Archive 26.01.2025

Robotics In Retail– How Zara Uses AI & Robotics To Automate Order Pickup

The inventions of automation and robotics in retail have overwhelmed many industries, but especially in the retail industry, its impact is beyond imagination.

Artificial Intelligence (AI) robots have been performing tasks faster than humans, saving time and improving productivity. From supply chain and logistics to back-office operations, warehouse operations, marketing & sales to customer-facing issues, the role of robots in the retail industry is incredible.

According to the reports, Robotics automation in the retail industry was estimated at US $19.4 billion in 2018, and it is predicted to reach 144.93 billion US dollars by 2026, increasing at a CAGR of 28.96 % from the year 2019 to 2026.

The major demand for AI-powered robotic solutions in retail is expected to derive from the retail, healthcare, and manufacturing sectors. Especially, most retailers are adopting intelligent AI applications to expand their business prospects.

• World’s leading retailers like Amazon own 100,000 robots across the globe at its warehouses to the meet demands of orders.
• Lowe’s has made an in-store robot named ‘LoweBot’ that helps customers with finding products in the store.
• Walmart deployed around 1500 autonomous shelves-cleaning and floor-cleaning robots in its retail stores.
• Best Buy has partnered with PaR Systems in order to launch an automated system named ‘Chloe’.

Also Read: Best Examples of Using AI for Retail Experiences And now,

Top Use Cases of Artificial Intelligence in Retail Industry

Today, we would like to discuss how Zara- a top fashion retailer, is using an AI robot for collecting products from the warehouse for customers who ordered products online.  

Zara Introduced AI & Robotics In Retail To Speed Up Online Order Pickup

Zara, a famous fashion retailer, has adopted the BOPIS (Buy Online, Pick-Up in Store) or the Click and Collect concept to improve in-store customer experiences. Consumers can place their orders online, and then reach Zara’s brick-and-mortar store to collect their goods either at the customer service or checkout queue. A more number of customers have preferred to order online before coming to the store to select their products, and it takes much time to pick up from the queue. To avoid the long lines and cut down the waiting time of customers, Zara is aiming to improve its BOPIS process with automation. Zara’s robot can pick up 2,400 packages simultaneously.

How can robotics be used in retail store?

To solve the issue of customers encountering long queues, the retail store is introducing a hi-tech solution for its BOPIS or “Click and Collect” service. Artificial Intelligence Robots will fetch the products’ barcodes, scan them, and pick them up from the warehouse by matching the barcode. Purchasers who have placed the orders through online can enter their PIN code and scan the bar code at the collection point in-store retail. Then, AI Robot will instantly search for the requested order and get it to checkpoints where the consumers can collect it. Zara has tested this technology at first at a Spain store near its headquarters, and it has worked most efficiently.

In addition to creating in-store experience retail with AI robots, Zara has also launched a completely digitally enhanced store called ‘Pop-up Store’ in a shopping mall in the UK. The Mall is located at 2,150-square-foot for men’s and women’s clothes and accessories. Zara also has a retail store located in London’s Westfield Stratford mall will help purchasers collect their online orders. The automated store employees use Smartphones to assist consumers’ purchases. Customers who place orders online before 2.00 PM can have a chance to collect their goods the same day. The remaining customers who place orders after 2.00 PM should wait for the next day to pick up orders and pay through Bluetooth credit cards.

In addition, the Zara automation has offered amazing features like an item recommendation system that functions with a mirror. Consumers can scan their product in at mirror using Radio Frequency Identification, and the system displays other products that pair well with the correct size and chosen outfit.

Let’s see a few more advantages of robotics in the retail industry

Advantages Of Robotics In The Retail Industry

Though robotics use cases are infinite in the retail sector, here are a few effective applications for robotics in retail:

• AI robots used in retail better manages warehouses
• In-store retail AI robots for navigating customers across the store and better engaging them
• Use of Autonomous Mobile Robots in Retail is on the rise for faster checkouts and the generation of auto-invoicing
• AI-powered inventory levels tracking robots are one of the robotics in retail top trends
• AI-powered robotic applications using machine learning capabilities will analyze the market conditions and derives valuable insights from sales data.
• Enhanced productivity is one of the top advantages of robotics in the retail industry
• AI robots in the retail industry offer real-time tracking of product movement
• Streamlined stocking and accurate tagging
• AI robots with facial and voice recognition features ensure high-level store security and also play a vital role in tracking the in-store customer’s emotions

Like these, robotics use cases in retail are continuously emerging in line with digital market trends. The emergence of robotics in retail industry is also spreading for modernizing supply-chain management and warehouse & distribution operations.

Types of robots in the retail industry

Here are the key tops of robots in the retail industry. 

1. Service Robots

Service robots assist customers by providing directions and information. Robots like Pepper answer queries and help in store navigation, making the shopping experience better.

