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IoT Data Monetization in Programmable Economy

Kateryna — Thu, 12 Jun 2025 08:54:18 +0000

Transforming Insights into Revenue

Data is to the 21st century what oil was to the 20th – a powerful force driving innovation and economic growth. Yet, just like crude oil, raw data holds little value until it is harnessed and used effectively.

Today businesses and individuals are sitting on a vast goldmine of IoT data, continuously generated by connected devices. This real-time data has the potential to revolutionize products, enhance services, and create seamless customer experiences. However, without a clear strategy for monetization, it remains nothing more than untapped potential.

The rise of the programmable economy presents an opportunity to bridge this gap, but for many startups and enterprises, turning IoT data into scalable revenue remains a complex challenge.

What is Programmable Economy?

Programmable economy is one where technology is used to shape how businesses operate without the need for constant human intervention.

In particular, the driving force behind this transformation is IoT data. Instead of just providing insights, IoT data becomes a catalyst for value creation.

At its core, the programmable economy redefines how value is exchanged in the digital world. It enables businesses and individuals to monetize data, make automated decisions, and create new economic models based on real-time information.

Just as the internet transformed how we communicate, the programmable economy is poised to reshape how we do business. Whether in supply chains, smart cities, or beyond, the programmable economy is set to redefine various sectors.

IoT Monetization: Turning Data into Value

IoT monetization refers to the process of transforming the data generated by connected devices into a valuable revenue stream. While the primary goal of many IoT products is to provide value to end users, the data collected from these devices can also be leveraged for additional profit. Furthermore, these insights are typically sold to third parties for additional revenue.

One common strategy involves offering IoT devices at little or no cost to the end user while collecting valuable data. The more devices deployed, the greater the data volume, therefore making it more attractive to third-party buyers.

Thus, this approach minimizes user barriers to entry, driving adoption and gathering diverse data that can be sold to interested parties.

Real World Example:

Devices that track driving behaviour provide value to users by offering insights into their habits.

However, the true beneficiaries are often insurance companies, which analyse patterns across users to assess risk and optimize pricing.

Smart Cities: Monetizing IoT Data for Sustainable Growth

Nowhere is the programmable economy more visible than in the development of smart cities.

In April 2024, Terminus Technologies, a China-based AI and IoT platform, secured $276 million in Series D funding, the largest IoT-related investment that year. Specializing in smart city solutions, Terminus has integrated LIDAR, AI, and smart cameras to enhance traffic management, public safety, and transportation. Indeed, their partnership with Dubai’s RTA improved automated traffic enforcement with 85% accuracy, showcasing their impact on urban innovation.

Cities around the world are increasingly using IoT devices to monitor and manage everything from traffic flow to energy consumption. By integrating data-driven technologies into infrastructure, cities, therefore, create a connected environment where IoT data is acted upon.

IoT data monetization models:

Data-as-a-Service

Cities can aggregate and anonymize data collected from smart traffic sensors, air quality monitors, and energy grids. Subsequently, this data can be offered as a service to businesses, researchers, and developers.

For example, traffic data can be sold to urban planning firms or ride-sharing companies to optimize route planning.

Energy data can be monetized by offering insights into energy consumption patterns to energy providers or renewable energy companies.

Dynamic Pricing Models

IoT-enabled systems can create pay-per-use models for public services like parking, electricity, or water usage.

For instance, smart parking systems can charge users based on real-time demand, adjusting prices according to traffic or space availability.

Similarly, smart meters can enable real-time billing for water and electricity usage, optimizing consumption and generating more accurate revenue collection.

Incentive-Based Systems

Cities can use IoT data to implement reward systems that encourage sustainable behaviour.

Notably, smart waste management systems can track recycling habits and reward residents with discounts for participating in eco-friendly activities.

These incentives can be funded by selling sustainability data to companies in green energy or recycling industries.

Public-Private Partnerships (PPPs)

Smart cities can collaborate with private companies to create joint ventures that leverage IoT data.

Telecom companies could collaborate with cities to implement sensor networks for smart city applications.

Meanwhile, both parties could share the revenue from new services, such as 5G connectivity or autonomous vehicle testing.

Introducing Our IoT Project: Turning Data into Profit

Tpoint, our latest IoT project aims not only to enhance user experiences but to generate tangible business value by integrating real-time data into the programmable economy.

