Emerging Tech: Talentscape for AI Services Leaders in Core AI Skills

30 May 2025 - ID G00823387 - 29 min read
By Annette Zimmermann, George Brocklehurst,  and 3 more
AI service providers have invested in core foundational AI techniques and GenAI employees with skills to successfully drive future AI projects and have an AI-driven workforce. AI services leaders should ramp up specialization skill sets such as agentic AI development to lead the next wave of AI innovation.

Overview

Key Findings

  • To compete in the AI race, C-level tech service executives must decide on how heavy to invest in key generative AI (GenAI) skills such as synthetic data engineering, domain-specific language model (DSLM) training and intelligent simulation.
  • Companies with significant investment in conversational AI (CAI) talent are strongly positioned to lead in scaling existing AI solutions across multiple industries to capitalize on AI advancements and expand their market share.
  • Synthetic data engineering will become an essential skill in the next three years, helping to navigate privacy regulations, accelerate AI development and enable complex future scenario planning, particularly in the context of intelligent simulations.
  • Due to the rapid growth of cloud-based and hybrid cloud AI deployments, talent investment in cloud computing and data integration is essential because, without these skills, service providers risk losing a competitive advantage.

Recommendations

  • Address the growing customer demand for GenAI technologies by rapidly ramping up specialization skills in your AI workforce, including DSLM training, CAI, agentic AI and data integration.
  • Take leadership in the agentic AI market by investing in skill sets that are at the intersection of CAI and GenAI to deliver projects at scale and monitor applications from a security, risk and governance perspective.
  • Develop a competitive advantage in talent skills by investing in data engineering skills to support design and implementation of simulation models and support adaptive scenario planning for domain/industry use cases.
  • Invest in AI-related infrastructure and architecture (I&A) skills that can handle new deployment environments and design, such as hybrid cloud and sovereign cloud architecture — the next frontier of cloud computing.

Strategic Planning Assumption

By 2028, the top 10 largest AI service providers will collectively invest over $10 billion in enhancing their workforce’s AI skills.

Analysis

Overview

In this research, we examined talent investment by leading AI service providers by analyzing nearly 40,000 employees with AI skills across AI-related jobs and member profiles from LinkedIn (see the Evidence section for more details). We specifically sought out a set of AI skills among the top AI service providers as an indicator of future talent and technology advancement.
We have identified several critical trends in how talent investment is flowing, concentrating around these five areas:
  1. GenAI: This group of skill sets includes knowledge of and techniques related to GenAI, large language models (LLMs) and advanced prompt engineering.
  2. Foundational techniques: This group focuses on foundational skills such as machine learning (ML), AI and data science, which are pivotal in enabling advanced AI solutions.
  3. Software and data engineering: This category can be divided into four subcategories: data engineering and architecture, general software development and engineering, specific programming language skills, and DevOps and IT operations.
  4. I&A: This group of skills can be broken down into four themes: I&A design, cloud computing for AI and data, cloud and networking solutions, and skills for specific development platforms and (integration) tools.
  5. CAI: This group of skills includes CAI as well as natural language processing (NLP), virtual assistants and AI agents.
For the accompanying analysis of AI talent investments and skills related to applying AI among leading AI service providers, see Emerging Tech: Talentscape for AI Services Leaders in Applied AI Skills.
Today’s investment in various AI skills among AI service providers signals a revolutionary future and can be an indicator of the future competitiveness of AI service providers within specific AI domains. From this research, Gartner has observed the following broad trends:
  • Foundational techniques are the most dominant area of AI skills among leading AI service providers. They are strategically leveraging foundational skills (e.g., ML, computer vision and AI skills) to establish robust AI infrastructure and capabilities.
  • GenAI is the new race for AI service providers, as they are looking to expand and scale GenAI projects, with a focus on expanding the application of GenAI across CAI use cases and expanding agentic AI expertise.
  • Software and data engineering is driving broad AI adoption via scaling AI efforts. DevOps automation is the critical enabler in this AI scaling, as it empowers software and data engineering teams to iterate faster, manage complex AI workflows effectively, and ultimately accelerate the delivery of AI-powered solutions to a broader user base.
These trends align with and extend our initial observations of the AI services market in 2023, when providers began significantly investing in AI skills. Organizations can increase their skill sets generally in three ways:
  1. Hiring new talent
  2. Acquiring a company with desired skillsets
  3. Reskilling and upskilling existing talent in the organization
From our case-based research in AI services, providers have been applying all three approaches. This has been key especially in the light of skills scarcity, which has limited the possibility of new hires. To improve the effectiveness of new hiring, AI service providers have been investing in partnerships with leading universities globally. And reskilling and upskilling programs have evolved over the past three years from very basic company-based courses and self-learning to increasing investments in professional training and certifications.
Figure 1 displays the selected key AI service providers by technology category. The vertical axis represents relative talent investment levels within the category. AI service providers that are higher on the vertical axis have a higher number of employees with AI skills than those lower on the axis.
Figure 1: AI Service Providers’ Employees With Skills by Technology
Accenture leads in AI service skills across foundational techniques, software, and data engineering, infrastructure, and architecture, generative AI, and conversational AI. IBM, EY, and Tata Consultancy Services also show strong capabilities across these areas, with varying skill levels.
Table 1 summarizes employee skills of the AI service providers that we examined.

