How to Build Software Powered By Artificial Intelligence

How to Build Software Powered By Artificial Intelligence - ScienceSoft

ScienceSoft applies 32 years of experience in software development and data science to develop software with artificial intelligence (AI) capabilities.

The Essence of Developing Software with AI Capabilities

Development of software with AI capabilities implies building new software or evolving existing software to output AI analytics results to users (e.g., demand prediction) and/or trigger specific actions based on them (e.g., blocking fraudulent transactions).

Supported by AI, an application can automate business processes, personalize service delivery and drive business-specific insights. According to Deloitte, 90% of seasoned AI adopters say that “AI is very or critically important to their business success today”.

Use Cases for Software with AI Capabilities

Business process automation

  • Chatbots
  • Search engines
  • Automated document generation
  • Optical character recognition engine for data extraction from paper documents
  • Job candidates screening and shortlisting

Production management

  • Predictive maintenance
  • Demand and throughput forecasting
  • Process quality prediction
  • Production loss root cause analysis

Customer analytics

  • Sentiment analysis
  • Customer behavior prediction
  • Sales forecasting

Risk management

  • Counterparty risk analytics
  • Potential damage prediction
  • Fraud detection

Supply chain management

  • Demand forecasting
  • Lead time forecasting
  • Inventory optimization

Personalized service delivery

  • Customer segmentation
  • Recommendation engines

Roadmap: Developing Software with AI Capabilities

The duration and sequence of the development stages will depend on the scale and the specifics of both basic software functionality and artificial intelligence you want to enrich it with.

1

Feasibility study

2

Business analysis to elicit AI requirements

3

Solution architecture design

4

Business processes preparation (in case of software development for internal use)

5

Software development (non-AI part)

6

AI module development

7

AI deployment

8

Maintenance and evolution of AI-powered software

Talents Required for Developing Software with AI Capabilities

The roles required in a software development project with an AI part vary according to the project’s goals and scope. The key roles include:

Project manager

To outline a project roadmap, manage the software & AI development life cycle, and foster collaboration between business and tech stakeholders.

Business analysts

To analyze business and user needs and translate them into technical requirements for software, AI, and integration between them.

Data scientists

To cleanse data for AI and engineer features; to build, train, test, and validate ML models. Domain experience is preferred.

Data engineer

To deploy AI and monitor it in production.

UX and UI designers

To design wireframes, create user stories and UI prototypes for AI-driven software, following the principles of user-centricity.

Software developers

To build the software back end and front end and build and implement APIs necessary for integration with AI, and further evolve software.

QA specialists

To design and implement a test strategy to validate software quality.

Sourcing Models of Developing Software with AI capabilities

All resources are in-house

Full control over the project, however, the lack of the required skills in AI is likely. Growing in-house AI capabilities can be a strategic decision if the development of software with AI functionality is a part of company-wide adoption of AI technologies.

All resources are in-house, except for data scientists

High control over the project and access to competencies unavailable in-house. If you’re looking to grow an end-to-end in-house team in the future, look for a resource vendor who provides knowledge sharing.

Non-AI part is developed in-house, while the AI part is outsourced

Optimal resource usage and access to competencies unavailable in-house. However, establishing smooth team collaboration may pose a challenge.

PM and BA are in-house, all technical resources are external

Sufficient control over the project and better process transparency, no problems with resource utilization after the project. There should be properly qualified PM and BA in-house.

Complete outsourcing

Access to rare talent and the latest technologies, which results in faster development and lower costs but higher vendor risks. Thus, we suggest requesting PoC from a chosen vendor.

Get Expert Help to Build Software with AI Capabilities

Having 31-year experience in software development and data science, ScienceSoft delivers reliable software with AI capabilities.

Cloud Services to Speed Up Development of Software with AI Features

AI platforms help quickly set up, automate and manage each stage of the AI module development with pre-configured infrastructure and workflows. ScienceSoft recommends considering platforms by major cloud providers: Amazon, Microsoft, and Google. All of them are leaders in Gartner’s Magic Quadrant for Cloud AI Developer Services and offer integrated development environments (IDEs) with the following capabilities:

  • Custom modeling with R/Python and supported frameworks (TensorFlow, PyTorch, scikit-learn, and others).
  • AI workflow orchestration and management.
  • Bias detection, explainability features, etc.
  • Automated model tuning.
  • Model performance monitoring.
  • Autoscaling of compute resources.
  • Advanced security.

