How can businesses seize opportunities as the technology services industry emerges from its longest deceleration in years?

Every year begins with questions about what lies ahead, and 2025 is no exception. As we stand at the cusp of what appears to be a pivotal year for technology services, it’s critical to assess where we are and where we’re headed.

Are we emerging from the longest deceleration in recent memory, or are we merely pausing before the next wave of challenges? More importantly, what strategies will define success in this shifting landscape?

Learn more in my blog on Forbes

Enterprises looking to adopt Artificial Intelligence (AI) initiatives are finding it difficult to implement them at scale due to data-related challenges, inability to acquire skilled talent, advanced IP, and lack of AI and cloud capabilities. Hence, they are turning to analytics and AI services specialists to serve their needs. In turn, these providers are improving their capabilities through investments in talent, products and platforms, partnerships, industry expertise, and AI-based solutions designed to serve specific client needs.

In this report, we present an assessment and detailed profiles of 22 analytics and AI services specialists featured on the analytics and AI services specialists PEAK Matrix®. Each provider profile presents a comprehensive picture of its service focus, key Intellectual Property (IP) / solutions, domain investments, and case studies. The assessment is based on Everest Group’s annual RFI process for the 2021 and 2022 calendar year H1 (January-June), interactions with leading analytics and AI services specialists, client reference checks, and an ongoing analysis of the analytics and AI services market.

• Analytics and AI Services Specialists PEAK Matrix® Assessment 2022

  

  What is in this PEAK Matrix® Report

  This report provides a detailed analysis of 22 analytics and AI services specialists and includes:

  • Everest Group’s PEAK Matrix® evaluation of analytics and AI service providers and their categorization into Leaders, Major Contenders, and Aspirants
  • An overview of enterprise analytics and AI priorities and key challenges in scaling AI
  • Key analytics and AI services trends
  • A detailed assessment of the strengths and limitations of the providers in terms of their market impact and vision and capability

  Scope:

  • Industry: data and analytics
  • Geography: global

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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.

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

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In this webinar, you’ll learn how to:

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

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

In the rapidly evolving world of automation, new concepts and buzzwords frequently spark excitement, anticipation, and sometimes confusion.

One such concept currently captivating the automation community is agentic AI. Many view agentic AI as the next transformative advancement beyond existing Intelligent Automation (IA) technologies. However, much of this excitement is clouded by misunderstandings about what agentic AI truly is and what it can achieve.

In this blog, we will clarify the concept of agentic AI, highlight how it differs from IA, and explain why it is such a significant leap forward for organizations poised to embrace the future of automation.

Reach out to discuss this topic in depth.

Understanding Agentic AI

First, let us define agentic AI. The term, agentic, refers to the idea of having the capacity for independent action and decision-making. Everest Group defines agentic AI as an evolved form of artificial intelligence (AI) that creates autonomous agents with high levels of autonomy, intelligence, decision-making, and adaptability. These agents can perform complex tasks based on simple and natural language inputs, including setting goals, planning, and taking actions in a dynamic environment with minimal human intervention.

Exhibit 1: Overview of agentic AI workflow

Source: Everest Group (2024)

As illustrated in Exhibit 1, an agentic AI system goes through a series of steps to transform user inputs, into meaningful outputs, utilizing a range of technologies at each step.

• Upon receiving an input or trigger through sources such as voice, chat, web applications, or Application Programming Interfaces (APIs), the system employs both traditional AI techniques such as Natural Language Processing (NLP), as well as generative AI (gen AI) to accurately interpret user intent. Additionally, it retrieves essential supplementary information to enhance contextual understanding through short-and long-term memory such as context windows, vector stores, and knowledge graphs
• Based on user intent, the system breaks down task into goals, utilizing specialized models and knowledge management tools
• It then analyzes potential actions and outcomes by employing various rules-based and AI models. Once a decision path is established, the system creates workflow and executes action. Task planning algorithms and automation tools organize tasks into an actionable sequence, which is then executed by automation robots or other tools
• The system continuously monitors and reflects on its actions, incorporating reinforcement learning and feedback to improve accuracy and efficiency over time

Through this workflow, agents can operate autonomously while adapting to new inputs and challenges dynamically, making agentic AI a powerful technology for complex decision-making and automation. Agentic AI has the potential to significantly transform business operations, way beyond what traditional IA technologies can do. To better understand this advancement, let us first review the role of IA.

