How AI is Transforming Retail Decision-Making
Published on October, 2026
Retail decisions used to be made in a simple way. A store manager looked at last year’s sales, trusted their gut feeling, and guessed how much stock to order before a big sale like Eid. Many businesses in Pakistan still work this way. But this is changing fast. Artificial intelligence is now helping retailers make smarter, faster, and more accurate decisions about:
- What to stock
- What price to set
- Who to sell to
- When to sell
Things in the retail business are decided through limited data and a lot of personal judgment. AI has not removed the need for good judgment. Also, it has completely changed the quality and speed of the data recorded to make serious decisions for the good of the company.
AI is not just a trend in today’s world. It has become a need for online businesses to make quick decisions and stay in a competitive market. The numbers prove it.
The Scale of the Change
Retailers no longer see AI as an extra feature. They see it as a core skill they need to build themselves. The numbers show how quickly AI has moved from experiment to essential.
- The AI in Retail market was worth USD 17.8 billion in 2026. It is expected to grow to USD 154.22 billion by 2036, with an annual growth rate of about 24.1% between 2027 and 2036. In 2027 alone, the market size is estimated to reach USD 22.09 billion.
- According to Deloitte’s 2026 retail outlook, 67% of retail leaders expect to have AI-powered personalization in place within a year.
- Globally, around 89 of retail and CPG companies are either using or actively testing AI.
- Personalization powered by AI commonly delivers a 5–15% revenue lift, and in some cases, higher.
- On the consumer side, AI is already part of the shopping journey. In Pakistan, a Visa study found that 82% of shoppers have used AI tools to assist with shopping, for comparing prices, checking reviews, or finding gift ideas. Globally, a growing share of consumers now start product research with AI tools, and AI-referred traffic to retail sites has grown dramatically.
These figures tell us something important, i.e., both brands and customers are adapting to this trend. The question is not this that whether to use AI. It is about how effectively it can be used by brands so they can make better decisions for their businesses through data driven by AI.
Where AI is Changing Decisions in Retail
Predicting demand and managing stock.
Traditional forecasting often relied on historical averages and seasonal patterns. AI models look at far more signals — browsing behavior, search trends, social mentions, weather, and even external events. This helps brands reduce stockouts and overstock at the same time. For mid-sized Pakistani brands, better demand prediction can mean the difference between tying up cash in slow-moving inventory and keeping working capital free for growth.
Smart Pricing and Promotions
Dynamic pricing and promotion optimization are becoming practical even for smaller teams. AI can test and adjust offers in near real time based on demand, competitor moves, and customer segments. Brands that still set prices mainly by cost-plus or competitor matching are leaving margin and volume on the table.
Customer Understanding and Personalization that Increases Sales
AI turns raw customer data into usable insight. It identifies which customers are likely to buy again, which are at risk of leaving, and what message or offer is most likely to convert them. Recommendation engines, personalized product rankings, and targeted campaigns are no longer limited to global giants.
Reducing fraud and failed deliveries
AI is also improving decisions around fraud detection, returns prediction, logistics routing, and customer service prioritization. These areas directly affect cost and customer experience, yet they are often managed reactively. AI makes them proactive.
Pakistani Brands Already Using AI for Decisions
Local examples show that this is not only a global story.
Daraz
Daraz has been investing in AI for:
- Search relevance
- Product discovery
- Seller insights
With more than 30 million shoppers and billions of searches, to understand shifting customer interests and make recommendations more relevant, the platform uses signals such as:
- Browsing
- Clicks
- Cart activity
- Transactions
This is decision support at marketplace scale.
Foodpanda
Foodpanda uses machine learning to:
- Optimize delivery routes
- Predict demand patterns across its restaurant network
Better prediction of when and where orders will come improves rider allocation and reduces delivery times. These were decisions that used to rely more on experience and manual adjustment before.
Fashion and lifestyle brands
Khaadi, for example, has been using digital tools and AI-supported capabilities for:
- Inventory ordering
- Customer behavior prediction
- Recommendation engines
Reports from the brand indicate measurable gains in conversion and average order value when AI-driven recommendations are applied. Other platforms such as PriceOye and various independent Shopify and WooCommerce stores are adding AI chatbots, better product recommendations, and basic predictive analytics.
These examples are important because they show that AI adoption in Pakistan is practical, not theoretical. Brands are using it to solve real local challenges, i.e.:
- High cash-on-delivery rates
- Variable demand
- Mobile-first customers
- The need to compete with larger marketplaces
Critical Perception
The opportunity and risks should not be denied, as they go side by side in every business.
First, data quality still decides the outcome
The use of AI proves helpful only if the data it receives is good. Many brands have fragmented data across:
- Marketplaces
- Their own online stores
- Offline channels
Without clean, connected data, AI produces noisy or misleading recommendations. Brands that treat data hygiene as a priority will get far more value from any AI investment.
Second, not every decision should be fully automated
AI is excellent at pattern recognition and speed. Humans remain better at judgment, brand positioning, and handling exceptions. The strongest results come when AI surfaces options and insights, and experienced teams make the final call, especially on:
- Pricing strategy
- Brand voice
- Major assortment changes
Third, local context matters
Global AI models and tools need to be adapted to Pakistani realities like:
- Mobile-heavy traffic
- Strong preference for Cash on Delivery
- Regional language nuances
- Different customer trust dynamics
Tools that work well in the US or Europe often need localization to deliver the same impact here.
Fourth, smaller brands can still benefit
There’s no need for a large data science team to begin. Practical first steps include:
- AI-powered product recommendations
- Basic demand forecasting
- Automated customer segmentation
- Smart chatbots for common queries
Many modern e-commerce platforms and third-party tools now make these capabilities accessible without heavy custom development.
What This Means for E-commerce Leaders
AI is shifting decision-making from reactive to predictive, and from periodic to continuous. Brands will move faster and waste less if they integrate AI into:
- Inventory
- Pricing
- Marketing
- Customer experience decisions
However, the real advantage will not come from simply “using AI.” It will come from building the right foundations, i.e.:
- Clean data
- Clear processes
- Teams that know how to interpret and act on AI outputs
“Technology should reduce guesswork, not replace responsibility”
What Often Not Discussed?
Not everything about AI in retail is simple or guaranteed to work. AI does not fix a business that already makes poor decisions. It usually makes the problem more visible, faster. If a brand’s different teams cannot agree on basic numbers, like how many units actually sold, an AI system will not quietly fix that. It will confidently give wrong predictions, and it will do it much faster than a person would.
Around the world, data privacy concerns are blocking many AI projects in retail, and connecting AI tools with older, existing systems remains a major challenge for about two-thirds of businesses trying to use them. Many business leaders also admit that AI’s actual results often don’t match the hype around it. In Pakistan specifically, newer brands often don’t have enough sales history for AI models to learn from, internet access outside big cities is inconsistent, and there is a real shortage of people who know how to properly use these tools, not just buy them.
The simplest advice for any business thinking about using AI is to fix the basics first. It can be done by bringing data related to stock, order details, and customers into one place. Then make use of AI tools.
“Relying only on AI will not lead to good business decisions. It strengthens whatever habits, good or bad, are already in place.”
Where This Is Headed
The direction is clear. AI is used specifically by online retailers for fast operations and quick decision-making. Within the next two to three years, AI-based demand forecasting, automatic pricing, and smart delivery routing are expected to become standard tools, not special features. The businesses that succeed in Pakistan’s retail future will be the ones with clean, connected data and those willing to trust what the numbers say, even when it goes against years of gut instinct.
Obaid Arshad
CEO & Co-Founder