Artificial intelligence has moved commercial writing from a bottleneck to a repeatable workflow. In 2025, Indian marketing teams across B2C and B2B plan, draft, translate and test copy faster because AI now sits where the work already happens. What used to be a chain of briefs, edits and approvals spread across tools is becoming a single flow where a model proposes options and humans decide what to ship. The shift is visible in email programs, product pages, performance ads, sales enablement and even internal playbooks. The question for practitioners is less whether to use AI and more how to keep output on brand, compliant and genuinely useful to customers.
A practical view of the stack helps. Most teams blend general purpose models with domain tools and suite native assistants. General workbenches such as ChatGPT, Google Gemini and Microsoft Copilot handle first drafts, translations and research scaffolding. Writing accelerators such as Grammarly Business, Jasper, Copy.ai, Anyword, Writesonic, Scalenut and Peppertype are used for tone control, headline testing, SEO briefs and multilingual variants. Suite assistants live inside the systems marketers already use. Salesforce and HubSpot now draft nurture emails and case study synopses inside the CRM. Zoho Zia and Freshworks Freddy summarise calls and tickets for follow ups that marketing can standardise for tone. On the lifecycle side, Indian platforms such as Netcore, MoEngage and WebEngage integrate subject line generation, content sequencing and send time prediction into everyday campaigns.
Marketers describe the change as one of method, not intent. As Vikram Sakhuja, Group CEO of Madison Media and OOH, put it at a recent industry forum, generative systems have fired up imaginations while the core principles of brand and consumer insight remain the same. His point matches what many Indian teams report. The brief still leads. AI multiplies options, compresses time and expands language coverage. The decision making stays with people.
Several signals frame the business case. First, large advertisers have publicly reported faster content production. Global FMCG majors have documented up to 30 percent reduction in asset creation time when AI drafts are paired with human review. Second, multinationals that run high volume testing have targeted 30 to 50 percent savings in content production costs as they scale generative tools across markets. Third, India remains among the most active adopters of workplace AI, which shortens the learning curve for marketing teams rolling out new workflows. Fourth, marketplaces and platforms that serve Indian sellers have begun to embed AI in listing creation and catalog maintenance, which reduces time to publish and raises the baseline quality for product copy. These data points vary by category, but together they explain why writing has become a systems problem rather than a standalone task.
Daily work reflects that systems view. In B2C, teams use AI to generate short lists of headlines and body copy for product pages in English and key regional languages, then push the strongest two or three variants into experiments. AI helps create replenishment and cross sell emails tuned to purchase intervals. App notifications and ad captions are drafted in batches, with the best options selected for test. Support transcripts and user generated content are summarised to refresh FAQ pages in plain language. A retailer might produce Hindi and Tamil variants first with a general model, then have native speakers edit for idiom. The result is faster coverage without eroding tone.
In B2B, the volume and precision problems are different. SDR teams draft first contact notes that reflect a buyer’s industry and role using CRM assistants. Product marketing turns hour long webinars into one page recaps and stage specific nurture emails within a day. Proposal teams maintain a glossary of positioning statements and approved phrases that AI can pull into scope sections. Legal and brand review sign off before send. The benefit here is consistency. Copy that used to vary widely by region or rep is anchored to shared language and facts.
Email remains the clearest proof point. As inbox rules have tightened, brands report that AI tuned subject lines, cadence and content blocks lift engagement when lists are clean and preferences are honoured. Send time optimisation models schedule per subscriber rather than per list. Generative tools speed up pre approved blocks for offers, policy updates and transactional messages. Human reviewers check claims and tone. The impact is felt in reduced fatigue, steadier deliverability and a measurable lift in qualified clicks.
Search and marketplace workflows are also changing. E commerce sellers must publish accurate descriptions and attributes quickly. Marketplace and platform tools now help create images and copy that meet listing standards. For independent D2C stores, the same approach links AI copy blocks to product inventory and price feeds. Marketing and merchandising teams spend more time on positioning and less on the mechanics of writing and re writing similar descriptions.
Speed is useful only if the work stays on brand. Most Indian teams now keep a living style guide, a library of approved phrases and a short facts source that models can reference. The facts source holds product names, plan tiers, fees, dimensions, warranty terms and other non negotiables that should not drift. Editors compare AI drafts to these assets and mark where the model gets tone right and where it needs prompt rules. Over time, teams build a small prompt library for common tasks. The result is a loop where models learn from better inputs and reviewers spend less time correcting the same mistakes.
Sachin Sharma, Director of LinkedIn Marketing Solutions in India, frames the accountability lens that many CMOs apply to AI programs. You have to prove that there is a return on whatever is being invested in marketing. You should be able to show short term and long term gains. For writing tools, that means measuring more than open rates. Teams track qualified pipeline in B2B, renewal lift in subscription categories and assisted revenue in commerce. This shift to value rather than volume aligns AI initiatives with budget owners.