A quick recap of Intelligent Automation (IA): the overview, benefits, and limitations

IA automates business processes by using various technologies such as Robotic Process Automation (RPA), process orchestration, and cognitive/AI-based automation, including Conversational AI (CAI) and Intelligent Document Processing (IDP). These IA technologies offer scalable, efficient solutions for the automation of repetitive, high-volume tasks such as processing invoices, managing customer service inquiries, and handling payroll, hence freeing human workers to focus on strategic goals, and enabling organizations to scale cost-effectively. Additionally, IA enhances governance and compliance with automated tracking and reporting.

Through augmentation with deterministic AI in the last few years, IA has been able to incorporate a decent level of self-learning, intelligence, and decision making. However, IA still has its limitations. It works well for structured, repetitive, low to medium complexity tasks, but needs human intervention for more complex scenarios requiring flexibility, judgment, or innovation, because of which it often does not achieve end-to-end process automation. This is where agentic AI steps in.

The leap: Why agentic AI is a game-changer?

The leap from IA to agentic AI can be synonymous with moving from a basic autopilot system on an airplane, to one capable of navigating unpredictable turbulence or emergency rerouting – all without needing human input at every step. Both existing IA technologies and agentic AI, reduces human intervention and boosts productivity, however, they differ significantly in terms of complexity, autonomy, adaptability, and decision-making.

Let us examine the attributes that set agentic AI apart from existing IA technologies:

• Scope: Both traditional IA and agentic AI can be integrated into existing business processes, but IA primarily manages well-defined, repeatable tasks such as data extraction, customer interactions, and data analysis. Agentic AI, on the other hand, extends beyond simple tasks to strategic problem-solving in complex, uncertain environments. Its broader scope allows for dynamic adjustments, making it useful in use cases such as autonomous demand forecasting and adaptive supply chains
• Task vs. goal-orientation: IA technologies such as RPA, IDP, and CAI focus on predictable, task-specific outcomes, such as processing an invoice or generating a report. In contrast, agentic AI adopts a flexible, goal-oriented approach, pursuing broader objectives and adapting its strategies to achieve them. These goals, typically informed by user interactions and environmental conditions, guide the agent’s actions, allowing it to prioritize tasks dynamically to meet high-level objectives
• Autonomy in decision-making and human oversight: Traditional IA relies on rules-based decisioning and predefined workflows. In scenarios falling beyond its defined parameters, either it stops or escalates for human intervention. In contrast, agentic AI operates with a high degree of autonomy, making decisions and strategic actions based on real-time data, without human intervention. While some oversight remains necessary for high-stakes or ethical decisions, agentic AI minimizes the need for constant monitoring
• Multi-agent collaboration: In traditional IA constructs, robots or digital workers based on technologies such as RPA, IDP, and CAI collaborate through a process orchestration layer / solution based on predefined rules, often needing human intervention for validations and adjustments. Agentic AI, however, can coordinate multiple agents, often using traditional IA technologies as tools, to manage subtasks either in parallel or sequentially. These agents collaborate, negotiate, and delegate autonomously, enhancing dynamic and goal-oriented collaboration. For instance, during a sudden surge in customer complaints, IA would process them as per its original workflow. In an agentic AI system, one agent addresses initial queries and identifies their nature, while another analyzes sentiment. If negative sentiment increases, cases are dynamically assigned to additional support agents specialized in similar issues, improving response times and customer satisfaction
• Workflow optimization: IA technologies such as process orchestration aim for process optimization by relying on structured workflows and rules, which require specific triggers and structured inputs. Agentic AI, on the other hand, continuously analyzes and adjusts workflows in real time to achieve set goals with minimal delay and optimal resource efficiency
• Self-learning: IA technologies such as RPA lacks the capability for self-learning, while AI-based tools such as IDP and CAI require retraining by humans and cannot autonomously adjust based on evolving goals. In contrast, agentic AI evaluates its actions’ effectiveness, creates an iterative self-improvement cycle, and adjusts future behavior based on successes or failures. For example, it refines its collaboration and prioritization rules over time, based on changing customer behavior, seasonality, and other business-specific insights
• Real-time adaptability: IA technologies have limited adaptability, requiring manual updates for rule changes or handling exceptions. If an IA system encounters an unexpected input, it may either fail or escalate for human intervention. Agentic AI, however, autonomously assesses and adjusts to new conditions. It modifies actions, creates new logic, and adapts strategies based on real-time data, all without human intervention. For instance, agentic AI can manage inventory in response to shortages, proactively communicate with suppliers, and re-prioritize orders to adapt to changing demand patterns, learning and improving continuously

