Category: Automation

Everest Group’s Artificial Intelligence (AI) Top 50™ List: 2024 report shines a spotlight on high-performance organizations across the globe | Blog

Releasing its first-ever artificial intelligence (AI) Top 50™ list in 2023, Everest Group has once again focused on the groundbreaking work and exceptional business performance carried out by AI technology providers from every corner of the globe. 

Continue reading to discover more about the leading AI-first providers featured in the top 50 list, as determined by Everest Group’s in-depth and continuous research. 

See Everest Group’s AI Top 50™ list 

The importance of recognizing high-performance AI organizations in 2024 

According to Everest Group estimates, AI technology has garnered substantial interest throughout the last few years, giving rise to more than 7,000 AI technology providers in the past decade alone, and many established providers are adapting to provide AI-based offerings themselves.  

The pursuit of AI’s potential to reveal hidden patterns—from weather predictions to insights on consumer behavior—has been underway for quite some time. However, the introduction of large language models (LLMs) marked a pivotal moment in AI’s evolution, possibly even in the history of technology itself. AI is now firmly in the spotlight, captivating consumers, businesses, and technologists alike. 

With AI now recognized as more than a passing trend, it’s increasingly important to focus on this tool that is reshaping nearly every industry and workflow. This list serves as a resource for enterprises to identify leading technology providers in the AI space that have achieved substantial scale, while also offering AI-first providers a way to benchmark themselves against the competition.  

How does Everest Group define AI?  

To define the terms in the report, we used a multi-faceted approach, drawing on a combination of exclusive proprietary data, including insights gathered directly from AI technology providers. Along with that, the methodology incorporates publicly accessible information such as reported revenue, total funding, and valuation data obtained from publicly accessible sources. This thorough methodology ensures the accuracy and breadth of the data. 

How is the Everest Group AI Top 50™ determined? 

Working with and analyzing over 2,000 AI technology providers from across the globe, a list was then narrowed down to 250+ providers, based on preliminary assessments, before the final top 50 was confirmed. 

The AI Top 50™ list represents AI technology providers that meet the following criteria: 

  1. AI-first: companies that develop and integrate AI as a key component in their products and solutions to a degree that – without it – their offerings would be fundamentally incomplete; the list excludes for example, providers that have AI as a feature that helps them improve their current offerings, such as automation-first vendors 
  1. Software-first: providers that develop and provide software-based AI solutions as their primary offering; the list excludes pure-play hardware and service-based AI providers 
  1. B2B focus/offerings: those that offer software products and solutions to meet other businesses’ technology needs; the list excludes providers that exclusively offer AI solutions for B2C purposes 

Similarly to the 2023 report, US-based OpenAI gained the top spot in 2024, in no small part thanks to their exemplary work and innovation across the board, with the US as a nation dominating the top 20, with 15 organizations hailing from the country. 

OpenAI also boasts top spots in the AI Top 10 providers by total private funding received and AI Top 10 providers by valuation lists. US firms Anthropic (2nd), Databricks (3rd), and Palantir (4th) also rank highly in all three lists, with French-based Mistral AI (5th) and Dataiku (6th) both flying the flag for Europe in the respective top 10s. 

To learn more about the 2024 Everest Group AI Top 50™, reach out to Vishal Gupta, [email protected], Abhigyan Malik, [email protected], Priya Bhalla, [email protected], and Abhiram Srivatsa, [email protected]. 

 

What is Everest Group Engage and the Pragmatic Edge? Announcing Everest Group Engage 2025 – London | Blog

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! 

Beyond Automation: How Conversational Artificial Intelligence (AI) Chatbots Enhance Customer Engagement | Blog

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

  1. 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

  1. 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

  1. 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

  1. 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.

Screenshot 2024 11 08 121007

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 ([email protected]) and Anubhav Das ([email protected])

 

 

Generative AI (gen AI’s) DEIB Dilemma: How ignoring Inclusion can be costly for businesses

In our previous blog, we discussed how the advent of generative AI in our day-to-day lives has skyrocketed in the past few years, helping individuals and companies efficiently tackle tasks through automation and reduce the time taken to complete them. 

Furthermore, new applications of gen AI for business solutions are being developed at a breakneck pace across industries such as Retail And Consumer Packaged Goods (RCPG) Retail and Consumer Packaged Goods, Banking And Financial Services, Healthcare and Life Sciences, and Human Resources , among others.  

Additionally, companies are now expecting more tangible results from the implementation of gen AI to avoid losing market share. This is true for all the previously mentioned stakeholders: technology providers, service providers, and enterprises.  

At the same time, these stakeholders must be mindful of their critical role in fulfilling the DEIB (Diversity, Equity, Inclusion, and Belonging) mandate, which includes promoting inclusive and equitable practices in gen AI development and deployment. The absence of comprehensive DEIB measures in gen AI models can have detrimental effects both internally and externally. 

Furthermore, equitable artificial intelligence (AI) learning is essential. A survey conducted by a leading consulting firm, indicates that only 10-15% of businesses have established AI roles focused on fostering diverse perspectives within their teams.  

Professionals’ lived experiences provide critical insights for mitigating bias—a truth that all stakeholders must embrace. Before exploring potential solutions, it’s important to investigate the root causes of bias, the different types of biases present, and their implications, as our analysts have done below. 

Reach out to discuss this topic in depth. 

