but many retailers report that they struggle to attract and retain talent with AI expertise.
Recruit for AI and Data Skills: Scaling AI requires skills in areas ranging from data engineering and model management to cybersecurity and infrastructure. Retailers must ensure they can access this expertise via either internal staff or external partners.
Build a Network of AI Champions: Retailers should identify employees who are eager to test new AI tools and share their knowledge with their peers. This “bottom-up” approach can sometimes accelerate AI adoption better than top-down mandates.
Offer Role-Specific Training: From the IT shop to the cash wrap, different retail employees will use AI tools in vastly different ways. Training should reflect this reality by addressing employees’ actual job responsibilities, rather than teaching generic AI literacy skills.
Measure Adoption and Impact: Retailers should track how employees are actually using AI tools, and whether those tools are improving business outcomes. Without impact data, AI training can quickly become a “checkbox” activity that fails to create real value.
Despite the clear opportunity presented by AI, many retailers are struggling to implement the technology effectively. By focusing on solving specific IT and business challenges, leaders can create AI ecosystems that yield real value today and adapt to changing conditions.
OPTION OVERLOAD: Retailers are inundated with new AI tools, platforms and vendors. Because the technology is so new and the marketplace is so crowded, leaders often have difficulty determining which options have the potential to solve real problems, and which are merely the product of hype. Rather than trying to find suitable uses for notable AI capabilities, retailers should start with specific business outcomes that they want to achieve, then explore ways that AI tools can help them meet these objectives.
UNCLEAR IMPLEMENTATION PATH: In addition to choosing among a large number of competing AI tools, organizations must decide on which use cases to pilot and scale first. This is especially challenging in retail, where potential applications differ greatly between settings that include cash wraps, warehouse floors and corporate headquarters. Workshops from partners such as CDW can help retailers identify quick wins that offer immediate value while also building internal AI expertise and laying the IT groundwork for future use cases.
FRAGMENTED DATA: Many retailers already struggle with siloed environments and disconnected data stores. This problem is exacerbated by the emergence of AI silos, where new tools fail to integrate effectively with existing platforms. According to Adobe, 41% of retailers report that fragmented data prevents them from delivering real-time personalization, and 35% say the issue causes inconsistent experiences across channels. To solve this challenge, retailers must implement data platforms that integrate information across different formats and structures.
INFRASTRUCTURE LIMITATIONS: The ongoing AI arms race has resulted in a scarcity of IT infrastructure elements such as GPUs, memory, storage and networking gear. Applications such as computer vision, real-time personalization and in-store analytics require low latency and high throughput, creating an additional need for edge processing power. Retailers can address infrastructure challenges through managed services, colocation providers and partner support to help determine which AI workloads are best suited to the public cloud.
CYBERSECURITY CHALLENGES: Issues such as shadow AI and improper data governance create new risks, particularly in retail environments, which handle sensitive customer data including payment information. Many retailers underestimate the importance of applying their own security measures to AI systems, incorrectly assuming that vendors universally provide sufficient protection. Retailers should bring security, legal and compliance teams into AI planning from the beginning, and also establish discovery and assessment processes to identify shadow AI and evaluate data exposure risks.
COST CONCERNS: Business leaders are beginning to pay more attention to AI costs, especially as vendors begin to move from flat-fee licenses to usage-based pricing. And some AI use cases will appear to be economical during pilots, then prove to be financial losers when inference costs become more substantial at production scale. To manage these challenges, retailers should build cost modeling into AI planning from the start. Before scaling pilot programs, leaders should estimate the total cost of ownership, which includes licensing, infrastructure, data integration and ongoing inference costs.
Kicking off AI experiments is typically much easier than scaling pilots into full production. Retail leaders must weigh the risk of falling behind their peers with the danger of overinvesting in solutions that will fail to scale.
