Join Everest Group experts Yugal Joshi, Partner, Alisha Mittal, Vice President, Mukesh Ranjan, Vice President, and Akshat Vaid, Partner, for an exciting webinar that will redefine how you view the future of the engineering and IT technology space.
Get ready for a deep dive into the transformative trends due to shape engineering and IT services in 2025. From the unstoppable rise of AI to breakthrough innovations on the horizon, our panel will deliver powerful insights into what’s next for the industry and how you can take advantage of this pivotal moment.

Expect engaging discussions, bold predictions, and data-driven insights designed to empower enterprises and service providers to navigate this key era. This is your chance to learn how to leverage technology trends, outpace the competition, and thrive in a rapidly evolving landscape.

Don’t miss this high-impact session—your roadmap to 2025 starts here.

What questions will the webinar answer?

• As a services provider, which areas should you invest in during 2025 for strategic growth?
• As an enterprise, what should you look for in outsourcing partners in 2025?

Who should attend?

• IT service providers CXOs
• IT strategy leaders
• IT business leaders
• Enterprise Procurement Managers
• IT/BPO Department Heads
• Global Sourcing Mangers
• Vendor Managers
• Senior Marketing executives

The rise of artificial intelligence (AI)-powered coding assistants has transformed software development, making coding faster, more efficient, and accessible.  

Tools like GitHub Copilot, Amazon Q Developer, Tabnine, and IBM Watson Code Assistant are leading this revolution, offering developers a new way to write, debug, and optimize code.  

This blog explores the growing capabilities of these tools, the competitive landscape, and the broader implications for developers and organizations. 

Reach out to discuss this topic in depth. 

The evolution of AI code assistants

AI-powered coding assistants have come a long way from simple code-completion tools to sophisticated platforms capable of generating entire code blocks, debugging, and optimizing code. Early iterations primarily provided auto-complete features, akin to predictive text on smartphones. Today, these tools leverage advanced Large Language Models (LLM) to understand context and provide nuanced code suggestions. As a result, AI coding assistants can now: 

• Suggest context-aware code snippets 
• Generate boilerplate code and documentation 
• Detect and fix potential bugs 
• Provide recommendations for code optimization 

These capabilities are not just about efficiency; they also help democratize coding by enabling citizen developers—non-professionals—to participate in software creation. 

A competitive and expanding landscape:

The AI coding assistant market is witnessing rapid growth, with established tech giants and emerging players entering the fray. GitHub Copilot, backed by Microsoft, has set a high standard, but competitors like Tabnine and Amazon Q Developer are making significant strides. 

Google has also made a significant entry into this market recently with Jules, an experimental coding assistant aimed at enhancing developer workflows. 

The increasing competition has also driven innovation. Many tools are now moving beyond code generation to adjacent capabilities such as: 

• Automated testing and quality assurance 
• Code refactoring and modernization for legacy systems 
• Enhanced debugging tools that provide explanations for errors and suggest fixes 

This expansion of functionalities demonstrates a shift from mere coding assistants to comprehensive development platforms. 

Opportunities in greenfield and brownfield development:

AI coding assistants are proving invaluable in both greenfield (new projects) and brownfield (existing systems) developments. For greenfield projects, these tools accelerate the initial development phase by automating repetitive tasks and generating foundational code structures, delivering significant productivity gains. In brownfield scenarios, AI can assist in understanding, maintaining, and modernizing legacy codebases, including those written in niche languages. 

While AI coding assistants offer tools to interact with and improve older systems, their effectiveness in modernization efforts can be hindered by challenges such as accurately interpreting complex legacy systems, ensuring compatibility with existing workflows, and addressing deeply entrenched technical debt. Nevertheless, they address a critical need by reducing the retraining required for developers and making legacy systems more accessible for updates and enhancements. 

Navigating concerns about code quality and costs: 

Despite their promise, AI coding assistants are not without challenges. Concerns about code quality, Intellectual Property (IP) violations, and potential security vulnerabilities persist. For instance, the reliance on publicly available code repositories raises questions about the legality and ethics of generated suggestions. 