The Case for DEIB in Gen AI:

While technology offers substantial benefits, a significant DEIB challenge persists within current gen AI frameworks, leading to adverse effects for individuals and organizations. AI algorithms – a host of which are trained on existing framework models, lack diverse perspectives, and can mirror the biases of their creators, perpetuating inequalities and harming marginalized communities.  

Cultural and social biases often infiltrate these systems, resulting in flawed outputs that do not accurately reflect varied experiences and knowledge. 

Some benefits of unbiased gen AI Models include:

At the same time, adopting unbiased gen AI models can significantly benefit organizations by: 

  • Enhancing Decision-Making: Eliminating biases allows for more accurate, objective insights, improving decision-making across scenarios 
  • Improving Customer Insights: Objective data analysis helps businesses better understand customer needs, facilitating targeted marketing 
  • Promoting Diversity in Hiring: Unbiased AI can eliminate discrimination in recruitment, supporting diverse candidates, including neurodivergent individuals 
  • Streamlining Operations: Reducing bias in automated processes optimizes operations, enhancing overall efficiency and productivity 
  • Fostering Innovation: Bias-free AI models yield more diverse and creative ideas, propelling innovation across sectors 
  • Improving Risk Management: Unbiased AI provides clearer, balanced assessments, aiding organizations in identifying and managing risks effectively 
  • Ensuring Compliance with Ethical Standards: Utilizing unbiased AI aligns with ethical norms and best practices, fostering trust and accountability 
  • Creating a More Equitable Workplace: By promoting fairness, unbiased AI contributes to a more inclusive environment, driving organizational growth 

A deep dive into the causes and types of the bias in terms of DEIB? 

Gen AI models are statistical by nature and prone to errors, especially when lacking domain expertise. Currently, a small, homogeneous group often determines the data used for training these models. Many models are built on foundational frameworks such as BERT or RooBERTa, which can carry inherent biases if not addressed from the outset. 

Types of DEIB bias include: 

Screenshot 2024 11 08 114257

The social and business cost for business by utilizing a biased gen AI model:

Addressing these challenges is paramount for companies when accounting for the vast use cases of this technology across sectors. For example, 19% of organizations are leveraging AI to develop new products and services across the RCPG space, according to an Everest Group insight 

Similarly, 40-45% of business leaders of mega enterprises (revenue exceeding US$ 1 billion) have reported successful implementation of gen AI across various operations in this Everest Group viewpoint. We expect this number to consistently increase in the coming years.  

If the models used for these products or services produce biased results or incorrect outcomes (an important component of ‘hallucinations’), it could negatively impact the companies’ reputations and their bottom lines. Thus, there are both direct and indirect costs associated with leveraging these models. The two key types of costs that businesses would suffer from are the following: 

Business Cost: The direct financial expenses incurred by a business, including production costs, operating expenses, and the costs of complying with regulations. These costs can be both internal and external to the business 

Social Cost: The total economic cost to society, including both direct costs borne by individuals and businesses, as well as indirect costs such as environmental damage, decreased quality of life, and social inequality 

Screenshot 2024 11 08 114452

While unbiased AI models are essential, their development and deployment can be costly. Collecting high-quality data for model training, designing and customizing AI models from scratch, and employing sophisticated techniques and specialized talent all contribute to the complexity.  

Additionally, scaling these models across large organizations or multiple geographies can introduce new biases due to variations in cultural, linguistic, and socioeconomic factors. Therefore, companies must be deliberate in identifying which products, services, or functions truly require such AI models.  

In response, some organizations have appointed Chief Diversity, Equity and Inclusion (DE&I) Officers, but this approach may be limited, as these officers typically focus on talent acquisition and retention.  

Effectively addressing AI’s DEIB impact requires input from multiple leaders, including the Chief Information Officer (CIO)/ Chief Technology Officer (CTO), Chief Product Procurement Officer (CPO), Chief DE&I Officer, and Chief Sustainability Officer, making it both resource- and cost-intensive. Furthermore, while algorithmic impact assessments are well-intentioned, they often fall short in fully capturing the broader social implications of AI models. 

To address this challenge, Everest Group has developed a framework that stakeholders can use to navigate these complexities effectively, with the overarching principle of the “Comprehensive Inclusion Framework” viewed from both an internal and external perspective. This principle is broken down into four key areas: 

  • Inclusiveness emphasizes broad representation in the entire AI development lifecycle. It ensures that diverse perspectives, experiences, and needs are considered when designing, developing, and deploying AI systems 
  • Impartiality ensures that AI decision-making processes are neutral, objective, and free from bias or unfair influence by continuously assessing the outputs of the model and checking for impartiality. Thus, blending in objective data driven insights 
  • Equity, in the context of AI ensures that all user groups experience fair and just outcomes from AI systems, regardless of their background, demographics, or identity  
  • Accessibility, focuses on making sure that AI technologies are usable and beneficial to all individuals, regardless of their socioeconomic status, disabilities, education, or geographic location 

The framework provides a comprehensive approach to integrating gen AI and DEIB policies within organizations across vertical and horizontal processes. It categorizes various policy combinations based on the level of emphasis placed on AI and DEIB and offers recommendations to achieve optimal alignment. The categories include: 

  1. Low DEIB Impact: DEIB efforts are not prioritized due to the lack of strong business or social cases 
  1. Medium DEIB Impact: DEIB efforts are focused on business and social benefits, with AI considered a tool to enhance these case 
  1. High DEIB impact: DEIB values are deeply integrated into organizational culture, using AI to drive inclusivity and equity throughout the business 

Screenshot 2024 11 08 114606 

The current state of the market in terms of DEIB embodiment by stakeholders: 

As mentioned in our last blog post, across stakeholders, the current level of DEIB integration according to our ROLE framework is as follows: 

Screenshot 2024 11 08 114705

As gen AI increasingly influences business operations, stakeholders must prioritize DEIB in their AI development and deployment efforts.  

