The sales services landscape is rapidly evolving due to disruptive technologies, elevating customer expectations, and enterprise focus on data-driven decision-making. Customers are increasingly seeking more tailored and customized experiences. In this space, inside sales services such as lead generation, customer segmentation, and lead qualification are vital revenue drivers. Furthermore, digital commerce and marketplace services are expected to see accelerated growth.

North America continues to dominate the sales service outsourcing landscape with businesses adopting emerging technologies and advanced analytics to deliver personalized customer experiences. The technology, and telecom and media sectors particularly see significant sales service outsourcing.

The sales services’ technologies suite includes diverse tools and solutions, such as sales forecasting platforms, lead generation and qualification tools, data management solutions, marketing and sales analytics, and agent assist applications. Advanced analytics, AI, and automation are increasingly shaping these solutions, enabling providers to meet the growing demands for more efficient and personalized sales processes. Several providers are partnering with technology vendors to boost their AI capabilities and broaden their service offerings.

• Sales Services PEAK Matrix® Assessment 2024

  

  In this report, we analyze 15 B2B and 9 B2C sales service providers’ capabilities across industries globally. Each profile examines the provider’s service focus, key IP and solutions, domain investments, and case studies.

  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

Read on to explore how AI is revolutionizing customer experience (CX) in today’s fast-paced digital landscape. In this expert analysis, you will learn how AI-driven solutions enhance customer journeys, personalize interactions, and streamline operations. Discover the pivotal role of human talent in ensuring the success of AI-driven CX and how businesses can harness these innovations to create seamless, customer-centric experiences. Reach out to us directly to discuss this topic further.

In today’s fast-paced digital landscape, customer experience (CX) stands at the forefront of business success. Companies are increasingly turning to AI to revolutionize their customer interactions, streamline operations, and provide a seamless journey from start to finish. AI-driven CX is not just a buzzword; it’s a game-changer, offering a host of benefits to both businesses and their customers alike. In this blog, we’ll delve into how customers stand to benefit from AI in their journey and highlight the pivotal role of human talent in ensuring the success of AI-driven CX.

Enhancing the customer journey with AI

Imagine a seamless, personalized experience tailored precisely to your preferences and needs, available at your fingertips. That’s the promise of AI-driven CX. From personalized product recommendations to proactive customer support, AI empowers businesses to anticipate and meet customer expectations like never before.

Elevated experiences: hyper-personalization for unparalleled connections

One of AI’s biggest benefits for CX is using data to achieve true personalization at scale. AI can analyze a company’s vast pools of customer data – from transactions to browsing behaviors to communication histories – to build rich customer profiles. It can then surface insights to deliver experiences tailored to each individual’s preferences, needs, and contexts.

For customers, this enables a new level of relevance from the brand interactions they receive. AI-powered recommendation engines, such as those from Netflix, Spotify, and Amazon, ensure customers always see the most fitting content or product suggestions.

For businesses, envisioning how AI-powered recommendation engines could work in their own context unlocks a new level of relevance in brand interactions. AI algorithms can ensure that customers receive personalized recommendations for support resources or solutions tailored to their specific needs and preferences. Moreover, AI can personalize messaging tones, channels, and cadences for outreach, creating smoother, more engaging experiences for customers. By leveraging AI in this way, customer service teams can deliver proactive and tailored support that addresses customer needs efficiently and enhances satisfaction.

Anticipating needs: proactive care redefining customer support

AI opens up powerful new possibilities for proactive customer care. With machine learning models analyzing data patterns, businesses can anticipate customers’ likely future needs or issues and be proactive about resolving them. This benefits customers by allowing problems to be addressed before any major disruption or frustration occurs.

For instance, a telecom company notices an increase in calls related to a network outage in a specific area. Instead of waiting for frustrated customers to flood the lines with complaints, the contact center proactively sends out automated notifications to affected customers, informing them of the issue and providing estimated resolution times. Additionally, the system identifies high-priority customers, such as those with critical business needs or medical emergencies, and prioritizes their inquiries for immediate resolution. Another example is when an insurance provider utilizes proactive outreach strategies, such as sending notifications when insurance coverage is expiring or subscriptions are ending, along with discounts or offers for renewals.

Efficiency amplified: faster, smoother, better resolution times

When customers do need to directly engage a brand for support, AI can accelerate resolution times and reduce frustration. Conversational AI assistants are becoming smarter at understanding complex language to quickly identify the true intent behind customers’ requests. From there, AI can directly handle simpler queries, tasks, or transactions through self-service.

