Speed wins in media and communications. And legacy systems are losing the race. Launching a new mobile plan, updating network capabilities, or rolling out a streaming feature like personalized recommendations or live event support can take weeks—or even months. Customers won’t wait that long. They expect instant, seamless, high-quality experiences. AI-assisted software development breaks through those barriers, helping media and communications leaders accelerate time-to-market, deliver updates faster, and meet rising expectations without compromising on quality.
This guide provides practical steps on how AI in media and communications can be used to responsibly implement automation and intelligence across the software development lifecycle to boost developer productivity, speed up releases, and deliver better customer experiences.
Guide on How to Elevate Software Development with AI
Step 1: Measure Developer Productivity the Right Way
To improve any process, it’s essential to track key performance indicators (KPIs) and set clear objectives. This applies equally to AI-assisted software development in media and communications.
While it’s common to focus on individual metrics—such as uptime of streaming services, latency improvements in content delivery networks, speed of deploying new features for OTT platforms, or reduction in bugs affecting video playback or call quality—it’s equally important to measure team-based outcomes. Productivity in AI-assisted software development is fundamentally collaborative, so assessments should also capture satisfaction, communication, efficiency, and overall workflow, as highlighted in frameworks like DORA and SPACE.
For example, in telecom and media streaming, you might track how effectively teams collaborate to deploy new content delivery optimizations or resolve network incidents affecting multiple users.
Conduct workshops and interviews to assess processes, challenges, and AI readiness, then use the insights to create an AI Engineering SDLC Index and roadmap for applying AI in media and communications development, testing, deployment, and governance.
Step 2. Boost Coding Efficiency with AI Tools
AI coding assistants can help not only speed up software development for telecom and media streaming but also accelerate time-to-market. Tools like GitHub Copilot act as intelligent assistants, offering code suggestions, autocompletion, debugging, and documentation support, which can increase developer productivity by 25–30%.1
This allows developers to focus on higher-value work, like improving streaming performance, adding new features, or enhancing the user experience.
Being intentional about selecting the right mix of AI tools—sometimes off-the-shelf, sometimes custom—and making sure they fit seamlessly into workflows across coding, testing, documentation, and deployment is key.
It’s also important to focus on the people using these tools: training, coaching, and clear guardrails help developers work confidently and responsibly with AI, embedding governance to move from experimentation to sustainable, AI-powered development.
Step 3. Transform QA with AI-Driven Testing
Incorporating AI in media and communications into testing processes allows Quality Assurance (QA) teams to focus on more complex tasks while AI handles repetitive ones. For telecom and media streaming, this could mean automatically testing video playback across devices, verifying user login flows, or checking that recommendation algorithms display content correctly. According to the 2025 Stack Overflow Developer Survey, 60.5% of developers expect to partially or predominantly use AI in code testing.²
Test Case Generation
AI can automatically generate test cases from user stories or product requirements. In streaming platforms, this could include scenarios like skipping ads, switching between video qualities, or simulating high-traffic streaming events. This improves test coverage and helps identify gaps that manual testing might miss.
Defect Prediction
By analyzing historical data, AI can identify areas prone to failure and vulnerabilities in production. For example, it could predict potential crashes when many users access a live stream simultaneously or highlight issues in network-dependent features like group calls or live chat. GitHub RCT shows higher unit test pass rates and fewer code errors when using AI, improving readability, reliability, maintainability, and conciseness.³
Visual Testing
AI models can spot subtle UI/UX issues. In telecom apps, this could include misaligned buttons, broken call or messaging icons, or incorrect signal and data usage indicators, and AI tools detect UI anomalies up to 30% faster than traditional methods. ⁴
Step 4. Revolutionize Knowledge Capture and Documentation
AI can revolutionize developer onboarding by providing relevant knowledge and making it accessible to new team members.
An AI assistant can guide users to pertinent documentation, highlight dependencies, suggest best practices, and offer contextual answers based on team discussions, codebases, and existing documentation. In telecoms, this could help new developers quickly understand network configurations, call routing logic, billing systems, or monitoring and troubleshooting processes for mobile and fixed-line services.
Additionally, AI enables dynamic documentation that evolves alongside the code being developed. Traditional documentation often lags behind product changes, but with AI, documentation can remain current and contextually useful by analyzing commits and changes in architecture.
Step 5. The AI Maturity Journey—from Experimentation to Excellence
The journey toward an AI-first engineering culture unfolds in five key stages, each building on the last:
Laying the Groundwork
Transformation begins with understanding. Organizations evaluate current engineering maturity, identify AI opportunities, and define a roadmap for responsible adoption. Through workshops and interviews, teams gain clarity on embedding AI across coding, testing, deployment, and governance. The result is an actionable foundation: an AI Readiness Assessment Report, a prioritized AI use cases backlog, a strategic AI integration roadmap, and an executive presentation and playbook.Embedding AI into Workflows
With a clear strategy, organizations move AI from concept to practice. This stage involves selecting the right tools, integrating them into development workflows, and establishing guardrails for responsible use. Structured training and real-time coaching empower developers to adopt AI confidently. By the end, teams have a functional Central Hub across developer tools, a network of AI advocates, and their first successful AI-enhanced delivery use case.Modernizing Legacy Systems
Legacy applications often slow agility. Using advanced AI analysis, organizations map codebases, uncover dependencies, and generate actionable documentation. The outcome is a modernization blueprint balancing security, scalability, performance, and maintainability, enabling confident decisions for cloud migration and long-term efficiency.Reinventing Quality Assurance
AI transforms testing from a manual checkpoint into an intelligent, continuous safeguard. Accelerated test creation, early regression detection, and accessibility monitoring improve software quality while reducing risk and maintaining compliance. Teams deliver more reliable, inclusive, and high-performing applications faster.Optimizing Delivery Pipelines
Finally, AI enhances CI/CD pipelines. Teams gain real-time insights into bottlenecks, deployment risks, and quality trends. Guided automation streamlines build validation and release readiness, resulting in faster release cycles, fewer errors, and stronger operational control.
These stages form a maturity model for AI-first engineering. The journey is modular and iterative, meeting organizations where they are while guiding them toward a culture where AI is fully embedded.
Embrace AI to Accelerate Innovation
As media and communications companies face increasing demands for speed and innovation, AI-assisted software development emerges as a powerful ally, driving both efficiency and quality. By leveraging AI in media and communications to enhance developer productivity, automate testing, simplify integration, and maintain up-to-date documentation, businesses can break free from the constraints of legacy systems.
AI is only the beginning. The next frontier of transformation lies in intelligent connectivity. Learn how network APIs are powering the next wave of agility and growth in telecom. Read “The Network API Advantage in Telecom.”
- “Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom,” McKinsey & Company, 2024.
- “Stack Overflow Developer Survey 2025,” Stack Overflow.
- “Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom,” Mckinsey, February 22, 2024.
- “Does GitHub Copilot improve code quality? Here’s what the data says,” Jared Bauer, GitHub, November 18, 2024.
- “AI In the Testing Industry Statistics,” WifiTalents Reports, June 1, 2025.