Tackling inherent biases and fostering fairness will not only mitigate risks but also enhance decision-making, customer insights, innovation, and workplace equity. By adopting frameworks such as Everest Group’s “Comprehensive Inclusion Framework”, organizations can effectively align their AI and DEIB strategies, ensuring long-term success and ethical compliance. 

We are actively tracking the evolution of artificial intelligence and its impact on the future of all sectors. To discuss the latest trends and their implications for brands, technology vendors, and service providers alike, feel free to reach out to Kanishka Chakraborty ([email protected]), Meenakshi Narayanan ([email protected]), Abhishek Sengupta ([email protected]), Abhishek Biswas ([email protected]), Rita Soni ([email protected]) and Cecilia Van Cauwenberghe ([email protected]). 

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, which delves deeper into the subject of gen AI. 

This is the first of a new series of blogs, with plenty more to come in 2024 and 2025! 

 

Bringing the Vision of Unified Customer Experience (CX) to Fruition: Shining a Spotlight on Sprinklr | Blog

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.

Picture1 2

(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.

Mid-Market Enterprises: The New Frontier for Digital Transformation Services | Blog

The digital transformation landscape is rapidly evolving, and mid-market enterprises (MMEs) are emerging as significant drivers of demand.  

While they may be smaller than Fortune 500 companies, MMEs are often more agile and willing to adopt innovative technologies  to gain a competitive edge.  

This has led service providers to recognize the untapped potential of this market and tailor their solutions to meet the specific needs of mid-sized businesses. 

Read on to discover how this has led to service providers recognizing the real untapped potential of this market, as they tailor their solutions to meet the specific needs of mid-sized businesses, and get in touch if you’d like to speak to an analyst on this subject. 

The Demand-Side Perspective: What Mid-Market Enterprises Want

Mid-market enterprises, traditionally overshadowed by larger corporations, are increasingly becoming the focus of digital transformation services. Unlike giants, MMEs demand personalized, cost-effective, and agile solutions. Their digital transformation initiatives often center on several key priorities, such as: 

  • Operational Efficiency: Leveraging technology to streamline operations and reduce costs. 
  • Customer Experience: Using digital tools to enhance customer interactions and satisfaction. 
  • Scalability: Implementing scalable technologies like cloud computing, artificial intelligence (AI), and data analytics to allow rapid growth. 

A notable trend is the growing adoption of cloud computing and AI-driven automation, which help MMEs extract valuable insights from their data, improve decision-making, and optimize operations.  

Additionally, many MMEs prefer bite-sized, phased digital transformation projects that minimize risks and provide quicker returns on investment. This preference for shorter, milestone-based engagements creates an opportunity for service providers to establish long-term partnerships based on incremental, success-driven outcomes. 

The Supply-Side Perspective: How Service Providers Are Responding

Service providers are increasingly adapting their strategies to align with the unique needs of mid-market enterprises. Key approaches include: 

  • Flexible Pricing Models: Given the limited budget MMEs typically have for large-scale, long-term transformation projects, service providers are adopting innovative pricing models that reflect the need for flexibility and scalability. These models include subscription-based or usage-based pricing, which can grow with the client’s evolving needs. 
  • Scalability and Hybrid Solutions: Cloud solutions, automation tools, and data analytics platforms are some of the most in-demand services. Providers are responding by offering scalable andhybridand hybrid solutions that allow MMEs to gradually expand their capabilities. 
  • Client Intimacy and Agility: Smaller service providers often have an edge when it comes to client intimacy, as they can deliver more personalized engagement and quicker responses compared to larger competitors. MMEs value hands-on support and strong partnerships, and they prefer service providers who demonstrate agility and a deep understanding of their business challenges. 

Strategic Partnerships and Co-Innovation

To effectively serve the mid-market segment, service providers must emphasize strategic partnerships and co-innovation with technology vendors. This is particularly evident in collaborations with hyperscalers and cloud service providers, where smaller vendors team up with larger players to develop solutions tailored to mid-market needs.  

These partnerships enable service providers to offer proven tools and accelerators, which can significantly reduce the time and cost required to implement digital transformation initiatives. 

Moreover, consulting-led engagements are another way service providers differentiate themselves. By providing strategic guidance alongside implementation services, providers position themselves as long-term partners, helping MMEs navigate complex digital landscapes while consistently delivering value. 

Growth Opportunities in the Mid-Market Segment

The mid-market segment is experiencing rapid growth, with service providers specializing in this area seeing compound annual growth rates (CAGR) of 9-10%. This surge is fueled by MMEs’ desire to modernize legacy systems, enhance customer experiences, and secure a competitive advantage through digital innovation.  

Unlike their larger counterparts, MMEs are often more willing to embrace  generative AI  (gen AI) and other cutting-edge technologies due to their agility and lower complexity. 