Summary of Examined AI Service Providers’ Employee Skills (Talent Investment)

AI category
Number of AI service providers examined
Total number of employees with skills
Number of employees with skills of highlighted AI service providers
Share of employees with skills held by highlighted AI service providers (%)
Generative AI
28
5,892
4,261
72%
Foundational techniques
29
12,539
10,016
80%
Software and data engineering
29
9,689
6,816
70%
Infrastructure and architecture
29
8,561
6,475
76%
Conversational AI
28
3,199
2,291
72%
Total
39,880
29,859
75%
Note: Data is for active job posts fetched in December 2024 for all geographies (except China) and recognized languages.
Source: Gartner (May 2025)

GenAI: AI Service Providers Are Shifting Strategies to Dominate

Analysis by Annette Zimmermann and Annette Jump
In this analysis, the AI service providers that we examined collectively have employees with nearly 4,300 skills related to GenAI. The vast majority of those skills — over three-quarters — are linked to generic GenAI capabilities. This is followed by LLM-related skill sets (17%), while 8% are around prompt engineering. This suggests a strategic focus on developing and deploying GenAI solutions across various applications.
Leading AI service providers show a significant number of employees with GenAI-related skills. This indicates a substantial investment in this area. Companies such as Accenture, Tata Consultancy Services, IBM, EY, and Deloitte exemplify this trend, with each having a high number of these skills as shown in Figure 1. This indicates that the top-tier service providers recognize the transformative potential of GenAI.
There is also a focus on actively integrating GenAI into existing service offerings, which could involve adapting GenAI to specific industries or applications. AI service providers such as Accenture, Capgemini, Cognizant and Microsoft represent this trend. Some companies are leveraging existing partnerships rather than developing significant in-house GenAI expertise (examples include Amazon and Dell). Such tactics point to a diversity of strategic approaches to developing GenAI talent skills and GenAI adoption. Additionally, AI service providers with higher numbers of GenAI-skilled employees might be implementing more aggressive recruitment or training programs versus their competitors.
Trends
AI service providers in the GenAI landscape are in a race to the forefront.
The data reveals a clear emphasis on GenAI skills among leading AI service providers, indicating a significant shift in the industry. While foundational skills such as ML and AI remain critical, the ability to leverage and implement GenAI is becoming a key differentiator. The overall distribution of skills suggests that some companies are heavily investing in building extensive GenAI capabilities, while others are adopting a more balanced or specialized approach, potentially focusing on integrating AI with existing services or targeting specific market segments.
Skills to support GenAI implementations are expanding.
Prompt engineering and LLM expertise are emerging as vital components for successful GenAI implementation. While GenAI skills outnumber prompt engineering and LLM expertise in our dataset, the data reveals a balanced approach with skills around these two other capabilities. This implies an understanding that these skills are interconnected. The lower numbers for prompt engineering and LLM skills compared with GenAI skills suggest that the industry is still in the early stages of developing these specific skills. This is an area where we can expect rapid growth in the near future, as well as around various LLM and other GenAI model training and integration into various applications.
