Adopting AI in SDLC: Unlocking Speed, Quality, and Strategic Focus

What the $8 Billion Deal Really Means for Enterprise Customers, Service Owners, and End Users 

Across the industry, one thing is becoming increasingly evident: Artificial Intelligence (AI) is reshaping how software is delivered. Going beyond changing development workflows, AI is transforming how technology teams generate value and how organisations build for the future.

At Innominds, we’ve been working closely with our customers to adopt AI throughout the Software Development Lifecycle (SDLC). Our customers have begun seeing significant, measurable improvements in their product delivery and operational efficiency.

Why Traditional SDLC Models Are No Longer Enough

Traditional SDLC models, while mature, come with inherent challenges:

• Delayed time-to-market due to long testing and release cycles
• Manual errors from repetitive coding and validation tasks
• Resource utilization on low-value activities rather than innovation
• Technical debt is creeping into each sprint

These challenges not only slow down innovation but also dilute the strategic potential of technology teams.

How AI adoption in SDLC is Changing the Game

By adopting AI through tools like GitHub Copilot and our internally developed AI accelerators, we’re enabling our customers to rethink and modernize their entire software development process. The impact has been significant leading to:

• 20–30% reduction in development time through AI-assisted code suggestions and boilerplate automation
• 20–25% faster architecture and API blueprinting, supported by design-generation tools
• 40–50% improvement in test case generation, as AI maps requirements to structured, reusable test coverage
• 20–30% increase in test automation efficiency, enabling more frequent and confident releases
• 25-30% fewer post-release defects, resulting in greater stability, reduced rework, and improved user experience

These gains are not theoretical, but are indeed being realised by teams using our AI-augmented approach across our customers’ projects.

It’s Not Just About the Tools — It’s About How We Use Them

The true power of AI lies not in the tools alone, but in how we use them. A key enabler of success in our journey and one we emphasise with every customer is prompt engineering.

Prompt engineering is the discipline of crafting inputs that guide AI tools toward precise, relevant, and high-quality outputs. At Innominds, we apply advanced prompt engineering techniques such as:

• Few-shot prompting: Providing 2–3 contextual examples to the AI to help it understand and replicate the desired structure—for instance, generating test cases or data models in a consistent format.
• Meta prompting: Instructing the AI how to think—e.g., “Generate a RESTful API schema, but explain each field based on business logic.”
• Reasoning-based prompts: Asking the AI to follow a step-by-step thinking approach—useful in scenarios like dependency mapping, impact analysis, or architectural decision support.

These techniques ensure the outputs from tools like Copilot, and our internal GenAI agents, are:

• Context-aware and business-aligned
• Consistent and reusable
• Free from hallucinations and logically sound

Here are simplified examples:

test('adds two numbers', () => {
  expect(add(2, 3)).toBe(5);
});

test('checks if number is even', () => {
  expect(isEven(4)).toBe(true);
  expect(isEven(3)).toBe(false);
});

test('multiplies two numbers', () => {
  expect(multiply(2, 3)).toBe(6);
  expect(multiply(-1, 5)).toBe(-5);
  expect(multiply(0, 10)).toBe(0);
});

{
  "paths": {
    "/register": {
      "post": {
        "summary": "Register a new user",
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "type": "object",
                "properties": {
                  "fullName": {
                    "type": "string",
                    "description": "User's full name"
                  },
                  "email": {
                    "type": "string",
                    "description": "User's email address"
                  },
                  "password": {
                    "type": "string",
                    "description": "User's password"
                  },
                  "phone": {
                    "type": "string",
                    "description": "User's phone number (optional)"
                  }
                },
                "required": ["fullName", "email", "password"]
              }
            }
          }
        }
      }
    }
  }
}

Step 1: Identify direct dependencies
- User analytics module
- Session management logic
- Audit logging reports

Step 2: Identify indirect dependencies
- WAU (Weekly Active Users) metric
- Admin dashboard widgets

Step 3: Impact summary
- Removing "last_login" would break analytics, session timeout rules, and dashboards.

Step 4: Mitigation steps
- Deprecate in schema v2 while keeping v1
- Use login events for session tracking
- Update reports to use new tracking

Risk Level: High  
Recommendation: Do not remove without a backward-compatible migration plan.

Through our work with customers, we’ve built internal expertise in this area, developed domain-specific prompts, and created best practices that significantly improve AI output quality and consistency.

How Our Customers Are Benefiting

By adopting AI into the SDLC, we’re helping customers shift from reactive development to predictive, intelligent delivery. With AI taking care of SDLC tasks, teams can now focus on:

• Solving domain-specific challenges
• Accelerating innovation and user-centric development
• Reducing QA cycles while increasing confidence in every release
• Delivering higher value with fewer resources

At Innominds, we help customers not just use AI, but apply it effectively across the full lifecycle—from design and development to testing, deployment, and beyond.

Looking Ahead

Software development is going to be intelligent, contextual, and faster than ever before. The organizations that will lead in this new landscape are those that not only adopt AI tools, but understand how to integrate them meaningfully into their software lifecycle.

At Innominds, we’re proud to be enabling that journey for our customers. Whether it’s AI-driven design, faster development, intelligent testing, or empowering teams through AI tools and prompt engineering, we’re focused on delivering results that matter. With the right strategy and the right partner, the results can be transformative.

Let’s build smarter, together.