For service providers, this presents an excellent opportunity to engage with a forward-thinking, fast-moving segment of the market that is eager to invest in the future. 

Conclusion

The mid-market enterprise segment is ripe for digital transformation, offering a wealth of opportunities for service providers that can meet their unique needs.  

As MMEs continue to prioritize agility, cost-effectiveness, and customer experience, service providers must adjust their strategies to offer scalable, innovative solutions that can deliver tangible business outcomes.  

By focusing on flexibility, personalized engagement, and strategic partnerships, service providers can position themselves as indispensable partners in the digital journeys of mid-market enterprises. 

Looking to capture the untapped potential of mid-market enterprises? Connect with our team to explore strategic insights from our recent study— Digital Transformation Services for Mid-Market Enterprises PEAK Matrix® Assessment 2024. 

If you found this blog interesting, check out our blog focusing on How Has Generative AI Evolved And Is Its Evolution Now Supporting CX Leaders More On The CXM Journey? | Blog – Everest Group (everestgrp.com), which delves deeper into another topic in the world of artificial intelligence. 

If you have any questions, would like to delve deeper into the Engineering & Information Tech market, or would like to reach out to discuss these topics in more depth, please contact Alisha Mittal and Parul Trivedi.

 

Agentic artificial intelligence (AI): From Science Fiction to Life Sciences Disruption | Blog

Remember when we were all buzzing about the metaverse like it was going to redefine reality? Yeah, that was just two years ago!

Fast forward to last year, and suddenly generative AI  (gen AI) has become the rockstar, spinning up content faster than we can say “machine learning.”

Now, as if we have blinked and missed a beat, we’re already asking, “what’s next?” – Enter Agentic AI, poised to not just assist, but act autonomously…

Could this be the game-changer for Life Sciences? Our expert analysts have found out just what this means for the sector going into 2025 and beyond!

Reach out to discuss this topic in depth.

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What is agentic AI?

Agentic AI is an evolved form of AI that creates autonomous agents possessing autonomy, decision-making, and adaptability. The agents can execute tasks in their entirety through natural language-based inputs. They can also set goals independently, plan accordingly, and act to accomplish the targets.

Key characteristics of agentic AI include:

  • Autonomy: perform tasks independently
  • Reasoning: make advanced decisions
  • Flexible planning: adjust plans based on prevailing circumstances
  • Workflow optimization: efficiently execute multistep, complex processes
  • Natural language understanding: comprehend and follow complex instructions
  • Continuous improvement: learn from historical data and feedback
  • System integration: integrate with diverse enterprise systems

The winning formula for agentic AI is training the models on diverse datasets with clear and concise instructions.

What does it mean for the life sciences industry?

Life sciences has always been a curious case for any emerging and next-generation technology – as it has always presented a unique challenge when it comes to adopting emerging technologies, whether it was Robotic Process Automation (RPA) a decade ago, cloud computing five years ago, or gen AI more recently, enterprises often display initial enthusiasm, diving into exploratory use cases and early proof of concepts (POCs).

However, as inherent challenges such as regulatory concerns, data privacy, and integration complexities emerge, majority enterprises take a step back and adopt a more cautious approach. This cycle reflects the industry’s general mindset—embracing innovation with enthusiasm, but always tempered by a significant degree of caution

Similarly, the industry is gradually transitioning from a cautious to a more pragmatic approach when it comes to adopting gen AI across various areas.

As enterprises continue to advance in this journey, Agentic AI can act as a powerful catalyst—particularly in targeted areas/segments—by driving efficiencies and accelerating time to return on investment (ROI). By automating decision-making and improving engagement processes, Agentic AI can help organizations realize the full potential of AI adoption faster and with greater impact.

While everyone was buzzing about “top use cases” in 2023, 2024 is all about getting strategic with scaled tech (hello, Gen AI!). Furthermore, just like its predecessor, Agentic AI is set to follow a similar trajectory—but expect this journey to be much faster.

In fact, there are a handful of areas where we predict Agentic AI will make the biggest splash in record time. So, without further ado, here are the top areas where Agentic AI will hit the ground running and deliver results in the short to medium term.

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How is it different from other chatbots or conversational assistants?

A key challenge with Agentic AI is understanding how it differs from existing conversational tools, such as chatbots and conversational assistants, which are steadily maturing in their capabilities.

This distinction is not just theoretical but critical, as each technology serves vastly different purposes. The complexity lies in unraveling these differences in both functionality and impact.

To simplify, the table below outlines the fundamental contrasts between chatbots, conversational assistants, and AI agents, with a focus on their technological architecture, autonomy, and practical use in life sciences. By illustrating these nuances, we can appreciate how AI agents go beyond basic interaction to deliver intelligent, autonomous decision-making in dynamic, real-world environments.

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What are the challenges?

  • Breaking the human barrier and trusting autonomous intelligence: Life sciences leaders and key stakeholders often approach disruptive technologies with caution, given the industry’s complex regulatory landscape and high-stakes environment. Gen AI has gained traction in part because its most successful applications involve a “human-in-the-loop” framework, where human oversight is embedded in AI decision-making processes. This model offers a balance between innovation and control, providing reassurance to organizations that value safety and accountability.

Agentic AI, however, shifts away from this hybrid model by significantly reducing or eliminating human involvement, relying instead on autonomous multi-agent interactions to manage decisions and workflows. For life sciences organizations, this presents a challenge: the need to develop a greater risk appetite and embrace potentially human-less frameworks. Successfully adopting Agentic AI will require not only trust in the technology, but also a shift in mindset, as companies learn to cede control to AI systems capable of operating independently.