FIND THE BUSINESS VALUE: Often, initial AI goals focus on industry-specific objectives such as reducing theft and increasing order size. However, AI tools can also help retailers automate portions of customer service, IT operations and back-office tasks such as document summarization. Identifying the right use cases is critical, and it is more challenging that it may seem at first, especially with the large number of offerings in the marketplace. Retail leaders may be tempted to chase trends to keep up with competitors, but instead they should take the time to align their AI investments with strategic goals and measurable outcomes. Structured workshops from AI experts can help retailers start their AI journeys on the right track. In CDW’s AI workshops, stakeholders from across the enterprise come together to weigh in on top priorities. These workshops help retailers speed up time to market, while also delivering long-term roadmaps for success.
BUILD A FOUNDATION: An AI foundation includes not only on-premises IT infrastructure but also public cloud environments, data governance, security frameworks and integration assessments. When retailers fail to lay this groundwork, AI initiatives can quickly grow costly and complex, with new challenges stalling each new use case. Often, retailers find that they must make investments in supporting infrastructure such as edge processing, in-store devices and new data center equipment. Use cases such as computer vision, real-time personalization and in-store analytics can place different demands on IT environments than back-office productivity tools, and investments must be aligned with specific use cases and goals. Additionally, it is important to standardize data management and governance so AI tools are able to draw from consistent stores of accurate, up-to-date information. CDW can help retailers assess their current environments and identify technologies needed to scale AI beyond the pilot stage.
TREAT AI AS AN ONGOING PRACTICE: Too often, leaders treat AI as merely a product, focusing chiefly on initial implementation and expecting to see positive results. But for individual AI efforts to succeed, leaders must surround these initiatives with a broader AI practice that includes ongoing training, cross-functional collaboration opportunities and deliberate refinement over time. This mindset shift is especially important for helping retailers to move beyond isolated AI pilots and toward enterprisewide impact, particularly for retail-specific applications. A chatbot or productivity assistant might be relatively easy to pilot, but tools that influence pricing or store operations will likely require additional systems integration and security capabilities. CDW can help retailers with transformation engagements and AI leadership workshops.
DON’T WAIT FOR PERFECTION: It is true that many organizations are racing to implement AI without taking the time to align their investments with a larger strategy. But it is also true that there is danger in waiting too long to find valuable applications of the technology. Already, a widening gap exists between companies that are actively adopting AI and those that are waiting, and organizations that continue to delay risk falling further behind. Retailers do not need to solve every AI challenge immediately. Instead, leaders should begin with targeted, high-impact use cases that can realistically deliver measurable value shortly after deployment. Then, they can build on the momentum and confidence created by these early successes. By acting both quickly and strategically, organizations can position themselves as AI leaders in an industry that will increasingly be defined by the technology.
- KEY RETAIL AI USE CASES
- BARRIERS TO ADOPTION
- SCALING AI IN RETAIL
Conversations about AI in business often get flattened into discussions about model power and token consumption. But in truth, AI in retail is not about implementing a single technological capability. Instead, retailers are finding success with a wide range of practical applications. When retail leaders are asked about real-world AI use cases, they consistently talk about tools that help them to enhance the customer experience, improve operational efficiency and create new revenue streams. For each of these pillars, success depends on retailers’ ability to connect and govern their data across physical store locations, digital channels and enterprise IT systems.
CUSTOMER EXPERIENCE: Retailers have long tried to find ways to target individual shoppers with tailored promotions and product recommendations. AI tools make this goal more achievable than ever. Through the use of loyalty program information, browsing history and buying patterns, these tools can suggest relevant products or upsells, as well as alter homepage and app experiences based on shoppers’ identities.
AI tools can also help consumers with product discovery. For example, instead of searching for “formal dresses,” a shopper might ask an AI assistant for clothing options under $200 for an outdoor fall wedding. A superior customer experience has the potential to dramatically increase customer retention and lifetime value, but many retailers struggle to deliver these experiences, despite significant investment in personalization and omnichannel capabilities. According to Adobe, only 15% of retail brands feel confident that their digital experiences delight customers, and shoppers themselves rate only 16% of brand experiences as excellent.