2. Inventory Management Robots

Scan shelves to track inventory levels. Robots like Tally help with inventory audits and stock shelves automatically when necessary.

3. Autonomous Mobile Robots (AMRs)

Impact of robots in retail industry for warehouse management is incredible. AMRs move items between warehouses and stores. Kiva robots accelerate order fulfillment and in other cases drop items at customers’ doors.

4. Robotic Checkout Systems

These allow customers to checkout independently. Self-checkout robots scan and pay for items, and robotic baggers enhance the efficiency of checkout.

5. Delivery Drones

Directly deliver products to customers. The drones improve efficiency and decrease human labor.

6. Robotic Pickers

Robotic pickers such as Locus Robotics assist in picking and sorting items in warehouse fulfillment centers, accelerating order fulfillment while minimizing errors.

7. Cleaning Robots

Cleaning robots autonomously clean store floors and disinfect heavily trafficked areas using UV light, ensuring cleanliness and safety.

Examples Of Real Industry Applications

Do you still have a question like How AI and Robots Revolutionize Retail? Here is the answer.

We have discussed here a few best examples of real industry applications robotics in retail industry.

These instances will give you a clear picture of the rise of Robotics in Retail.

• World’s leading retailer like Amazon has 100,000 robots across the globe at its warehouses to streamline the operations.
• Lowe’s has made an in-store robot named ‘LoweBot’ that helping customers with finding products in-side the store and improving their shopping experiences.
• Walmart, A big retailer store, has deployed around 1,500 autonomous shelves-cleaning and floor-cleaning robots in its retail stores.
• Best Buy has partnered with PaR Systems to launch an automated system named ‘Chloe’.

These are just a few examples of real industry applications of robotics. Many retailers are in plans to introduce the robotic services to transform their functionalities completely to the new digital stage.

Hence, the importance of robots in the retail industry is vital. Robots in retail marketing is another best application where AI robots help industry players better manage their customers, predict market trends, and grab opportunities into sales and customer base.

Impact Of Robots In Retail Industry

The impact of robots on the retail industry has been transformative, particularly in the space of operational effectiveness and consumer interaction. Robotic technologies such as automated checkouts, robotic stock, and delivery drones have streamlined store operations, reducing human error and expenditure. Robots can perform repetitive tasks such as stocking shelves, reorganizing stock, and price scanning, releasing human labor to more value-added functions such as customer interaction and complex problem-solving.

On the customer front, robots alter how companies interact with consumers. Robotic assistants and AI-driven chatbots offer personalized recommendations, aid shoppers in finding products, and deliver real-time responses.

On the front of customers, robots change the way companies communicate with customers. Robotic personal assistants and computerized chatbots provide personalized recommendations, assist in product discovery, and answer questions in real-time.

Besides, robots such as those applied in fulfillment centers accelerate the picking and packing of online orders to make delivery quicker. Although robot development has been a source of worry regarding job displacement, it also opens doors for employees to transition into jobs that demand higher-level skills, including operating and programming such automated machinery.

With robotics technology continuing to evolve, the retail industry is likely to see even greater efficiencies and innovations, both operationally and for the customer.

Final Words

The impact of robotics in retail industry and emerging AI applications will offer potential benefits and opportunities to retailers. Robots might perform only the assigned tasks but they accomplish tasks faster with zero errors. In conclusion, I’d like to state that the use of Artificial Intelligence and robotics in retail are on the rise. Retail stores are continuing to incorporate AI tools for better customer engagement and work efficiency. If you own a retail store and you want help with AI and robotic technology, Contact us. We would be happy to develop a best-in-class AI retail app!

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Generative AI holds incredible promise, but its potential is often blocked by poor app experiences. 

AI leaders aren’t just grappling with model performance — they’re contending with the practical realities of turning generative AI into user-friendly applications that deliver measurable enterprise value.

Infrastructure demands, unclear output expectations, and complex prototyping processes stall progress and frustrate teams.

The rapid pace of AI innovation has also introduced a growing patchwork of tools and processes, forcing teams to spend time on integration and basic functionality instead of delivering meaningful business solutions.

This blog explores why AI teams encounter these hurdles and offers actionable solutions to overcome them.

What stands in the way of effective generative AI apps?

While teams move quickly on technical advancements, they often face significant barriers to delivering usable, effective business applications: 

• Technology complexity: Building the infrastructure to support generative AI apps — from vector databases to Large Language Model (LLM) orchestration — requires deep technical expertise that most organizations lack. Choosing the right LLM for specific business needs adds another layer of complexity.

• Unclear objectives: Generative AI’s unpredictability makes it hard to define clear, business-aligned objectives. Teams often struggle to connect AI capabilities into solutions that meet real-world needs and expectations.