While these tools promise cost savings by reducing development time, they can introduce hidden costs. Errors in AI-generated code might require significant debugging efforts, offsetting initial time savings. Companies must weigh these risks and implement robust review processes to ensure quality. The licensing costs for these tools can also be prohibitive, especially since it can be difficult to evaluate their Return On Investment (ROI). 

Adapting to varied requirements: 

A key question is whether AI coding assistants primarily serve professional developers or empower citizen developers. The answer lies in their adaptability. For professional developers, these tools act as productivity multipliers, automating mundane tasks and enabling focus on complex problem-solving. For citizen developers, they lower the barrier to entry, allowing business professionals to create simple applications without extensive training. 

To maximize their impact, many AI coding assistant providers are partnering with system integrators as part of their go-to-market strategies. System integrators bring industry-specific expertise and help deploy these tools in enterprise environments. This collaboration is crucial for addressing unique organizational needs, such as compliance and scalability. 

While general-purpose coding assistants dominate the headlines, niche applications are gaining traction. Specialized tools tailored for Structured Query Language (SQL), Common Business-Oriented Language (COBOL), and other domain-specific languages are emerging to address specific industry challenges. These tools often combine AI-driven insights with domain expertise, providing highly targeted solutions. 

The future: AI agents with reasoning capabilities:

Looking ahead, the next generation of AI coding assistants may evolve into full-fledged AI agents capable of reasoning and decision-making. These agents could: 

• Understand project requirements at a high level 
• Suggest architectural patterns 
• Manage dependencies and workflows 
• Collaborate dynamically with teams by adapting to feedback 

Organizations must stay informed and strategically adopt these tools to remain competitive in a rapidly changing landscape. With the promise of reasoning capabilities and domain-specific applications, the future of AI in coding is not just bright but transformative. 

At Everest Group, we’re closely tracking the modern application development space. Our recently published Innovation Watch report assesses 14 providers that offer generative AI (gen AI)  solutions to address software development use cases, positioning them on Everest Group Innovation Watch Assessment as Luminaries, Fast Followers, Influencers, and Seekers. 

If you found this blog interesting, check out our blog focusing on Building Purpose-Driven Generative AI (gen AI) – Why We All Have A Role To Play In The Future Success Of The Gen AI Ecosystem  | Blog – Everest Group (everestgrp.com), which delves deeper into the topic of artificial intelligence. 

To discuss this and other insights from our research on modern application development, please reach out to manukrishnan.sr@everestgrp.com and alisha.mittal@everestgrp.com. 

Enterprises are investing in change management to navigate complex organizational transformations due to technology advances and evolving business models. Historically, several organizations struggled to realize their digital transformation initiatives’ full potential due to the absence of a strategic change management approach. Thus, change management consulting has become pivotal in ensuring digital transformation success by offering roadmaps to address organizational culture, leadership alignment, and employee engagement.

Effective change management requires a balanced focus on human and operational aspects of change. With innovation, sustainability, and employee experience becoming increasingly important, providers must integrate technology with their change management capabilities and offer post-implementation support for change initiatives’ long-term sustainability. Additionally, monitoring change initiatives’ performance, analyzing stakeholder feedback, and refining strategies based on data-driven insights are vital for efficient change adoption.

• Digital Transformation Consulting Services PEAK Matrix® Assessment 2024 – Change Management

  

  Scope

  • All industries and geographies
  • This assessment is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with leading sales service providers, client reference checks, and an ongoing analysis of the sales services market

  Contents

  In this report, we:

  • Examine the sales services market
  • Position the providers on the Everest Group’s PEAK Matrix® framework as Leaders, Major Contenders, and Aspirants
  • Compare providers’ key strengths and limitations

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

Watch the annual Everest Group analyst relations webinar to hear highlights from the market study, “Mapping the Next: Key Priorities for 2025,” and to learn how AR professionals can identify their key priorities for 2025.

Everest Group experts Katrina Menzigian, Vice President, Alisha Mittal, Vice President, and Rajesh Ranjan, Managing Partner, talked through the leading AR priorities to consider for 2025 in order to help your organizations tackle key trends related to technology adoption, generative AI, digital transformation, customer experience, sustainability and more.