For instance, an AI assistant could autonomously resolve a customer asking to return a parcel by walking through the process and initiating a refund. For more complex cases, AI can automatically route the conversation to the right human agent or department for seamless escalation.

This AI-powered triage and assistance cuts down tedious back-and-forth, reducing resolution times and customer effort. When human employees are looped in, they have full context to focus on providing a tailored, speedy resolution.

The human touch: why human talent matters in AI-driven CX

While AI brings transformative capabilities, human agents remain vital for delivering empathetic, personalized customer experiences – especially for complex, emotional situations. AI can augment and empower human workers, rather than replace them. Here’s why human talent is crucial in AI-driven CX:

Empathy and emotional intelligence

AI may excel at analyzing data and predicting behavior, but it lacks human empathy and emotional intelligence. Empathetic human interactions are essential, especially in sensitive situations or complex inquiries where understanding and compassion are paramount. Human agents can empathize with customers, actively listen to their concerns, and provide personalized solutions that resonate on an emotional level.

Complex problem solving

While AI can handle routine queries and tasks with efficiency, complex issues often require human intervention. Human agents possess critical thinking skills and domain expertise to navigate intricate problems, adapting to unique situations and finding creative solutions. By combining AI’s automation capabilities with human problem-solving skills, businesses can deliver comprehensive support that addresses the full spectrum of customer needs.

Building trust and loyalty

Trust is the foundation of customer relationships, and human interactions play a vital role in fostering trust and loyalty. Customers value authentic connections with human representatives who demonstrate understanding, sincerity, and integrity. While AI can streamline processes and deliver personalized experiences, it’s the human touch that cultivates meaningful connections and builds long-term loyalty.

Continuous improvement

Human feedback is irreplicable for refining AI algorithms and enhancing the customer experience. Human agents act as the last checkpoint before messages reach customers, ensuring authenticity and relevance. Moreover, with AI’s potential to hallucinate, skilled agents play a crucial role in validating AI-generated insights and enhancing the overall customer experience. By fostering collaboration between humans and AI, businesses can achieve continuous improvement in their customer operations and stay ahead in a competitive market.

Conclusion: the future of customer experience (CX)

In the dynamic realm of CX, the fusion of AI innovation and human expertise emerges as a cornerstone for success. As businesses embrace AI-driven solutions to streamline operations and personalize interactions, they can unlock unprecedented levels of efficiency and customer satisfaction. However, amidst these technological advancements, the pivotal role of human agents cannot be overlooked. With their empathy, creativity, critical thinking, and problem-solving abilities, human agents add an indispensable touch to customer interactions, fostering trust and loyalty in an increasingly digital landscape. By striking the right balance between AI-driven innovation and human connection, businesses can navigate the complexities of CX, delivering seamless experiences that resonate with customers and propel their brands to new heights of success.

Learn more about how to utilize AI and its latest iteration, generative AI, for anticipating CX needs in the LinkedIn Live session, Leveraging AI for CX.

Join Everest Group Practice Director Anish Nath and a panel of experts for an insightful webinar on groundbreaking AI integrations and the emergence of agentic intelligence. Explore how technology is redefining business operations and customer interactions, including advancements in personalization, the resurgence of voice communication, and the impact of evolving regulations on AI innovation.

Register

In this webinar, you’ll learn how to:

• Gain a competitive edge with insights into emerging trends that will dominate in 2025.
• Prepare your organization to embrace transformative technologies and opportunities.
• Get the predictions and insights you need in just 30 minutes.

Register

Anish Nath
Practice Director, Everest Group
Marni Carmichael
Vice President Marketing, ImageSource
Greg Council
Director of Product Marketing, ImageSource

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

In today’s fast-evolving customer experience (CX) landscape, data is often referred to as the “new oil”—a critical resource powering innovation and efficiency.  

Nowhere is this truer than in contact centers, where data from customer interactions fuels everything from service delivery to strategic decision-making. With generative AI (gen AI) entering the scene, this data is not just an asset but a requirement. For contact centers, data is the lifeblood that enables artificial intelligence (AI) to deliver real-time, personalized experiences, optimize workflows, and streamline issue resolutions. 

But before you jump into implementing gen AI, there’s a key question to address: Is your data ready?

