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AI- Artificial Intelligence
Developing software with AI capabilities entails creating new software or adapting existing software to output AI analytics findings to users (e.g., demand forecasting) and to trigger particular actions depending on them (e.g., blocking fraudulent transactions).
With the assistance of AI, a business application may automate operations, customize service delivery, and provide company-specific insights. 90% of seasoned AI users believe that “AI is vital to their business’s success today.
Case Studies for AI-Enabled Software
Business process automation
Engine for optical character recognition used to extract data from paper documents.
Production management
Analysis of the fundamental causes of production losses and forecast of process quality.
Customer analytics
Customer sentiment analysis and behavior prediction.
Risk management
Analyses of counterparty risk and possible harm prediction.
Supply chain management
Forecasting lead times and inventory optimization
Personalized service delivery
Segmentation of customers and recommendation engines.
Strategy For Developing AI-Enabled Software
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Feasibility study
Duration: 1 month
Developing a proof of concept (PoC) for AI to determine the technical and economic viability of incorporating it into software, as well as the scope of work, timeframe, budget, and hazards. Outlining software needs AI at a high level (in the case of new software). -
Conducting a business study to ascertain AI needs
Duration: 1-6 weeks
Defining comprehensive functional and non-functional criteria for AI, such as the needed degree of AI accuracy. In certain circumstances, the value may be generated with as little as 65-80% accuracy, explainability, fairness, and privacy, as well as the required reaction time. -
Designing a solution architecture
Duration: ary depending on the software's overall complexity
Choosing patterns and methods for integration. Architecting the solution's architecture, including points of interaction between its components, including integration with an AI module. -
Preparation of business procedures
Duration: 1-3 months
Launching a program to integrate AI into mission-critical software may need organizational adjustments to ensure the initiative's success and uptake. -
Software Advancement
Duration: 3-36 months
Developing software's front and back end (the server-side and APIs, including necessary APIs for AI module integration). Conducting quality assurance methods throughout the software development process to ensure the program's quality. -
Development of AI modules
Duration: 1-2 weeks
Consolidating data from various relevant sources (internal and external, which can be acquired via one-time purchase or a subscription). The generated data is divided into three sets: training, validation, and test. -
ML model training
Duration: 1-4 weeks
Choosing appropriate machine learning algorithms and developing machine learning models. After training the models on training data and validating them against a validation dataset, fine-tuning hyperparameters enhance their performance.
Cloud Services to Boost AI Software Development
Amazon SageMaker
- Pre-configured data labeling workflows and pre-screened data labeling vendors.
- One-click data import, 300 pre-configured data transformations, and data visualization.
- Machine learning traits are stored in a central database.
- Pre-built machine learning algorithms and models are available in the marketplace.
Azure Machine Learning Services
- A drag-and-drop user interfaces for developing low-code models.
- Our data labeling solution helps you manage projects and automate iterative procedures.
- Azure offers a variety of deployment options, including a hybrid cloud.
- Cost control via the use of quotas at the workspace and resource level.
Ascertain the Feasibility of Your AI Development Initiative
Blocknize may undertake a feasibility study on your behalf to ascertain the unique combination of elements that will determine the path for incorporating AI capabilities into software in your specific scenario.