Exhibit 2: Comparison between traditional IA and agentic AI

Source: Everest Group (2024)

As summarized in Exhibit 2 above, agentic AI surpasses IA in its ability to adapt, learn, and autonomously make decisions. Its broader scope, goal-driven focus, and real-time adaptability enable it to handle complex environments with minimal human oversight, enhancing both operational efficiency and resilience.

Understanding the boundaries of agentic AI

While it is crucial to understand what agentic AI is capable of, it is also important to understand the boundaries in which it functions.

Agentic AI systems show remarkable autonomy and can make decisions based on data, but they are ultimately bound by the parameters set by their programming. They make data-driven decisions within a defined framework. This structured approach ensures that their operations align with specific objectives determined by human developers.

Fundamentally, these systems function as sophisticated tools designed to enhance efficiency and productivity, rather than as sentient beings capable of independent thought or emotional reasoning. They lack the free will or emotional depth that characterizes human decision-making processes, which are influenced by a complex interplay of subjective experiences, values, and social contexts. This distinction is crucial for businesses and users to comprehend, as it helps set realistic expectations regarding the capabilities and limitations of these technologies.

Thus, agentic AI’s decision-making abilities stem from advanced algorithms and data processing techniques – not from any form of independent consciousness. The leap from automation to agentic AI is substantial, but it is still a leap within the confines of human-controlled programming and objectives.

Conclusion

Agentic AI will be crucial for enabling organizations to automate tasks, decisions, and goals. This shift offers operational autonomy but introduces challenges, including the need for strong data governance, orchestration, ethical decision-making frameworks, and defined limits on AI power. Responsible and transparent integration of agentic AI with the surrounding ecosystem is essential for its effective use.

Furthermore, understanding agentic AI deeply is essential for effective industry implementation. The shift from traditional IA to agentic AI-led automation is significant, but it should be viewed as a technology that enhances human capabilities such as decision-making but does not replace human judgment or creativity. Therefore, organizations must evaluate the implications of using agentic AI for critical decisions and assess the context for its deployment.

By acknowledging the boundaries of agentic AI, organizations can leverage its strengths while avoiding potential pitfalls. This approach ensures agentic AI acts as a powerful ally in the digital landscape, empowering businesses to thrive responsibly in a rapidly evolving world.

If you found this blog interesting, check out our Innovation Watch: Agentic AI Products report which delves deeper into another topic worked regarding agentic AI. You may also be interested in our latest blog Navigating the Agentic AI Tech Landscape: Discovering the Ideal Strategic Partner | Blog – Everest Group)

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 Vaibhav Bansal (vaibhav.bansal@everestgrp.com), Samikshya Meher (samikshya.meher@everestgrp.com) and Divya Chandak (divya.chandak@everestgrp.com).

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The Rising Enterprise Adoption of Agentic AI

Agentic AI has been capturing popular imagination in the past few months and has now started proliferating from the more intuitive consumer use cases to the more elaborate enterprise applications.

As the agentic AI ecosystem evolves, so will its potential to deliver greater benefits which in turn will increase utilization and reliance on the technology, leading to widespread permeation. But enterprises have already started realizing the rewards and extraordinary accomplishments this technology promises to yield, including increased productivity, optimized workflow and improved decision making.

The early adoption pattern of agentic AI reflects a strong preference for horizontal use cases (Exhibit 1) with functions such as sales & marketing, customer support and HR leading the pack, though the industry-specific processes are also expected to pick up soon. 

Reach out to discuss this topic in depth. 