Guardrails and governance are a recurring theme. The practical approach is to automate drafts for routine channels such as organic social and lifecycle snippets while keeping human review for message integrity, pricing or offer claims and cultural nuance. Many teams set two thresholds. Low risk content can move on a lighter review path. High reach or regulated content gets deeper review. Records note where AI assisted a draft to simplify compliance if disclosure norms for AI generated content strengthen over time.
Privacy and data ethics sit beside performance. India’s consent based data direction means brands are tightening how they collect and use preference signals. Marketers using AI to personalise must be clear about what data informs copy and must provide easy controls. Some brands disclose AI assistance in sensitive contexts to preserve trust if content is reposted without original captions. Others route sensitive queries to a human agent by default. The unifying idea is simple. Personalisation should feel helpful, not invasive.
Tool choices reflect team size and needs. Enterprises tend to standardise on suite native assistants in CRM and service platforms so writing sits inside sales and support workflows. Mid market brands often rely on a general model plus Grammarly Business for tone and style, supplemented by a writing accelerator for SEO briefs and multilingual variants. Agencies mix and match based on client stack, leaning on Indian founded tools for cost and local language support, and on global suites for integration depth. In all cases, prompts and examples matter as much as model choice. Teams that maintain a prompt log and examples library report steadier quality.
Creative speed should not flatten voice. This is where human editors and creative leads remain central. Copy that is optimised for clicks but stripped of human detail peaks fast and fatigues audiences. The craft is to keep a recognisable rhythm in sentences and a consistent way to describe the product while allowing room for variation. This is not a soft skill. It is a competitive advantage in markets where many brands now use the same tools.
Recent examples illustrate how Indian brands are using AI writing tools with purpose. Large consumer companies have documented faster creative throughput by pairing AI drafts with brand kits and human review. E commerce and travel companies use AI to prepare multi language variants of offers and alerts, then test by cohort rather than by channel. Financial services marketers rely on AI summarisation for policy updates, service emails and monthly statements, with strict legal and compliance checks. B2B tech firms convert long webinars and technical notes into clear one page explainers for sales within a day. Education platforms adapt lesson summaries and email digests for parents in regional languages while teachers review for accuracy and tone.
Somasree Bose Awasthi, Chief Marketing Officer at Marico Limited, describes the practical gains. We have leveraged GenAI tools to streamline content creation and generate compelling captions, product descriptions and marketing copy, saving time and resources. Her team’s experience mirrors a broader pattern in consumer goods and D2C where repetitive, high volume writing has become faster and more consistent under human review.
Anupam Mittal, founder of Shaadi.com, has cautioned against leaning on AI as a crutch. His view is that while AI can fake intelligence it cannot fake human qualities like courage and judgement. Content teams read that warning as a call to keep AI as a co pilot. The brief remains human. The workflow is augmented. This balance supports both velocity and brand character.
There are limits to note. AI trained on narrow datasets can miss idiom or reinforce bias. Teams that publish in mixed English and regional languages have to check for nuance that models may flatten. Some categories require explainability. Financial and health content needs a clear chain from claim to source. That is one reason many teams now publish a small facts block on key pages with dates and figures and review it weekly. If a model draws from that source, it is less likely to drift.
Choosing where to start can be as important as the tools. Practitioners suggest a narrow use case where latency hurts outcomes. Examples include follow ups on demo requests, abandoned cart nudges, back in stock alerts and webinar recap emails. Pick one, measure against a baseline and then expand. Early wins build confidence with stakeholders who control budgets and risk.
A short playbook that Indian teams have found useful looks like this. First, consolidate the brand kit and publish a structured facts source that holds your non negotiables. Second, define five reusable content blocks with golden examples. These might be product cards, value props, social hooks, step lists and proof points. Third, pilot AI drafting for low risk channels and install a pre send checklist for links, alt text, accessibility basics and any mandatory disclosures. Fourth, add one regional language and measure engagement shifts by cohort. Fifth, instrument outcomes. Track not only opens and clicks but assisted revenue, qualified pipeline or renewal rate. Sixth, keep a governance loop with brand, legal and performance so reviews evolve as your use grows.
Two trends will define the next year. First, vernacular depth. As assistants improve in Indian languages and as more tools localise interfaces, brands will expand regional content without bloating headcount. Second, agentic workflows. Writing assistants will begin to take simple actions such as opening tickets for legal review or scheduling sends under human gates. Teams that invest in data quality and shared definitions will pull ahead because their models will learn from cleaner signals.
The takeaway for leaders is steady and practical. Keep the brief human and the workflow augmented. Use AI to handle scale and personalisation, then measure outcomes that matter to the business. Or, as Sachin Sharma of LinkedIn India reminds peers, you have to prove that there is a return on whatever is being invested in marketing, and you should be able to show short term and long term gains. Between those poles sits the everyday discipline of writing at speed, in the languages people prefer, with the clarity that earns attention and trust.
Disclaimer: All data points and statistics are attributed to published research studies and verified market research. All quotes are either sourced directly or attributed to public statements.