The success of AI initiatives hinges not just on the quantity of data but its quality, accessibility, and structure. Data readiness becomes the foundation for any successful AI deployment, ensuring that your contact center is prepared to harness AI’s full potential. But what exactly is data readiness, and how can you assess whether your contact center has it? Let’s dive deeper…

Reach out to discuss this topic in depth. 

What is data readiness?

Data readiness is the state of your organization’s data in terms of quality, quantity, structure, and availability to support AI-driven solutions. In the context of gen AI in contact centers, it refers to having the right kind of structured and unstructured data—voice transcripts, chat logs and customer relationship management (CRM) interactions, etc.—that AI systems can process, learn from, and use to generate intelligent insights or automate workflows.

Think of it like cooking a gourmet meal: You can have the best kitchen equipment (AI tools), but if your ingredients (data) are spoiled or mismanaged, the final dish will be a disaster. The same goes for AI implementation in contact centers — without properly prepared data, even the most advanced gen AI tools can underperform.

What should your data be like for gen AI success?

For gen AI to work effectively in contact centers, the data needs to meet several key criteria:

Exhibit 1: Checklist for data readiness 

Source: Everest Group

Stages of data readiness

Understanding where your organization stands in its data readiness journey is key to planning for gen AI. Here are typical stages:

Exhibit 2: Stages of Data Readiness 

Source: Everest Group

To identify your state in data readiness, answering the following questions will give you a clear picture of your current state:

Exhibit 3: Questions to identify your current state in data readiness 

Source: Everest Group

Tangible steps to improve data readiness

Once you identify your stage, you can follow the below approach to progress toward AI readiness:

Exhibit 4: Tangible steps to improve data readiness 

Source: Everest Group

Leveraging technology, automation, and AI to boost data readiness

Automation tools and AI can play a big role in speeding up the data readiness process. For instance:

• Automation for data cleaning: Deploy Robotic Process Automation (RPA) for cleaning and structuring large datasets, eliminating repetitive manual tasks

• AI-based feedback loops: Implement AI to constantly monitor data quality, identifying gaps and correcting inconsistencies in real-time

• Self-learning systems: As your gen AI models evolve, create a feedback mechanism where the AI itself learns from the data it processes, ensuring continuous improvements

Conclusion

Data readiness isn’t a destination—it’s a continuous journey that ensures your contact center is prepared to maximize the potential of gen AI.

Whether you’re in the early stages of organizing your data or already utilizing advanced AI systems, taking steps to improve your data’s quality, accessibility, and contextual richness will dramatically impact your AI’s effectiveness.

As gen AI continues to evolve, ensuring your data is prepared will not only enhance the customer experience, but also drive efficiency and innovation across your contact center operations. Don’t let poor data hold your AI back—start optimizing today!

If you found this blog interesting, check out our blog focusing on Generative AI In Healthcare – A Game Changer Or Another Fad? | Blog – Everest Group (everestgrp.com), which delves deeper into another topic regarding gen AI. 

If you have questions or want to discuss CX strategies and solutions, please contact Mohit Kumar or Chhandak Biswas. 

Following the conclusion of Everest Group’s inaugural Engage 2024, an event in which attendees left feeling energized and equipped with practical insights and actionable strategies, there is now more to come! 

The event, which offered a deep dive into the evolving world of outsourcing and service delivery, brought together industry veterans, thought leaders, and Everest Group   advisors, all of whom explored the past, present, and future of global services.  

What was the goal of this you may ask? Put simply, it was to arm leaders with pragmatic solutions that drive business impact, in the “do more with less” and generative AI (gen AI) eras. 

At its core, Everest Group Engage revolves around “The Pragmatic Edge”—a philosophy that blends practical and actionable strategies, with forward-thinking insights. 

This approach enables organizations to build sustainable advantages in service delivery, combining innovative thinking with real-world applicability.  

The event highlighted how businesses can leverage this mindset to remain competitive, all while embracing the next phase of transformation: the Business Value Mosaic. This new model emphasizes the need to understand and capture business value holistically—redefining how services create impact across industries. 

Everest Group Engage 2024 explored the megatrends shaping the industry today. Sessions delved into topics ranging from pricing models and gen AI adoption, to shifts in global delivery models. Attendees were challenged to think beyond traditional service paradigms and embrace new frontiers—like the growing relevance of Africa as a key player in global delivery with its emerging talent pool. 

The event also sparked critical conversations about the future of work. Experts emphasized the need for frictionless service models—delivering seamless experiences—and the importance of developing a “prime” model, with built-in flexibility to adapt in an ever-evolving ecosystem. Whether discussing artificial intelligence (AI) , automation, or evolving workforce strategies, the message was clear: success in this era requires agility, collaboration, and bold leadership. 

