Exploring the future of digital transformation: Cognizant

In an exclusive interview, I sat down with Hari Parameswaran, SVP & Global Delivery Head – Application Development & Management Practice at Cognizant, to discuss the critical challenges and emerging strategies shaping the future of enterprise technology.

Excerpts:

The Gartner report points to organisational inertia and skill gaps as major hurdles in digital transformation. Beyond technology, what key strategies can leaders use to overcome these non-technical challenges?

In today’s digital transformation era, speed and complexity have increased exponentially, especially with the non-deterministic nature of systems introduced by generative AI and autonomous agentic systems. Leaders must simultaneously build the organization of the future, deliver results today, and manage change effectively.

It starts with Leadership defining a bold “true north” and inspiring teams through clear communication. Empowering employees with the right tools to learn relevant skills and experience how their roles are augmented by AI helps scale the transition effectively.

Key strategies include:

• Establishing a steering committee, that owns the implementation strategy, drives the right behaviours needed for transformation, ensures leadership alignment, and manages funding model.
• Adopting a simplified operating model to reduce change fatigue by streamlining initiatives and building agile foundations with clear value measurement.

With legacy systems consuming up to 80% of IT budgets, what innovative financial models are enterprises using to fund and justify large-scale modernization projects?

Regardless of the percentage of IT budget allocated to legacy systems, enterprises are increasingly adopting a combination of self-funded and gain-share models to drive large-scale transformation projects.

Organizations are leveraging AI-enabled platforms and automation tools to optimize IT operations and reduce cost. These efficiency gains can be reinvested into modernization efforts, creating a self-funding cycle for digital transformation. Platforms that support intelligent operations and accelerated development workflows help streamline legacy modernization and enabling faster time-to-market.

Additionally, outcome-based gain-share models are gaining traction, especially in large scale vendor consolidation and transformation programs. These models tie financial investment to measurable outcomes, making modernization efforts more financially viable and strategically aligned.

How do you see generative AI fundamentally reshaping the entire software development lifecycle, from requirements gathering to maintenance, and what new operational models will emerge?

The software development lifecycle (SDLC) is rapidly evolving into an AI-embedded paradigm, where many activities are no longer performed solely by humans.  Generative AI is transforming every stage, from requirements gathering and design to coding, testing, deployment, and maintenance. AI pair-programmers are already reducing task time, boosting developer confidence, and enabling faster delivery with improved quality. This shift allows teams to focus more on creative problem solving and innovation.

The percentage of code written by AI is steadily increasing and this trend is expected to grow significantly in the coming year. A key enabler is enterprise-wide skilling in AI, which empowers developers, especially those early in their careers to leverage AI tools effectively.

AI-assisted coding enables rapid prototyping and experimentation, though rigorous validation is required to translate this code into enterprise-grade applications and systems.

As organizations adopt AI-first approaches, new operating models are emerging, including:

• AI-centric automation of manual tasks across the SDLC.
• Continuous learning systems that integrate data with models for adaptive development.
• Human-AI collaboration becoming the foundational way of working.

Looking ahead, we anticipate the role of AI-augmented PODs, agentic development factories, and digital personas that complementing human roles. These models will amplify engineering rigor through strong governance, policies frameworks, and human accountability, ushering in a new era of scalable, intelligent software delivery.

As DevOps, cloud-native architectures, and AI converge, what key synergy points will define modern application delivery in the next 3-5 years?

The convergence of DevOps, cloud-native architectures, and AI is reshaping modern application delivery, with a strong emphasis on end-user value, agile autonomy, and continuous business impact. 

As organizations integrate generative AI, virtualization, DevOps and cloud-native workflows, key synergy points are emerging around:

• Modularity and headless architecture: These enable greater portability, improved quality, and the development of highly resilient and nimble applications. 
• AI-infused operations: Once deployed, applications are increasingly managed within autonomous ecosystem governed by humans but powered by AI-driven self-serve and self-heal capabilities.
• Feedback loop: Intelligent systems provide high-value feedback that accelerates change delivery and enhances operational efficiency.

This evolution is driving a shift toward AI-native engineering models, where automation, observability, and continuous learning are embedded across the lifecycle, setting the stage for faster innovation and more responsive digital experiences.

Low-code/no-code platforms are on the rise. How can companies balance speed and agility with enterprise-grade requirements like security and scalability, and avoid creating a new type of 'legacy'?

Low-code/no-code platforms has evolved significantly, and Vibe coding platforms have taken this to the next level with a distinct AI-driven approach that uses natural language to generate readable and editable code quickly, thereby offering greater flexibility.

To balance speed and agility with enterprise-grade requirements, organizations must embed robust engineering practices into their low-code strategies. This includes:

• Inputs validation
• Secure secret management
• Robust authorization mechanisms

These guardrails ensure that rapid development does not compromise security, compliance, or maintainability.

Advanced platforms now integrate and automate workflows using leading frameworks and AI capabilities from LLMs (large language models), golden templates, and tools across the development lifecycle. This ensures rapid delivery of quality code while minimizing security and compliance risks, enhancing transparency, and improving developer experience.

An API-first approach, combined with data separation strategies, helps reduce system coupling and improve portability, laying the foundation for modular, scalable applications. Treating applications as products with built-in observability, portability and repeatability ensures long-term scalability and helps prevent the emergence of a new form of legacy.

How does Cognizant's approach to application modernization deliver measurable business value and ROI beyond traditional project completion metrics?

Cognizant’s AI-enabled Application Modernization approach focuses on simplifying legacy systems complexity and reducing operational costs. The core modernization outcomes are in ROI principles, making businesses more agile, resilient and aligned with KPIs (key performance indicators).

Success lies in identifying the business outcomes that modernization can positively impact on, including: 

• Regulatory and compliance metrics.
• Enhanced customer experience.
• Improved time-to-market and revenue growth.

In our experience, these outcomes are consistently underpinned by reduced technology costs and improved operational efficiency, delivering measurable business value beyond traditional project metrics.

Can you provide a real-world, anonymized example of how a platform like Skygrad or Neuro IT Operations has helped a client achieve specific, transformative outcomes?

For a large media company in the US,

We leveraged our Neuro IT Operations platform, to drive a machine-first operations environment, resulting in greater stability and enhanced business resilience in serving end customers. Tangible outcomes included:

• A substantial improvement in system resilience.
• A significant reduction in business outages.
• A strong AIOps foundation that strategically eliminated technical debt.

We also leveraged our Flowsource platform, to turbocharge the SDLC, through end-to-end orchestration. This included automated user stories, productivity gains via code assistants, and the delivery of secure, reliable, high-quality code. Tangible outcomes included:

• Significantly faster time-to-market.
• Productivity gains across the entire life cycle.

For a leading retailer in the US, we developed a cloud-native, AI-enabled Order Management System (OMS) to replace their legacy infrastructure. This modern OMS enabled:

• New revenue opportunities through AI-powered search capabilities.
• A substantial increase in order processing throughput.
• Significant projected savings in infrastructure costs.