New Trends in AI Use Among Retail Professionals

CAP found that brands experienced uplift in ROAS, with gains ranging from 100% to nearly 300%. CAP’s findings suggest that AI can outperform rule-based dynamic creative optimisation, making it a scalable and efficient tool for improving conversions in real-world marketing environments. The mature virtual retail of 2025 is about practical innovation that actually enhances the shopping experience. Think virtual dressing rooms that work better than the real thing and 3D product inspections that make online shopping feel more real than ever.

EWeek stays on the cutting edge of technology news and IT trends through interviews and expert analysis. Gain insight from top innovators and thought leaders in the fields of IT, business, enterprise software, startups, and more. AI data analytics uses artificial intelligence to analyze large datasets, uncover patterns and trends in these vast volumes of data, and interpret the findings for more accurate business predictions or recommendations.

However, the condensed holiday timeline—there’s one less week between Cyber Week and Christmas this year—adds an additional layer of complexity. Loyalty programs are becoming an increasingly important tool for retailers, ai trends in retail particularly as inflation continues to shape consumer behaviour. Salesforce’s research indicates that 66% of Canadian consumers are consolidating their purchases around retailers that offer loyalty programs.

Contact center retention is still a struggle

These digital natives are discerning consumers who prioritize experiences over products and choose brands that align with their values. If you’re still thinking of social media as just a marketing channel, you’re missing the bigger picture. In 2025, platforms like TikTok Shop and Instagram Shopping aren’t just part of the retail landscape – they’re reshaping it. Gen Z shoppers are more likely to trust a live-streamer’s product review than a traditional advertisement, and successful retailers are following the eyeballs (and wallets) to these platforms.

Data-driven business software, which integrates AI to optimize decision-making processes and automate operations, is a significant part of this investment. From predictive analytics to real-time customer engagement, businesses are finding that AI is no longer optional, it is a competitive necessity. One of the main advantages of artificial intelligence (AI) is its ability to rapidly process vast amounts of data, far exceeding human capabilities. However, humans are still instrumental for contextualizing the processed data and gleaning relevant insights for decision-making. AI data analytics simplifies and automates this process for business users, further eliminating manual efforts and reducing the overhead required to go from raw data to actionable intelligence.

Evolution of Layer 1 and Layer 2 Solutions

With better access to data, contact centers can make more informed decisions about staffing, customer service strategies, and overall operations. Detailed analytics help predict trends, identify areas for improvement, and enhance the customer experience, all based on accurate, up-to-date information. Data collection in contact centers is becoming more accessible and affordable due to advancements in AI, automated speech recognition, cloud computing, and automation.

By applying predictive analytics to the playing experience, game developers can anticipate whether a player will likely make an in-game purchase, click on an advertisement, or upgrade. This enables game companies to create more interactive, engaging game experiences that increase player engagement and monetization. AI data analytics helps physicians, researchers, and healthcare professionals diagnose diseases more accurately. You can foun additiona information about ai customer service and artificial intelligence and NLP. Because it can analyze complex medical data and surface patterns undetectable by humans, AI algorithms enable a high degree of diagnostic accuracy while reducing false positives and human error.

For contact centers, this means breaking down the barriers between different customer support channels and adopting technologies that enable seamless handoffs between agents. This can also include using social media platforms like Instagram, Twitter, and TikTok as alternative support channels. Growth in demand for re-commerce options from online retailers will continue to build as consumers become increasingly mindful of the retail industry’s ChatGPT environmental and community impact. From accessing top-tier talent and achieving cost efficiency to ensuring scalability and data security, the benefits are clear and compelling. As the business landscape continues to evolve, outsourcing offers a way for companies to stay competitive, innovative, and agile. Moreover, as AI becomes more embedded in business processes, the demand for specialized solutions will only increase.

This vast data reservoir spans sectors from agriculture to healthcare, where insights into local behaviours, health patterns, and market trends could be invaluable on the world stage. AI service providers have pre-built models and frameworks that can be customized and deployed quickly. They also have data pipelines and cloud infrastructure ready to scale, enabling businesses to launch data-driven business software in as little as three to six months.

This rapid deployment is essential in industries like retail, where consumer trends can change overnight, or in healthcare, where timely data analysis can save lives. With unpredictable disruptions and rapidly changing consumer preferences, relying on traditional supply chain models is no longer enough. Retailers must focus on building a resilient, customer-focused supply chain with AI that goes beyond just predicting demand. The key is moving from reactive strategies to proactive, insight-driven decision-making that anticipates disruptions before they can impact consumers.

