In an era of rising lifestyle-related diseases, as well as amidst climate anxiety and the proliferation of ultra-processed foods, the statement 'We are what we eat' becomes evident. In this context operates Foodwize—a social enterprise based in Bangalore that helps people build healthier relationships with food while simultaneously contributing to the creation of more sustainable food systems.
Foodwize implements its work through several initiatives. The first is STHIRA, a youth innovation contest, through which about a thousand students developed ideas related to soil health in food systems and nutrition education.
The second focus is on educating consumers and the corporate sector, helping people become 'food-wise.' Furthermore, the company's B2B segment, named Foodwize Ignite, focuses on integrating sustainable development principles into the culinary practices of chefs, hospitality professionals, and businesses in general.
As more people consume food prepared outside the home, whether through delivery services or cloud kitchens, commercial kitchens bear increasing responsibility for how products are sourced and served from a sustainability perspective.
Venture funding for Indian startups showed a sharp increase last week following two weeks of moderate capital inflow. Investors began distributing their bets across various sectors, with artificial intelligence emerging as the leading driver of this growth. The total funding volume for the third week of July reached $431 million across 23 deals.
The funding attracted by the AI-based startup Emergent is viewed as a positive signal indicating that Indian AI startups are gaining investor attention. Other AI-based startups, such as Reo.Dev and Groyyo, also secured funding this week. This significant surge in funding brings great relief to the entire Indian startup ecosystem, which now anticipates a brighter future.
When discussing artificial intelligence in logistics, people often picture obvious elements: robots in warehouses, routing algorithms, or the rare use of drones. While this is not incorrect, such a view focuses on the wrong level of change.
Automating physical labor primarily leads to cost reduction, but today, the decisive factor for success is not cost. The change that will reshape the logistics landscape in India is less visible; it lies in how decisions are made. What used to be a company's competitive advantage—having trucks, warehouses, and postal code coverage—is being replaced by something much harder to copy: what the company has learned through its operational activities.
Artificial intelligence is already being actively implemented in Indian logistics. According to the market assessment 'India AI in Logistics' by the analytical firm Markets and Data, the AI in logistics market is expected to grow from $756 million in fiscal year 2024 to $6.8 billion by fiscal year 2032. Meanwhile, NASSCOM estimates the level of AI adoption in Indian enterprises at 2.45 out of 4.
Most people stop at these figures, seeing only increased efficiency—approximately a 15% reduction in logistics costs and up to a 50% increase in forecasting (Markets and Data). However, this is only a superficial aspect. Efficiency is the first-year dividend. The advantage that expands over time only manifests if the company is built with this in mind, and most companies are not.
Over the last decade, logistics software provided visibility—dashboards showing cargo location and the profitability of a specific route. The publicly listed company Delhivery built a significant part of its scale precisely on this visibility infrastructure. Visibility remains important, but it is no longer what distinguishes leaders. The main question now is whether your mechanism can act autonomously based on the data received. Knowing that a warehouse might run out in two days is useful; but a system that has already moved the goods before you receive the notification is an entirely different level.
This transition from simply reporting a problem to resolving it marks a shift from reporting to decision making, and all other processes develop below this level. Consider the example of a solar company aiming to increase daily installations from 12 to 50. The obvious answer is to hire more workers. However, the real bottleneck is often related to judgment: one operations manager keeps track of installer schedules, panel inventory, and site readiness in their head, with most information processed over the phone. This process depends on genius, and geniuses have limits; they reach their limit around 20 installations per day, and this limit can collapse when the manager receives a better offer.
Implementing a system that consistently organizes installations based on current inventory and personnel availability allows achieving the 50 target without the need for such rapid hiring. What has been acquired is not just a good quarter, but a new way of working that persists as metrics grow. This is the true meaning of the phrase 'systems over heroes'—a concept that is duller but more valuable than it seems.
This is where the advantage accumulates, which is often underestimated. Every time the system works, errors, especially those that occur, serve as the basis for the next decision. A platform that has processed millions of such cases for multiple brands and cities possesses not just a large volume of data; it has accumulated operational experience that cannot be bought, but can only be gained through practice. This is cumulative intelligence, and this is why the category is ranked by learning speed, not by who has raised the most capital.
The greatest benefit is enjoyed by brands that have long been ignored by the market. Companies with annual revenues between 20 and 500 crore rupees are too large to manage logistics with spreadsheets and phone calls, but too small for the bespoke approach that Flipkart receives. AI makes serving the mid-segment economically viable, allowing a brand in Jaipur to send goods to Siliguri with a level of orchestration previously reserved for metropolises. Experts believe that a significant part of India's export and job growth will depend on this.
If you manage such a business, stop measuring yourself by the volume of the controlled network. Instead, ask yourself: does your system fix problems or just report them? Will your advantage survive after the best employee quits on Friday? Does every order make the next decision more accurate, or does it just add to a history from which you extract nothing? If your answer is yes to all these questions, you are achieving a cumulative effect. If not, you are simply automating routine tasks and calling it transformation.
There is an important caveat: this is not universally applicable. In the segment of bulk commodity transportation, where the game is determined by cost per kilometer and asset density, learning speed is insignificant, and the old asset-based barrier remains valid. The argument applies to complex tasks: many SKUs, multi-node execution for fast-growing brands, which aligns with the direction of India's consumer economy development.
Achieving the $5 trillion economy constantly discussed for 2030 is impossible without a substantial improvement in trade turnover. The companies that lead this will not just automate warehouses—that will become commonplace. They will automate a more complex task—judgment, which was previously in the heads of several people, turning improvised supply chains into operations that make decisions and continuously improve themselves. It is this, not robots, that is rewriting logistics with AI.
