Anish Modi writes about practical AI adoption for Indian small businesses, explaining how to identify repetitive work and build focused tools around real business needs. Their posts also describe building an automated outbound system.
Anish Modi is most useful for practical AI adoption education aimed at Indian small and midsize businesses.
Best for
A B2B AI consulting firm or vendor selling implementation to Indian small and midsize businesses.
Not for
An enterprise software vendor promoting a flagship CRM or ERP for reach and demos.
Key takeaways
A generic chatbot lacks business context, so a tool connected to customer data works better.
An outbound system processed 5,000 leads, but nearly half were unclear due to weak targeting.
Modi says small firms may adapt faster because they have less legacy infrastructure to defend.
Check first: Confirm availability, format, terms, and current audience demographics before contacting them.
Main ideas
The problem is fit, not the model: generic output is a design failure · Waiting for the right time is the expensive choice · Small and custom-fit now beats big and standardised
Anish Modi writes about practical AI adoption for Indian small businesses.
In these LinkedIn posts, they advise starting with a business problem—often repetitive work—and then building or choosing a focused tool around it.
They also urge founders to prioritize customers and margins over attention, and argue that small firms may be able to adapt faster than larger companies.
Their account of an outbound system that processed 5,000 leads adds a counterpoint: the automation worked, but nearly half the leads were classified as unclear because the targeting definition was weak.
Readers weighing AI projects or growth advice can follow how they connect tools to operating choices—and warn against mistaking activity or scale for results (advice for founders).
Anish Modi's posts make one argument in several forms: Indian small businesses get value from AI when a specific business problem comes first and a focused, data-connected tool comes last, while competitors quietly collect the cost of waiting.
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Recurring priorities are fit over novelty, repetition as the trigger for starting rather than readiness or budget, smallness and low cost as the current advantage over larger, more standardised competitors, defining a lead or target before scaling, and operator margin over audience reach.
The posts test those claims against their own work and conversations with founders in four industries.
They are written for owners of manufacturing units, services firms and other businesses that look nothing like a creator business.
The problem is fit, not the model: generic output is a design failure
Anish Modi's most repeated point is that a failed chatbot trial says little about what AI can do.
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They describe founders who read about AI, sign up for ChatGPT, ask it to write a marketing plan, get something generic and conclude AI is overhyped.
That output is expected: a general chatbot has no access to a company's customer data, no memory of earlier conversations and no workflow that ends in a result.
Value comes from building a specific tool around the model, connecting it to customer data, training it on past responses and pointing it at a task rather than a paragraph.
In another post, they report what founders in hospitality, fashion, manufacturing and marketing told them: they know they should use AI, do not know where to start, are pitched a different tool by everyone, have tried two that did not stick and have teams tired of new tools.
Cost, access and awareness are not the bottleneck; the missing role is someone who sits with a business, finds the real friction and designs a focused fix.
The framing sharpens across the two posts: founders never see the better version of AI because nobody around them is building it, and the barrier is absent diagnosis rather than an absent example.
A founder asks ChatGPT to write a marketing plan, gets generic output and writes off AI.
Modi sums up the gap as the distance between “I tried it” and “I use it,” and says the next five years of Indian small-business advantage will sit in that gap.
Founders in hospitality, fashion, manufacturing and marketing described near-identical experiences with tools that did not stick.
Waiting for the right time is the expensive choice
Anish Modi reports that every small-business founder they meet says a version of the same line: they intend to use AI, but not now — after the dust settles, after the right tool appears, after the team is ready, after the budget opens.
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While they wait, a competitor quietly builds a tool that pulls every customer query into a dashboard, drafts replies and saves the team 15 hours a week, with no launch and no announcement.
They put a date on the delay: founders holding back will catch up around 2027, about 18 months late.
The recommended trigger for starting is not readiness but repetition — adopt AI where the work is boring, repetitive and taking time from work that matters.
The argument is about the cost of inaction rather than the technology.
The line Modi hears, as they report it: “Haan haan, AI use karna hai.
Lekin abhi nahi.”