GenAI is speeding up the shift to AI-driven solutions and insights.
The distribution of GenAI, prompt engineering and LLM skills among the leading AI service providers highlights strategic choices and potential future trends in the industry. Companies with strong presence in all three skills areas with balanced skills distribution appear to be positioning themselves as leaders in the full spectrum of GenAI services. They are investing heavily in talent and resources to provide end-to-end GenAI and AI-based solutions and insights. Those investments will enable AI service providers to support large-scale digital transformation projects powered by AI, as evidenced by AI service providers such as Accenture, IBM and Tata Consultancy Services.
The rapid adoption of GenAI also indicates a shift toward offering AI-driven insights and solutions to clients across various industries. This is evidenced by AI service providers such as Deloitte and EY. With many adopter organizations now looking for impact and quantifiable benefits from GenAI projects, there is increased focus on impact versus productivity benefits. This will require AI service providers to expand talent skills around model training and customization, synthetic data, and predictive analytics.
Near-Term Implications and Actions
  • Invest in training and hiring. Leading AI service providers are significantly invested in GenAI skills. The ongoing development and expanding application of GenAI technologies suggest a continued rise in demand for these skills, requiring companies to continually invest in training and hiring talent in various GenAI techniques, model training, small language models (SLMs), advanced prompt engineering and multichain LLM management.
  • Focus on domain specialization and predictive analytics. In the next two to three years, there will be a growing requirement for specialization, so AI service providers will need to develop capabilities for domain-specific solutions. Combining AI and real-time data analytics for a specific industry or domain is a growing trend supported by advancements in DSLMs. Therefore, the expertise to leverage GenAI for real-time insights and predictive analytics will be in high demand.
  • Engage in ethical and secure AI practices. As GenAI capabilities advance, there will be a growing requirement in talent and skills around ethical AI practices, especially in areas such as AI avatars, AI-personalized content and disinformation security. This will drive demand for professionals skilled in implementing ethical safeguards, including consent and control capabilities for technologies, as well as working with industry advisors and regulators. Model bias detection and mitigation as well as model explainability will also be in demand. To tackle the growing threats of disinformation, particularly those heightened by GenAI technologies, AI service providers will need to enhance their internal talent capabilities or collaborate with security firms to effectively address these increasing challenges.
  • Support scalable deployment. As many adopter organizations will move from small pilots to broader enterprise adoption of GenAI solutions in 2025, there will be a requirement for talent around user-friendly AI solutions and deployment best practices. This is because only a limited number of adopting organizations possess such expertise, and the emerging nature of the technology makes it challenging to find these skills.

Three Postures in Foundational Skills: AI Infrastructure Innovators, Data Insight Leaders and Advanced AI Solutions