Attendees came away with a good understanding of the investments most likely to help analyst relations professionals drive value for their internal stakeholders, as well as actionable insights into building momentum during 2025.

What questions did the webinar answer for the participants?

• What are the key priorities that will shape overall technology and services markets in 2025?
• What are the specific ways in which AR teams can support their company in light of these priorities?
• How can AR teams position their expertise in working with analysts, and give visibility to research to better prepare their organization to compete in 2025?
  

Who should attend?

• Analyst relations professionals
• Advisor relations professionals

The rise of artificial intelligence (AI)-powered coding assistants has transformed software development, making coding faster, more efficient, and accessible.  

Tools like GitHub Copilot, Amazon Q Developer, Tabnine, and IBM Watson Code Assistant are leading this revolution, offering developers a new way to write, debug, and optimize code.  

This blog explores the growing capabilities of these tools, the competitive landscape, and the broader implications for developers and organizations. 

Reach out to discuss this topic in depth. 

The evolution of AI code assistants 

AI-powered coding assistants have come a long way from simple code-completion tools to sophisticated platforms capable of generating entire code blocks, debugging, and optimizing code. Early iterations primarily provided auto-complete features, akin to predictive text on smartphones. Today, these tools leverage advanced Large Language Models (LLM) to understand context and provide nuanced code suggestions. As a result, AI coding assistants can now: 

• Suggest context-aware code snippets 
• Generate boilerplate code and documentation 
• Detect and fix potential bugs 
• Provide recommendations for code optimization 

These capabilities are not just about efficiency; they also help democratize coding by enabling citizen developers—non-professionals—to participate in software creation. 

A competitive and expanding landscape: 

The AI coding assistant market is witnessing rapid growth, with established tech giants and emerging players entering the fray. GitHub Copilot, backed by Microsoft, has set a high standard, but competitors like Tabnine and Amazon Q Developer are making significant strides. 

Google has also made a significant entry into this market recently with Jules, an experimental coding assistant aimed at enhancing developer workflows. 

The increasing competition has also driven innovation. Many tools are now moving beyond code generation to adjacent capabilities such as: 

• Automated testing and quality assurance 
• Code refactoring and modernization for legacy systems 
• Enhanced debugging tools that provide explanations for errors and suggest fixes 

This expansion of functionalities demonstrates a shift from mere coding assistants to comprehensive development platforms. 

Opportunities in greenfield and brownfield development: 

AI coding assistants are proving invaluable in both greenfield (new projects) and brownfield (existing systems) developments. For greenfield projects, these tools accelerate the initial development phase by automating repetitive tasks and generating foundational code structures, delivering significant productivity gains. In brownfield scenarios, AI can assist in understanding, maintaining, and modernizing legacy codebases, including those written in niche languages. 

While AI coding assistants offer tools to interact with and improve older systems, their effectiveness in modernization efforts can be hindered by challenges such as accurately interpreting complex legacy systems, ensuring compatibility with existing workflows, and addressing deeply entrenched technical debt. Nevertheless, they address a critical need by reducing the retraining required for developers and making legacy systems more accessible for updates and enhancements. 

Navigating concerns about code quality and costs: 

Despite their promise, AI coding assistants are not without challenges. Concerns about code quality, Intellectual Property (IP) violations, and potential security vulnerabilities persist. For instance, the reliance on publicly available code repositories raises questions about the legality and ethics of generated suggestions. 

While these tools promise cost savings by reducing development time, they can introduce hidden costs. Errors in AI-generated code might require significant debugging efforts, offsetting initial time savings. Companies must weigh these risks and implement robust review processes to ensure quality. The licensing costs for these tools can also be prohibitive, especially since it can be difficult to evaluate their Return On Investment (ROI). 

Adapting to varied requirements: 

A key question is whether AI coding assistants primarily serve professional developers or empower citizen developers. The answer lies in their adaptability. For professional developers, these tools act as productivity multipliers, automating mundane tasks and enabling focus on complex problem-solving. For citizen developers, they lower the barrier to entry, allowing business professionals to create simple applications without extensive training. 

