It’s fascinating to see how DeepSeek currently seems to be everywhere on our social media feeds throughout the last couple of days. 

Unsurprisingly, it’s now sparking a mix of excitement and anxiety across the industry. For some, it’s a revolutionary breakthrough. For others, it’s raising serious concerns (and the deep correction in most tech stocks on NASDAQ on January 27^th only confirms that). 
 
The big debate: Whether DeepSeek is truly better than what OpenAI, Anthropic, or others have achieved. The internet is still split over which model outdoes the others. Some early tests do indicate that DeepSeek R1 manages to outscore its peers on some of the testing benchmarks. 

Reach out to discuss this topic in depth. 

Benchmark performance of DeepSeek-R1

Image source: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Sheer compute power isn’t the only lever artificial intelligence (AI) creators have. An AI model can stand out in two ways in the current market – firstly by being state-of-the-art or secondly, by being almost as good but far cheaper. DeepSeek appears to have mastered the latter, and that’s what makes it such a revolution right now. 

Its founders claim to have developed DeepSeek with a budget of under US$6 million. And it took them less than two months to do that. Secondly, all the training was done on older NVIDIA hardware rather than using the most expensive chips – H100s.

While there are some questions being raised about the authenticity of that number (such as possibly not accounting for all the hardware costs because the hedge fund that owns DeepSeek was already in possession of the hardware, or DeepSeek being built off of Open AI and Anthropic models, thus significantly bringing down the costs), DeepSeek has clearly shown that you don’t need multi-billion dollar budgets or the most shiny hardware in the market to build truly competitive AI models anymore.  

It’s not just the development costs: What makes DeepSeek interesting is not just the development cost, but also the fact that it delivers the best that any AI company out there has to offer for free, at least for personal use. For commercial use, the cost per million tokens is considerably less at $0.55 per million input tokens and $2.19 per million output tokens, compared with OpenAI’s Application Programming Interface (API), which costs $15 and $60, respectively.  

What’s more? It is open source. Everything it has achieved is documented and available for others to replicate. This means that it should open the path for more such models to come up on the market, further putting pressure on pricing.  
 
Only good news for enterprises: For those who’ve been eager to embrace AI but held back by the high costs, DeepSeek might be the breakthrough they’ve been waiting for. Imagine a future where you don’t need to budget millions to train and run AI agents, don’t need super sophisticated hardware, and don’t need large, AI-skilled teams!  

DeepSeek might also open doors for self-hosting soon, which so far hasn’t been possible because of the hardware and computer costs associated with it. And it’s not just DeepSeek either, there are others out there, such as Kimi k1.5, Alibaba’s Qwen, and 01.AI. These newer models could lead to faster adoption, even among Small and Mid-Sized Businesses (SMBs). The eureka moment for Generative AI (gen AI)  might be now. These are exciting times for AI. With DeepSeek, it’s become clear that innovation in this space doesn’t require billion-dollar-deep pockets anymore and that is what truly makes it a game changer.

If you found this blog interesting, check out our blog focusing on AI-Powered Coding Assistants: Shaping The Future Of Software Development | Blog – Everest Group, which delves deeper into another topic relating to AI. 

To learn more about DeepSeek, what it may mean for the services industry, and the evolution of AI, please contact Aishwarya Barjatya (aishwarya.barjatya@everestgrp.com) or Sharang Sharma (sharang.sharma@everestgrp.com).  

CXM Providers

CXM Services

Digital CXM

Inside sales outsourcing is being transformed by a wave of leading-edge technologies, including AI, automation, and advanced analytics. These powerful technologies are enhancing lead scoring and predictive analytics, maximizing automation’s role in streamlining tasks, and advancing data-driven strategies for smarter selling.

This session will also unveil the latest trends and explore the dynamic potential of hybrid AI-human sales models, setting the stage for the future of sales excellence.

During this collaborative LinkedIn Live session, we explored:

• How data and real-time analytics can help sales teams adapt strategies and achieve better targeting and close rates
• How AI-driven tools enhance lead scoring, predictive analytics, and customer insights to improve inside sales performance
• What the emerging market trends are within inside sales outsourcing

The Customer Experience Management (CXM) market in 2024 saw a lot of exciting developments, many of which will transform the way we work, think and act as customers.  

The industry saw groundbreaking advancements in artificial intelligence (AI), automation, and data analytics, all of which made enterprises rethink how they engage with their customers. 

As we now look ahead to 2025, the pace of transformation shows no signs of abating. 2025 promises to raise the transformation bar a notch higher, by bringing seismic shifts that will redefine how enterprises connect, engage, and delight customers for the short and long term. These trends are more than changes; they’re bold leaps into the future.  

 Our analysts have dived deep into Everest Group’s top ten predictions for the CXM market, all of which promise to alter the dynamics of this market. 

