Rajat Sharma, President, Digile Inc. A leading platform-led AI transformation firm focused on automotive and construction Engineering.
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We are already beginning to see the market react to the potential disruption AI may bring to traditional IT services. Several major outsourcing firms are trading well below their historical highs, reflecting growing questions about how AI agents and automation will reshape service delivery.
At the same time, the industry is moving toward a "Service as Software" (SaaS) model, where AI agents increasingly perform work that was previously delivered by large teams of human consultants and operators.
If this trend continues, AI will fundamentally change how IT services are designed, built, implemented, secured, managed and consumed.
Let's look at how the IT services industry has evolved, and how they have stayed resilient from legacy Y2K to SaaS.
Initially, IT services firms deployed thousands of engineers to help businesses prepare for Y2K in the late 1990s. Many firms thought revenue would evaporate by the year 2000, however most were able to adapt. They trained their workers in new skills for the growing dot-com era and continued to grow. When the dot-com boom ended, they shifted again by moving more work offshore to lower costs and stay competitive.
IT services companies quickly adapted to this change by offering managed services instead of simply providing workers on hourly contracts. In these deals, the service provider took responsibility for running a company's IT operations. They often lowered costs by moving work offshore and improving productivity over several years.
Over time, these companies improved their approach. They built shared service centers and created their own tools to stand out from competitors and provide better service. While these tools reduced the amount of work needed, clients continued to give service providers more projects and responsibilities.
Next came the age of automation. Companies moved from automating simple tasks to automating entire workflows through technologies like robotic process automation (RPA) and intelligent process automation (IPA). This further reduced the services headcount for managed services; however, it started a new service stream of implementing RPA and IPA as stand-alone projects. So, this era again created more work for IT service providers.
Then came cloud computing. Before public cloud services became widespread, service providers found another growth opportunity by including infrastructure and software licenses in managed services contracts. In many cases, infrastructure and licensing made up more than 60% of the total contract value, creating a significant new source of revenue.
Another major growth driver was cloud migration and modernization. Companies needed help moving their systems to the cloud, and cloud providers often funded these projects. As a result, even more business flowed to IT service providers.
Through all of these technology waves, large IT services companies continued to grow. Managed services expanded in two ways: More companies adopted managed services, and each new technology created additional managed service opportunities.
Now the industry is entering the age of AI. Better and faster computing power, advances in large language models from companies like OpenAI and Anthropic, and new tools for building AI agents are changing how businesses operate. At the same time, new AI architectures are making it easier for multiple AI agents to work together and complete complex tasks.
As a result, many companies are focused on experimentation, proof-of-concept projects and rethinking how their organizations are designed. Instead of signing large outsourcing contracts, business leaders are asking a new question: How can service providers help us build and operate an AI-powered enterprise?
To respond, some IT services firms have announced large partnerships with AI companies and plans to deploy AI tools to hundreds of thousands of employees. However, simply rolling out AI at scale does not guarantee business value. Without a clear AI strategy, operating model and governance framework, these investments may not deliver the expected results.
This challenge is not limited to traditional IT services firms. Even the large consulting firms are still working to define what the AI-powered enterprise should look like and how it should operate.
When selecting an AI service provider, business leaders should focus on five key areas:
• Business value
• Human oversight
• Security
• Cost management
• Operational consistency
Ask providers how they deliver measurable outcomes, keep people involved in critical decisions, protect data and AI systems, control AI costs, and balance innovation with reliable business processes. These factors will help ensure your AI investments create lasting value.
We have recently seen Google Cloud emerge as a fully integrated AI company, announcing a $750 million agentic AI partner fund for IT service providers. This is likely only the beginning. From my view, we can expect to see similar investments not only from the Magnificent Seven companies, but also from AI infrastructure providers, semiconductor companies, large language model providers and SaaS companies as they look to accelerate AI adoption and sustain their growth.
Just as hyperscaler investments helped drive cloud adoption, the next phase of AI adoption will likely be fueled by investments across the AI ecosystem, from infrastructure providers to companies building agentic AI platforms and applications.
On the services side, managed services operations are likely to reemerge as "agentic outsourcing."
In this model, AI agents will perform a growing share of operational work, bundled with LLM token consumption, infrastructure and software services. Human workers will remain responsible for orchestration, governance and outcome management.
Successful agentic outsourcing will require a new operating model. Instead of focusing primarily on people, processes and technology, organizations will need to manage agents, policies and human controls.
Service-level agreements (SLAs) will increasingly be tied to business outcomes rather than staffing levels or hours worked. Pricing models may also evolve toward a single resource unit that combines AI infrastructure, AI agents, LLM usage, token consumption, software licensing and human oversight.
The transition to agentic outsourcing will not happen overnight, but the road map is becoming clearer as enterprises continue to experiment with AI and rethink how work gets done.
Instead of cloud modernization funding cloud operations, I believe we will see that AI modernization will drive demand for agentic operations and new AI-enabled service models.
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