Contributed by: RVKS
At the Tata Consultancy Services (TCS) Annual General Meeting the Chairman made one of the more serious predictions for corporate India in recent years. He said that in a company with half a million employees, it is increasingly likely there will also be half a million AI agents working alongside them. Rather than a threat, he sees this as an opportunity for collaboration between people and AI. Concerns about disruption, he argued, often overlook how enterprise spending will evolve, with AI representing the biggest opportunity in enterprise IT.
TCS is a global information technology (IT) services, consulting, and business solutions company headquartered in Mumbai, Maharashtra, India. A part of the Tata Group, TCS is one of the largest IT service providers worldwide and a key player in digital transformation and AI-driven enterprise technology.
The three-year horizon is not an abstraction as TCS’s AI revenues have been growing at a compound quarterly rate of more than 22 percent and have reached an annualised figure of USD 2.5 billion.
What is being described is not replacement of the workforce but a structural doubling, where every human worker has a corresponding AI agent running in parallel, handling portions of the work. There was an acknowledgment, for the first time, that hiring will slow because certain portions of current work will go to AI agents, though the impact was described as being restricted to the transition phase. Looked at from a longer horizon, this structural doubling is also the credible starting point of something considerably more profound: the formalisation of the era of digital twins for persons and professions.
While the TCS announcement is rooted in the context of an IT services company, the structural logic it describes is not confined to that sector. It applies with equal force to every profession in which knowledge, judgment, and client relationships are the primary currency. Lawyers, doctors, architects, consultants, engineers, financial advisors, and accountants all operate in a domain where the same structural doubling is approaching, carrying with it the same opportunities and the same risks.
This is the context worth sitting with before one rushes to either alarm or celebration.
What an AI Agent Actually Does
An AI agent, in the sense being used here, is not merely a tool one prompts with a question. It is a semi-autonomous system that can receive a task, break it into steps, call external tools and databases, produce outputs, and act on them, with a human either reviewing the result or setting the guardrails in advance. The distinction from the AI assistant most professionals already use is one of autonomy and continuity. The assistant responds whereas the agent executes.
In a services context, this means the agent can handle workflows end to end: draft, check, format, cross-reference, log, and file, while the professional’s role shifts to task definition, judgment on edge cases, and client interface. A lawyer’s agent drafts the first cut of a contract and flags clause-level risks. A doctor’s agent reviews patient history, surfaces relevant literature, and prepares consultation notes. An architect’s agent generates code-compliant preliminary drawings. A financial advisor’s agent monitors a client’s portfolio against stated objectives and prepares review materials overnight.
AI governance was highlighted at the Annual General Meeting (AGM) as a potentially significant recurring revenue stream, with the observation that AI agents learn, act, drift off course, and can deteriorate. The argument made was that if maintaining applications was the defining annuity of the last era, governing intelligence will be the defining annuity of the next. That observation carries weight for every professional services domain, not only technology.
The Digital Twin – From Engineering Concept to Professional Reality
The digital twin concept, has been used in manufacturing and engineering, refers to a real-time digital replica of a physical system, one that mirrors the original closely enough that simulations can be run, failures predicted, and interventions tested on the twin before being applied to the real thing. The AI agents described at TCS are, in their current form, workflow executors: systems trained to carry out defined tasks within defined parameters. They shadow the human workforce in volume but not yet in judgment. The question worth asking seriously is whether that is merely the first chapter of something considerably deeper.
The progression from AI agent to digital twin of a person requires something beyond task execution. It requires the accumulation of context, preference, relationship history, reasoning patterns, and professional judgment in a form that can be retrieved and applied situationally. That is not yet what most agentic systems do, but it is the direction in which the more sophisticated systems are moving. A professional who has spent twenty years developing a particular way of reading a situation carries knowledge that is currently irreproducible by any AI system. The question is how long that remains true, and more interestingly, what a professional could do deliberately to accelerate and shape that reproduction.
A large language model trained on general domain knowledge can produce a competent first draft in most professional disciplines. A system fine-tuned on a specific professional’s years of correspondence, analysis, case notes, and judgment on particular client situations can produce something that reads, reasons, and advises in that professional’s voice, with that professional’s contextual intelligence. That is not a tool but is closer to a professional extension, a form of intellectual leverage that has no real precedent in the history of any profession.
Succession, Continuity, and the Transfer of Embedded Knowledge
Professional service firms across every discipline have always carried a structural fragility at the point of senior transition. The senior partner, the lead consultant, the principal physician carries the client relationship, the institutional memory, the contextual knowledge of numerous cases and engagements built over years. When that person retires or exits, the firm transfers a name on a letterhead and hopes the client follows. The knowledge does not transfer because it was never externalised.