Against it, they set the competitor's unannounced dashboard-and-drafting tool that saves 15 hours a week.
Small and custom-fit now beats big and standardised
Two posts make the same commercial case from different angles.
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In the first, enterprise software once worked as a moat: a ₹200 crore company could buy a ₹1 crore CRM, spend six months customising it and leave smaller rivals fighting a system they could not afford to match.
In Modi's account, that math has flipped: a founder with Claude, a Supabase database and a weekend can build a CRM that fits their business better for roughly ₹25,000.
The advantage moves from owning the biggest tool to knowing what the business actually needs.
In the second post, the smallest Indian businesses can move faster than medium ones: a five-person company has no legacy software, internal politics or established way of doing things, while a fifty-person company has standard procedures, an IT vendor, managers protecting their patch and a ₹8 lakh CRM that takes a quarter to change.
They conclude that size used to be the advantage and smallness is now, and that the cost of doing nothing has never been higher.
The comparison Modi draws: ₹1 crore and six months of customisation for an incumbent's CRM against a weekend build on Claude and Supabase for about ₹25,000; a five-person firm rebuilding workflows in a weekend against a fifty-person firm where a CRM change takes a quarter.
Scale does not fix a weak assumption; it prints it
In this post, Anish Modi describes building and running an outbound operation that processed 5,000 leads in 90 days.
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The scrapers ran, the enrichment ran and the drafting held up.
What broke was their definition of a good lead: nearly half of the 5,000 landed in a bucket labelled MISC — not manufacturing, not D2C, not services, just unclear.
They read the result as the machine holding up a mirror: a fuzzy targeting thesis printed itself five thousand times.
The general rule they draw is that scale exposes a weak assumption at volume, with receipts.
Their priority is to settle qualification and targeting definitions before automating volume; they end by asking readers what scaling their current process would expose.
Of 5,000 leads processed in 90 days, close to half fell into the system's MISC bucket, meaning they fit none of the stated target categories.
Advice for operators, not for the attention economy
Anish Modi argues that most advice founders follow was written for people whose product is advice.
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Post daily, build in public, grow your personal brand: fine counsel if your business model is selling counsel, but a distraction dressed as a growth plan for someone running a manufacturing unit or a services firm.
They argue that the creator economy taught a generation of operators to optimise for attention when they should be optimising for margin — reach is not revenue and an audience is not a customer base.
Their recommendation is to take advice from people who run a business shaped like yours, not from people who sell the advice itself.
The post is addressed to operators and pushes back on content habits associated with creators.
Modi closes by asking readers what they have been doing for reach that has never once brought a paying customer.
Shows what Modi argues about AI adoption and the problem they keep returning to.
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It is the clearest entry point to the idea running through most of their other posts.
Modi describes a pattern they keep seeing: a founder reads about AI, signs up for ChatGPT, asks it to write a marketing plan, gets generic output, decides AI is overhyped and moves on.
Their argument: the model is not what failed.
A general chatbot has no access to a company's customer data, no memory of previous conversations and no workflow that ends in a finished result, so generic output is expected.
Value appears, they write, when someone builds a specific tool around the model, connects it to customer data, trains it on past responses and points it at a task rather than a paragraph.
They sum up the gap as the distance between "I tried it" and "I use it," and say the next five years of Indian small-business advantage will lie in that gap.
For brands planning a campaign
A talk or content partnership built on this diagnosis gives founders who have tried AI and given up something to recognise themselves in, rather than a general "AI for business" introduction.
Explains why Modi thinks small businesses delay AI and what they believe waiting costs.
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Useful for judging whether they can hold an audience's attention on a topic it is avoiding.
Modi reports that every small-business founder they meet says some version of the same line — "haan haan, AI use karna hai.
Lekin abhi nahi" — meaning after the dust settles, after the right tool appears, after the team is ready, after the budget opens up.
Meanwhile, they write, a competitor quietly builds a tool that pulls every customer query into a dashboard, drafts replies and saves the team 15 hours a week, with no launch and no announcement.
