Executive Perspective
In the first week of February 2026, a single product announcement by Anthropic travelled unusually fast from Silicon Valley to Dalal Street. Indian IT stocks fell sharply, and global software and data services names sold off in tandem. The proximate trigger was Anthropic’s release of new Claude capabilities, including AI plug-ins aimed at routine legal work and a major model upgrade (Claude Opus 4.6) designed to run longer, more reliable, multi-step tasks with “agent” style workflows.
For business owners and promoters, the headline story is not whether one more AI assistant can draft emails or write code. The more important shift is this: investors are increasingly pricing in the idea that AI will compress paid effort in repeatable knowledge work, and that customers will respond by renegotiating contracts, tool stacks, and workforce needs. That combination threatens the economics of labor-intensive outsourcing, per-seat enterprise software, and several adjacent professional services models.
What Anthropic actually Launched and Why it Matters
Anthropic’s legal plug-in is positioned as a tool designed to handle routine legal work such as contract checks, non-disclosure agreement reviews, legal summaries, and standard drafting tasks. It has been positioned as part of Claude and “like a plugin” for in-house legal teams, while also stating that it is not legal advice and requires lawyer review before use.
On the model side, Claude Opus 4.6 is framed as an upgrade designed to handle sustained tasks with better reliability and meaningful gains in coding and finance workflows. Alongside this, Anthropic is pushing the ability to process extremely large prompts and to divide work across multiple autonomous agents through developer-focused tooling. This matters because many corporate tasks are not one prompt. They are workflows: read, compare, decide, draft, revise, validate, and package.
Why this Combination is Strategically Significant:
First, it signals movement from “assisted output” to “assisted execution.” A typical enterprise does not spend heavily on standalone writing. It spends on workflows: reviewing a contract, preparing a legal brief, extracting obligations, updating trackers, redlining, drafting, and routing approvals. When AI becomes competent at the workflow level, the impact is no longer incremental productivity. It becomes structural.
Second, the legal domain is a high leverage beachhead. Legal work sits at the intersection of revenue protection, risk, and compliance. If AI tools become good enough to handle first-pass review and drafting at scale, they can reduce the load on internal teams and external vendors, and pressure incumbent legal research and information services players.
Why Stocks Fell: The Market was Reacting to a Margin Reset, not a Feature Release
The announcement led to sharp sell-offs across European legal software companies, with prominent legal information and workflow businesses falling materially in a single session. Indian IT names also dropped sharply intraday, reflecting a broader global re-rating.
The selloff was driven by fears that advanced AI tools could disrupt a labor-intensive outsourcing model and compress per-seat software economics. The trigger was the belief that AI can automate tasks across legal, sales, marketing, and data analysis, reducing billable hours and the need for large teams.
Under the Hood, three Valuation Assumptions were Challenged at Once:
One, “visibility premium” in software. AI is eroding the historical visibility premium because the speed of AI advancement makes long-term valuations harder to defend, especially when AI can allow businesses to do more with fewer staff and threatens per-user pricing logic.
Two, the “headcount to revenue” linkage in outsourcing. If customers believe AI can reduce effort in testing, documentation, L1 support, analytics, and contract review operations, they will push for fewer FTEs, lower blended rates, and productivity sharing.
Three, tool consolidation. If generalist AI platforms can complete tasks previously split across multiple paid tools, procurement will reduce vendor sprawl.
This is why the market reaction should not be dismissed as noise. It is the market trying to anticipate how procurement behavior changes once AI becomes credible in production workflows.
Will this replace Human Brains? Not in the way People Fear, but it will replace Large Amounts of Paid Effort
The most common misunderstanding is to frame this as “AI versus people.” In practice, AI competes with a narrower target: repeatable, text-heavy, rules-driven work where the output format is standardized and the cost is primarily labour.
At the same time, the tool’s positioning explicitly acknowledges two limits: accountability and risk. In real operations, the more accurate outcome is a redistribution of human time:
More time shifts to judgement and supervision. Someone must decide what to automate, define acceptable risk, set review thresholds, and sign off.
More time shifts to client-facing work and domain interpretation. AI can summarize a contract; it cannot independently decide how an organization should change a negotiation stance given its broader commercial strategy.
More time shifts to governance. Enterprises will require controls around data access, auditability, output validation, and policy compliance.
So, AI will not eliminate the need for human thinking. It will reduce the paid minutes required to produce first-pass outputs, and it will compress the number of people needed in the middle layers of delivery. For labour-based services models, that distinction is existential.
What Changes when AI moves into Corporate Legal Work
Legal is a useful lens because it shows how adoption will play out across other functions. Three practical consequences are likely.
First, a “first pass automation” wedge. Routine checks, clause extraction, summary generation, and standard drafting become faster and cheaper. That reduces demand for junior-heavy workstreams, both in-house and outsourced.
Second, a change in buying behavior. In-house legal teams may shift spend from external drafting and review hours to implementation, governance, and specialist escalation. Vendors who cannot demonstrate measurable cycle-time improvement will face pressure.
Third, a rapid rise of hybrid workflows. Even when humans retain final decision rights, AI will be used to prepare, compare, and draft. That changes the staffing pyramids and unit economics.
The same pattern then extends beyond legal into sales operations, marketing operations, finance analytics, and internal reporting, which are precisely the task areas now being discussed as potential AI automation targets.
What this means for IT Services Firms: Expect Contract Conversations to change before Delivery does
For Indian IT and global outsourcing firms, the disruption is unlikely to arrive as an overnight collapse. The more realistic pathway is commercial.
Clients will start asking uncomfortable questions in RFPs and renewals: How much of this work can AI do? If you are using AI internally, why should my rate card remain the same? What part of the savings do I get? Can we move to outcome-based pricing? These conversations will arrive faster than the full-scale re-platforming of delivery.