In fact, it was designed to transform traditional sports courts into fully IoT-enabled spaces allowing clubs to manage and track facility usage effortlessly.

The system allows clients to configure lighting, AC, music, score-tracking, game video recording, and live streaming, enhancing player interaction while streamlining management for operators.

How it Generates Revenue

Pay-Per-Use & Dynamic Pricing: Court owners can monetize idle time by dynamically adjusting pricing based on demand. Real-time IoT data on court usage, peak hours, and cancellations allows for flexible pricing strategies, maximizing occupancy and revenue.
Subscription & Membership Models: Players can purchase club memberships, game passes, or premium services (e.g., video recordings, live streaming, and score-tracking). This transforms a one-time payment model into a recurring revenue stream.
Data-as-a-Service (DaaS): By aggregating player behaviour, peak usage times, and facility conditions, clubs can sell anonymized insights to sports brands, advertisers, and event organizers for targeted promotions and sponsorships.
Automated Energy Management: Smart lighting, AC, and music systems reduce operational costs by adjusting settings based on real-time occupancy, cutting unnecessary energy expenses and maximizing profit margins.
In-App Marketplace & Sponsorships: The app can integrate partnered brands, offering players sports gear, nutrition products, and coaching services directly through the platform. Brands can pay for targeted advertising based on usage patterns.

For businesses, startups, and urban planners, the message is clear: those who invest in innovative IoT monetization strategies today will shape the smart economies of tomorrow. The question is no longer whether IoT can drive value, it’s about how you’ll harness it, along with emerging tech trends, to stay ahead of the competition.

Practical FinOps Tips for AWS Cost Optimization

Kateryna — Mon, 09 Jun 2025 16:34:35 +0000

The Growing Challenge of Cloud Cost Overruns

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Imagine this: Your cloud bill arrives, and once again, it’s higher than expected. You start digging through the details – unused instances, oversized resources, surprise data transfer fees. Meanwhile, finance is demanding cost cuts, engineering aims for smooth operations, and you’re left balancing it all. It’s a challenge that’s becoming increasingly common.

According to an independent study commissioned by Boomi, 72% of companies exceeded their cloud budgets last year. The primary reasons? Lack of cost visibility, suboptimal resource allocation, and inefficient scaling strategies.

This is where FinOps comes in. No more surprises, no more inefficiencies—just smart, data-driven decisions that maximize every dollar spent in the cloud.

What is FinOps ?

FinOps (short for Financial Operations) is a framework and cultural practice that brings financial accountability to cloud spending, enabling organizations to maximize the value of their cloud investments.

AWS cost optimization integrates financial management, engineering, and operations to assist businesses in tracking, analyzing, and optimizing cloud expenses in real time.

Real World Example

Recently, a client approached us, struggling with soaring cloud costs. After a thorough analysis, we uncovered the root cause: their **Kubernetes **architecture, designed for large-scale operations, was powering a system used by just five managers.

While scalable and robust, it was like using a rocket to deliver a pizza.

By implementing FinOps practices, we made the following optimizations:

Shut down unused instances
Optimized data transfer costs
Right-sized over-provisioned resources
As a result, the client saw a 65% reduction in cloud costs, saving saving $322/month, which amounts to $3,864/year and $19,320 over the course of five years.

But what if these issues could have been avoided from the start?

Let’s dive into some of the core principles that can help any organization achieve the same level of control and efficiency.

Top FinOps Best Practices to Maximize Cloud ROI

Achieve Cloud Cost Visibility
The first step in managing cloud costs is gaining visibility into expenses, as a lack of clear insights makes it difficult to optimize spending and forecast costs accurately.

**Granular Cost Insights: **Use AWS Cost Explorer to track hourly, daily, or monthly cost trends, and third-party tools like CloudHealth and Apptio for deeper insights and multi-cloud environments.
Implement a tagging strategy: Tag cloud resources with AWS Resource Groups & Tag Editor or use third-party tools like CloudCheckr for enhanced visibility and automation.
Alerts and Notifications: Set custom cost and usage budgets with AWS Budgets or choose Spot to receive alerts via Slack, email, or custom workflows.

Optimize AWS Cloud Commitments

By strategically selecting commitment options, businesses can significantly reduce their cloud spend without sacrificing flexibility. Balance your commitments carefully: too many results in wasted capacity, too little means high costs.

AWS offers a range of pricing models, and choosing the right commitment strategy is key to managing costs effectively:

On-Demand: Flexible but expensive. Aim to minimize On-Demand usage to reduce costs.