To maximize their impact, many AI coding assistant providers are partnering with system integrators as part of their go-to-market strategies. System integrators bring industry-specific expertise and help deploy these tools in enterprise environments. This collaboration is crucial for addressing unique organizational needs, such as compliance and scalability. 

While general-purpose coding assistants dominate the headlines, niche applications are gaining traction. Specialized tools tailored for Structured Query Language (SQL), Common Business-Oriented Language (COBOL), and other domain-specific languages are emerging to address specific industry challenges. These tools often combine AI-driven insights with domain expertise, providing highly targeted solutions. 

The future: AI agents with reasoning capabilities: 

Looking ahead, the next generation of AI coding assistants may evolve into full-fledged AI agents capable of reasoning and decision-making. These agents could: 

• Understand project requirements at a high level 
• Suggest architectural patterns 
• Manage dependencies and workflows 
• Collaborate dynamically with teams by adapting to feedback 

Organizations must stay informed and strategically adopt these tools to remain competitive in a rapidly changing landscape. With the promise of reasoning capabilities and domain-specific applications, the future of AI in coding is not just bright but transformative. 

At Everest Group, we’re closely tracking the modern application development space. Our recently published Innovation Watch report assesses 14 providers that offer generative AI (gen AI)  solutions to address software development use cases, positioning them on Everest Group Innovation Watch Assessment as Luminaries, Fast Followers, Influencers, and Seekers. 

If you found this blog interesting, check out our blog focusing on Building Purpose-Driven Generative AI (gen AI) – Why We All Have A Role To Play In The Future Success Of The Gen AI Ecosystem  | Blog – Everest Group (everestgrp.com), which delves deeper into the topic of artificial intelligence. 

To discuss this and other insights from our research on modern application development, please reach out to manukrishnan.sr@everestgrp.com and alisha.mittal@everestgrp.com. 

Welcome to the second blog in our series on transitioning to the Product-Led Operating Model (PLOM). In the first installment, we discussed the strategic imperative for organizations to adopt PLOM, and why traditional operating models may no longer serve the fast-paced, customer-driven market. If you missed it, you can read it here.

In this blog, we take a deep dive into the 4Rs — Reinvent organizational design, Redefine processes, Reshape talent, and Reimagine core technology components — that form the backbone of a successful PLOM transformation.

Reach out to discuss this topic in depth.

Together, these pillars create a comprehensive framework for aligning teams, processes, and technologies with a unified product vision. Let’s explore each dimension and uncover how enterprises can build a more agile, customer-centric organization.

Reinvent organizational design: break down silos, build agility

A product-led transformation starts with rethinking organizational structures. Traditional hierarchies and functional silos stifle agility, innovation, and collaboration. To overcome these challenges, enterprises must align teams with business value streams — shifting from a technology-focused setup to a product-centric model.

Key considerations:

• Empowered, cross-functional teams: Establish product teams that blend business and IT expertise, creating accountability for both revenue generation and innovation. Models like Spotify’s squads and tribes offer excellent templates
  
• Clear roles and accountability: Define responsibilities such as product owners, agile coaches, and chapter leads to drive focus and clarity
  
• Generative culture: Foster a culture where teams are encouraged to innovate, fail fast, and continuously improve

Organizations that embrace alignment-enabled autonomy—where teams have the freedom to make decisions but stay aligned with overarching goals—experience better collaboration and faster decision-making. This approach positions enterprises to respond swiftly to market changes.

This exhibit illustrates the core structure of a product-aligned operating model that integrates business and technology teams, to deliver value through well-defined value streams. It emphasizes the role of persistent, cross-functional, and autonomous teams, supported by lean processes like Agile, DevSecOps, and automation-first approaches. The model is underpinned by robust platforms (business, technology, and foundational) that ensure efficiency, scalability, and consistency, governed by a lean framework for agile decision-making.