A digital twin built deliberately over time, one that captures not merely the outputs of a professional’s work but the reasoning behind them, the client-specific calibrations, the judgment calls and their rationale, would change that entirely. The successor inherits not just the account but something closer to the accumulated intelligence of the predecessor’s engagement with that client or case. Succession ceases to be a relationship hand-off and becomes, for the first time, a genuine transfer of professional knowledge.
For risk mitigation, the implications run in two directions. The first is continuity risk, which is what succession addresses. The second is the risk of scale without judgment. A firm that deploys AI agents to expand its client base rapidly but has not embedded sufficient professional judgment into those agents is creating a different kind of exposure, one where the volume of output grows faster than the quality of oversight. A digital twin of a senior professional, properly constructed, is precisely the mechanism by which judgment scales without diluting.
The Professional Firm and the Structural Parallel
Every professional practice, regardless of discipline, shares a common architecture. It has a body of repeatable, rule-governed work that forms the operational core, and a smaller but more consequential body of judgment-intensive work that constitutes the real value proposition. In law, the first category is document review, standard drafting, compliance filings; the second is litigation strategy, deal structuring, regulatory navigation. In medicine, the first category is routine diagnostics, standard protocols, documentation; the second is differential diagnosis in complex cases, treatment planning under uncertainty, patient counselling. In architecture and engineering, the first is drawing production, code checking, specification writing; the second is design resolution, client interpretation, problem-solving under constraint.
The first category in every profession is precisely where AI agents will operate at scale and at speed within a very short horizon. The second is where each profession’s value proposition will either deepen or dissolve, depending entirely on what its practitioners have chosen to do with the transition.
This is not a distant forecast. Professionals across disciplines who have experimented seriously with AI tools over the last two years already know that the routine, documentation-heavy portions of their work are now significantly faster. What is coming is the agentic layer, where these tasks run without manual initiation, continuously, across a full client portfolio simultaneously. The implications for capacity, pricing, and competitive positioning are substantial.
On Capacity and the Cost Structure of Professional Practice
The unit economics of most professional firms are built around billable hours and seniority ratios. Senior professionals oversee teams; the teams do the operational work; billing is tied, loosely or directly, to time. If AI agents compress the time required for the operational layer significantly, the firm either contracts its billing or expands its capacity, which means serving more clients without proportionate cost growth.
Firms that understand this early will be able to offer better pricing, faster turnaround, or a materially higher quality of senior attention per client, or some combination of all three. That is not a marginal competitive advantage. For clients who are cost-sensitive, time-pressured, or historically under-served by the depth of senior professional attention they receive, this is genuinely transformative.
The risk is equally clear. A firm that does not deploy these tools will face clients who notice that faster, more attentive, and often cheaper alternatives exist. The loyalty that professional relationships have historically generated is real but not unlimited, and it will not compensate indefinitely for a visible gap in responsiveness or value.
On the Nature of Professional Value
The more important question, harder to quantify, is what the professional brings once the operational layer is substantially automated. This is where every knowledge-based profession faces a reckoning it has largely deferred.
Professional training across disciplines has historically emphasised technical mastery of the rules, the standards, the procedures, the codes. These remain necessary but are not sufficient. What the AI transition will surface, fairly quickly, is whether a professional practice has genuine advisory depth or whether its value was primarily procedural.
Advisory depth means the ability to read a situation and see what it reveals beyond the presenting question. It means understanding that a legal matter is sometimes a relationship problem, that a medical symptom is sometimes a lifestyle question, that a financial restructuring is sometimes also a succession question. It means being present when a client is making a significant decision and having something worth saying that the client cannot access from an AI interface. It means the kind of judgment that is built over years of engaged practice, disciplined reading, and genuine curiosity about the human and institutional contexts in which professional work takes place.
This kind of judgment cannot be replicated by an AI agent operating within a workflow. The profession’s preparation for the agentic era depends, therefore, not on learning to operate AI tools, which will come naturally, but on investing seriously in the advisory dimension before the procedural one is commoditised away.
On Governance as a Professional Service
As organisations deploy AI agents for internal processes across every function, finance, legal, human resources, procurement, operations, someone has to govern those agents. Someone has to ask whether the agent’s output is compliant, whether it has drifted from its intended behaviour, whether the audit trail is sound, whether the decisions it is making are defensible under the applicable regulatory framework. That governance function does not sit naturally inside a technology team. It sits with the professionals who understand the regulatory and institutional context in which the agent is operating.
A legal firm that can provide AI governance services to clients deploying agentic legal tools is offering something that did not exist three years ago. A medical practice that can certify the clinical validity of AI-assisted diagnostic workflows is addressing a need that every hospital system will face. A financial advisory firm that positions itself as a governance layer for a client’s AI-powered finance function, not just reviewing the outputs but auditing the intelligence that produces them, has a service line with no historical precedent and potentially significant recurring value.