  • Tactical use case enthusiasm eclipsing long-term strategic execution: The adoption of new technologies in life sciences often follows a pattern of initial excitement, where enterprises focus on specific use cases without fully considering the broader strategic framework. This was evident with gen AI, where enterprises quickly launched pilots across various segments without a cohesive, long-term strategy. Agentic AI faces a similar risk, where organizations may rush to deploy AI agents for isolated use cases—such as patient engagement or health care professional (HCP) interactions—without fully integrating the technology into a comprehensive, scalable architecture. This fragmented approach can limit the long-term value and scalability of AI in life sciences.
  • Domain specific training data for agents: AI models are only as good as the data they’re trained on, and in life sciences, domain-specific data is critical. Agentic AI systems require vast amounts of high-quality, structured, and unstructured data to function effectively, whether for patient monitoring, drug discovery, or HCP engagement. However, obtaining and curating training data that is both relevant and comprehensive is particularly challenging in life sciences, where data is often siloed across different systems, protected by privacy regulations like health insurance portability and accountability act (HIPAA), and involves a complex mix of clinical, genomic, and behavioral information. Without access to specialized datasets, AI agents risk underperforming or producing inaccurate results, which could undermine both their efficacy and the trust in their outputs; thus, leading to underwhelming ROI discussions thereafter.

In conclusion, Agentic AI presents a transformative potential for the life sciences industry, pushing the boundaries beyond traditional chatbots and conversational assistants.

However, its adoption will require overcoming industry-specific challenges such as trust, strategic implementation, and the availability of domain-specific training data. As life sciences enterprises gradually embrace this technology, Agentic AI could revolutionize engagement, decision-making, and operational efficiency, but only if organizations are ready to adapt to its autonomous, human-less frameworks.

If you found this blog interesting, check out our blog focusing on The Healthcare Professional (HCP) Engagement Blueprint: Winning Strategies For Building Lasting Connections | Blog – Everest Group , which delves deeper into another topic worked on by our HSL service line.

If you have any questions, would like to gain expertise in Agentic AI and artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Rohit K, Durga Ambati, and Chunky Satija.

Agentic Artificial Intelligence (AI): The Next Growth Frontier – Can It Drive Business Success for Banking & Financial Services (BFS) Enterprises? | Blog

Artificial intelligence is evolving faster than a quarterly earnings report, and just when we’ve started to master generative AI , a new breakthrough is emerging: agentic AI!  

This isn’t just another buzzword to add to your corporate lexicon either—it’s a game-changer that’s set to redefine AI’s capabilities.  

Reach out to discuss this topic in depth. 

What is agentic AI? 

Agentic AI is an evolved form of AI that creates autonomous agents possessing autonomy, decision-making, and adaptability. The agents can execute tasks in their entirety through natural language-based inputs. They can also set goals independently, plan accordingly, and act to accomplish the targets. Key characteristics of agentic AI include: 

  • Autonomy: perform tasks independently 
  • Reasoning: make advanced decisions 
  • Flexible planning: adjust plans based on prevailing circumstances 
  • Workflow optimization: efficiently execute multistep, complex processes 
  • Natural language understanding: comprehend and follow complex instructions 
  • Continuous improvement: learn from historical data and feedback 
  • System integration: integrate with diverse enterprise systems 

The winning formula for agentic AI is training the models on diverse datasets with clear and concise instructions. 

What does it mean for the banking and financial services industry? 

In Banking and Financial Services, agentic AI could be the key to optimizing operations, automating complex processes, and delivering hyper-personalized customer experiences.  

Agentic AI assesses the need for actions before executing them and continuously learns from its experiences to improve decision-making.  

Now let’s dive into why this innovation is catching the attention of technology  and financial leaders and how it could now transform the financial services industry. 

In the fast-moving world of trading and investment , agentic AI has the potential to transform portfolio management. These AI agents can analyze market trends, make rapid trading decisions, and adapt investment strategies in real time based on economic data and news events.  

Beyond trading, agentic AI could enhance risk management by autonomously identifying potential market disruptions or regulatory changes and adjusting exposure accordingly. In personalized banking, it could optimize customer service, offering tailored financial advice, automated portfolio management, and fraud detection systems that continuously learns and adapts by the second.  

This combination of real-time decision-making and autonomy could lead to more efficient markets, improved risk mitigation, and potentially higher returns for investors and clients alike. 

What are the high priority use cases for agentic AI in banking and financial services? 

Agentic AI is a transformative force driving exponential growth for banks by revolutionizing customer engagement, decision-making, and operational efficiency. 

With its ability to incorporate a “chaining” capability in decision making, banks can deliver hyper-personalized products and services, significantly boosting customer loyalty and unlocking new revenue streams through targeted cross-selling and upselling.  

Agentic AI will empower banks to make smarter, faster decisions on investments and lending, while superior risk management enables more aggressive growth with minimized losses.  

The following exhibit highlights the most relevant use cases from a banking and financial services perspective. 

Agentic AI blog infographic scaled

Which technology providers are riding the agentic AI wave already? 

The vast ecosystem of core banking technology providers, are still familiarizing themselves with the nuances of embedding AI into core baling modules offered via their plaforms. Our conviction is that core augmentation providers, hyperscalers, and niche agentic AI start-ups are going to lead the agentic AI revolution for this industry. 

