TCS Wants 80% of New Hires to Have AI Skills

Tata Consultancy Services (TCS) plans to make digital and artificial intelligence (AI) capabilities a requirement for a much larger share of its future hiring, with around 80% of new hires expected to have new-age technology skills. Chief Operating Officer Arathi Subramanian shared the target in comments to Press Trust of India on October 12, 2026, as the IT services company expands its AI business and adjusts its workforce strategy to changing customer demand.

The figure needs an important distinction: approximately 25–30% of the 4,500 employees TCS added in the September quarter had specialised AI skills, while nearly 80% had broader digital and new-age technology skills. The company is not saying that 80% of its new employees will be AI researchers or machine-learning specialists. Instead, the target reflects a wider shift towards workers who can use AI, data, cloud and other modern technologies in enterprise projects.

Key takeaways

• Hiring target: TCS expects around 80% of future hires to have digital and AI-related new-age skills.
• Specialised AI talent: Around 25–30% of the 4,500 employees added in the July–September quarter had specialised AI skills.
• AI revenue contribution: AI-led work accounted for approximately 10% of TCS revenue in the September quarter.
• Business opportunity: TCS believes expanding AI services could support profitability as the company delivers more AI-led projects.
• Hiring strategy: The company plans to invest in specialised talent, partnerships and acquisitions while continuing to develop employees internally.
• Skills matter beyond coding: Enterprise AI work also requires data knowledge, cloud capabilities, cybersecurity, business understanding and the ability to integrate systems.
• What it means for jobseekers: AI skills are becoming more relevant, but the 80% target does not mean every role requires advanced AI engineering expertise.

What TCS’s 80% AI and Digital Hiring Target Means

TCS’s hiring target reflects a broader change in the skills required across the IT services industry. Businesses increasingly want technology partners that can integrate AI into existing applications, automate processes, analyse data and redesign workflows rather than simply maintain traditional software systems.

For TCS, that means more employees will need some combination of digital and new-age technology capabilities. These may include AI tools, data analytics, cloud computing, cybersecurity, software engineering and enterprise technology platforms.

The target is broader than hiring specialists who build AI models from scratch. A software engineer might use AI coding assistants to develop and test applications. A data analyst could use AI-enabled tools to extract insights from business information. A cloud engineer may need to understand how AI workloads are deployed securely and efficiently.

Likewise, consultants and project managers increasingly need to understand where AI can improve a client’s operations, what data is required and how the technology can be integrated into existing systems.

This distinction matters because AI adoption is changing jobs across technology teams, not only creating demand for a small group of highly specialised researchers.

Subramanian’s comments suggest that TCS expects the broad skills requirement to continue in future hiring. However, the company has not said that every job will require the same AI capabilities or that all new employees will be assigned to AI projects.

How TCS’s Hiring Numbers Break Down

The company’s September-quarter hiring figures help explain the difference between specialised AI expertise and broader digital capabilities.

Hiring measure	Reported figure
Employees added during Q2 FY27, as cited in the hiring comments	Approximately 4,500
Share with specialised AI skills	Around 25–30%
Share with broader new-age technology skills	Nearly 80%
AI-led work as a share of Q2 revenue	Approximately 10%
Annualised AI revenue reported for Q2 FY27	More than $3 billion

Source: TCS management comments reported by PTI and financial publications. The 80% figure refers to broader new-age skills, not exclusively specialised AI expertise.

The numbers show that TCS is trying to align its hiring profile with the changing composition of its business. Specialised AI talent remains important, but the larger pool of employees with digital and new-age skills is expected to support a wider range of client engagements.

The 80% target should be understood as a forward-looking hiring strategy rather than a statement that the current workforce already has that exact skill composition. The proportion of employees hired with these capabilities can also vary by quarter, job category and business demand.

AI Business Now Contributes 10% of TCS Revenue

The hiring shift comes as AI-related work becomes a larger part of TCS’s business.

During the September quarter, AI-led work accounted for approximately 10% of the company’s revenue. TCS also reported annualised AI revenue of more than $3 billion, highlighting the growing commercial importance of AI-related services.