As someone who’s been analyzing business and technology trends for decades, I’m particularly excited about how 2025 is shaping up to be a watershed year where science fiction meets shopping reality. Retailers are bracing for the holiday shopping surge, a period that can make or break annual revenue. With consumer demand on the rise, businesses must effectively manage inventory both for the immediate rush and the months that follow. Accurate forecasting is crucial to avoid the twin pitfalls of overstocking and stockouts, which can tie up capital and frustrate customers, leading to lost sales. Investing in upskilling programs in data science, AI, and machine learning is essential to create a steady pipeline of skilled professionals.

Canadian retailers are preparing for a crucial holiday season, with a projected 2% increase in sales over last year. According to Caila Schwartz, Director of Consumer Insights and Strategy for Retail & Consumer Goods at Salesforce, Canadian online retail sales are expected to reach approximately $14.7 billion (USD) between November and December. This growth reflects broader trends seen in major markets, including the U.S., where a similar 2% increase is anticipated.

The top key trends online retailers should add to cart in 2025

She graduated from the University of Gloucestershire with a Bachelor of Science in sports science and marketing management. In conclusion, embracing AI is not just a technological upgrade; it is a strategic necessity for brands aiming to thrive in the dynamic world of e-commerce. For example, AI can power recommendation engines that suggest products based on past purchases and browsing behaviour. During the high-stakes shopping season of Black November, personalised recommendations can significantly enhance conversion rates. Examining Bangladesh’s demographics, sector-specific data sources, and regional positioning can show why the country has a particular edge in data.

The adoption of data-driven business software will be a significant driver of this growth, as companies look to integrate AI into every aspect of their operations, from marketing to supply chain management. As of 2024, global spending on AI is projected to reach $500 billion, according to the latest estimates from the International Data Corporation (IDC). This represents a 20% increase from the previous year, signaling the intensifying race to adopt AI technologies.

Projects like Filecoin, Chainlink, and Polkadot are enabling Web3 development, while decentralized storage and identity verification continue to grow. The environmental impact of cryptocurrencies, especially Proof of Work (PoW) mining, has led to growing interest in green crypto solutions. Ethereum’s transition to Proof of Stake (PoS) has set a trend for sustainable crypto practices, and other networks, including Cardano and Algorand, are promoting eco-friendly approaches. By 2025, “green” cryptocurrencies could dominate the market, aligning with global environmental goals. As governments increase surveillance on financial transactions, privacy-focused cryptocurrencies like Monero and Zcash are expected to gain attention. These assets prioritize user anonymity and are becoming appealing alternatives in a data-driven world.

Modern Commerce vs. Legacy Systems: Know the Costs

With a current market capitalization exceeding $45 billion, DeFi applications, including lending, borrowing, and yield farming, are attracting both retail and institutional investors. By 2025, the DeFi market could reach a $100 billion valuation as more platforms like Aave, Compound, and MakerDAO solidify their roles within the ecosystem. Navtej Paul Singh, a Senior Data Analyst with over 15 years of experience across various industries—financial services, healthcare, banking, and manufacturing—highlights how AI is reshaping the field. While larger retailers are leading the way in AI adoption, smaller businesses are also finding ways to leverage AI to compete more effectively. With a contact center CRM integration, all customer information is in one place — and you can access it right from your contact center screen without having to switch between different systems. This is not a new trend, but I think it’s been exacerbated by the surveillance-style monitoring that some employers use.

Interoperability among blockchains, facilitated by cross-chain protocols like Polkadot and Cosmos, will likely accelerate DeFi adoption, enabling users to seamlessly access services across different networks. As industries evolve and new technologies emerge, success will belong to those who can innovate, learn continuously and adapt to the digital transformation. Companies across various sectors are now recognising that innovation and growth are increasingly ChatGPT App driven by human ingenuity – something that AI can aid, but not exactly replicate. Just a few years ago, implementing AIs to evaluate feelings could have required heavy investments on infrastructure and risky contracts with unproven products. Today, companies deliver AI in microservices, meaning contact centers can leverage them through easy-to-integrate APIs. Contact centers have been using AI to analyze customer sentiments for almost a decade already.