From a  core augmentation provider perspective, we see technology platforms in the areas of experience, data & analytics androbotic process automation (RPA) leading the way , in order to guide and augment the core banking platforms, and enabling access to latest technologies.  

In recent days, we have already seen the launch of Agentforce by Salesforce that is positioned as suite of autonomous, and personalized assistive agents to support employee’s workflow with specific tasks.  

On the other hand, RPA providers are sitting on a base architecture that enables them to manage and automate tasks. Automation Anywhere is offering AI Agent Platform to build its own AI agents, while UiPath is also incoporating these capabilities into its existing RPA offerings.  

Additonally, financial crime remains a particularly ripe area for disruption by agentic AI, as technology providers are deploying AI agents to fight financial crime such as WorkFusion.  

We also see Google with its Vertex AI Agent Builder and Microsoft with its AutoGen, offering to build AI agents, that provide the necessary frameworks to accelerate agentic AI development. 

There are also a few niche providers such as EMA that are catering to use cases for the financial services industry and it will be interesting to see how other firms evolve and adapt in the weeks and months to come. 

Potential challenges on the road to adoption 

Adopting agentic AI faces several challenges, including high costs and an uncertain return on investment (ROI). Change management and acquiring the right talent  are critical hurdles. 

Existing technology investments, such as process automation, orchestration, and core modernization efforts, can complicate integration. Additionally, data readiness for training AI models and the substantial effort required to train and integrate these solutions into the value chain are among the other obstacles currently facing firms. 

What support do banks and financial service (FS) firm need? 

Looking at the technology estate of banking and financial services firms, we see a spider like mesh of various systems and applications that have evolved over the years.  

Streamling them to accomplish a workflow, retrieving the right set of data, and arriving at the meaningful insight is no singular feat and one that continues to be amongst the biggest challenges for enterprises today.  

Agentic AI can help jump through various of these applications to automate tasks while needing support from other agents to complete the tasks.  

Banking and financial services enterprises thus need to ensure their data assets are ready to be uttilized by agents while the workflow and processes are clearly defined. It is on this bedrock that these enterprises will be able to deploy agents. 

 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, would like to gain expertise in Agentic AI and artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Pranati Dave, Ronak Doshi and Kriti Gupta.

 

Building Purpose-driven Gen AI – Why We All Have a Role to Play in the Future Success of the Gen AI Ecosystem | Blog

Gen AI’s rapid adoption is evident from its early success; for example, ChatGPT 3.5 amassed one million users within five days of its 2022 launch, and now has over 180 million users – these numbers simply can’t be ignored!  

Organizations across industries are now leveraging gen AI to transform operations, enhance decision-making, personalize customer experiences, and foster innovation.  

However, this rapid adoption comes with significant environmental and social challenges. Our analysts have delved deeper into the topic, to decipher how and why gen AI needs to be nurtured and understood throughout every ‘step of the ladder’ in the marketplace. 

Reach out to discuss this topic in depth. 

The current landscape: 

The environmental footprint of gen AI is notable; generating a response from gen AI uses six to ten times more energy than traditional internet searches, exacerbating the information technology (IT) carbon footprint in every sector.  

Socially, gen AI also faces issues such as bias and ethical concerns, with biases in gen AI outputs perpetuating discrimination and misinformation. The particular concern around fair use doctrine is also emerging, with the New York Times suing OpenAI to use its news articles without permission, to train its model.  

To address these multifaceted challenges, it is crucial to understand the roles of various stakeholders in the gen AI ecosystem. Each plays a distinct part in promoting sustainability and mitigating negative impacts.  

The gen AI’s ecosystem involves various stakeholders—technology providers, service providers, enterprises, regulatory bodies, and research/coalition building organizations. Technology providers can enhance model efficiency and inclusivity, while service providers develop energy-efficient and responsible artificial intelligence(AI) solutions.  

Enterprises, as end users, can demand sustainable practices and influence market demand. Regulatory bodies also play a crucial role by establishing and enforcing standards and regulations. Meanwhile, research and coalition building organizations drive innovation and offer insights into emerging best practices and technologies for sustainable gen AI. Together, these stakeholders form a cohesive ecosystem essential for advancing sustainability in the gen AI landscape. 

Everest Group explores how key stakeholders influence gen AI’s path to sustainability

At Everest Group, we view gen AI’s sustainability through the lens of the planet and people. To ensure a sustainable future for gen AI, we have identified three themes:  

  • Decarbonization and energy management: Reducing energy consumption and lowering the carbon footprint of gen AI technologies. 
  • DEIB (Diversity, Equity, Inclusion, and Belonging): Promoting inclusive and equitable practices within gen AI development and deployment. 
  • Accessibility: Ensuring gen AI technologies are accessible and usable for everyone, regardless of their disability status. 

Three stakeholders—technology providers, service providers, and enterprises—are pivotal in translating these mandates into practical actions. Understanding their contributions is essential for advancing gen AI sustainability.  

Technology providers, service providers, and enterprises are directly involved in implementing and influencing sustainable practices, making their involvement critical for tangible progress. While regulatory bodies, governments, and research organizations and industry coalitions play a complementary part by establishing standards, regulations, and guiding research, the immediate impact on sustainability stems from the actions and commitments of these primary stakeholders. 