They put a date on the delay: the "abhi nahi" founders catch up in 2027, about 18 months late.
Their recommended trigger for starting is not readiness but repetition: adopt AI where work is boring, repetitive and taking time from more important tasks.
For brands planning a campaign
"Abhi nahi" is a usable hook: a session, article or video series built around the excuse founders use, rather than the technology, is easy to promote and gives audiences a clear way to recognise themselves.
Shows where Modi thinks the real barrier lies and who should remove it.
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Read this if you are evaluating them for a diagnostic or advisory-style engagement.
After speaking with founders in hospitality, fashion, manufacturing and marketing, Modi reports hearing almost the same sentence word for word: they know they should be using AI, they do not know where to start, everyone is selling a different tool, they tried two and neither stuck, and the team is tired of the constant new thing.
Their conclusion: cost, access and awareness are not the bottleneck.
The bottleneck, they write, is that nobody sits with a business, finds the actual friction and designs a focused fix.
Their closing line is blunt: the market is full of AI products looking for a problem, and the tool comes last even though everyone sells it first.
For brands planning a campaign
This positions Modi for discovery-focused work — a diagnostic workshop, an audit or customer-education content — rather than a product demonstration. Vendors seeking a credible voice against tool-first selling can use it directly.
The only post where Modi describes building and running a system themself, including what went wrong.
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Read it to see how they reason about their own work rather than comment on other people's.
Modi built an outbound system that processed 5,000 leads in 90 days.
The scrapers ran, the enrichment ran, the drafting held up; the machine worked.
What broke was their definition of a good lead.
Nearly half of the 5,000 landed in a bucket the system labelled MISC — not manufacturing, not D2C, not services, just unclear.
Their reading: the machine did not fail so much as hold up a mirror.
Their targeting thesis was fuzzy, and running at scale printed that fuzziness five thousand times.
They extend this into a general rule — scale does not fix a weak assumption, it exposes it at volume — and end by asking readers what scaling their current process would expose.
For brands planning a campaign
An honest "here is what broke" story makes a strong event or content asset: Modi can speak from firsthand delivery rather than opinion, and give audiences a question they can apply to their own pipeline.
Sets out Modi's economic argument for how AI changes the competitive position of small firms.
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Use this when a decision-maker needs the commercial case in one screen.
Modi contrasts two eras.
Previously, a ₹200 crore company would buy a ₹1 crore CRM, spend six months customising it, and create a system every smaller competitor had to fight against — an advantage they could not afford to match.
That math has flipped, in their telling: a founder with Claude, a Supabase database and a weekend can build a CRM that fits their business better than a large vendor's product, for roughly ₹25,000 instead of ₹1 crore.
Their conclusion: the advantage is no longer who owns the biggest tool, but who knows what their business actually needs.
Enterprise software, they write, protected incumbents from competition, and AI dismantled that protection.
For brands planning a campaign
A budget-holder argument: spend less, fit better. It is short, concrete and quotable enough for a pull quote, a slide or a webinar aimed at owners rather than IT buyers.
Explains which businesses Modi expects to move first and why, showing how precisely they segment an audience.
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Modi argues that the smallest Indian businesses are about to leapfrog medium-sized ones.
A five-person company has nothing to defend, they write: no legacy software, no internal politics, no "we have always done it this way," so it can rebuild every workflow around AI in a weekend if it chooses to.
A fifty-person company has standard procedures, an IT vendor, three managers protecting their patch and a CRM bought for ₹8 lakh two years earlier, so changing anything takes a quarter.
Size used to be an advantage; right now, in their view, smallness is.
They call the next 24 months the strangest cycle Indian B2B has seen and say the cost of doing nothing has never been higher.
For brands planning a campaign
Use it to define a target segment precisely — very small firms, not mid-market. It also offers a timeline claim a campaign can test with its own audience rather than simply repeat.
Shows what Modi tells founders to ignore—a useful test of how they would handle a thought-leadership campaign that criticises hype rather than adds to it.
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Modi argues that most advice founders follow was written for people whose product is the advice itself.