Mobile payments will soon be consumers’ preferred way to pay worldwide and demand for split payments, gift card integration, and deferred payments continue to increase. The same IDC report notes that 55% of organizations that attempted to build AI capabilities in-house encountered significant roadblocks, such as talent shortages, skyrocketing costs, and operational inefficiencies. This struggle has led many companies to reconsider their approach and look toward outsourcing as a viable and advantageous alternative. According to Glassdoor figures, AI data analytics and AI data/data science-related professionals can make between $164,000 and $269,000 a year. When integrating AI with existing data workflows, consider whether the data sources require special preparation, structuring, or cleaning.

They employ cutting-edge encryption methods, conduct regular security audits, and have dedicated teams to ensure compliance with local and international laws. This level of security is difficult to replicate with an in-house team, especially for businesses without prior experience in data governance. Outsourcing AI and data analytics allows businesses to remain laser-focused on their core competencies while still benefiting from advanced data insights. This approach frees up internal teams to work on strategic initiatives rather than getting bogged down in the complexities of AI model training or data pipeline management.

By outsourcing, a company can quickly bring in the expertise needed to develop, deploy, and maintain sophisticated AI solutions without the heavy burden of hiring and training staff. The Cost of Missed CommitmentsConsumers today have less patience for late deliveries or out-of-stock products. When brands miss business commitments, such as failing to meet service-level agreements (SLAs) with suppliers or partners, the impact trickles down to the consumer.

These programs not only allow users to create compelling visual content, but also help them develop the kind of creative thinking that is increasingly valued in today’s changing job market. Speaking of micro-credentials, Adobe Education Exchange offers many such short courses that allow professionals to keep up with the latest technological developments. Looking ahead, Schwartz emphasized that the holiday season will be a critical time for Canadian retailers. “It’s going to be an incredibly competitive season, and retailers that listen to their consumers and deliver personalized experiences will be the ones that succeed,” she concluded.

When giants like Ikea, Levi’s, and Zara are launching their own resale platforms, you know the game has changed. Meanwhile, platforms like Vinted and Depop have transformed from quirky marketplaces into retail powerhouses. Emerging economies like India and Brazil have already begun to harness their population-scale data as a valuable resource, demonstrating the potential to create exportable datasets that power international AI development. Even before Trump was elected, 2025 was already looking like it might be another year of upheaval on the social, political and technological fronts.

By automatically uncovering insights hidden within deep expanses of data, AI data analytics enables data analysts and strategists to make highly accurate business decisions quickly—with a greatly reduced margin of error. The MMA’s Consortium for AI Personalization (CAP) conducted studies demonstrating significant returns on ad spend (ROAS) for e-commerce through AI-driven personalisation. CAP offers a valuable opportunity to marketers to improve customer experience by leveraging machine learning and generative AI to personalize ads and optimise digital campaigns. Gen Zs are known for their tech-savvy and strong inclination toward authenticity and social responsibility.

Continuous Innovation: Staying Ahead of the Curve

He underscores the idea that digital skills are not static, unlike other fields like accounting, which have remained relatively unchanged for many decades. In contrast, digital skills evolve rapidly, necessitating continuous learning to remain valuable in the job market. I expect companies who focus on empathy will temper that impulse, deliver excellent service at key moments in the buyer journey, and win a high-degree of loyalty from customers. For those considering this strategic shift, partnering with a trusted provider can be a game-changer. The right outsourcing partner can deliver customized, data-driven business software solutions tailored to your needs, empowering your company to harness the full potential of AI without the risks and complexities of in-house development. AI data analytics has become a fixture in today’s enterprise data operations and will continue to pervade new and traditional industries.

Online shoppers are seeking convenience, personalisation, and an unforgettable, seamless shopping experience. They’re being careful where they spend their money, and retailers must earn shopper loyalty now more than ever. Every company has its core competencies, areas in which it excels and creates value for its customers. Diverting resources to build and maintain AI solutions in-house can detract from these core activities, reducing overall productivity and strategic focus. For instance, a financial services firm should prioritize risk management and client advisory, not data infrastructure maintenance.

New models, such as generative AI and edge AI, are constantly emerging, while best practices in data analytics continue to evolve. Keeping up with these advancements requires significant investment in research and development. Most in-house teams simply cannot match the speed and depth of innovation seen in specialized AI firms. These providers have robust security protocols, often certified by industry standards like ISO or SOC 2.