Everest Group has developed an assessment framework to define the roles the primary stakeholders play in making gen AI more sustainable.  

Our ROLE framework evaluates how much pressure existing AI regulations place on stakeholders, their operational control across the gen AI value chain (from conceptualization to end-of-life), their leadership in partnerships and engineering research & development (ER&D), and their expertise in shaping sustainable gen AI.  

The ROLE framework is depicted in Exhibit 1.

Exhibits Generative AI gen AI – why we all have a role to play in the future success of the gen AI ecosystem 2

After scoring the three stakeholders across the parameters defined in our ROLE framework, our assessment has categorized stakeholders into three roles:

Screenshot 4

 

  • Architect: Reflects stakeholders with high engagement and significant influence on advancing gen AI sustainability.

Technology providers are currently in the role of Architect. They drive innovation and set the standards for sustainable gen AI technologies. Their involvement spans the entire lifecycle of gen AI, from development to deployment, and they are at the forefront of integrating sustainability into their solutions.  

  • Contributor: Indicates stakeholders who actively support and engage with sustainability efforts but do not lead them.

Service providers fall into the Contributor category. They play a vital role in implementing and supporting sustainable practices within gen AI solutions, yet their influence is more supportive rather than leading the charge in sustainability initiatives.  

  • Influencer: Denotes stakeholders who monitor or follow sustainability developments with minimal direct involvement but may shape discussions and perceptions through their observations.

Enterprises are classified as Influencers. While they adopt gen AI solutions, their involvement in driving sustainability is limited. They largely follow industry trends without actively shaping or leading sustainability efforts. However, they can shape the demand for more sustainable gen AI through discourse, forming industry-coalitions to adopt best practices, or co-innovating sustainable gen AI solutions with tech partners. 

The ROLE framework provides a comprehensive assessment of the market and the contributions of various stakeholders.  

It categorizes stakeholders based on their overall impact within the ecosystem. However, we recognize that some players are making exceptional efforts that could elevate their roles—from Influencers to Contributors or from Contributors to Architects. This nuanced view acknowledges that individual players can surpass their general category and assume a more influential position in driving sustainability. 

The evolving roles of technology providers, service providers and enterprises present valuable opportunities for further advancements. By exploring these dynamics, we can better understand how each stakeholder can contribute to a more sustainable gen AI ecosystem. 

Everest Group will keep digging deeper to understand the gen AI sustainability ecosystem better. Stay tuned for our upcoming blogs, where we’ll explore strategies for tackling gen AI’s complex sustainability challenges. We’ll delve deeper into each stakeholder’s evolving role and offer insights on bridging the gaps in their current efforts. 

If you found this blog interesting, check out our recent blog focusing on Unleashing The Power Of Advanced AI Engines: Transforming Business Operations For The Future | Blog – Everest Group (everestgrp.com), which delves deeper into the topic of advance AI and gen AI. 

If you have questions or want to discuss these topics in more depth, please contact Meenakshi Narayanan, Rita N. Soni and Cecilia Van Cauwenberghe. 

 

Race for Artificial Intelligence (AI) Infrastructure: Navigating the Best Path to Supercharge Your AI Strategy | Blog

As we stand on the brink of a new technological era, the rise of AI is reshaping our interactions with the digital world.  

The rapid proliferation of AI has intensified the demand for scalable, high-performance computing resources, in the process exposing the limitations of traditional infrastructure.  

Enterprises are now seeking significant upgrades and expansions to their traditional information technology (IT) infrastructure, in order to keep up with the rising demands of AI workloads.  

This has since driven considerable investment into specialized AI infrastructure and tools and services, that can now create the necessary environment for core hardware and infrastructure components to operate at their best.

  • According to Everest Group research, 81% of enterprises plan to allocate 50% or more of their infrastructure budget this year to upgrading capabilities specifically for AI

Managing Investments in the Face of Rising AI Demands and Evolving landscape 

To accelerate AI development and maintain an edge in the evolving digital landscape, enterprises are increasingly investing in core hardware and infrastructure components. This had led to the surge in demand for high-performance critical computer hardware, networking, and storage infrastructure necessary for AI computations and data management including (Graphics Processing Units) GPUs, (Tensor Processing Units) TPUs, and Virtual Storage Platforms (VSP).  

  • As per Everest Group research, 46% of enterprises prioritize upgrading computing power such as, graphics processing units (GPUs), central processing units (CPUs), and tensor processing units (TPUs), as one of their top three priorities in AI infrastructure investments

Reach out to discuss this topic in depth. 

Providers are now significantly increasing investments to upgrade their supply and secure their positions in a rapidly evolving marketplace.  

They are adopting multifaceted strategies to differentiate themselves, secure market share, and address the evolving needs of enterprises for their AI needs. As the market transforms, leading players are making bold strides in the AI arena: 

As Nvidia rides the AI wave, AMD battles to disrupt its market dominance in the GPU market 

Nvidia, known for its high-end graphics cards for gaming personal computers (PCs), has now crossed US$3 trillion in market cap, owing to the rising demand for its AI chips, critical for advanced AI infrastructure. As Nvidia stands at the forefront of the AI infrastructure market, its GPUs are indispensable for training and deploying sophisticated AI models, including OpenAI’s ChatGPT, leading to its market dominance in the GPU sector.  

While Nvidia remains a dominant force in the AI field, other competitors are gradually emerging, aiming to gain market share and driving innovation to break Nvidia’s dominance.  