Annualised revenue is a measure that projects a recent revenue run rate over a full year. It is not the same as revenue actually earned during the full financial year, and it does not guarantee that the same rate will continue.

The growth reflects demand from enterprises looking to apply AI to software development, customer service, business operations, data analysis and other processes. Companies are increasingly moving beyond small experiments and exploring how AI can be deployed across larger parts of their operations.

For an IT services provider, this creates opportunities to help clients select models, prepare data, integrate AI into existing applications, establish security controls and monitor the performance of AI-enabled systems.

However, AI revenue does not automatically translate into higher profits. Projects may require investments in skilled employees, infrastructure, partnerships and software. The financial outcome depends on how efficiently TCS delivers these services, the pricing it secures and the extent to which AI changes the cost of completing client work.

Why TCS Believes AI Can Improve Profitability

TCS management has indicated that a larger share of AI-led work could help the company improve profitability. The argument is that higher-value services and AI-enabled delivery models may create opportunities to improve productivity and the economics of client engagements.

Traditional IT services can involve large teams maintaining applications, managing infrastructure and delivering recurring technology support. AI tools can automate portions of these activities, assist software developers and speed up selected tasks.

If a company can deliver the same or better outcome with less repetitive work, it may be able to improve productivity. AI may also create new projects involving business transformation, data platforms and enterprise automation that command different commercial terms from routine services.

But the impact on margins is not automatic. Enterprise AI projects can be complex, and customers may expect some of the productivity benefits to be reflected in lower prices. Providers also need to invest in training, infrastructure, security and quality assurance.

TCS has said it is conservative when classifying a deal as AI-related, while noting that some AI component is now present in virtually all the work it does. This suggests that the company is attempting to distinguish clearly identifiable AI-led engagements from broader projects that use AI as one component.

The profitability effect will therefore depend on the mix of projects, the cost of delivery and whether productivity improvements can be converted into sustainable financial gains.

What Skills Will Matter in TCS’s AI Hiring Strategy?

TCS has not published a single list of skills that every new employee must possess to qualify under the 80% target. Its public comments point to a broad shift towards new-age technology capabilities rather than one mandatory qualification.

Several skill areas are likely to be relevant to the wider strategy.

1. AI and generative AI fundamentals

Employees may need to understand how AI systems work, what large language models can and cannot do, and how generative AI tools can be applied to business tasks.

For technical roles, this may extend to model evaluation, retrieval-augmented generation, AI application development and the integration of models with enterprise data. For non-specialist roles, the requirement may focus more on using AI tools responsibly and checking their outputs.

2. Programming and software engineering

Programming remains important for many technical positions. Languages and frameworks are selected according to the role, but software engineers benefit from understanding core programming concepts, debugging, APIs, databases and testing.

AI coding tools can assist with writing code, but developers still need to evaluate whether the generated code is correct, secure and maintainable. Strong fundamentals remain useful when AI tools produce errors or when a project involves complex systems.

3. Data analytics and engineering

AI applications depend on data. Employees working on data projects may need skills in SQL, data preparation, analytics, data pipelines and data quality.

Data engineers help move and organise information, while analysts use data to answer business questions. Both roles can contribute to AI projects even when they are not building machine-learning models themselves.

4. Cloud computing and cybersecurity

Many enterprise AI systems run on cloud platforms or connect to existing cloud-based applications. Knowledge of cloud infrastructure, identity management, security, monitoring and deployment can therefore be relevant.

Cybersecurity is also important because AI systems may process sensitive corporate or customer information. Companies need controls to protect data, manage access and reduce the risks of insecure integrations.

5. Business and communication skills

Enterprise AI projects are not purely technical. Employees need to understand the problem a client wants to solve, identify suitable use cases, explain trade-offs and measure whether a solution delivers value.

Communication, teamwork and domain knowledge can be especially important for consultants, analysts and employees who work directly with clients.

For jobseekers, the practical lesson is to develop a combination of technical foundations and role-specific skills rather than learning AI terminology alone.

TCS Is Also Investing in Internal Training

Hiring new talent is only one way for TCS to develop AI capabilities. The company has also invested in training existing employees and giving them access to modern tools and learning platforms.