At the same time, leading firms are already securing AI-led deals and rolling out domain-specific platforms as clients accelerate adoption. That is exactly the tension. Demand for AI transformation services rises even as traditional managed services margins face pressure.
The likely Impact will be Uneven:
Firms with deep domain platforms and managed outcome offerings will defend margins.
Firms overly exposed to staff augmentation or low differentiation application maintenance will see rate compression.
Mid-tier firms exposed to a narrow set of workflows, especially documentation-heavy or analytics-heavy operations, may see quicker demand shifts.
The market reaction, in that sense, is a forward-looking repricing of future margin structure rather than an immediate judgement on today’s revenues.
What this means for Non-IT Businesses: Your Competitors may become Faster, Leaner, and more Aggressive
For manufacturing, trading, logistics, healthcare, and financial services, AI will not “destroy the business.” It will change competitive capability in three ways.
Cycle time becomes a weapon. Companies that compress turnaround times in proposals, contract negotiation, procurement evaluation, and reporting can respond faster to customers and suppliers.
Cost-to-serve declines. Customer support, internal documentation, and analytics can become cheaper, allowing more aggressive pricing or higher margin retention.
Decision quality can improve if governance is sound. Better summarization, obligation tracking, and variance analysis can reduce surprises, although poor governance can introduce new risks.
The risk is not that AI replaces your leadership team. The risk is that competitors adopt it, compress overhead, and out-execute you while you keep running legacy processes.
Where the Situation is Likely to go: Three Phases, with a Narrow Window for Proactive Repositioning
The market is currently in a phase of narrative acceleration. Tools are advancing fast, while enterprise operating model change moves slower. That gap creates a window where leaders can redesign before they are forced to.
Phase 1 is internal productivity. Companies deploy AI to reduce drafting time, accelerate analysis, and improve internal responsiveness. Benefits are real, but not yet strategic.
Phase 2 is workflow redesign and procurement reset. This is when contracts, rate cards, and KPIs change. It will be driven by CFOs and procurement, not by innovation teams.
Phase 3 is business model reconfiguration. Vendors and service providers converge on outcome-based delivery, proprietary accelerators, and deeper integration into client operations, with stronger governance expectations.
A Pragmatic Forecast: The Direction is Clear, the Speed will differ by Function and Risk Appetite
A credible forecast should not claim precise dates. It should identify where adoption will be fastest and where constraints will slow it.
Routine, text-heavy, low-to-medium risk processes will move first. Legal operations support; sales operations, marketing operations, and internal analytics are in that bracket.
Highly regulated, high-stakes decisions will move slower. Not because the models are incapable of generating text, but because organizations will demand audit trails, validation standards, and liability clarity.
Over a 12 to 24 month horizon, the visible impact is likely to be commercial and organizational: fewer junior-heavy roles, new governance roles, and tougher client procurement terms. Over a 24 to 48 month horizon, the impact is more structural: pricing models reset, and firms that fail to build differentiated IP face commoditization.
What Businesses should do now: Not “Buy AI,” but Redesign how Work gets Done
The right response is not to chase the newest model. It is to identify where paid effort sits in your operating model and decide what you want AI to do, under what controls.
Start by mapping your cost of friction. Where does work stall because information is scattered, approvals are unclear, drafts are repeatedly rewritten, or teams cannot find the “latest version”? These are workflow problems, not technology problems.
Then Decide your Posture in Three Categories:
If you are exposed, defend margins and renegotiate how value is framed. Move conversations from “hours” to “outcomes,” supported by measurable cycle-time or quality metrics. Repackage delivery as a managed service with clear SLAs.
If you are a buyer of knowledge work, take the productivity dividend deliberately. Redesign processes, set controls, and reduce your cost-to-serve without taking unnecessary risk.
If you want to enter a segment adjacent to AI disruption, avoid building yet another generic wrapper. Build around a defensible wedge: proprietary datasets, domain workflows, integration partnerships, or regulated compliance capability.
How Hmsa can Help
Generic consulting does not help here. Companies need targeted, situation-specific intervention.
For IT services and software firms at risk, the priority is credible margin defense. This typically means a rapid portfolio and contract exposure review, identifying revenue streams most vulnerable to automation, pinpointing clients likely to demand productivity pass-through, and redesigning offerings toward productized or outcome-based delivery. It also requires reshaping the delivery pyramid and putting in place governance that clients can trust.
For non-IT businesses, the priority is risk-controlled adoption and operational readiness. The consultant’s role is to review systems to identify high-impact workflows, redesign them end-to-end, define approval and audit controls, and align deployment to business KPIs rather than experimentation.
For entrants, the priority is differentiation and go-to-market. Winning plays will focus on a narrow, valuable domain problem with strong enterprise integration, clear compliance boundaries, and measurable ROI, supported by a defensible commercial roadmap.
The core shift is speed: AI is forcing faster portfolio decisions. A consultant helps quantify exposure, design the response, and support restructuring or transaction pathways where required.
Hmsa’s Opinion
The stock market reaction to Anthropic’s announcement should be seen as an early warning signal, not a final verdict. The announcement matters because it reinforces a broader transition: AI is moving from assistance to execution in corporate workflows. The central risk is not that AI becomes “smarter than humans.” The central risk is that customers stop paying for effort once they believe effort has been compressed.
If you run an IT services or software business, treat this as a contract and operating model reset cycle. If you run a non-IT business, treat this as a competitiveness cycle-time opportunity. If you are planning entry or exit in an exposed segment, treat this as a timing window where thoughtful moves now will be priced very differently 18 months from now.
Reference: NDTV World