Reach out to discuss this topic in depth. 

1. Voice of the customer becomes a predictive powerhouse
   Very few things define superior Customer Experience (CX) better than proactive support. Over the next year, AI-driven digital twins—virtual replicas of customers built from real-time data—will start making this a reality. By analyzing patterns and trends from integrated data sources, these twins will predict behaviors, solve problems proactively, and change the dynamics for customer loyalty.

Why it matters: 

• Businesses can build deeper customer trust by delivering hyper-personalized interactions that align with individual preferences and expectations 
• Streamlining operations through data-driven predictions enhances efficiency and helps businesses anticipate and address customer needs effectively 

What to watch out for: 

• Steep initial investments in AI infrastructure can act as a significant barrier for many businesses 
• Another concern is addressing privacy issues as data collection scales up, requiring robust frameworks to protect customer information 

2. Voice channels roar back with intelligence

Voice as a channel is here to stay! In 2025, voice channels will be powered by cutting-edge AI, capable of real-time sentiment analysis, language translation, and seamless integration with digital channels. Voice will shine as the go-to channel for emotionally charged, high-value interactions, making it a game-changer for businesses. 

Why it matters: 

• Enhancing customer relationships with empathetic, real-time conversations is crucial for fostering trust and satisfaction 
• Boosting retention by resolving complex issues effectively through voice interactions strengthens customer loyalty and drives long-term engagement 

What to watch out for: 

• Further developments in this space include AI-powered real-time voice cloning, enabling hyper-personalized interactions and proactive customer engagement; deeper integration with virtual and augmented reality (VR/AR) environments to create immersive support experiences; and enhanced multi-modal customer experiences that seamlessly blend voice, video, and text. While these innovations hold immense potential, they are anticipated to require several more quarters to achieve mainstream adoption 
• Privacy concerns as data collection increases, along with scalability issues and risks, need constant monitoring to maintain trust and security 

3. The rise of data sovereignty ecosystems

In today’s era of heightened data privacy, regional ecosystems—networks of local technology   providers, regulators, and businesses—are set to emerge. These ecosystems aim to give customers greater control over their data, while ensuring compliance with local regulations. By fostering regional collaboration, these systems can balance personalization with privacy.  

Why it matters: 

• Empowering customers with greater control over their data builds unparalleled trust, making businesses the preferred partners in privacy-conscious markets. This trust directly translates to improved customer loyalty and long-term engagement 
• Streamlining operations using data-driven predictions enhances efficiency and supports the development of stronger regional partnerships and compliance strategies 

What to watch out for: 

• Fragmented regulatory requirements across regions can create challenges in standardizing processes and ensuring compliance globally 
• Building region-specific ecosystems involves significant costs and operational complexities, requiring careful planning and localized expertise 

4. The synergy of CX and employee experience (EX)

Happy employees drive happy customers, and 2025 will redefine this connection! We predict that the integration of CX and EX will create a new era of collaboration and impact. Unified dashboards and advanced analytics will link employee satisfaction to customer outcomes, proving that an engaged workforce is the secret to extraordinary customer journeys. Businesses will foster a culture where people and technology collaborate seamlessly, unleashing unmatched potential. 

Why it matters: 

• AI and automation will empower employees like never before, handling repetitive tasks while enabling teams to focus on creativity, problem-solving, and high-impact initiatives. This synergy of human intuition and machine precision will transform customer and employee experiences alike 
• Boosting productivity requires prioritizing employee satisfaction, as it directly impacts motivation and overall performance 
• Creating a ripple effect of positivity ensures that satisfied employees deliver an exceptional service, ultimately enhancing customer experiences 

What to watch out for: 

• Resistance to change within organizations can hinder the adoption of new practices and tools aimed at aligning CX and EX 
• Aligning CX and EX data streams into cohesive strategies is often challenging due to disparate systems and a lack of any unified frameworks 

5. Africa and emerging markets take the spotlight

Africa’s potential as a CX hub has long been recognized, fueled by its cost-effective and skilled talent pools. Recent advancements in accent neutralization, real-time translation, and AI-driven training are now accelerating this momentum, further solidifying Africa and other emerging markets as vital contributors to the global CXM landscape  

Why it matters: 

• Expanding into diverse and untapped talent markets unlocks opportunities for growth and resource optimization in emerging economies 
• Driving innovation with fresh perspectives from new regions can lead to novel solutions and enhanced customer engagement 

What to watch out for: 

• Navigating cultural differences and regulations requires careful planning and adaptability to local norms and legal frameworks 
• Maintaining consistent quality across global teams is critical to ensuring a seamless customer experience and upholding brand standards 

6. The emotional intelligence revolution

While automation excels at handling routine tasks, the ability to empathize remains uniquely human. Soon, emotionally intelligent agents will play a pivotal role in addressing complex and high-stakes customer interactions. Businesses will begin to leverage Emotion-as-a-Service (EaaS) as a key differentiator, offering personalized and empathetic experiences that stand out in competitive markets. 

