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August 26, 2025
Sand Technologies
This guide aims to help companies choose AI models and tools by providing a clear, three-part framework to navigate this complex landscape. To determine the appropriate tool, companies should focus on a systematic selection criterion by evaluating project goals, available data and technical resources.
Teams must initiate the AI model selection process by clearly defining the business or research problem to be solved. Start by determining what success looks like. Everything hinges on answering this question first. Once a clear goal is defined, it is possible to map it to a specific category of AI tasks— Regression, Classification, Clustering, or Generation.
An AI regression model is the go-to when companies need to forecast a continuous number, such as estimating revenue increases by adjusting cell tower settings to optimize performance. Standard algorithms for this model include Linear and Polynomial Regression, Decision Trees and Gradient Boosting models.
A primary use case for a regression model is energy load forecasting in the electric utility sector. The model’s objective is to predict a continuous value: the amount of electricity (in megawatts) needed for a specific area over the next hour, day, or week. The model trains on a variety of high-quality historical data, including:
A regression model (like Gradient Boosting or a time-series model like ARIMA) learns the complex relationship between all these input variables and the resulting energy demand. For example, it knows that for every degree the temperature rises above 80°F on a weekday afternoon, the energy load increases by a specific amount due to air conditioning.
The model provides the utility with accurate, automated predictions of future electricity demand. With this information, they can optimize power generation by determining which power plants to operate and when, ensuring a sufficient supply without overgeneration, which saves money and reduces emissions. It can help provide grid stability by anticipating demand surges, thereby preventing blackouts or brownouts. Moreover, the utility can schedule maintenance for power plants and transmission lines during periods of forecasted low demand, limiting disruptions.
The task of the classification model involves assigning a categorical label to each data point. It categorizes data for better context. For instance, considering whether sensor data falls within an acceptable range or if a pressure reading indicates a pipe leak. For these tasks, algorithms such as logistic regression, support vector machines (SVMs), random forest and various neural networks are good options.
A robust use case for a classification model is an electric utility’s vegetation management program, which utilizes predictive risk assessment to help prevent power outages. The classification model’s goal is to examine a tree near a power line and assess its risk of causing an outage within the following year. The categories are “Low Risk,” “Moderate Risk,” or “High Risk.”
The efficacy of the model depends on the quality and scale of its training dataset. The objective input for training is a diverse set of data, including high-resolution satellite or LiDAR imagery (to determine tree height, species and proximity to lines), historical weather data (such as wind speeds and ice storm frequency) and soil moisture levels. This scenario illustrates the importance of accurate and consistent labeling. The model learns what to look for by studying historical outage records. Data scientists use each past tree-related outage to label a tree with similar characteristics as “High Risk.” Without accurate historical records, the model learns the wrong lessons, perfectly illustrating the “Garbage In, Garbage Out” principle.
Instead of using a costly, inefficient and time-based trimming cycle (e.g., trimming every tree in a region every five years), the utility can use the model’s classifications to prioritize its work. The utility dispatches field crews to proactively manage only the trees classified as “High Risk,” preventing outages before they happen. This data-driven approach enables the utility to allocate its resources more effectively, enhance grid reliability, reduce costs and improve safety.
When data lacks pre-defined labels but the goal is to identify natural groupings within the data, clustering is the model to use. A good example of this case is applying a clustering algorithm to smart city or public sector accident data to find hidden patterns that can reduce traffic accidents. Widely used models for data clustering include K-means, DBSCAN, and Hierarchical Clustering.
A key use case for clustering models in the water utility sector is customer segmentation for demand management. Utilities collect vast amounts of data from smart water meters, which can include hourly water consumption, time of day, day of the week and seasonal usage for thousands of customers. The challenge is that a utility can’t treat all customers the same. A family of five has an entirely different water usage “fingerprint” than a restaurant or a single-person apartment. Understanding these different patterns is crucial for managing the water supply and identifying problems.
The utility applies a clustering algorithm to the smart meter data. It automatically groups customers with similar consumption patterns without being told what to look for. The model might identify several distinct clusters, such as:
By identifying these clusters, a water utility can transition from a reactive to a proactive management approach, thereby helping to conserve water and enhance operational efficiency.
Few fields in artificial intelligence are generating as much interest as Generative AI. These models generate new content based on the training data, including creating marketing copy, generating stunning images from text prompts, or even composing music. The tools of the trade include large language models, such as the GPT family, for text, and models like DALL-E or Midjourney for images.
A compelling use case for a generative model in the telecom industry is creating synthetic network performance data for robust testing and validation. The purpose of the generative model is to develop realistic, artificial datasets that mimic real-world network traffic and performance under a wide range of conditions, including rare but critical events such as massive outages or cyberattacks.