OPERATIONAL EFFICIENCY: In a high-overhead industry such as retail, any technology that promises to reduce expenses is certain to command attention. Stores are using AI features such as computer vision and video analytics to catch theft and unscanned items in real time, helping to reduce shrinkage rates. By giving employees AI-enabled tools, retailers are enabling store associates to find product information more quickly and make on-the-spot, personalized recommendations to customers.
At the store and regional manager level, leaders are using AI tools to forecast demand and identify trends, helping to prevent common problems such as stockouts and excess inventory. Retailer leaders should also consider the potential impact of AI tools on back-office productivity. Like organizations in many knowledge work sectors, retailers can leverage AI to streamline reporting and financial tasks, offer immediate answers to natural language queries and automate routine workflows in departments such as marketing and human resources.
REVENUE GENERATION: For a time, many large companies were content to spend millions of dollars on AI experiments, with no real promise of ROI. Increasingly, though, business leaders are demanding that AI investments pay for themselves in the form of both improved efficiency and new revenue. In retail, organizations are looking to emerging models such as agentic commerce and retail media networks to help them create new revenue streams.
In an agentic commerce model, AI agents can help customers discover new products and compare options. Retail media networks, meanwhile, allow retailers to monetize their owned channels by selling targeted advertising and precise campaign measurement to brands. Other potential revenue opportunities include dynamic pricing (where stores use AI to adjust prices based on real-time supply and demand) and subscription offerings that use AI-enabled replenishment.
Click Below To Continue Reading
Employees are essential to implementing an effective AI strategy, but many retailers report that they struggle to attract and retain talent with AI expertise.
Recruit for AI and Data Skills: Scaling AI requires skills in areas ranging from data engineering and model management to cybersecurity and infrastructure. Retailers must ensure they can access this expertise via either internal staff or external partners.
Build a Network of AI Champions: Retailers should identify employees who are eager to test new AI tools and share their knowledge with their peers. This “bottom-up” approach can sometimes accelerate AI adoption better than top-down mandates.
Offer Role-Specific Training: From the IT shop to the cash wrap, different retail employees will use AI tools in vastly different ways. Training should reflect this reality by addressing employees’ actual job responsibilities, rather than teaching generic AI literacy skills.
Measure Adoption and Impact: Retailers should track how employees are actually using AI tools, and whether those tools are improving business outcomes. Without impact data, AI training can quickly become a “checkbox” activity that fails to create real value.
Despite the clear opportunity presented by AI, many retailers are struggling to implement the technology effectively. By focusing on solving specific IT and business challenges, leaders can create AI ecosystems that yield real value today and adapt to changing conditions.
OPTION OVERLOAD: Retailers are inundated with new AI tools, platforms and vendors. Because the technology is so new and the marketplace is so crowded, leaders often have difficulty determining which options have the potential to solve real problems, and which are merely the product of hype. Rather than trying to find suitable uses for notable AI capabilities, retailers should start with specific business outcomes that they want to achieve, then explore ways that AI tools can help them meet these objectives.
UNCLEAR IMPLEMENTATION PATH: In addition to choosing among a large number of competing AI tools, organizations must decide on which use cases to pilot and scale first. This is especially challenging in retail, where potential applications differ greatly between settings that include cash wraps, warehouse floors and corporate headquarters. Workshops from partners such as CDW can help retailers identify quick wins that offer immediate value while also building internal AI expertise and laying the IT groundwork for future use cases.
FRAGMENTED DATA: Many retailers already struggle with siloed environments and disconnected data stores. This problem is exacerbated by the emergence of AI silos, where new tools fail to integrate effectively with existing platforms. According to Adobe, 41% of retailers report that fragmented data prevents them from delivering real-time personalization, and 35% say the issue causes inconsistent experiences across channels. To solve this challenge, retailers must implement data platforms that integrate information across different formats and structures.
INFRASTRUCTURE LIMITATIONS: The ongoing AI arms race has resulted in a scarcity of IT infrastructure elements such as GPUs, memory, storage and networking gear. Applications such as computer vision, real-time personalization and in-store analytics require low latency and high throughput, creating an additional need for edge processing power. Retailers can address infrastructure challenges through managed services, colocation providers and partner support to help determine which AI workloads are best suited to the public cloud.
