Hello everyone. My name is Kata, and today I'm excited to talk about something that can radically improve how we manage operational cost in urban mobility and logistics, AI power. Pricing for street-based tasks. If you're dealing with task-based operations, whether it's fleet deployment logistics, or on demand workforce management, you already know that strategic and strategic pricing models don't work efficiently. They lead to cost inflation, poor task distribution, and inefficiencies. Over the next 10 minutes, we'll walk you through how AI can solve this problem using real time data to dynamically adjust pricing, improve efficiency, and reduce costs. Before we dive into AI powered pricing, let me briefly introduce myself and share how I ended up working in this space. I actually started my career in construction industry working at mr, which is one of the biggest developers in the world. I was a structured, it was a structured, process driven industry, but I was drawn to something different, fast paced, high growth businesses that led me to move to London where I transitioned into startups. And I initially joined Oya, which is a SoftBank backed hospitality company known for its aggressive growth strategy across the globe. It was an intense high speed environment. I learned a lot about scaling operations quickly after Covid, I piloted. Back into Property Invest, investing industry in the uk joining Brick Lane, which is a company that is using AI power decision making to make property investment decisions within the UK and optimize these decisions. This was where I developed my understanding of AI in business strategy and how it can be leveraged. Finally I joined Lyme where I'm now the general manager. I oversee operations, one of the lar world's largest micro mobility markets. One of our biggest challenge today is ensuring that task-based operations, like moving and rebalancing bikes and scooters are. Priced fairly and efficiently. This challenge isn't just unique to Lyme. It's a problem across the entire industry, and that's why AI powered pricing is a such a game changer for this problem. Let's take a look at why pricing is such a critical issue in micro mobility. Micro mobility is one of the fastest growing transportation sectors. Worldwide. Cities are embracing shared mobility because it offers affordable, convenient, and eco-friendly alternatives to cars. Governments are pushing for sustainable transportation options, and we are seeing cities build more bike lanes, pedestrian forni zones, and smart mobility infrastructure. But with this growth, operational complexity is also increasing. Managing thousands of scooters and bikes across large urban areas is challenging and inefficiencies in deployment, retrieval and maintenance at significant costs. So where is this industry heading next? The micro mobility industry is projected to grow from $61.5 billion in 2024 to 223.6 billion. By 2033. That's nearly a four times increase in less than a decade, but this massive growth doesn't automatically mean profitability. Many micro mobility companies are struggling with high operational cost, pricing inefficiencies, and logistical challenges. So what does the future of micro mobility look like? To sustain this level of growth, micro mobility companies need to become operationally smarter, not just bigger. The industry is evolving beyond simple vehicle sharing, into data-driven, AI powered urban mobility networks, fleet efficiency, task automation, and dynamic pricing will be key factors that separate successful companies from that fo that fail. But how do we actually achieve this? The answer lies in AI driven decision making. Companies that use AI to optimize pricing, task allocation, and fleet management will outperform competitors. But before we talk about solutions, let's first have a look at the biggest challenges the industry faces today. There are four major challenges we face in micro mobility industry today. Number one. Parking and infrastructure limitations. City struggle with poorly accommodating fleets as there's simply more demand and no more fleets than parking infrastructure, and that needs to be improved. Number two, fleet and rebalancing issues vehicles pile up in low demand areas instead of being where they needed to be to increase the usage. So that requires incre rebalancing the fleet. To optimize where vehicles are at the point where they need to be. Three, high operational cost, manual decision making leading to inefficiencies, especially in the logistics industry where it, which is the backbone of the whole micro mobility industry at the moment, which causes us to move vehicles from one point to another place. And last point is the pricing strategies start static. Pricing does not reflect real world condition, and that needs to change so we can actually in include the real time data while giving prices. One of the biggest levers that we can pull to fix these inefficiencies and easiest one solve is the pricing. Pricing affects everything. If it's too high, costs inflate unnecessarily. If it's too low tasks go unfilfilled, because workers won't take them and complete them. Dynamic pricing is crucial because it ensure tasks get completed efficiently at the lowest possible cost. Now let's talk about how AI pricing works, that we understand why pricing is crucial in micro mobility. We are taking a deeper look about how we are using AI in pricing and how we are solving the problems that we are facing in terms of pricing in micro, multi industry traditional pricing models assume that every task is equal, but in reality, not all tasks have the same complexity, urgency, or cost. For example, moving an e scooter two blocks away in low traffic area should not cost the same as retrieving a broken scooter from a high congestion, high demand city center. A rush hours there is, this is where we are using and leveraging AI powered pricing, and we are. Getting into play, AI uses real-time data to analyze demand fluctuations, worker availability, and geographic vi variables to adjust pricing dynamically instead of a flat, inefficient price. AI ensures that each task is priced at its optimal rate to encourage fulfillment while controlling costs. But why doesn't fixed pricing work? Let's explore the key flows of