Best for:_ Running a temporary application or proof of concept (PoC) for 1-2 months without predictable resource demand.
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Reserved Instances (RI): Offers discounts (up to 75% off On-Demand) for long-term commitments to specific instance types. Use for predictable workloads.

Best for: _Hosting a database or critical business applications that run 24/7 with predictable demand for resources.
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Savings Plans (SP): Ideal for more flexible, long-term savings across various resources. Discounts up to 72% off On-Demand.

Best for: Applications with scaling needs, like microservices, that require flexibility in instance type and region but still have consistent base-level resource demand.
Spot Instances: Best for non-critical, interruptible workloads where cost reduction is a priority.

Best for: Running large data processing jobs, batch computing, or machine learning training tasks that can tolerate interruptions.

Continuous Rightsizing

One of the most effective ways to reduce cloud expenses is through rightsizing—ensuring that your cloud resources are properly aligned with actual usage. Rightsizing these instances involves:

Monitoring Usage Patterns: Utilize cloud monitoring tools like AWS Compute Optimizer to gather real-time data on CPU, memory, and network usage.
Comparing Instance Performance: AWS recommends evaluating current usage against two benchmarks: the existing instance type and the next smaller instance.
Downsizing Based on Utilization: If an instance consistently operates below 40% of its capacity or is within 80% of a smaller instance’s capability, switching to a smaller, more cost-effective instance can lead to savings.

Rightsizing for EC2 Instances Example

One of our clients was running multiple EC2 instances to support its growing workload, but after a deep dive into usage patterns using AWS Compute Optimizer, we discovered that they were over-provisioned.

The analysis revealed that their t3.large instances were more powerful than needed for their actual demands. By right-sizing to **t3.medium **instances, they were able to reduce unnecessary capacity while maintaining performance.

This AWS cost optimization led to an impressive $3,000 savings on their cloud bill over the course of a year, helping the company reallocate resources more efficiently without impacting productivity.

Managing Non-Production Environments

Non-production environments (Dev, Test, QA) don’t need to run 24/7. Use AWS Instance Scheduler or CloudHealth to shut down these environments outside of business hours, you can reduce costs by 60-70%, ensuring resources are only active when needed.

Regular Well-Architected Reviewing
The AWS Well-Architected Review (WAR) is a comprehensive evaluation of your cloud infrastructure to ensure that you are following the best practices outlined in the AWS Well-Architected Framework.

By reviewing your architecture against this framework, you can identify areas for improvement and optimize performance, security, and cost-efficiency.

Focus on Five Pillars:

Operational Excellence: Focuses on running and monitoring systems to foster continuous improvement and deliver business value.
Security: Ensures the protection of cloud systems and the confidentiality of your data.
Reliability: Looks at minimizing disruptions that could impact on your services and ensuring they are resilient.
Performance Efficiency: Aims to use computing resources efficiently to meet demand, maximizing performance.
Cost Optimization: Focuses on meeting your IT needs while minimizing unnecessary expenses.

Cloud cost management doesn’t have to be an overwhelming challenge.

Implementing these AWS cost optimization strategies can help companies to stay ahead of the latest tech trends and to take control of their cloud expenses, ensuring they scale efficiently without overspending.

As an AWS Certified Partner, Trident Software helps a multitude of clients overcome complex cloud challenges, enabling them to use computing resources efficiently while minimizing unnecessary expenses.

Think, Act, Repeat: Task-Oriented AI Agents

Kateryna — Wed, 19 Feb 2025 13:54:52 +0000

Beyond Automation to Intelligent Problem Solving

Imagine an AI that goes beyond responding to your questions or automating simple tasks. Moreover, it truly acts as a multi-step problem solver across platforms, optimizing workflows and driving business efficiency.

In fact, task-oriented AI agents are the next big leap in AI, moving beyond simple automation. They streamline operations, enhance customer interactions, and enable new business models.

Welcome to the world of agents in artificial intelligence, the game-changing technology transforming how businesses operate.

What are Agents in Artificial Intelligence?

AI agents are intelligent virtual assistants that autonomously perform tasks by processing data, making decisions, and improve through ongoing interactions. By collaborating with other agents and leveraging advanced technologies like Large Language Models, they efficiently tackle complex challenges.