AMD presents a significant challenge to Nvidia in the GPU sector and is working on providing compelling alternatives, particularly for budget-conscious buyers. AMD’s MI300 chip has gained substantial traction amongst startups, as well as with technology giants like Microsoft. It is also constantly investing in this space to bolster its position, as evidenced by its recent multi-billion-dollar acquisition of ZT Systems.

Intel – the computing giant facing challenges, but could that change soon with Gaudi 3? 

Intel, traditionally focused on CPUs, has faced challenges in gaining a strong foothold in the GPU market and has been facing stiff competition from competitors, with Nvidia surpassing Intel in annual revenue 

Intel is now intensifying its efforts to close the gap in the AI market. At the recent Intel Vision event, Intel highlighted the forthcoming release of Gaudi 3, an AI accelerator, claiming to be able to outperform Nvidia’s powerful H100 GPU in training large language models (LLMs).  

Intel also stated that the Gaudi 3 could deliver similar or even superior performance compared to Nvidia’s H200 for large language model inferencing. Additionally, it claims that Gaudi 3 is focused on reducing energy consumption and has greater power efficiency than the H100, for specific use cases.  

Intel’s strategic push to challenge Nvidia’s dominance occurs against a backdrop of persistent shortages in AI accelerator chips, which has created substantial obstacles for tech companies. 

Hyperscalers – Nvidia’s largest customers today, potential rivals tomorrow? 

Major cloud providers such as Google, Microsoft, Amazon, and Oracle, who together contribute significantly to Nvidia’s revenue, are making a strategic shift toward developing their own processors and in-house chips, to reduce dependency on Nvidia’s GPUs, as well as to drive their own innovation.  

Amazon has been rolling out its AI-focused Inferentia and Tranium chips for AI inference and training, offering these through Amazon web services (AWS), as cost-effective alternatives to Nvidia’s products.  

Google, a long-time advocate of its Tensor Processing Units (TPUs), recently introduced Trillium, its sixth generation TPU to power its AI models, which it claims is 5 times faster than its predecessor.  

Microsoft is also making strides by developing its own AI processors and chips, including the Cobalt 100 CPU, an arm-based processor used for running general purpose computer workloads on the Microsoft Cloud and Maia 100 AI Accelerator. 

Emergence of new players and trailblazing startups Disrupting the AI landscape with innovative approaches? 

Several startups are making significant strides within the AI infrastructure landscape, with their innovative approaches.  

Cerebras Systems, known for its Wafer-Scale Engine (WSE) designed for high-performance AI workloads, has recently introduced an AI inference service that it claims to be the fastest in the world.

Groq’s Language Processing Unit (LPU) stands out for its high speed in AI inference tasks, offering substantial performance gains for large language models. Groq has also recently raised $640 million for its AI chips.

Groq’s rival SambaNova, has also launched its AI inference platform SambaNova cloud. Similarly other startups like Blaize, an AI chip maker, is developing competitive AI chip technology, with its own unique focus and specialization.  

Although Nvidia holds a dominant position, Groq, Cerebras Systems, SambaNova, and other startups are emerging as serious contenders in the marketplace, offering innovative and competitive solutions. It will now be interesting to see how the new players in this space can challenge the technological giants. 

AI chips and accelerators 1

Exhibit 1: AI chips and accelerators landscape 

How to take the next steps? 

As the AI landscape continues to evolve, challenges remain, as enterprise demand for GPUs exceeds supply, leading to a shortage.  

This imbalance, combined with high demand, has also driven up GPU prices, making it challenging to find affordable alternatives. As a result, organizations are increasingly exploring alternatives to the dominant players in the AI chip and accelerators market.  

But, with so many options, it’s crucial for organizations to carefully evaluate their requirements, budget, and strategic goals, to choose the most suitable options for leveraging AI power effectively. We suggest a two-pronged approach to align organizational AI strategy: 

Assess and analyze

Assess your requirements on parameters such as:  

  • Organizational capabilities and budgetary flexibility: Assess which strategy would suit your budget – purchasing or renting GPUs. Weigh in the initial investment needed, maintenance costs, and long-term operational savings
  • AI current workload requirements: Analyze your requirements based on the types of AI workloads and business use-cases (e.g., is your need centered around high-performance training or low latency inference or both)
  • Future adaptability: Consider whether your AI workloads may evolve, necessitating reconfigurable hardware or if the efficiency of specialized chips is more important
  • Power and space: Assess your organization’s energy efficiency, hardware footprint, and power consumption needs

Align and augment

After the initial assessment and once you have a clear understanding of your AI requirements, develop a roadmap that supports your AI strategy, taking the 5 S into consideration – Scalability, Sustainability, Security, Simplicity, and Stability 

  • Ensure your AI strategy is directly aligned with business objectives, such as innovation, operational efficiency, or scaling products, while also being adaptable to future AI workloads
  • Augment existing AI infrastructure by partnering with the right vendors that can help you meet your AI workload demands

Slide2 1 

Exhibit 2: By adopting this two-pronged approach, you can effectively chart the best path to supercharge your AI strategy.  

If you found this blog interesting, check out our report, Navigating AI Infrastructure: The Backbone of the AI-Driven Era.

If you have any questions, would like to gain expertise in artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Praharsh Srivastava, Zachariah Chirayil, and Tanvi Rai.

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