Everest Group Engage is more than a conference. It’s a call to action for leaders to expand their toolsets, shift their mindsets, and embrace the future of service delivery with confidence. Whether you are new to the industry or a seasoned professional, this event provides the knowledge, connections, and strategies needed to thrive amid constant change. 

Announcing Everest Group Engage 2025 — London 

We are delighted to announce the next chapter of Everest Group Engage will be coming to London, from March 31 – April 1, 2025. Building on the momentum of the 2024 event, Everest Group Engage 2025 — London will delve deeper into The Pragmatic Edge: Global Impact—exploring how practical strategies can drive sustainable growth in a complex, interconnected world. 

This 1.5-day event will connect executives from GBS , sourcing, vendor management, IT services, global locations, and next-gen technology, with top experts and thought leaders on hand to co-create solutions for future challenges.  

With hands-on workshops, solution-focused discussions, and immersive networking, attendees will leave prepared to make confident, data-driven decisions and transform their organizations. 

Mark your calendars now! London awaits—offering you the opportunity to sharpen your pragmatic edge and achieve global impact. Learn more about Everest Engage 2025 in London! 

In today’s digital-first world, customer expectations have evolved rapidly…

Modern customers now expect fast, accurate, and personalized interactions from the brands they engage with. Furthermore, meeting these heightened expectations has become a challenge for businesses, driving the adoption of advanced technologies to enhance customer engagement.

At the forefront of these technologies is Conversational AI (CAI), an increasingly transformative solution reshaping how companies interact with their customers.

In this blog, we will explore how CAI technology is revolutionizing engagement across the entire customer journey, and how businesses should integrate CAI into their tech stack for providing an efficient, scalable, and personalized engagement to the modern customer.

The evolution of CAI:

CAI has been one of the biggest beneficiaries of the AI revolution over the past decade. Early solutions were rule-based, functioning on pre-programmed scripts that limited their ability to adapt to diverse inquiries or provide truly personalized service.

Today’s AI-powered bots can use sophisticated Machine Learning (ML) algorithms to understand context, intent, and sentiment, enabling more natural and engaging interactions across the plethora of channels that exist i.e. voice, chat, email, and social media.

Now with the addition of generative AI (gen AI) and the ability to effectively leverage customer data, CAI bots have grown more adept at handling complex queries, offering dynamic and customized responses, often with limited human intervention.

Supercharging the customer journey: A CAI-powered approach:

One of the most impactful aspects of CAI is in its true versatility i.e. its ability to assist customers at every stage of their journey, from initial engagement through to post-purchase support. From the moment potential customers discover a brand, CAI bots can engage with them in real time 24/7, as explained below.

1. Lead generation

Generating high-quality leads is one of the most crucial tasks for sales and marketing teams. CAI can enhance lead generation efforts by engaging potential customers on websites or social media channels in real time. Through outbound campaigns, they can gather essential data and seamlessly hand off qualified leads to sales teams

2. Product discovery

Instead of browsing through static menus or endless product categories, users can rely on conversational search to find what they’re looking for faster. CAI systems, especially when integrated with enterprise applications like customer relationship management (CRMs) and customer data platforms (CDPs), can analyze user preferences, behavior, and past interactions across various channels

3. Purchase support

CAI can provide insights on bundle deals, warranty options, and related products, helping customers make informed purchase decisions. If a customer hesitates at checkout, the chatbot can step in with timely offers or discounts to encourage completion of the purchase. Furthermore, these chatbots seamlessly integrate with payment gateways like PayPal and Apple Pay, allowing secure transactions directly within the chat interface, adhering to industry-standard security protocols

4. Post-purchase assistance

CAI can conveniently help customers with order confirmation, receipt generation, and next steps such as shipping details. It enables brands to check in with customers, asking about their experience and offering tips for maximizing product use. The chatbot can also assist customers with returns, refunds, and exchanges making the process hassle-free

5. Customer retention

CAI can schedule follow-up interactions with customers after they’ve left, sending personalized emails or messages highlighting new features, improvements, or exclusive return offers. Automating win-back efforts ensures the brand maintains a connection and demonstrates a commitment to addressing any previous issues.

To illustrate the comprehensive support CAI provides, the following exhibit showcases how a potential customer navigates a fictional e-commerce website, TechTrends, that has embraced CAI across the customer journey.