Some of the platforms’ distinctive features are outlined below:

Amazon SageMaker

Description

  • Powerful, enterprise-ready infrastructure offered by AWS (e.g., Amazon EC2 and Amazon S3-based) to support AI-related projects.
  • Pre-configured data labeling workflows, access to pre-screened vendors offering data labeling services.
  • One-click data import, 300 pre-configured data transformations, data visualization capabilities.
  • A unified repository to store, organize and reuse ML features.
  • Marketplace with pre-built ML algorithms and models.

best for

Enterprise-scale AI integration initiatives.

Pricing

Payment for compute and storage resources consumed. Pricing depends on the region, the services used within the platform, their configuration and hours of usage.

Azure Machine Learning Services

Description

  • Drag-and-drop UI for low-code model development.
  • Data labeling service to manage and monitor labeling projects and automate iterative tasks.
  • Flexible deployment options offered by Azure, including the hybrid cloud.
  • Cost management with workspace and resource level quota limits.

best for

Flexible AI deployment (on-premises/hybrid cloud).

Pricing

Payment for compute and storage resources consumed. Pricing depends on the region, the services used within the platform, their configuration and hours of usage.

Google AI Platform

Description

  • Accelerated AI performance due to integrated proprietary Tensor Processing Unit (TPU).
  • Advanced support of Kubernetes orchestration.
  • Integration with BigQuery (Google’s hyperscale data warehouse) datasets.
  • Data labeling service that connects companies with human labelers.
  • Support of TensorFlow Enterprise.
  • Pre-configured virtual machines and optimized containers for AI based on deep learning.

best for

Integration of resource-intensive deep learning AI into software; startup-friendly.

Pricing

Pricing depends on the region, the services used within the platform, their configuration (type and number of instances) and hours of usage.

Cost Factors of Developing Software with AI Capabilities

The cost of software powered by artificial intelligence can vary greatly:

  • For example, an AI-powered solution that automatically extracts unstructured data from several sources, classifies it using an ML algorithm of modest complexity, and provides outputs in batches may cost $100,000-$200,000.
  • A more complex AI-powered solution that processes data of various types and from a large number of sources with an advanced, expertise-demanding ML algorithm of high accuracy (as it’s critical to business processes) may cost $500,000-$650,000.

The estimates rely heavily on the specifics of AI module development:

  • Data volume used for AI and the number of data sources to process.
  • Data type (unstructured data is more expensive to work with than structured).
  • Data origin (there may be a need to buy external data) and whether it needs labeling (tagging data samples with the desired output).
  • Data quality (issues in data require more resources for cleansing).
  • Required accuracy rate for AI (the higher it is, the more time-consuming and expertise-demanding ML model tuning will be).
  • Complexity of ML algorithms.
  • Deployment type (AI outputs are in batches or in near-real-time).
  • AI maintenance costs (AI operating in a changeable data environment, e.g., feeding on dynamic user data, needs regular retraining).
  • Infrastructure costs.

Ensure Feasibility of Your AI Development Initiative

ScienceSoft can conduct a feasibility study for you to understand the unique mix of factors that will shape the roadmap of integrating AI capabilities into software in your particular case.

Consider Professional Services for Development of AI-Powered Software

ScienceSoft applies 31-year experience in software development and data science to create solid software with AI capabilities.

Consulting: software development with AI capabilities

Our consultants help:

  • Conduct a feasibility study on integrating AI into your software (potential benefits, risks, and costs).
  • Outline a risk management strategy to mitigate AI-related risks.
  • Outline a development, deployment and integration plan for building software with AI capabilities.
  • Choose an optimal sourcing model.
  • Select a fitting technology stack for software and its AI part prioritizing open-source frameworks to optimize development time and costs.

Outsourced development of software with AI capabilities

We cover all the stages of development:

  • Feasibility study (including PoC).
  • Business analysis: eliciting requirements for software and AI.
  • Software development: UX and design, front-end and back-end development, QA.
  • AI development: data preparation, ML model building, training and tuning.
  • AI integration with software, deployment (MVP and full-scale rollout) and testing.
  • User training.
  • Software maintenance and evolution.
About ScienceSoft

About ScienceSoft

ScienceSoft is an international IT consulting and software development company headquartered in McKinney, TX. Relying on 32-year practice in software development and data science for 30 industries, including manufacturing, healthcare, financial services and retail, we develop software enhanced with AI to optimize workflows and reduce operating costs, improve decision-making, and increase customer engagement.