Why it matters: 

• Businesses can stand out by offering deeply personalized and empathetic interactions that resonate with individual customer needs and preferences 
• To be able to delight customers in situations where automation cannot provide the necessary human touch, ensuring satisfaction and loyalty 

What to watch out for: 

• Recruiting and training emotionally intelligent agents for these high-value roles can be resource-intensive and challenging 
• Balancing the use of automation with human-driven empathy requires a careful strategy to avoid an over-reliance on either approach 

7. Key Performance Indicators (KPIs) reimagined for the future

The metrics we use to measure success are changing. By 2025, KPIs such as Emotional Engagement Scores (EES), Personalization Effectiveness Index (PEI), and Predictive Engagement Metrics will start becoming prominent, offering deeper insights into what customers really value. 

Why it matters: 

• Businesses can make smarter decisions by leveraging forward-looking metrics that provide predictive insights into a customer’s behavior 
• Measuring success through new KPIs offers a more accurate reflection of customer satisfaction and engagement 

What to watch out for: 

• Adapting legacy systems to accommodate these new KPIs may require significant time and investment 
• Educating teams and stakeholders about these nuanced metrics will be critical to their successful implementation and adoption 

8. The rise of platforms – the fall of point solutions

2025 will see hyperscalers such as Amazon and Microsoft continue to expand their influence in the CXM landscape, with integrated ecosystems that simplify operations and drive efficiency. However, these hyperscalers represent just one part of the platform equation. A wide range of other players, including smaller niche providers and regional leaders, also offer competitive and specialized solutions tailored to specific business needs. 

Why it matters: 

• Consolidating tools into one ecosystem helps businesses cut costs by reducing redundancies and simplifying resource management 
• Streamlining CX operations through seamless integration improves efficiency and ensures smoother workflows 

What to watch out for: 

• Relying on a few dominant players may increase dependency and limit flexibility in choosing alternative solutions 
• Generic platforms can lead to a loss of customization options, potentially limiting their effectiveness for specific business needs 

9. Agentic AI: autonomous CX on autopilot

The future is autonomous. Agentic AI—AI that learns, decides, and acts independently—will revolutionize CX operations. These systems are expected to increasingly take over parts of customer journeys, providing more personalized experiences while reducing operational strain. 

Why it matters: 

• Scaling CX without adding headcount allows businesses to expand their reach and maintain efficiency without significant increases in labor costs 
• Faster response times and reduced costs are achievable through autonomous decision-making, enhancing operational agility and customer satisfaction 

What to watch out for: 

• Ethical challenges in AI-driven decision-making, include biases, lack of transparency, and potential misuse of data, which require proactive management 
• Risks of errors in unsupervised operations could lead to customer dissatisfaction or reputational damage, necessitating careful monitoring and fallback mechanisms 

10. Synthetic experiences redefine reality

Virtual meets reality in 2025. Generative AI (gen AI) and synthetic media will create immersive experiences that blur the line between digital and physical worlds. Virtual assistants, lifelike avatars, and personalized environments will transform customer engagement. 

Why it matters: 

• Businesses can captivate customers by creating jaw-dropping experiences that merge cutting-edge technology with creativity, leaving lasting impressions 
• Standing out in a crowded marketplace requires innovative solutions that differentiate brands and enhance customer engagement 

What to watch out for: 

• Deploying synthetic environments often involves high costs, which can be a barrier for smaller organizations 
• Skepticism about the authenticity of interactions in synthetic experiences must be addressed to build trust and acceptance among users 

2025: The CXM revolution awaits 

The year 2025 presents significant opportunities for businesses to redefine customer engagement by embracing key trends in CXM.  

These developments offer pathways to sustained growth and relevance, but they also come with challenges.  

Rapid technological adoption needs careful planning to avoid operational disruptions, while increasing customer expectations and evolving regulatory landscapes demands thoughtful strategies.  

Organizations must strike a balance between innovation and practicality, ensuring agility and resilience in the face of market and technological complexities. By approaching these changes strategically, businesses can not only navigate the challenges but also make meaningful progress in the CXM space. 

If you found this blog interesting, check out our blog focusing on Unleashing The Power Of Advanced AI Engines: Transforming Business Operations For The Future | Blog – Everest Group, which delves deeper into another topic relating to CXM and AI. 

To discuss these 2025 predictions and other insights from our research on current and future CXM trends and evolutions, please reach out to Jagrit Kasera (jagrit.kasera@everestgrp.com) and Sharang Sharma (sharang.sharma@everestgrp.com) 

Gen AI Adoption

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

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)

Adoption of Generative AI

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