Exhibit 1: Adoption of agentic AI by business function 

Source: Everest Group (2024) 

Emerging Agentic AI tech landscape 

The tech landscape for agentic AI is evolving rapidly. Everest Group’s Innovation watch assessment on agentic AI products (Exhibit 2) published in September 2024, mapped out the key agentic AI technology providers on their market performance and ecosystem drivers. While market performance is linked to the scale of operations and maturity of the product, ecosystem drivers include key partnerships and investments.  

Exhibit 2: Everest Group innovation watch assessment for agentic AI products 

Source: Everest Group (2024) 

As you can expect, the pace of change and innovation in this budding market is very high. In the short time frame since this assessment was launched, many providers have made announcements or have ventured into this space. Some notable ones are: 

1. Salesforce Agentforce: launched at Dreamforce 2024, Agentforce comes with multiple prebuilt agents such as sales development agents, sales coaches, personal shopping agents, service agents, and campaign agents. It also supports agent customization via Agent Builder, Model Builder, and Prompt Builder​ 
2. ServiceNow Xanadu: Agentic AI integration into the ServiceNow platform is expected to be available from November 2024 with Customer Service and information technology (IT) Service agents being the first in a set of agents that will continue to be added through 2025 
3. UiPath: Agent builder was launched at the Forward event in October 2024. UiPath is also expected to come up with a comprehensive agent orchestration platform with required security and agent ops 
4. Kore.ai: Kore.ai launched its platform named GALE to build, test, integrate and deploy AI agents and applications. The platform also comes with essential guardrails and analytics capabilities

With the slew of tech providers continuously entering this space, the market dynamics are bound to change. In the current landscape, our analysis (Exhibit 3) reveals four categories coming to the fore:  

1. Hyperscalers: hyperscalers are sizeable providers engaged in providing broader cloud, network, technology, and data services. Microsoft, Google, and AWS are the biggest names in this category, each with their own Agent frameworks, application programming interfaces (APIs) and even off the shelf agents in some cases. Hyperscalers have the financial clout and customer base to make significant advances and gain market share in Agentic AI 
2. Pureplay providers: pureplay providers operating in this field are mostly startups and AI native companies offering specialized capabilities for use cases, ranging from broad based to highly targeted ones. These players operate with an undivided focus on agentic AI and aim to continuously innovate in this category. Some notable names here are Ema, crew AI, Newo.ai, Relevance AI, Lyzr, Dust AI and so on 
3. Enterprise platforms: enterprise platforms are tech players that provide a centralized hub for a range of comprehensive software products/solutions such as customer relationship management (CRM), enterprise resource planning (ERP) and supply chain management (SCM). These are advanced software solutions that assist enterprises by integrating multiple functionalities into a cohesive system. As mentioned earlier, Salesforce and Service Now have already made announcements on their Agentic AI offerings on top of their existing platforms and others are now expected to follow suit  
4. Intelligent automation providers: these players are focused on streamlining business processes through a combination of rule-based automation and AI, using technologies such as robotic process automation (RPA), IDP, conversational AI, process orchestration and process intelligence. Some of these providers, such as Automation Anywhere, UiPath and Kore.ai have already launched their agentic platforms / capabilities, while others are on course to do the same in the near future

Apart from these tech providers, the ecosystem of tech services providers is also emerging where system integrators, managed services providers and consulting players are all expected to play a significant role in the agentic AI space through a range of advisory, solution development and operations services 

Exhibit 3: Emerging Agentic AI tech landscape 

Source: Everest Group (2024) 

How should enterprises go about selecting the right tech partner for Agentic AI?  

With the deluge of providers with diverse specialties operating in this space, the question being faced by enterprises is how to develop the right agentic AI ecosystem. Should they extend their relationships with their existing partners or explore new specialized partners focused on agentic AI?  

Identifying the right partner(s) begins with identifying what enterprises want to achieve in their agentic AI journey, and how the partner’s capabilities align with that vision. Here (Exhibit 4) is a framework that enterprises could use to evaluate their potential tech partner(s) for their agentic AI journey. 

Exhibit 4: Agentic AI tech provider evaluation framework 

Source: Everest Group (2024) 

As a part of this framework, enterprises can evaluate providers across 2 dimensions, ease of adoption and comprehensiveness of solution. Ease of adoption is defined by the ability of the provider to deploy a fit for purpose solution at a fast pace with minimum disruption. On the other hand, comprehensiveness of solution covers the exhaustiveness, robustness and flexibility of the solution.  