How it works:

User Instructions:
The process begins when a user provides high-level instructions through a chat interface, such as “create a shopping cart from this grocery list.”
Navigating the Web: The AI agent uses a browser extension to navigate websites and interact with them, just like a human would. For instance, it searches for the listed items and adds them to the shopping cart by mimicking human actions—clicking, scrolling, and filling out forms.
AI’s Autonomy:
Once the task is defined, the agent breaks it into subtasks and executes them without needing continuous input. Additionally, it can ask for clarification when necessary, like confirming how many of a certain item to buy.
Cloud Processing:
The agent sends screenshots of the browser window to the cloud (e.g., ChatGPT), where data is processed. Subsequently, ChatGPT sends back instructions to continue the task.
User Control:
While the agent works autonomously, users retain control. For example, sensitive actions like making payments or accepting cookies are left to the user, ensuring privacy and security.

Key Technologies

These core technologies empower task-oriented AI agents to efficiently automate, learn, and adapt to complex tasks:

Natural Language Processing (NLP):
Enables agents to understand and respond to human language (e.g., transformer models like GPT).
Machine Learning (ML):
Allows agents to learn from data and improve over time (e.g., reinforcement learning for decision optimization).
Deep Learning:
Uses neural networks (CNNs for visuals, RNNs for sequential tasks) to handle complex patterns and tasks.
Planning Algorithms:
Optimizes task execution using algorithms like A search* and Markov Decision Processes.

AI Agents in Action

Hospitality:

These smart systems can adjust to user preferences, optimize service processes, and foresee needs, allowing them to not only meet but also anticipate guest expectations.

Personalized Guest Experiences:
Tailored services, customizing guest experiences by adapting room temperature, lighting, and even entertainment options based on past stays.
Smart Concierge Services:
24/7 access to concierge services, answering questions, booking reservations, and offering local recommendations.
Operational Efficiency & Resource Management:
Optimizing workflows by automating tasks like housekeeping schedules and inventory management.

Healthcare:

Task-oriented AI agents are reshaping healthcare by solving complex, multi-step problems across various platforms.

Diagnostic Assistance:
Analyzing medical images, helping radiologists detect anomalies like tumors or fractures more accurately.
Patient Monitoring:
Real-time health guidance, reminders, and support, while also enabling remote monitoring for early detection of health issues.
Administrative Task Automation: Appointment scheduling, medical transcription, and billing, saving time and reducing errors in healthcare settings.

Logistics:

By leveraging AI to analyze vast amounts of historical data, companies can forecast demand, optimize inventory levels, and ensure timely delivery.

Predictive Demand & Inventory Management:
AI analyzes trends and historical data to forecast demand and optimize stock levels.
Smart Route Optimization:
AI-powered algorithms determine the most efficient delivery routes, reducing fuel costs and transit times.
Automated Warehousing & Robotics:
AI-driven robots manage sorting, packing, and inventory control to streamline warehouse operations.

Hiring:

Task-oriented agents streamline the hiring process by automating multi-step workflows and predictive talent analytics to enhance decision-making.

Automated Candidate Screening:
Streamlines recruitment by analyzing resumes, filtering applicants, and ranking candidates based on job requirements.
Chatbots for Engagement: Interact with applicants, answer queries, and schedule interviews, enhancing communication.
Predictive Analytics for Talent Acquisition:
Assesses candidate success probability using historical hiring data and performance metrics, improving decision-making.

Race for AI Supremacy

The new frontier in the battle for AI supremacy lies within our computer screens.

OpenAI has recently released Operator, its first AI agent. Operator is powered by the Computer-Using Agent (CUA) model, which interacts with graphical user interfaces (like buttons and menus) to execute tasks such as booking restaurant tables, purchasing tickets, or completing online grocery orders.

Compared to rival tools like Anthropic’s Computer Use and Google DeepMind’s Mariner, Operator reportedly excels. In contrast, CUA breaks tasks into steps, backtracks when stuck, and uses the same interfaces humans use, eliminating reliance on APIs and expanding task compatibility.

Ultimately, AI moves beyond generating text and images to taking real action. Agents in artificial intelligence are poised to reshape industries by unlocking new opportunities for businesses to optimize their operations and elevate user experiences.

In this new reality, businesses will not just respond to customer needs, but anticipate and effortlessly fulfill them, setting a new standard for personalized, efficient service. The future of AI is here, and it is agent driven.

Originally published at https://trident-software.ch/blog/