This governance opportunity is, in a sense, the natural extension of what every profession already does. Every profession exists partly because society has decided that certain judgments require accountable human expertise. The arrival of AI agents does not dissolve that need. It relocates and deepens it.
What Professional Practices Should Be Doing Now
There are a few things worth being specific about rather than leaving at the level of principle.
The first is internal deployment. A firm that has not yet deployed AI tools meaningfully across its own operational practice should begin now, not to be fashionable, but to understand where the gains are real and where the judgment layer remains essential. The experience of deploying these tools on one’s own work is the only reliable education. Reading about it is insufficient.
The second is building documentation discipline. AI agents work best when processes are written down cleanly: what information is needed, what the output format is, what the review criteria are. Most professional practices run on implicit knowledge, the senior associate who remembers how this client’s situation was handled, the partner who knows the client’s preferences and sensitivities. That knowledge needs to be articulated and systematised, or the agentic layer will not function well. This documentation discipline is also the foundational act of building a digital twin. The professional who begins externalising judgment today is investing in an asset that will compound over years.
The third is investing in the advisory vocabulary. Writing, reading widely, engaging with client situations beyond the operational mandate: these are not soft adjuncts to practice. They are the substance of the value that will survive automation. A professional who can produce a clear analytical note on a client’s situation, who can contribute meaningfully to a strategic conversation, who understands the broader context in which a client’s specific question is situated, is not replaceable by an agent. A professional who primarily manages a workflow and reviews junior work faces a more difficult transition.
The fourth is beginning, even tentatively, to think about building a personalised professional system. A professional who spends the next three years not merely using AI tools but deliberately training a system on the texture of his own reasoning, his own client interactions, his own analytical frameworks and judgment patterns, is doing something qualitatively different from one who simply adopts off-the-shelf agentic tools. The former is building an asset that compounds over a career. The latter is renting a capability that will be available to everyone at roughly the same price.
The fifth is rethinking the training pipeline for the next generation. If AI agents absorb a large portion of the operational work that currently trains junior professionals, every profession faces a pedagogy problem. Where will the next generation of senior professionals develop craft judgment if the early stages of practice are automated? This deserves serious institutional attention from professional bodies across disciplines, though individual firms can address it by designing mentorship deliberately rather than relying on work volume to transmit professional knowledge.
A Philosophical Note on Self-Knowledge
There is a dimension to the digital twin question that is worth acknowledging separately. The process of building such a twin, of externalising one’s judgment in a form that a system can learn from, would itself be a rigorous act of professional self-examination. It would require a professional to ask, perhaps for the first time with real discipline, what exactly he knows and how he knows it. Most professionals never do that work. The tacit knowledge accumulated over a career remains tacit, useful in practice but unavailable for transfer or scrutiny.
Those who undertake that work in the next few years will understand their own practice more clearly, and will have built something of lasting value, whether or not the technology ultimately delivers on its more ambitious promises. The digital twin of a professional is, in a sense, the formalisation of a question that every knowledge-based profession has always avoided asking. The era of AI agents makes that question unavoidable.
The prediction made at TCS’s AGM is significant less for what it says about one company and more for what it signals about the pace and direction of change across the entire landscape of knowledge work. An organisation of six hundred thousand people, already at USD 2.5 billion in AI revenue, making a public commitment to achieve a one-to-one ratio of agents to humans within three years, is not presenting a thought experiment. It is a structured plan from an organisation with the resources and intent to execute it. The rest of the professional world will follow the same curve, at varying speeds but in the same direction.
For every professional practice, the question is not whether this wave arrives. It is whether the practice is standing on higher ground when it does, with a genuine advisory depth, a governance capability, and, for those who move early enough, the beginnings of a personalised professional intelligence that carries the full weight of accumulated judgment into a new form.
The era of digital twins for persons and professions is not fully here yet. The TCS prediction is a credible starting point. What makes it a starting point rather than an endpoint is precisely whether organisations and individuals will use this transition passively, letting agentic tools handle the routine, or actively, building personalised, judgment-rich systems that carry professional experience forward in a form that survives, and ultimately outlasts, the individual who built it.
Related Reading – AGN Global Business:
- AI Adoption in a Modern Firm – AGN Roundtable Insights
- The Future of Accounting – Insights from AGN NextGenner
- Succession Is No Longer a Relay Race – Leadership, Talent & Value Transfer at a Turning Point
- Funding the Evolving Firm – Capital, Capability and Career Choices in an Era of Consolidation
Contributed by:
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