Analysis by George Brocklehurst
In this analysis, we examine the strategic positioning of AI service providers based on their core AI skill sets. The focus is on foundational skills such as ML, AI and data science, which are pivotal in enabling advanced AI solutions. Among the providers, a significant portion of skills is dedicated to these foundational areas, highlighting a strategic focus on building robust AI infrastructure and capabilities. This foundational expertise positions these companies to exploit high-value AI use cases, enhance decision intelligence and develop domain-specific solutions. The analysis reveals a diverse range of strategies, from leveraging existing partnerships to developing in-house expertise, reflecting the varied approaches companies are taking to capitalize on AI advancements.
The analysis categorizes AI service providers into three distinct groups based on their strategic focus and core skills. The first group, including companies like Cognizant and Microsoft, emphasizes AI infrastructure by focusing on foundational skills such as ML and big data. This positions them to build scalable platforms that support AI-driven automation and analytics, addressing critical adoption gaps like AI centers of excellence and data quality. The second group, with firms like Deloitte and EY, is naturally aligned to exploit high-value GenAI use cases in decision intelligence. Their expertise in data science and analysis allows them to extract actionable insights and enhance decision-making processes, although further advancements in AI reasoning capabilities are needed. The third group, featuring Accenture and IBM, has developed skills to deliver domain-specific AI solutions, focusing on advanced techniques like deep learning and computer vision. Cognizant has also invested broadly in skills supporting domain-specific AI solutions. This positions these vendors to create tailored applications for industry-specific challenges, bringing technology and service providers into closer competition as they vie for specialized AI opportunities.
Trends
AI scalable infrastructure providers double down on core AI and data skills to address key enterprise adoption inhibitors.
AI scalable infrastructure providers like Cognizant and Microsoft are focusing on core skills to address critical gaps in AI adoption. These companies have a higher percentage of expertise in ML, AI and big data compared with other vendors, enabling them to build robust and scalable AI infrastructure. One of the main challenges in AI adoption is the absence of a capable AI center of excellence and issues related to data quality and skills. These gaps often result in longer pilot cycles and lower conversion rates from pilot to production. By concentrating on these core areas, AI scalable infrastructure providers are well-positioned to help organizations establish AI centers of excellence that drive innovation and best practices. They also tackle data quality challenges by employing advanced data management techniques, ensuring AI models are trained on reliable datasets. This strategic focus not only strengthens their infrastructure offerings but also empowers businesses to overcome adoption hurdles, allowing them to accelerate AI deployment and achieve competitive advantage.
Data insight leaders are positioned to exploit high-value decision intelligence opportunities.
Companies such as Deloitte, EY and PwC, all data insight leaders, are naturally positioned to exploit high-value use cases for AI, particularly in decision intelligence. These firms possess a higher percentage of expertise in data science, data analysis and AI compared with other vendors, enabling them to excel in extracting actionable insights from data. Leveraging their capabilities in data visualization and advanced analytics, they are well-equipped to augment with GenAI to create solutions that enhance strategic decision-making processes. This inherent positioning allows them to address complex business challenges by providing clients with predictive insights and strategic recommendations. Their expertise in data-driven decision intelligence empowers these companies to guide organizations in optimizing operations, improving customer experiences and driving innovation. However, to fully capitalize on these opportunities, they must advance GenAI in reasoning capabilities, which requires improvements in chain-of-thought faithfulness and the development of cognitive reflection to address diverse reasoning styles and problem-solving needs.
Advanced AI solutionists bridge technology and service for domain-specific excellence.
Companies such as Accenture, Capgemini and IBM, known as advanced AI solutionists, have honed their skills to deliver specialized AI solutions for various industries. By focusing on advanced techniques such as deep learning, computer vision and neural networks, they create tailored AI applications that tackle specific industry challenges. This expertise enables them to offer sophisticated solutions for sectors such as healthcare, automotive and manufacturing. As a result, other technology and service providers are striving to seize opportunities in specialized AI solutions. Adopting AI, especially GenAI, demands deeper integration and domain specialization to achieve the contextual outcomes necessary for delivering business ROI. For success, these vendors must also develop training and tuning capabilities to transform their foundational skill sets into composite AI solutions. With these capabilities, they can effectively leverage their advanced AI skills to lead in providing transformative solutions that offer significant competitive advantages.
Near-Term Implications and Actions
  • Enable AI centers of excellence. Service providers should prioritize establishing enablement capabilities to help customers develop better AI center of excellence practices and empowerment. The focus should be on data quality and ownership over AI use-case prioritization, architecture and deployment. By fostering a culture of continuous learning and collaboration, providers can enhance their capabilities and reduce time to market as well as greater adoption for AI solutions.
  • Enhance AI reasoning capabilities. To fully leverage decision intelligence opportunities, providers need to invest in advancing GenAI reasoning capabilities. This includes partnering with model providers and offering specialized training, demonstrating improved chain-of-thought faithfulness and a roadmap geared toward developing cognitive reflection. By doing so, providers can offer more nuanced insights and strategic recommendations, enhancing their value proposition to clients.
  • Develop domain-specific expertise: With the increasing demand for specialized AI solutions, service providers should focus on building domain-specific expertise. This involves becoming experts in building model training metacurricula that can ingest customer domain data and supply-optimized training pipelines. By aligning their skills with industry needs, providers can differentiate themselves and capture new market opportunities.