For training, ML models require high-quality data that is free from formatting errors, inconsistencies, and missing values—for example, columns with “NaN,” “none,” or “-1” as missing values. You should also implement data monitoring mechanisms to continuously check for quality issues and ongoing model validation measures to alert you when your ML models’ predictive capabilities start to degrade over time. Choosing the right AI tooling depends on which solution fits their particular scenario, use case, and environment. For example, organizations handling lots of structured data and looking to seamlessly integrate functionality from popular third-party apps can opt for a solution with an expansive app marketplace like Snowflake or Databricks.

Gartner’s 2024 report on AI infrastructure highlighted that 75% of organizations using outsourced AI solutions were able to scale operations efficiently and cost-effectively. This flexibility ensures that businesses can remain agile and adapt to changing needs without being locked into rigid and costly infrastructure investments. Companies may experience sudden surges in data processing needs, whether due to seasonal trends, product launches, or global events. Scaling an in-house operation to handle these fluctuations is not only expensive but also complicated. Expanding infrastructure requires capital investment, while hiring additional talent can be slow and costly. According to a 2024 report from McKinsey, the average cost of setting up an internal AI operation for a mid-sized enterprise can range from $1 million to $7 million, depending on the scale and complexity of the projects.

Without real-time analytics, companies risk empty shelves, missed delivery windows, and losing customers to more reliable competitors. AI’s ability to offer tailored shopping experiences is particularly valuable in a crowded marketplace, where retailers are vying for consumer attention. Schwartz emphasized that AI can help businesses deliver the right message at the right time, which is critical during key sales periods like Cyber Week. According to MarketsandMarkets, the global AI outsourcing market is projected to reach $170 billion by 2028, driven by increasing complexity in AI use cases, talent shortages, and the need for cost optimization.

• With unpredictable disruptions and rapidly changing consumer preferences, relying on traditional supply chain models is no longer enough.
• If your agents are testy or hostile with them, your company’s reputation can take a serious hit.
• AI’s role in crypto extends to price forecasting and risk management, enabling more accurate predictions and secure investments.
• Retailers are bracing for the holiday shopping surge, a period that can make or break annual revenue.

AI tools can be used to analyze various types of data, whether in the form of Excel spreadsheets, PDFs, Word documents, or web pages, among others. Snowflake started as an enterprise data warehouse solution but has since evolved into a fully managed platform encompassing all components of the AI data analytics workflow. The Snowflake AI Data Cloud also incorporates the Snowflake Marketplace, which effectively opens the platform to thousands of datasets, services, and entire data applications. Brands can also implement chatbots that provide instant support and personalised product suggestions, to fulfil on Gen Z’s expectations of immediate, on-demand service. Gen Z’s desire for high-quality and unique products has a huge influence on their shopping habits.

As we step into 2025, artificial intelligence and digital innovation are revolutionizing the retail … [+] landscape in unprecedented ways, from hyper-personalized shopping experiences to sustainable second-hand luxury. Unpredictable disruptions in the supply chain, such as seasonal storms, labor strikes, or global transportation issues can lead to delivery delays, frustrating consumers who expect timely service. Adobe’s tools remain industry standards for creative roles such as graphic design, video production, digital media and more.

As DeFi expands, institutional adoption rises, and Web3 matures, the cryptocurrency landscape will likely undergo significant changes. Increased regulatory clarity and advancements in blockchain scalability are poised to attract new users, while privacy, sustainability, and decentralization remain key themes. By staying attuned to these developments, investors and participants can navigate the evolving crypto space with confidence. In 2025, Web3 adoption could accelerate, impacting industries from social media to financial services.

We’re talking about dynamic pricing that adapts to individual budgets, loyalty programs that actually understand what you value, and product recommendations that feel like they’re coming from a friend who really gets you. In an age where consumers want to know the life story of their morning coffee, transparency isn’t just nice to have – it’s essential. Walmart’s AI is already playing party planner, customizing Super Bowl spread suggestions based on your previous game day purchases. Meanwhile, Dutch supermarket Albert Heijn has turned your random fridge photos into gourmet meal plans. In today’s landscape, data is often described as “the new oil” because it fuels AI and machine learning algorithms, offering insights and capabilities that would otherwise be unreachable. Without diverse, high-quality datasets, even the most sophisticated AI models are ineffective.