CYBERSECURITY CHALLENGES: Issues such as shadow AI and improper data governance create new risks, particularly in retail environments, which handle sensitive customer data including payment information. Many retailers underestimate the importance of applying their own security measures to AI systems, incorrectly assuming that vendors universally provide sufficient protection. Retailers should bring security, legal and compliance teams into AI planning from the beginning, and also establish discovery and assessment processes to identify shadow AI and evaluate data exposure risks.
COST CONCERNS: Business leaders are beginning to pay more attention to AI costs, especially as vendors begin to move from flat-fee licenses to usage-based pricing. And some AI use cases will appear to be economical during pilots, then prove to be financial losers when inference costs become more substantial at production scale. To manage these challenges, retailers should build cost modeling into AI planning from the start. Before scaling pilot programs, leaders should estimate the total cost of ownership, which includes licensing, infrastructure, data integration and ongoing inference costs.
Kicking off AI experiments is typically much easier than scaling pilots into full production. Retail leaders must weigh the risk of falling behind their peers with the danger of overinvesting in solutions that will fail to scale.
FIND THE BUSINESS VALUE: Often, initial AI goals focus on industry-specific objectives such as reducing theft and increasing order size. However, AI tools can also help retailers automate portions of customer service, IT operations and back-office tasks such as document summarization. Identifying the right use cases is critical, and it is more challenging that it may seem at first, especially with the large number of offerings in the marketplace. Retail leaders may be tempted to chase trends to keep up with competitors, but instead they should take the time to align their AI investments with strategic goals and measurable outcomes. Structured workshops from AI experts can help retailers start their AI journeys on the right track. In CDW’s AI workshops, stakeholders from across the enterprise come together to weigh in on top priorities. These workshops help retailers speed up time to market, while also delivering long-term roadmaps for success.
BUILD A FOUNDATION: An AI foundation includes not only on-premises IT infrastructure but also public cloud environments, data governance, security frameworks and integration assessments. When retailers fail to lay this groundwork, AI initiatives can quickly grow costly and complex, with new challenges stalling each new use case. Often, retailers find that they must make investments in supporting infrastructure such as edge processing, in-store devices and new data center equipment. Use cases such as computer vision, real-time personalization and in-store analytics can place different demands on IT environments than back-office productivity tools, and investments must be aligned with specific use cases and goals. Additionally, it is important to standardize data management and governance so AI tools are able to draw from consistent stores of accurate, up-to-date information. CDW can help retailers assess their current environments and identify technologies needed to scale AI beyond the pilot stage.
TREAT AI AS AN ONGOING PRACTICE: Too often, leaders treat AI as merely a product, focusing chiefly on initial implementation and expecting to see positive results. But for individual AI efforts to succeed, leaders must surround these initiatives with a broader AI practice that includes ongoing training, cross-functional collaboration opportunities and deliberate refinement over time. This mindset shift is especially important for helping retailers to move beyond isolated AI pilots and toward enterprisewide impact, particularly for retail-specific applications. A chatbot or productivity assistant might be relatively easy to pilot, but tools that influence pricing or store operations will likely require additional systems integration and security capabilities. CDW can help retailers with transformation engagements and AI leadership workshops.
DON’T WAIT FOR PERFECTION: It is true that many organizations are racing to implement AI without taking the time to align their investments with a larger strategy. But it is also true that there is danger in waiting too long to find valuable applications of the technology. Already, a widening gap exists between companies that are actively adopting AI and those that are waiting, and organizations that continue to delay risk falling further behind. Retailers do not need to solve every AI challenge immediately. Instead, leaders should begin with targeted, high-impact use cases that can realistically deliver measurable value shortly after deployment. Then, they can build on the momentum and confidence created by these early successes. By acting both quickly and strategically, organizations can position themselves as AI leaders in an industry that will increasingly be defined by the technology.