Software and Data Engineering: Driving Broad AI Adoption by Scaling AI Efforts

Analysis by Mark Driver
In this analysis, the AI service providers that we examined collectively have employees with over 6,816 skills related to software and data engineering. We found four underlying subcategories or themes of job skills:
  • 43% related to specific programming language skills
  • 29% related to DevOps and IT operations
  • 14% related to data engineering and architecture
  • 13% related to general software development and engineering
Accenture, EY, IBM and Tata Consultancy Services were the service providers with the largest overall number of skills across all combined segments. Accenture, IBM, Deloitte and Cognizant were the service providers with the largest overall number of skills in the programming language segment. Deloitte, Accenture, Cognizant and IBM were the service providers with the largest overall number of skills in the DevOps and IT operations segment. Accenture, IBM, EY and Tata Consultancy Services were the service providers with the largest overall number of skills in the data engineering and architecture segment. Accenture, Microsoft, Deloitte and Cognizant were the service providers with the largest overall number of skills in the general software development and engineering segment.
Skills in the Python programming language and DevOps automation skills stood out as dominant, broadly reflected in all the vendors measured. The Python and Java programming languages stood out as dominant skills in the programming language segment. General DevOps skills and automation stood out as key skills in the DevOps and IT operations segment. General database and extraction, transformation and loading (ETL) skills dominated the data engineering and architecture segment. And frameworks and API skills dominated the general software development and engineering segment.
Leading AI service providers show a significant number of employees with software and data engineering AI-related skills. This indicates a substantial continued investment in this area. Companies such as Accenture, IBM and Tata Consultancy Services exemplify this trend with their strong skills presence. This indicates that the top-tier service providers recognize the role of AI in driving state-of-the-art software development.
An examination of the top-skilled software and data engineering AI service providers revealed the following trends and insights.
Trends
AI service providers show that Python remains the dominant programming language among AI development efforts.
The Python language has been a cornerstone of many IT projects for over a decade. Its popularity as a general-purpose “Swiss army knife” language has attracted developers for a wide range of projects — ranging from e-commerce web solutions to big data processing, and most recently, it’s been the preferred language foundation for next-generation AI projects. At 16%, it ranked highly as one of the dominant skills across all segments and across all the providers measured; as a result, it is nearly universally positioned as a must-have skill set for AI service providers. Companies such as Accenture, Deloitte and Tata Consultancy Services stand out as particularly focused (although certainly not exclusively) on Python skills. This evidence shows that Python will continue to be a go-to technology for AI-centric software development for the near future — even as newer specialized languages are likely to emerge in coming years.
DevOps automation is the key to scaling AI-centric software development.
Enterprises are increasingly turning to AI service providers to help scale/drive adoption from early proof of concepts (POCs) to broad enterprise maturity. At 11% of the overall skills focus, DevOps automation is a key proof point and evidence of the need of mainstream IT leaders to keep up with demands to incorporate next-generation AI technologies into line-of-business projects. Both EY and IBM stand out as leaders in this skills segment.