The model trains on vast amounts of real, historical network data, including metrics such as data throughput, latency, packet loss and signal strength from millions of users over time. The quality of this historical data is paramount; if it’s noisy or incomplete, the model will generate flawed synthetic data. A sufficient quantity of diverse data is also needed to capture all the complex patterns of network behavior.
Real network data is often sensitive due to customer privacy and may be limited in its ability to represent extreme scenarios. A generative model, such as a generative adversarial network (GAN), can learn the underlying patterns of real data and then produce entirely new, synthetic datasets. Engineers can instruct the model to generate data that simulates specific scenarios, such as a sudden 1000% spike in traffic in a particular city, without having to wait for such an event to occur naturally. This high-quality synthetic data helps telecom companies improve their models in the following ways:
| Project Goals Determine the AI Model | ||
| Goal | Example | Model type |
| Forecast a continuous number | Estimating revenue increases by adjusting cell tower settings | Regression model |
| Need data categorized | Does sensor data fall within an acceptable range | Classification model |
| Identify data groupings | Applying a clustering algorithm to smart city or public sector accident data to find hidden patterns that can reduce traffic accidents | Clustering model |
| Data creation | Create marketing copy and images from text prompts | Generative AI |
A core principle of AI is that the data governs the possible solutions. The quantity, quality and type of data are the most significant factors influencing the choice of algorithm. When selecting an AI tool, be realistic about the data assets. Data issues are the primary reason AI projects fail.
Is the data neatly organized in rows and columns, like in a spreadsheet or a database? If so, it means the data is structured, and it’s well-suited for traditional machine learning algorithms. When handling unstructured data—such as raw text, images, or audio files—developers need more specialized models, like convolutional neural networks (CNNs) for images or Transformer-based models for text.
Determining your needs involves looking at the model and the available data. Traditional machine learning models, such as linear regression, logistic regression, or decision trees, are not computationally intensive. They run perfectly well on a standard CPU. Complex models, such as deep learning (neural networks for image recognition or transformers for text), involve massive parallel calculations. Developers use the specialized processing power of GPUs or tensor processing units (TPUs) to train them in a reasonable amount of time.
Evaluating the dataset size is another important consideration. For small to medium-sized datasets, a modern CPU with sufficient RAM can handle training simple models on datasets with millions of rows. When training complex models on enormous datasets (e.g., millions of high-resolution images), GPUs become essential. Processing a vast amount of data necessitates both the high processing speed of a GPU and a substantial amount of video random-access memory (VRAM) or the GPU’s dedicated memory. A practical approach is to start with a flexible cloud-based solution (such as AWS, Google Cloud, or Azure), benchmark the model’s performance on a standard GPU instance and scale resources up or down according to the project’s specific needs.
The computational needs for a live, deployed model (inference) can differ significantly from those for training. Training is the most resource-intensive phase. It’s where the most powerful hardware (often high-end GPUs) is needed to process all data and learn the patterns. The process of using a trained model to make predictions is called inference. The application’s requirements for speed (latency) and volume (throughput) drive the needs in this case. The hardware must be able to deliver predictions at the speed your application requires (e.g., real-time fraud detection vs. a daily report). However, sometimes a less powerful GPU or even a CPU is sufficient for inference, resulting in significant cost savings.
Assessing a team’s expertise is as fundamental as evaluating the data. A brilliant idea is not enough if you don’t have the right people to maintain it. This analysis has a direct impact on the project’s scope, timeline and ultimate success.
Integrating, testing and deploying an AI model requires specific skills, such as data and software engineering. Data teams must deeply understand the data, actively participate in its cleaning, and help integrate it into the model. Software engineers need to shepherd it into production. Companies can employ upskilling programs for their employees or leverage consulting to fill any identified gaps.
Additionally, project teams must consider the available time and budget. Is a quick solution needed, and is there a tight budget? Consider using a pre-trained model and fine-tuning it on company-specific data to achieve optimal results. Pre-trained models can save an enormous amount of time and computational cost. For a long-term, core business project, building a custom model from scratch may be a justified investment.
Selecting the right AI model isn’t about finding the single “best” one; it’s about finding the most appropriate one for the unique context. By systematically evaluating goals, data and resources, companies can move from a state of overwhelming choice to one of strategic clarity.
Remember, this is an iterative process. The best approach is often to start with a simple problem to solve and a simple model to use. In the starter project, build a baseline of experience and understanding. Iterate from there. The main point to remember is that basing choices on the problem at hand is one of the key drivers in choosing an effective and impactful AI model or tool.
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