Best practices for implementing CAI solutions:

While CAI presents significant opportunities for businesses, successful implementation requires thoughtful planning and execution. The following best practices are recommended to successfully implement and harness the capabilities of CAI.

• Start small with careful planning: Before implementing any CAI solution, it’s essential to define clear objectives, as well as identifying small pilots that can deliver a quick return on investment (ROI). This approach allows organizations to test the CAI solution, gather feedback, and gradually expand into more complex areas as they gain confidence with the technology
• Customer-centric conversational flow: Conversational flows should be designed mindfully, ensuring they are intuitive and user-friendly. This includes incorporating fallback mechanisms, such as human handover options, to provide seamless transitions when the chatbot encounters complex queries or customer frustration
• Establish a robust data infrastructure and integrations: Enterprises should ensure all customer data sources, including CRM, past chat logs, and behavioral data, are unified and regularly updated as usage scales. There also must be a focus on building application programming interface (APIs) and middleware that allows context transfers across channels for omnichannel deployments
• Utilize modular architecture for scalability: Modular, microservices-based architectures allow for easy upgrades, testing, and scaling, making it possible to refine and scale specific parts of the CAI solution without affecting the entire system
• Prioritize AI transparency and governance: Besides complying with regulations, it is vital to implement AI explainability, especially in regulated industries such as finance and healthcare, to help agents and customers understand the basis of AI recommendations
• Embrace change: Transitioning to CAI also requires a cultural shift, emphasizing that it is a tool to assist, not replace, human roles. Providing training and fostering an open mindset will help customer facing teams to effectively leverage CAI

Conclusion:

CAI’s capabilities can transform what was once a series of disjointed transactions into a fluid, intuitive, and highly personalized customer journey.

This streamlined approach saves time for the customer, increases conversion rates for the business, and ultimately creates a more satisfying and efficient experience.

Looking ahead, the future of CAI is poised for remarkable advancements. CAI bots will evolve into agentic systems, becoming autonomous digital colleagues, capable of higher-order planning and independent decision-making.

Through the combination of deep learning and reinforcement learning, these systems will be able to process large amounts of data, recognize complex patterns, and learn from their actions and experiences in real-time environments.

The bottom line for enterprise leaders remains the same, conversational AI’s real impact is not just in introducing it in a siloed fashion, but embedding it deeply across the customer journey, into the core of business processes, where it can be of deliverable measurable value.

If you have any questions, would like to delve deeper into the Experience, Sustainability & Trust market, or would like to reach out to discuss these topics in more depth, please contact Simran Agrawal (simran.agrawal@everestgrp.com) and Anubhav Das (anubhav.das@everestgrp.com)

In today’s rapidly evolving business landscape, traditional operating models are struggling to keep pace with the needs of digital-first enterprises.  

The shift toward product-led operating models (PLOM) has also become a strategic priority for many companies seeking to innovate faster, deliver superior customer experiences, and maintain a competitive edge. But what does a product-led operating model really entail? How does it differ from conventional models, and why should companies adopt it? 

This is the first blog in our series on product-and-platform led operating model transition. We aim to cover several dimensions on PLOM, including organizational design, processes, tools and technology and talent  in the coming blogs.  

This blog explores the essence of the product-led operating model, its impact on organizations, the challenges of adopting it, and the benefits it can deliver. Now, let’s dive deep into the principles, enablers, and outcomes of a product-led transformation… 

Reach out to us to discuss this topic further with our expert analysts.  

The dawn of the product-led era: More than just a buzzword: 

A product-led operating model structures the organization around products rather than projects. Each product—whether a service or software application—is treated as its own mini-business unit. Unlike the traditional project-based approach, PLOM is focused on long-term value creation, with dedicated teams responsible for each product’s lifecycle. This fosters accountability, continuous improvement, and faster innovation. 