Redefine processes: from linear to agile

Processes define how work gets done, and outdated, linear project-based approaches can no longer keep up with today’s demands. Enterprises must adopt agile and DevSecOps methodologies to enable continuous product delivery and innovation

Key process shifts include:

• From projects to products: Replace project-based management with iterative, product-focused delivery cycles
  
• Integrated security and compliance: Incorporate security into the development process (DevSecOps) rather than treating it as an afterthought
  
• Outcome-driven metrics: Shift from measuring outputs (e.g., number of features delivered) to outcomes (e.g., customer satisfaction, time-to-market)

By adopting a lean product development mindset, organizations can reduce lead times, improve responsiveness, and ensure processes remain customer focused. Furthermore, OKRs (Objectives and Key Results) can help align teams with business goals and track progress effectively.

Reshape talent: from specialists to product champions

The success of a PLOM depends heavily on the people who execute it. Traditional role definitions and rigid expertise boundaries must evolve to create a dynamic workforce equipped for product-centric delivery.

Transformational steps:

• Upskilling and cross-training: Equip employees with skills beyond their immediate technical domain, enabling them to contribute to broader business outcomes
  
• Cross-functional collaboration: Encourage collaboration between business and IT professionals, fostering shared accountability for product success
  
• Talent deployment models: Implement flexible talent pools (e.g., chapters and guilds) to dynamically allocate resources based on evolving business needs

Reshaping talent not only empowers employees but also increases engagement. When individuals see the direct impact of their work on customer outcomes, it creates a culture of ownership and innovation.

Reimagine core technology: your strategic battlefield, not just infrastructure

A product-led operating model requires a robust and modern technology foundation. Legacy systems and fragmented tech stacks can undermine even the most well-designed models. Enterprises need to rethink their technology infrastructure to enable agility, scalability, and speed.

Core technology initiatives:

• API-first architecture: Enable seamless integration and interaction between systems, allowing product teams to access infrastructure and data efficiently
  
• Cloud-native solutions: Transition to scalable, cloud-based platforms that support rapid development and deployment
  
• Low-code/no-code platforms: Empower business teams to build and iterate on solutions without heavy reliance on IT resources
  
• Data-backed decision-making: Centralize analytics to provide teams with real-time insights for better decision-making

By reimagining technology as a platform rather than a collection of tools, enterprises can create an environment where innovation thrives. A modern tech stack also ensures consistency, reduces costs, and accelerates time-to-market.

Looking ahead: learning from pioneers

As enterprises embark on their PLOM journeys, the 4Rs offer a comprehensive framework to navigate the complexities of transformation. However, each organization’s path will be unique, influenced by its industry, size, and strategic objectives.

While this blog has explored the foundational pillars, the next in our series will reveal key insights from enterprises that have successfully adopted PLOM.

Based on Everest Group’s survey of 200 global enterprises, we will uncover the challenges, lessons, and tangible benefits experienced by leaders in this space.

Stay tuned as we bring you the perspectives and learnings from these pioneers to help you refine your approach to PLOM and set your organization up for success.

If you found this blog interesting, check out our blog focusing on Mid-market Enterprises: The New Frontier For Digital Transformation Services | Blog – Everest Group, which delves deeper into another topic covered by the Engineering & Information Tech service line.

If you have any questions, would like to gain expertise regarding PLOM, contact Krishna Zawar (krishna.zawar@everestgrp.com), Alisha Mittal (alisha.mittal@everestgrp.com), Parul Trivedi (parul.trivedi@everestgrp.com), Ankit Nath (ankit.nath@everestgrp.com) or Lalith Kumar (lalith.kumar@everestgrp.com).

Join us as we reveal findings from the annual “Mapping the Next: Key Priorities for 2025” study, offering an inside look into the top concerns, expectations, and goals of industry leaders for the coming year. This session will spotlight leaders’ core priorities, such as technology adoption, digital transformation, generative AI, customer experience, and sustainability.

Our experts will also discuss critical global services decisions, including build vs. buy strategies, in-house vs. outsourcing, onshore vs. offshore operations, and the balance between long-term and short-term contracts.