In its FY2026 integrated report, TCS said employees acquired more than 270,000 higher-order AI, machine-learning and generative AI skills. It also reported 5.2 million external certifications across the organisation and highlighted training in areas including generative AI, cybersecurity, data and analytics, cloud and industrial autonomy.

These figures show the scale of the company’s effort to build capabilities internally. They are not a count of 270,000 separate employees necessarily becoming AI specialists; the measure refers to skills acquired and should be interpreted in that context.

Internal training is important because enterprise projects often require people who understand both a client’s existing technology environment and the new tools being introduced. Retraining current employees can help companies fill new roles while preserving institutional knowledge.

It can also support internal mobility, allowing employees to move towards projects that require newer capabilities. For a company with a workforce of hundreds of thousands, developing skills at scale is likely to remain as important as recruiting specialists externally.

What the Shift Means for Freshers and Entry-Level Jobs

TCS’s announcement is relevant to students and early-career jobseekers because it signals that technology skills are becoming more central to hiring decisions.

However, the 80% figure should not be interpreted as a guarantee that a candidate needs advanced AI expertise before applying for an entry-level role. The company has distinguished between specialised AI talent and broader new-age technology skills, and different roles will have different requirements.

A fresher targeting software development, for example, can start with programming fundamentals, problem-solving, data structures and basic software development. They can then learn how AI tools are used in coding and testing.

Someone interested in data analytics could begin with spreadsheets, SQL and basic statistics before progressing to Python and data visualisation. A candidate interested in cloud or cybersecurity would need a different learning path, although programming and networking fundamentals may still be useful.

Practical projects can help candidates demonstrate their knowledge. A small application, a data-analysis project or a simple AI-powered tool can provide evidence of skills beyond a certificate alone.

Candidates should also read individual job descriptions carefully. TCS’s broad hiring direction does not mean that all positions require the same technology stack, experience level or certification.

How TCS’s Strategy Reflects a Wider IT Industry Shift

TCS is not making this change in isolation. IT services companies are increasingly investing in AI capabilities as clients look for ways to improve productivity, automate workflows and redesign business processes.

The opportunity extends beyond building chatbots or generating code. Enterprises need help integrating AI into existing systems, preparing data, establishing governance, evaluating model performance and ensuring that deployments meet security and compliance requirements.

This creates demand for a range of roles, including software engineers, data specialists, cloud architects, cybersecurity professionals, AI engineers and consultants who understand business operations.

At the same time, AI can put pressure on traditional service models. If clients can automate more tasks or complete work faster, they may reconsider how much they pay for certain services. Providers need to demonstrate measurable business outcomes rather than relying only on the number of employees or hours supplied.

TCS’s hiring target reflects that dual challenge: it needs people capable of delivering AI-enabled services while adjusting its workforce to a changing mix of client requirements.

The Bigger Picture

TCS’s plan to have around 80% of future hires possess digital and new-age technology skills highlights how AI is becoming part of the broader technology workforce, rather than remaining a specialised research field. The distinction between the 80% broader skills target and the 25–30% share with specialised AI skills is central to understanding the announcement. Most employees supporting an AI-led business do not necessarily need to be model researchers, but they may need to understand how AI fits into software, data, cloud and enterprise operations.

The company’s growing AI revenue gives it a commercial reason to accelerate this shift. Yet the benefits will depend on whether AI services continue to attract customer spending and whether productivity gains improve delivery economics. Hiring, training and AI investment are inputs; sustained revenue growth, client outcomes and profitability will determine whether the strategy succeeds.

Looking Ahead

TCS’s future hiring mix will be a useful indicator of how quickly its workforce is adapting to demand for AI and digital services. Investors will watch AI revenue, margins, hiring levels, training investments and the company’s ability to win and deliver larger AI-led projects. The 80% target describes a skills direction, not a guarantee that every new employee will be assigned to AI work or that the same proportion will be achieved in every hiring cycle.

For jobseekers, the most useful response is to build a solid foundation in a chosen field and learn how AI tools fit into it. Programming, data analysis, cloud computing, cybersecurity and business problem-solving can all support different career paths. AI familiarity can strengthen those capabilities, but practical skills, sound judgement and the ability to deliver reliable work remain essential.