the static and fixed pricing models. Fixed pricing model has been in the industry and has been the standard for many years, but there are major flaws with that. Here's couple of four of the main problems that we are seeing with it. Number one, it ignores real-time labor a avail. If there are too few workers tasks pile up and the work, the works will get into delayed if there are too many workers. And with the same pricing, we will be paying the same price. And that means we'll be overpaying to workers for simple jobs by looking at labor availability. And similar to what Uber uses with on the demand, comparing demand and driver availability, excuse me, driver and demand availability, we are able to adjust the price accordingly. So that's why it's important to include the real time labor. second one or least overpaying and underpaying pricing doesn't change based on location or demand, meaning some of the workers reject low pay tasks while others get overpaid for low effort jobs. Going back to the example which we talked in the previous slide, moving a scooter from two blocks away versus moving a scooter in a high congested area. It's not the same job, so they need to have a different pricing depending on the situation that they're in. Third one, first to consider task density. Some of the areas need urgent vehicle stri distribution, but workers have no pricing incentivize to prioritize these areas over these jobs. That's one of the areas we can solve by using AI pricing strategies. Last one does not adjust for real world demand shifts. Mobility, demand changes based on weather, traffic, and events, but static pricing ignores all of these factors. Clearly we need a better way to price tasks dynamically and efficiently, and this is what we are trying to achieve, and this is where we are using AI powered pricing changes. AI powered pricing is designed to adapt real world changes in real time. Instead of setting one fixed price, AI uses real live data, to adjust pricing automatically based on a set of key factors that is predetermined. Again, think of it like Uber's search pricing model when demand is high and drivers are low. Prices go up when demand is low, prices are down. The same princip applies and the prices go up. And the same thing happens in the micro mobility task pricing. But how exactly does AI determine the right price for each task? Let's take a look at the key inputs AI considers. AI pricing models don't just make random adjustments. They analyze multiple real-time factors to calculate the most efficient price. These include task density, how many tasks that exist in a given area. If there's a backlog, AI increases payouts to attract workers. Worker availability if there are too few workers. AI raises task prices to balance supply and demand time sensitivity. Urgent tasks, example, retrieving the scooter from no parking zone are priced at a higher priced to ensure fast completion, environmental conditions. AI are adjust pricing based on weather, traffic, and city events that affect the operational costs. By combining all of these data points, AI pricing ensures that every task is priced at optimal level balance, optimal level to balance, cost, and efficiency. Now let's look at, let's explore how the system actually improves operational efficiency. Number one, optimizes labor costs. AI prevents unnecessary spending while ensuring tasks are completed. Number two, balances, demand and supply. AI raises prices in underserved areas and reduces them where work availability side. Number three, incentivizes urgent tasks. Critical jobs get higher payouts to ensure quicker fulfillment. Number four, eliminates pricing inefficiencies. No more overpaying for simple task or underpaying for complex ones. Last one, scales with business growth ai. Pricing adapts automatically as fleet size expands or changes or decreases. instead of manually adjusting and manually setting prices, AI automates the process to keep costs low while improving task completion rate and times. let's break down the AI pricing model and how it actually works. AI pricing model follows a structure four step process to continue optimize pricing. Decision number one is data collection. As you can imagine, AI aggregates real time task worker and citywide data collects them. number two, predictive analysis machine learning algorithms forecast upcoming demand fluctuations. Number three, dynamic adjustments. AI updates pricing automatically in real time based on the predictions. Number four, continuous learning. AI refines pricing strategies based on historical data and performance outcomes. The same AI driven pricing model is being used in other industries as well. Let's like a look at, Uber, how Uber is using, similar strategies. As Uber is one of the most well-known examples of AI powered pricing optimization. Their search PRI pricing model automatically increases right prices when demand high, ensuring the better, driver availability. Uber uses AI to adjust first based on realtime demand spikes when rider demand is high. Prices increased at truck drivers, geographic hotspots. Pricing adjust for airports, pickups, stadiums, events, specific events zones, weather based adjustments, bad weather reduces drivers, so prices tries to compensate, and Uber isn't the only company using AI powered pricing. Amazon also applied dynamic pricing strategies. Amazon continuously adjust product prices based on market demand, competitor pricing and inventory levels. Their AI pricing model works by analyzing competitor prices. AI scans, competitor prices and adjust Amazon prices accordingly. Demand-based adjustments. If demand for a product rises, prices automatically increase personalized pricing. AI tracks customer behavior to offer customized discounts on recommendations. Now we ex explored how AI is pricing works in different industries. Let's wrap up with the key, takeaways. Summarize, here's what we cover today. Fixed pricing models are outdated. Static pricing creates inefficiencies. AI optimizes pricing based on real time data, balancing cost and efficiency. AI pricing is already used in multiple industries, proven successful in Uber and Amazon. That concludes the presentation today. Thank you very much for your time, and thank you for joining the session.