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Investing.com -- IT services firms are facing an inflection point as the rapid evolution of generative AI (Gen AI) reshapes enterprise demand and technology strategies. Companies across industries are looking to integrate AI into their operations, but the path to implementation remains complex.
IT services providers, acting as key enablers, are adapting their business models to meet this demand, balancing innovation with the practical challenges of AI adoption.
One of the most important factors driving enterprise investment in Gen AI is the promise of improved productivity.
Clients expect AI-driven efficiencies to be reflected in cost savings and business transformation, which places pressure on IT services firms to demonstrate tangible returns on investment.
As per Bernstein analysts, major consulting firms such as Accenture (NYSE:ACN) and Deloitte indicate that 85-90% of AI-enabled productivity gains will be passed on to clients, with only 10-15% retained by service providers.
Despite this, firms anticipate overall revenue growth as AI adoption drives new project engagements and expanded service offerings.
Accenture, for instance, reported $500 million in Gen AI-related revenue in the November quarter of 2024, an increase from previous quarters. Gen AI bookings have also surged, reaching $1.2 billion, accounting for 6.4% of the company’s total bookings.
These numbers highlight a shift from experimental AI pilots to substantial, enterprise-wide deployments.
The initial wave of Gen AI investment was largely focused on large language models (LLMs), but IT services firms are now seeing a shift toward smaller, more specialized language models (SLMs).
These models are designed for targeted applications, offering lower computational costs and greater efficiency in enterprise environments. IT services companies are actively guiding their clients in deploying SLMs for specific use cases, such as banking, IT operations, cybersecurity, and enterprise applications.
Indian IT firms like Infosys (NSE:INFY) have already moved in this direction, developing domain-specific SLMs to support industries with tailored AI solutions.
According to Bernstein, Infosys has built four SLMs designed for banking, IT operations, cybersecurity, and general enterprise functions, each leveraging proprietary datasets to deliver more relevant insights while reducing reliance on massive, resource-intensive LLMs.
A more recent development within AI adoption is the rise of Agentic AI, a concept that enables AI models to coordinate actions autonomously within enterprise processes. IT services firms are working with clients to explore how AI-driven agents can streamline operations in HR, finance, customer service, and supply chain management.
While many of these use cases remain in early development, some firms are already deploying AI agents in limited capacities.
Accenture and Deloitte report increasing interest from enterprises looking to develop their own AI-powered agents that can interact with existing software stacks and business applications.
However, full-scale implementation remains a long-term objective, as companies work through the complexities of AI orchestration and integration.
The debate between open-source and closed-source AI models is another challenge that IT services firms must navigate.
While closed-source models from providers such as OpenAI and Google (NASDAQ:GOOGL) Gemini dominate enterprise deployments today, open-source alternatives like Mistral AI and DeepSeek are quickly catching up in terms of performance and cost-effectiveness.
Despite the rapid advancement of open-source models, Bernstein analysts suggest that they will not be ready for enterprise-wide adoption before 2026-27.
IT services firms are monitoring this space closely, as enterprises weigh the benefits of lower costs and greater transparency against the risks of security and compliance.
IBM (NYSE:IBM) Consulting has highlighted specific cases in financial planning and accounting where open-source models have already matched or exceeded closed-source alternatives in both cost and performance.
While the momentum behind Gen AI is strong, IT services firms acknowledge several barriers to widespread adoption.
Data readiness remains a major obstacle, as many enterprises struggle with unstructured, incomplete, or outdated datasets that hinder AI model training and deployment.
Beyond data challenges, enterprises must also refine their business processes and technology stacks to fully integrate AI capabilities.
Regulatory uncertainty further complicates AI implementation. As governments introduce stricter rules around AI transparency, ethical considerations, and security, enterprises are looking to IT services firms to ensure compliance with evolving regulations.
Service providers, in turn, are investing in AI governance frameworks to help clients navigate these complexities.
As Gen AI continues to evolve, IT services firms are positioning themselves as strategic partners for enterprises looking to harness AI-driven efficiencies.
Whether through the development of industry-specific AI models, support for AI-powered automation, or guidance on regulatory compliance, IT service providers are playing a crucial role in shaping the AI landscape.
The coming years will likely see further shifts in AI adoption strategies, with open-source models gaining ground, Agentic AI becoming more sophisticated, and productivity expectations continuing to rise.
As Bernstein analysts highlight, IT services firms that successfully bridge the gap between AI potential and enterprise execution will emerge as key beneficiaries of the Gen AI boom.