The framework can be used by the enterprises to identify the category of the tech provider (as defined earlier in this blog) that will suit their needs and/or to evaluate and choose among multiple tech providers in one or more categories. Let’s consider a couple of scenarios to understand how enterprises can benefit from this framework. 

Scenario 1:  

Situation: company A is one of the largest financial services companies in the world and deals with origination and servicing of personal and property loans. The company is required to process large amounts of data in a highly secure manner. It has already fostered a partnership with a hyperscaler for its data, cloud and AI needs.  

In recent times, the company has been seeing a decline in its business and there have been many unrecovered loans.  The company aims to increase the loan volume as well as strengthen its loan disbursement mechanism to avoid losses.

For this, it wishes to deploy an agentic AI system that reduces loan disbursement time and precisely identifies loan requests that are unlikely to be recovered. For this purpose, it is looking for an ideal tech provider that has the ability to deliver a scalable solution coupled with robust security measures. In the longer term, the company also plans to extend this solution to other standard as well as non-standard organizational processes. 

Choosing the right partner: from the perspective of comprehensiveness of solution, company A is looking for an agentic AI provider with the capacity to deliver a solution with progressively increasing levels of complexity as the magnitude of the work increases. It should also exhibit robust security guardrails and ease of integration into the existing ecosystem. From an ease of adoption standpoint, an ideal partner would be a provider capable of rapid agentic AI development and deployment. In this case, it may make sense for company A to partner with a hyperscaler, preferably the existing partner, that can develop a robust customized agentic AI solution on top of the existing infrastructure and scale the solutions later as per organizational needs 

Scenario 2: 

Situation: company B is a small-scale logistics services provider operating a fleet of trucks for road-based transport. The company is not very mature in terms of leveraging automation except aspects of the business that are very standardized or repetitive in nature with little to no exceptions or dynamic situations. This means there is heavy reliance on manual workforce and dependence on their decision-making skills which is often not backed by sufficient data. 

Unanticipated weather conditions or political situations frequently require a proactive rerouting of shipments to ensure timely delivery. However, in the current scenario, this is more reactive, costing company B significant time and money. It wants to deploy an agentic AI solution that can reroute shipments on its own with minimal manual intervention. Given the cost associated with each route and rerouting, there is an urgent need to deploy a solution with minimum build and rollout time. 

Choosing the right partner: as company B is a relatively small organization in the logistics market, it has limited resources available to invest in agentic AI. It is looking for a specialized solution with a low cost of investment. It should opt for a partner that is already servicing clients with its proven Agentic AI solutions and use cases in the logistics industry. In this case, it may make more sense for company B to partner with a pure play / niche agentic AI provider operating in the logistics space to launch a best-of-breed solution in a cost-effective manner.  This will help it focus on its core competencies without big investments in the underlying solution development 

Conclusion 

While the agentic AI partner evaluation framework is a good aid, there is no one size that fits all approach that can be used in terms of partner selection.

Ultimately, enterprises will have to make a choice between one or more players in the agentic AI tech landscape, keeping their needs, goals, and priorities in mind. For a big enterprise, a combination of multiple providers might work out as a better strategy, while for a small sized enterprise, being more focused might be a good strategy to begin with.  

Additionally, the agentic AI market is still very nascent and there are still a lot of unknowns. As every provider comes up with their own set of agents and solutions, we are likely to see an agent sprawl. This will necessitate the need for proper orchestration, integration and governance mechanisms within the agentic AI solutions and ultimately these could become the deciding factor over everything else.

With the number of start-ups burgeoning every day, we can also expect a significant consolidation in the months and years to come. Placing your bets on likely leaders and an ability to be agile can go a long way in emerging as a winner in the agentic AI journey! 

If you found this blog interesting, check out our blog on Agentic AI – Exploring Its Enterprise Potential | Blog – Everest Group, which delves deeper into the topic of agentic AI. 

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 Vaibhav Bansal (vaibhav.bansal@everestgrp.com) or Vershita Srivastava (vershita.srivastava@everestgrp.com)