*Based on Everest Group’s survey of 200 global enterprises with revenue more than US$ 1 billion 

 The 4 core tenets of a product-led operating model are: 

1. Decentralized information technology (IT) teams: IT is integrated into business units, allowing teams to work closely with stakeholders and understand the business context deeply
2. High autonomy: Teams are granted ownership over products, driving accountability and more meaningful outcomes for the business
3. Shift from milestones to objectives: The focus is on achieving business objectives, not merely meeting project milestones. This allows for iterative development and continuous delivery of value
4. Prioritize speed over perfection: Teams prioritize agility and responsiveness over lengthy perfectionism, speeding up time-to-market for products

Why your current operating model might be holding you back?: 

The traditional project-led approach, with its rigid timelines, fixed budgets, and siloed teams, is increasingly becoming a liability in today’s fast-paced digital landscape. Here’s why: 

1. Slow time-to-market: In an age where first-mover advantage can make or break businesses, the lengthy approval processes and waterfall methodologies of project-led models are detrimental
2. Disconnect from customer needs: Project-led approaches often prioritize internal metrics over customer satisfaction, leading to products that miss the mark
3. Innovation bottlenecks: With resources tied up in predefined projects, there’s little room for experimentation and pivoting based on market feedback
4. Talent drain: Top talent is increasingly drawn to environments that offer autonomy and end-to-end ownership – something project-led models struggle to provide

*Based on Everest Group’s survey of 200 global enterprises with revenue more than US$ 1 billion 

The product-led advantage: More than just faster delivery: 

While speed is a significant benefit of PLOM, its advantages extend far beyond just getting products to market faster: 

1. Enhanced customer loyalty: By continuously iterating based on customer feedback, product-led organizations create solutions that truly resonate with users, fostering long-term loyalty
2. Increased innovation: The autonomy granted to product teams creates a fertile ground for breakthrough ideas and disruptive solutions
3. Better resource utilization: Resources are allocated based on product performance and potential, ensuring optimal use of both human and financial capital
4. Improved employee satisfaction: The sense of ownership and impact fostered by PLOM leads to higher employee engagement and retention

Case in point: Spotify – Harmonizing innovation at scale: 

Spotify’s famous Squad model is often cited as a prime example of PLOM in action. By organizing into small, cross-functional teams (Squads) focused on specific product areas, it has maintained its innovative edge despite rapid growth. 

Spotify’s model demonstrates how PLOM can be scaled effectively in large organizations, maintaining agility without sacrificing coordination. 

Key Enablers of a Product-Led Operating Model: 

To successfully transition to a product-led model, companies must enable several foundational elements: 

1. Leadership commitment

Top-down leadership is essential for driving the shift to a product-led mindset. Chief Information Officers (CIOs) must evolve into Chief Influence Officers, ensuring that technological decisions align with broader business objectives​. Product-centric IT governance should also integrate strategic business priorities into product development. 

2. Agile and DevOps practices

PLOM requires an agile environment with continuous integration and continuous deployment (continuous integration (CI)/continuous delivery (CD)) capabilities. DevOps practices are essential for ensuring that new features can be tested, rolled out, and reverted back to quickly if necessary​. 

3. Effective use of data and feedback

Data plays a critical role in driving product decisions. Tools for clickstream analysis, user feedback, and feature performance helps teams understand how products are being used and where improvements are needed​.   

Metrics and KPIs: The Move from SLA to OKRs: 

A significant shift in a product-led model is the way success is measured. The traditional model focuses on service level agreements (SLAs) and key performance indicators (KPIs), while PLOM focuses on objectives and key results (OKRs). OKRs are ambitious, outcome-driven goals, that align with long-term business objectives rather than operational metrics. 

For example: 

• OKR: Increase active user base by 15% over the next quarter by enhancing product user experience (UX). 

• KPI: Did we meet our objective of reducing the churn rate by 5%? 

This shift from operational to business-centric metrics encourages teams to align their activities with broader business goals. 

Key challenges in adopting PLOM: 

1. Cultural shift: A product-centric IT model requires a change in mindset, moving from a ’defect-avoidance’ culture to a ‘fail-fast, learn-fast’ approach​. Organizations need to instill trust, ownership, and collaboration across teams, fostering a ’generative’ culture where experimentation is encouraged
2. Structural complexity: PLOM often introduces a matrix organizational structure, where IT and business functions operate closely. This setup can be complex, especially for larger enterprises that previously functioned in silos. For instance, defining decision rights—whether it’s feature sets, budget allocation, or vendor selection—can lead to friction between IT and business leaders
3. Governance and scalability: While PLOM emphasizes autonomy, central IT functions such as cybersecurity, compliance, and architectural standards remain crucial. Managing this balance between decentralization and centralized governance can be difficult

Overcoming the hurdles: Transitioning to a product-led model: 

While the benefits of PLOM are clear, the transition can be challenging. Here are some key considerations:  