This evidence suggests that to keep up with customer demand, AI service providers will increasingly focus on a range of “automation” mechanisms. One of the most impactful innovations in this area is the emergence of AI agents. As a result, a focus on DevOps automation will be a major factor in agentic AI features and services added to AI service provider portfolios in the near future (see How AI Agents Will Disrupt Software Engineering).
Near-Term Implications and Actions
  • Expand skills around Python. Product leaders should continue to incorporate and drive skills around the Python programming language (and related frameworks such as TensorFlow, PyTorch, etc.) to maintain a strong ongoing foundation of AI-centric software and data engineering skills.
  • Prioritize DevOps automation. Automated capabilities for Kubernetes, Docker, CI/CD pipelines and so forth will be essential to support growing mainstream customer demands for scalable, reliable infrastructure. As AI services move beyond early adopters, customer expectations for 99.9% uptime and seamless scaling will become non-negotiable. Companies without robust DevOps talent will struggle to meet these reliability standards.
  • Prioritize AI agent investments. The emergence of AI agents represents the most impactful strategic shift among AI service providers, fundamentally changing how businesses operate and deliver value. We’re in the early stages of the AI agent revolution. Companies that build expertise in agent architecture, multiagent coordination and human-AI workflows now will dominate their markets by 2027.

Infrastructure and Architecture: Shift From I&A Design and Implementation to Big Data and Cloud for AI

Analysis by Annette Zimmermann and George Brocklehurst
AI service providers cumulatively have employees with over 8,500 skills related to LLM capabilities, with these four underlying subcategories:
  • I&A design
  • Cloud computing for AI and data
  • Cloud and networking solutions
  • Skills for specific development platforms and (integration) tools
    • In particular, AI service providers invested in the area of technology integration, leveraging various tools and solutions to drive business outcomes.
IT consulting and data processing and management in Hadoop-based architectures were also part of the I&A category.
Of the providers we examined, Accenture, Microsoft, IBM and Tata Consultancy Services have the largest overall number of skills in the I&A segment. This is aligned with these companies’ positioning in the market. Accenture not only has been well-known for its technology integration and IT consulting capabilities, but also is growing its skills in data for AI as well as business consulting capabilities. Microsoft, as one of the leading GenAI vendors in the industry, is manifesting its position further for cloud computing for AI and DevOps automation. IBM leads with a focus on hybrid cloud strategies automation and therefore has acquired related skills to address customer demand in specific geographies such as Europe, where hybrid cloud deployments for AI are growing. Tata Consultancy Services invested early on (2023) in R&D for AI infrastructure-related areas, such as processing power to handle complex AI algorithms and large-scale simulations and cloud computing for AI.
Trends
Expect explosive growth in AI-driven cloud computing, AI-powered cloud solutions and hybrid cloud deployment skills.
Skills in cloud computing, development and data integration have been highly sought after as cloud-based and hybrid cloud AI deployments are rapidly growing. Our data suggests that some skills requirements are platform-specific, such as capabilities to deploy and manage scalable Microsoft Azure solutions and AWS-based solutions. There are different factors driving the demand for these skills. Most enterprises want to take advantage of AI in a cloud-based environment for workload optimization and scalability. As a result, the hyperscalers are growing their AI service business.
Cloud computing for AI and data will likely be the most critical element in the future. The vast majority of advanced AI and data processing will increasingly occur in the cloud due to the need for scalability, access to data lakes and warehouses, specialized services, the use of cloud-specific AI/ML platforms, services and hardware (like GPUs and TPUs), and to achieve cost-effectiveness at scale.