• Talent and expertise: Generative AI moves fast, but skilled talent to develop, manage, and govern these applications is in short supply. Many organizations rely on a patchwork of roles to fill gaps, increasing risk and slowing progress.

• Collaboration gaps: Misalignment between technical teams and business stakeholders often results in generative AI apps that miss expectations — both in what they deliver and how users consume them.

• Prototyping barriers: Prototyping generative AI apps is slow and resource-intensive. Teams struggle to test user interactions, refine interfaces, and validate outputs efficiently, delaying progress and limiting innovation.

• Hosting difficulties: High computational demands, integration complexities, and unpredictable outcomes often make deployment challenging. Success requires not only cross-functional collaboration but also robust orchestration and tools that can adapt to evolving needs. Without workflows that unite processes, teams are left managing disconnected systems, further delaying innovation.

The result? A fractured, inefficient development process that undermines generative AI’s transformative potential.

Despite these app experience hurdles, some organizations have navigated this landscape successfully. 

For example, after carefully evaluating its needs and capabilities, The New Zealand Post — a 180-year-old institution — integrated generative AI into its operations, reducing customer calls by 33%.

Their success highlights the importance of aligning generative AI initiatives with business goals and equipping teams with flexible tools to adapt quickly.

Turn generative AI challenges into opportunities

Generative AI success depends on more than just technology — it requires strategic alignment and robust execution. Even with the best intentions, organizations can easily misstep.

Overlook ethical considerations, mismanage model outputs, or rely on flawed data, and small mistakes quickly snowball into costly setbacks.

AI leaders must also contend with rapidly evolving technologies, skill gaps, and mounting demands from stakeholders, all while ensuring their models are secure, compliant, and reliably perform in real-world scenarios.

Here are six strategies to keep your initiatives on track:

1. Business alignment and needs assessment: Anchor your AI initiatives to your organization’s mission, vision, and strategic objectives to ensure meaningful impact.
   
   
2. AI technology readiness: Assess your infrastructure and tools. Does your organization have the tech, hardware, networking, and storage to support generative AI implementation? Do you have tools that enable seamless orchestration and collaboration, allowing teams to deploy and refine models quickly?
   
   
3. AI security and governance: Embed ethics, security, and compliance into your AI initiatives. Establish processes for ongoing monitoring, maintenance, and optimization to mitigate risks and ensure accountability.
   
   
4. Change management and training: Foster a culture of innovation by building skills, delivering targeted training, and assessing readiness across your organization.
   
   
5. Scaling and continuous improvement: Identify new use cases, measure and communicate AI impact, and continually refine your AI strategy to maximize ROI. Focus on reducing time-to-value by adopting workflows that are adaptable to your specific business needs, ensuring that AI delivers real, measurable outcomes.
   

Generative AI isn’t an industry secret — it’s transforming businesses across sectors, driving innovation, efficiency, and creativity.

Yet, according to our Unmet AI Needs survey, 66% of respondents cited difficulties in implementing and hosting generative AI applications. But with the right strategy, businesses in virtually every industry can gain a competitive edge and tap into AI’s full potential. 

Lead the way to generative AI success

AI leaders hold the key to overcoming the challenges of implementing and hosting generative AI applications. By setting clear goals, streamlining workflows, fostering collaboration, and investing in scalable solutions, they can pave the way for success.

To achieve this, it’s critical to move beyond the chaos of disconnected tools and processes. AI leaders who unify their models, teams, and workflows gain a strategic advantage, enabling them to adapt quickly to changing demands while ensuring security and compliance.

Equipping teams with the right tools, targeted training, and a culture of experimentation transforms generative AI from a daunting initiative into a powerful competitive advantage.

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The post Solving the generative AI app experience challenge appeared first on DataRobot.

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Image credit: Deep Robotics – deeprobotics.cn

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X30 robot dog conducts autonomous inspection day and night in any weather stably, the operating temperature range of X30 has been extended to between minus 20°C and plus 55°C, the load capacity can be up to 85KG

Image credit: Deep Robotics – deeprobotics.cn

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Various Challenges for the systems above: Enhancing autonomy, improving power efficiency, and refining adaptability to diverse environments.

Future Goals for the systems:
Integrating AI-driven perception systems for smarter navigation.
Expanding industrial, public safety, and service applications.
Developing robots for seamless, collaborative human-robot interactions with superior performance in real-world scenarios.

Videos:
https://www.youtube.com/watch?v=zxGwOEYYFVo
https://www.youtube.com/watch?v=NNkxlKLMoMM&t=19s
https://www.youtube.com/watch?v=iL833P0Vino
https://www.youtube.com/watch?v=lCFyfh3mLOQ

The content and media above is provided to us by Deep Robotics. www.deeprobotics.cn