1. Cultural transformation: Moving to a product-led model requires a significant shift in mindset across the organization. Leaders must champion a culture of experimentation, learning from failure, and customer obsession
2. Talent and skill gap: PLOM demands a different skill set, particularly in product management and data analytics. Organizations need to invest heavily in upskilling and potentially bringing in new talent
3. Technology infrastructure: Legacy systems can be a major roadblock. Investing in modern, flexible tech stacks is crucial for enabling the agility required in a product-led model
4. Metrics and KPIs: Traditional project-based metrics won’t cut it. Organizations need to develop new ways of measuring success, focusing on customer value and business outcomes rather than just delivery timelines
5. Governance and risk management: Balancing the need for autonomy with necessary oversight is crucial. Organizations must develop new governance models that enable speed without compromising on compliance and risk management

 *Based on Everest Group’s survey of 200 global enterprises with revenue more than US$ 1 billion 

The future is Product-Led: Are you ready? 

As we move deeper into the digital age, the ability to rapidly innovate, adapt, and deliver customer value will be the defining factor between market leaders and laggards. The Product-Led Operating Model offers a proven framework for achieving this agility and customer-centricity. 

However, the transition to PLOM is not a one-size-fits-all solution. Organizations must carefully assess their current state, culture, and goals to determine the right approach. It requires commitment from leadership, investment in people and technology, and a willingness to fundamentally rethink how value is created and delivered. 

The question isn’t whether your organization should consider a product-led approach, but rather how quickly you can begin the transformation. In a world where customer expectations are constantly evolving, standing still is no longer an option.

The product-led revolution is here – will you lead, or be left behind?

In our next blog, we will explore in-depth the key dimensions of PLOM: organization design, processes, core technology and talent. We will discuss at length a framework that serves as a blueprint for establishing the foundational principles of a product-and-platform-led operating model and assists enterprises throughout their transformational journey. 

If you found this blog interesting, check out our blogs page at Everest Group | Blog (everestgrp.com), which delves deeper into a wide range of topics across all of our service lines. 

To discuss this topic in more detail or for an even more detailed analysis, please contact Krishna Zawar and Parul Trivedi.

WATCH THE WEBINAR ON-DEMAND

After experiencing significant growth post-pandemic, the customer experience management (CXM) market hit turbulence in 2024. Enterprises are now more cautious about their spending, pushing service providers to do more with less. At the same time, generative AI (gen AI)-led use cases are moving into production, which is revolutionizing how contact center leaders are thinking about their future operating models.

Watch this webinar to hear our CXM experts examine how the CXM market has evolved throughout 2024, and share what can be expected for 2025.

What questions did the webinar answer?

• How does the CXM service provider landscape across regions look in 2024
• How was the year for the broader CXM market, and which themes have materialized?
• What should we expect from the CXM market in 2025?

Who should attend?

• CEOs, CCOs, CIOs, CTOs
• BPO strategy/global heads
• Heads of CXM outsourcing
• CXM strategy heads
• Heads of customer service
• Heads of CXM service delivery
• Senior sales and marketing executives

After previously zooming the lens in on how Salesforce has helped global enterprises to provide a holistic customer experience approach through its integrated set of offerings, this time we focus on another CX tech vendor, Sprinklr, that offers a unique category of enterprise software, which it terms as “Unified Customer Experience Management”.

Unified Customer Experience Management (Unified-CXM) empowers all customer-facing teams in an enterprise, from support, to sales and marketing, in order to then collaborate effectively, communicate across digital channels, and leverage an artificial intelligence (AI)-powered platform to deliver consistent and cohesive customer experiences at scale. In this blog, we shine a spotlight on Sprinklr and its evolution. Reach out to discuss this topic in depth.

Today’s consumers interact with brands across a range of touchpoints. Naturally, the modern customer journey is a complex and multi-faceted one, often involving a combination of channels and modalities.

Enterprises want a comprehensive view of these interactions—from marketing, through sales, to post-sales support—to maintain effective customer engagement across the lifecycle.

However, most enterprises still rely on legacy customer relationship management (CRM) systems that are not tightly integrated with customer facing tools and applications, which becomes a hindrance to delivering real-time personalized engagement. This leads to customer dissatisfaction and a loss of trust in many cases.

Sprinklr’s Unified-CXM platform is designed to address these challenges by helping enterprises eliminate silos, access and analyse unstructured digital data and leverage AI to generate a unified view of each customer’s journey. This approach allows customer facing teams to better assist customers, share knowledge, and collaborate, ultimately enhancing the overall customer experience.