I&A design will likely remain the second most important skills category, as well-designed infrastructure is the bedrock upon which successful and scalable AI deployments are built. In the future, the complexity of AI architectures (e.g., distributed training, real-time inference at the edge, MLOps pipelines) will necessitate even more sophisticated and optimized I&A skills. In contrast, poor design can lead to performance bottlenecks, high costs and deployment failures.
There is growing demand for hybrid cloud AI deployments, specifically driven by those organizations that have not migrated to the cloud yet (and may not be able to do so in the near term). Hybrid cloud offers “the best of two worlds,” where enterprises can maintain their sensitive data in the private cloud/on-premises while leveraging scalability for AI workloads in the cloud. AI service providers such as IBM, NTT DATA and Wipro position themselves in the market with hybrid cloud, which is resonating in certain industries and geographies. We expect that, in addition to hybrid cloud deployment skills, regions such as Europe, where sovereign cloud is seeing demand (see Forecast Analysis: Sovereign Cloud IaaS, Worldwide), a new skill set around sovereign cloud deployment will develop.
Data engineering is the essential future skill.
Data engineering and, increasingly, synthetic data engineering will become indispensable skills for AI service providers to invest in during the next three years. While data engineering is currently helping with scalable and reliable AI deployments, synthetic data engineering will enable AI service providers to offer differentiated capabilities in the future.
Synthetic data engineering will help with navigating privacy regulations and accelerate AI development and testing and will also enable complex future scenario planning, particularly in the context of intelligent simulations. Many future scenarios will involve dynamic elements and require the integration of real-time or near-real-time data streams (e.g., traffic flow for urban planning, energy consumption for resource management, disease spread updates for pandemic readiness). Future talent investment in data engineering skills will support design and implementation of simulation models, allowing for more responsive and adaptive scenario planning (including complex scenarios with a sequence of events).
Mastery of sovereign cloud and data will be an absolute necessity.
As sovereign cloud has surfaced as a relatively recent trend among vendors, our dataset does not offer any insights on this topic. However, Gartner client inquiries provide evidence that there is growing interest in this topic (see Sovereign Cloud Portfolio Design for Cloud IT Services Providers). Organizations’ growing concern about the sovereignty of data, infrastructure and operations hosted in foreign-owned cloud service offerings leads to stricter requirements for data residency and data control, and more demand for technological control and long-term autonomy and governance.
Near-Term Implications and Actions
  • I&A design. With I&A design expected to remain the second most important skills category, it will need continuous investment in terms of talent acquisition and upskilling existing employees. Compute power, storage and scalability are critical focus areas for AI service providers to enable next-generation AI applications and solutions.
  • Hybrid cloud. AI service providers should not underestimate enterprise demand for hybrid cloud offerings, especially in Europe and certain verticals. Recent research provides evidence that there is continuous demand for hybrid cloud-based AI solutions. Cloud adoption is slower in certain verticals, and cloud migration represents a significant cost factor, to which hybrid offerings can then be a viable option.
  • Sovereign cloud offerings. Sovereign cloud offerings that address key customer requirements in regions such as Europe will be part of the AI growth story. AI service providers need to invest in skills that can address AI deployments based on sovereign cloud design and hence provide sovereign AI strategies.