This is your chance to hear exclusive, high-impact insights and gain an edge so you can make more informed decisions that will improve organizational resilience, competitiveness, and chances of future success for 2025.

What questions will the webinar answer for the participants?

• What are the trends that will shape the global services market in 2025?
• What are the likely changes in sourcing spend, sourcing strategy (in-house vs. outsource), and locations?
• What is the enterprise outlook on tech and AI adoption in 2025?
• Which digital services and next-generation capabilities are expected to be in demand?
• How are outsourcing deals, enterprises’ leverage of service providers, and bill rates expected to change?

Who should attend?

• Enterprise/business leaders (CIOs, CDOs, CTOs, CFOs, CPOs)
• Global sourcing leaders
• GBS/shared services center heads
• Leaders at IT and BP providers

Two big shifts are under way in the world of software development. Since the launch of ChatGPT in 2022, bosses have been falling over themselves to try to find ways to use generative AI (gen AI). Most efforts have yielded little, but one exception is programming. Surveys suggest that developers around the world find generative AI so useful that already about two-fifths of them use it.

Read more about what Everest Group Vice President Alisha Mittal had to say on the matter at: The Economist

Organizations are grappling with ensuring quality and reliability in their AI applications and systems, while keeping pace with rapid technology advances and evolving regulatory requirements. As AI adoption increases, enterprises need Quality Engineering (QE) service partners that can not only validate AI models and systems, but also embed responsible AI principles throughout the development life cycle. Organizations seek providers that understand AI testing’s unique challenges and can blend traditional testing approaches with AI-specific methodologies, while remaining compliant with emerging AI regulations and ethical guidelines.

Providers offering comprehensive AI testing frameworks, specialized validation tools, and deep expertise in detecting biases, explaining models, and monitoring performance are uniquely positioned to support enterprises in their AI quality assurance journey. These providers must demonstrate technical proficiency in AI testing and a strong understanding of domain-specific AI applications, regulatory requirements, and ethical considerations.

• Quality Engineering (QE) Services for AI Applications and Systems PEAK Matrix® Assessment 2024

  

  What is in this PEAK Matrix® Report

  In this report, we assess 21 providers featured on the Quality Engineering (QE) Services for AI Applications and Systems PEAK Matrix® Assessment 2024 and categorize them as Leaders, Major Contenders, and Aspirants based on their capabilities and offerings. The study will enable buyers to choose the best-fit provider based on their AI testing requirements, while providers will be able to benchmark their performance against each other.

  Scope:  

  • All industries and geographies
  • Service: QE

  Contents:

  • This report features detailed assessments, including strengths and limitations, of 21 providers focusing on delivering QE services for AI applications and systems.

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers

Mid-market enterprises are trying to strengthen their Quality Engineering (QE) foundations while simultaneously exploring fresh frontiers through cutting-edge technologies such as generative AI. In an ever-evolving market landscape, they want to engage with a provider capable of delivering superior technical implementations and also serving as a true strategic partner. While these enterprises want to focus on excellence and innovation when selecting their provider, they also do not want to be lost in a sea of deals when engaging with larger providers. In their pursuit, mid-market enterprises want partners that are relationship-driven, cost-efficient, agile, nimble, and committed to delivering business impact and return on investment at every step of the transformation.

Providers with inventive solutions, accelerators, and strong advisory capabilities can efficiently guide these enterprises through their QE transformation journey.

• Quality Engineering Services for Mid-market Enterprises PEAK Matrix® Assessment 2024

  

  What is in this PEAK Matrix® Report

  In this report, we assess 32 providers featured on the Quality Engineering Services for Mid-market Enterprises PEAK Matrix® Assessment 2024 and categorize them as Leaders, Major Contenders, and Aspirants based on their capabilities and offerings. The study will enable buyers to choose the best-fit provider based on their sourcing considerations, while providers will be able to benchmark their performance against each other.

  Scope:

  • All industries and geographies
  • Service: QE

  Contents: 

  This report features detailed assessments, including strengths and limitations, of 32 providers focusing on delivering QE services to mid-market enterprises.

  READ ON

What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

LEARN MORE ABOUT Top Service Providers