“The New Litigation Skill CAs Were Never Trained For: Writing for Algorithms, Not Officers”

 

Introduction

For generations, tax litigation was shaped by human interaction. Arguments were framed for officers, appeals were drafted with persuasion in mind, and representation relied heavily on professional intuition and experience. Chartered Accountants were trained formally and informally to write for people.

That reality has changed. Today, many tax disputes are first examined not by an assessing officer, but by systems. Algorithms shortlist cases, flag risks, prioritise issues, and even influence the direction of scrutiny. By the time a human officer reads a submission, the system may already have formed an opinion.

This has created a new, largely unacknowledged skill gap. CAs are still drafting as if they are writing for officers, while decisions are increasingly being shaped by machines. Litigation success now depends as much on how data is structured and presented as on the legal merit itself.

 

How Algorithms Entered the Litigation Process

Faceless assessments, risk-based selection, system-generated scrutiny notices, and automated mismatch identification have quietly altered the litigation landscape. Cases are no longer picked randomly or purely on discretion. They are selected based on patterns, deviations, ratios, and historical behaviour.

Submissions, replies, and explanations are scanned digitally, cross-matched with databases, and evaluated for consistency. Keywords, numerical thresholds, reconciliations, and even silence on specific points can trigger further action.

In this environment, drafting is no longer just advocacy it is data signalling.

 

Why Traditional Drafting Is Falling Short

Traditional litigation drafting relies heavily on narrative flow. Professionals explain facts chronologically, justify positions conceptually, and rely on the reader’s ability to connect the dots. This approach works well when the reader is human.

Algorithms, however, do not infer intent or appreciate context unless it is explicitly structured. They look for direct answers, numerical reconciliation, and alignment with known data sets. A beautifully written explanation that does not clearly map figures, address mismatches, or mirror system language may be ignored or misread.

This is why many strong cases are still escalating unnecessarily. The issue is not the position it is the presentation.

Writing for Algorithms: What Actually Changes

When writing for algorithms, clarity overtakes persuasion. Precision replaces narrative depth. Every assertion must anchor to data already available with the department or clearly reconcile deviations.

Instead of saying “the difference is due to timing issues,” the drafting must explicitly quantify the difference, identify the source, and tie it back to reported returns or financials.

Instead of broad explanations, structured responses with clear headings, tables, and reconciliations perform better in digital scrutiny environments.

A Shift in Drafting Mindset

The difference between traditional drafting and algorithm-aware drafting can be summarised as follows:

Traditional Drafting	Algorithm-Aware Drafting
Narrative explanations	Structured, data-linked replies
Context-heavy arguments	Point-wise issue resolution
Assumes human interpretation	Assumes zero inference
Legal emphasis first	Data reconciliation first
Reactive clarifications	Preventive disclosures

This shift does not dilute legal strength. It strengthens it by ensuring the case survives the system filters before reaching a human decision-maker.

 

Common Triggers CAs Overlook

Many escalations begin with small drafting omissions. Failure to address auto-populated figures, ignoring portal data mismatches, or not mirroring notice language can lead systems to assume non-compliance.

Even silence can be interpreted negatively. If a notice raises three issues and the reply strongly addresses two, algorithms may flag the third as admitted or unresolved, regardless of intent.

This makes comprehensive, issue-wise responses critical even when issues appear minor or repetitive.

 

Why This Skill Was Never Taught

Professional training has historically focused on law, standards, and reasoning not on system behaviour. Algorithms were not part of the litigation ecosystem when most CAs were trained.

As a result, drafting styles evolved organically through mentorship and experience, both of which assumed human readers. The profession is now adjusting in real time, often through trial and error.

This is not a failure of training it is a structural evolution of the regulatory environment.

 

What CAs Can Do Differently Today

CAs need not become technologists, but they must become digitally conscious drafters. This includes aligning submissions with portal data, using consistent terminology, incorporating reconciliation tables, and explicitly addressing every system-raised issue.

Internal review processes should now ask a new question: “Will this make sense to a system before it reaches an officer?”

Firms that adopt this mindset early are already seeing fewer follow-up notices, quicker closures, and reduced escalation.

The Bigger Picture

This shift goes beyond litigation. It affects replies to GST notices, refund explanations, faceless assessment submissions, and even responses to MCA and sectoral regulators.

As regulatory systems become more sophisticated, professional success will depend on the ability to communicate clearly with both humans and machines. Drafting is no longer just an art it is a structured discipline.

 

Conclusion

The next competitive advantage for Chartered Accountants will not come from knowing more law, but from presenting compliance and litigation positions in a system-friendly manner. Writing for algorithms does not mean abandoning professional judgment; it means packaging it intelligently.

Those who adapt will find litigation becoming more predictable and manageable. Those who do not may continue to fight strong cases that never get a fair human hearing.

In the new litigation environment, how you write can matter as much as what you argue.