Conversational AI: Investment Shifts From NLP to Agentic AI

Analysis by Danielle Casey and Annette Jump
The top nine AI service providers that we examined for the CAI space collectively have employees with nearly 2,300 skills, accounting for over 72% of all CAI skills among leading AI service providers. Nearly 76% of those skills are around natural language processing (NLP), while the other skills are related to virtual assistants, AI agents and CAI. This reveals that NLP is a foundational skill underlying development of many CAI applications.
Accenture and IBM demonstrate clear leadership in the number of CAI skills, underscoring their prominence in investment within this area. This indicates that these two companies have the largest overall capacity in deploying CAI-related expertise. This concentration of skills highlights that they have heavily invested in this technology space for the last five years. The majority of next-tier AI service providers — Tata Consultancy Services, Deloitte and EY — exhibit a substantial presence in the CAI skills landscape, with a stronger emphasis on NLP skills. This could be a foundation for future growth and specialization in the evolving CAI market.
CAI technology is well-established with all leading service providers, so many of them aim for a broad and balanced skills distribution. This is exemplified by Cognizant and Microsoft, indicating intention to build comprehensive solutions that integrate multiple technologies and be versatile in their approach. There is also some specialization, as exemplified by Capgemini potentially offering specialized services or solutions.
An examination of the top-skilled AI service providers in CAI revealed the following trends and insights.
Trends
Scalability and innovation are enabled through CAI skills investment.
Companies with significant investment in CAI skills are likely to lead in the ability for scaling existing solutions across multiple industries and domains. The extensive resources will also help to drive innovation and allow for the development of advanced AI/CAI models and the ability to handle large-scale deployments. A large pool of talent available will aid the deployment and management of large-scale implementations and the handling of increased user demand. This is exemplified by Accenture, Deloitte, IBM and Tata Consultancy Services.
Future agentic AI applications require CAI and GenAI synergy.
The growing enterprise focus on agentic AI necessitates a blend of CAI and GenAI expertise. A strong foundation of CAI skills combined with GenAI, LLMs and prompt engineering skills is crucial for developing and leveraging agentic AI.
Agentic AI relies heavily on advanced conversational capabilities beyond simple virtual assistants. AI service providers with higher skills in CAI will have an advantage in core AI functionalities required for innovating toward more sophisticated agentic AI systems.
GenAI skills enable the creation of more nuanced, context-aware and humanlike interactions, which are fundamental for agentic systems that need to understand complex instructions, generate coherent responses and engage in more natural dialogues. Vendors such as Microsoft and Cognizant actively investing in these areas are at the forefront of developing agentic AI and multiagent systems (MAGs).
Domain specialization can result in developing targeted CAI innovations.
Specialization in specific CAI areas can contribute to domain or niche innovations within intelligent applications and agentic AI. While a balanced approach is generally advantageous, AI service providers with a strong focus on a particular area can drive innovation in how specific agent functionalities are delivered through conversational interfaces. This specialized expertise can be valuable in developing highly effective agents for targeted domain- or industry-specific applications, as evidenced by IBM.
Near-Term Implications and Actions
  • Drive innovation. AI service providers who are building expertise in interconnected areas are poised to drive future innovation in the CAI space. Investing in R&D to push the boundaries of AI technology can differentiate AI service providers as leaders in innovation.
  • Support intelligent automation. There is a growing demand among enterprises to leverage CAI and GenAI to enhance productivity, streamline workflows and empower employees with intelligent automation. Development of selective DSLMs, advanced AI agents, agentic platforms and domain-specific advanced virtual assistants that integrate seamlessly into various business processes and applications will become a competitive necessity in the next six months.
  • Navigate ethical and security challenges for scaling. Ethical and security considerations serve as critical gatekeepers in the transition from POC to full deployment and scaling of CAI and intelligent automation projects. Addressing these concerns and developing the required skills in the organization is essential, as they play a pivotal role in ensuring the development of trustworthy and reliable AI applications. This focus enhances user confidence and compliance with regulations but also positions AI service providers as leaders in delivering both innovative and responsible CAI.
  • Develop strong data management capabilities. The importance of data (both real and synthetic data) is again at the forefront for any AI development. Expertise in handling large datasets, synthetic data generation, ensuring data privacy and security, and providing actionable insights through AI is crucial for the success of AI projects.

Evidence

This research is based on social listening analysis leveraging LinkedIn to complement other fact bases represented in this document. The job and member data was extracted using specific keyword combinations related to AI (“AI” or “artificial intelligence”), providing a detailed view of job availability and member roles across 29 leading AI service providers (including Accenture, Capgemini, Cognizant, Deloitte, EY, IBM, Microsoft, PwC and Tata Consultancy Services). To prepare the dataset for review, Gartner applied analyst-designed algorithms, and analysts examined the AI-augmented dataset to uncover patterns and develop the insights presented. For strategic decision making, clients should consider this analysis in conjunction with other indicators to build a holistic view of technology emergence and growth.
Social media data referenced is for active job posts fetched in December 2024 for all geographies (except China) and recognized languages. Due to its qualitative and organic nature, the results should not be used separately from the rest of this research. No conclusions should be drawn from this data alone.