Sprinklr’s platform is comprised of four product suites—Service, Marketing, Insights, and Social—which when brought together support enterprises in better managing the end-to-end customer journey.

These suites operate on a single, unified AI-powered platform, enabling enterprises to streamline customer interactions across multiple touchpoints. Each product suite offers distinct capabilities, which will be examined in more detail below.

(Image courtesy: Sprinklr)

Sprinklr’s product suite:

With the rise of digital channels such as Instagram, TikTok, and WhatsApp, among others; customers are now more connected and empowered than ever before, offering continuous real-time feedback to express their concerns or frustrations.

This shift makes personalized and real-time customer engagement crucial for brands. Sprinklr’s product suite addresses these evolving needs, offering enterprises solutions to enhance engagement, gain insights into customer sentiment, and take proactive measures when necessary. Each suite provides a range of solutions that enterprises can implement either individually or as a bundle –

• Sprinklr Social – This suite offers AI-powered tools to unify social media publishing and engagement across more than 30 channels. It enables enterprises to manage and analyze social media content, monitor conversations, and improve customer interactions. Key products include:
  • Social Publishing & Engagement: Supports teams with digital asset management, editorial calendaring, and omnichannel publishing
  • Employee Advocacy: Enables organizations to leverage employees in brand promotion, boosting awareness, and generating leads
• Sprinklr Insights – Sprinklr Insights unifies data across customers, competition, as well as the industry, from both traditional and digital channels, allowing enterprises to monitor customer sentiment and industry trends in real time Key products include:
• Social Listening: Which enables enterprises to understand unstructured data from 15+ digital channels, as well as automatically identify trends/anomalies to act upon
• Competitive Insights & Benchmarking: Which enables enterprises to benchmark their social performance against competition and monitor influencers across eight social channels
• Sprinklr Marketing – Focused on planning, executing, and optimizing marketing campaigns, this suite enables enterprises to manage content creation, collaboration, and performance tracking across multiple channels. Key products include:
• Campaign Planning & Content: Marketing which has capabilities like brand governance, cross-channel publishing/distribution, briefing, copy assistance and localization

Ads Comment Moderation: Which aids enterprises in managing comments on paid posts at scale, brands can moderate testimonials, product feedback, and urgent customer service queries

• Sprinklr Service – Sprinklr’s Service Suite is a comprehensive Contact Center as a Service (CCaaS) solution for managing customer support across voice and digital channels. It integrates AI-driven automation, self-service, and agent assistance tools, in order to provide customer care at scale through voice, messaging, social media, and other digital platforms. Products within this suite include –
• Sprinklr Voice: For managing inbound and outbound interactions with capabilities such as Interactive Voice Response (IVR), Automatic Call Distribution (ACD), Agent Assist, AI-driven nudges and predictive dialers, and omnichannel workflows
• Conversational AI chat and voice bot solution: Which comes with a use case library and industry-specific/intent-based bot workflows, as well as Workforce Management & Quality management for contact center managers

Some of Sprinklr’s strategic differentiators include:

• Unified architecture: Sprinklr’s single-codebase platform allows enterprises to seamlessly integrate channels, unify customer journeys, and accelerate innovation through a “build once, deploy everywhere” model
• Advanced listening: The platform captures unstructured data from 450 million daily conversations, providing comprehensive social listening and analytics
• Purpose-driven AI: While it has its proprietary AI models which are industry-trained, it also allows enterprises to integrate other industry-leading generative AI (gen AI) models, which it calls Sprinklr AI+. Sprinklr AI+ leverages generative AI in all four Sprinklr product suites and is powered by integrations with OpenAI, Google Cloud’s Vertex AI and Microsoft Azure OpenAI Service
• Scalable enterprise-grade platform: This platform is designed to meet industry security standards, including ISO 27001, HIPAA, PCI-DSS, and SOC compliance, making it a scalable solution for large enterprises

Powered by Sprinklr AI+, it has also recently launched Sprinklr Digital Twin, a new AI technology designed to enable enterprises to build and deploy autonomous and intelligent AI applications, that can mirror and enhance the capabilities of customer-facing teams.

While Sprinklr’s service suite has become an established offering, the company’s broader vision remains becoming the core operating system for all front-office teams supporting all conversations that an enterprise can have with its customers.

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.

If you have any questions, have further interest as we continue to investigate best in-class vendors to support your CX transformation journey, or would like to reach out to discuss these topics in more depth, please contact Anubhav Das and Sharang Sharma.