Post daily, build in public, grow your personal brand: good counsel, they say, if your business model is selling counsel, but often a distraction dressed up as a growth plan for someone running a manufacturing unit or services firm.
The creator economy, in their account, taught a generation of operators to optimise for attention when they should be optimising for margin: reach is not revenue, and an audience is not a customer base.
They tell founders to take advice from people who run a business shaped like theirs, not from people who sell advice, and ask readers what they have done for reach that has never brought in a paying customer.
For brands planning a campaign
Suits a brand seeking content critical of marketing hype. It also signals that Modi may resist campaigns that make reach, follower growth or engagement metrics the goal.
Anish Modi argues that the most common AI story in Indian business is a founder signing up for ChatGPT, asking it to write a marketing plan, getting generic output, and concluding AI is overhyped.
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The problem is not the model, they write; ChatGPT alone lacks context, access to company data, and memory of prior conversations.
Real value appears when a business builds a specific tool around the model, connects it to customer data, trains it on past responses, and gives it a process that ends in a result rather than a paragraph.
Most founders never see that version because nobody around them is building it.
Contrasts a general chatbot with a tool built around the model to diagnose the adoption gap.
For brands planning a campaign
A consulting or software partner can build a campaign around moving buyers past general chatbot trials to focused, data-connected AI tools. It supports a talk or content series on choosing AI implementation over prompt experimentation.
The “abhi nahi” delay and quiet competitor advantage
Every SMB founder Anish Modi meets has a version of “Haan haan, AI use karna hai.
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Lekin abhi nahi,” meaning they will use AI later.
While they wait for the right tool, team readiness, or budget, a competitor can quietly build an AI tool that pulls customer queries into a dashboard, drafts replies, and saves the team 15 hours a week.
Modi argues that the “abhi nahi” founders will catch up in 2027, 18 months late.
The right time is when work is boring, repetitive, and taking time from more important work.
For brands planning a campaign
An urgency-led campaign for Indian SMB owners can focus on finding repetitive support, sales, or admin work and implementing a narrow AI fix now. A partner can frame the cost of waiting in terms of competitor productivity, not hype.
After speaking with founders in hospitality, fashion, manufacturing, and marketing, Anish Modi heard almost the same sentence: businesses know they should use AI, do not know where to start, are sold a different tool by everyone, have tried two that did not stick, and have teams tired of constant new tools.
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The bottleneck is not cost, access, or awareness.
It is that nobody helps a business find what is worth building for its specific situation.
The market is full of AI products looking for a problem; what is missing is someone who will sit with the business, find the real friction, and design a focused fix.
The tool comes last.
Cross-industry founder conversations in hospitality, fashion, manufacturing, and marketing.
The founders described had already tried tools that did not stick, making implementation fit—not awareness—the central issue.
For brands planning a campaign
A software or consulting partner can use this for workshops, diagnostic audits, or co-branded content that starts with a business problem, not a product demo. Anish Modi can be positioned as a practical guide to SMB AI adoption.
Anish Modi warns that most advice founders follow was written for people whose product is advice.
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Post daily, build in public, and grow a personal brand are fine counsel if the business model is selling counsel, but often a distraction for a manufacturing unit or services firm.
The creator economy taught operators to optimize for attention when they should optimize for margin.
Reach is not revenue, and an audience is not a customer base.
Take advice from people who run a business shaped like yours.
For brands planning a campaign
A B2B brand can steer its campaign away from generic personal-brand tactics and toward operator-relevant proof: margin, customers, and process improvement. This is a strong content angle for manufacturing, services, and other non-creator SMB audiences.
Small-business speed can leapfrog larger competitors
Anish Modi argues that the smallest Indian businesses are about to leapfrog medium ones.
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A 5-person company has nothing to defend: no legacy software, no internal politics, no “we have always done it this way,” and can rebuild workflows around AI quickly.
A 50-person company has SOPs, an IT vendor, managers protecting their patch, and an expensive CRM that takes a quarter to change.
Size used to be an advantage; right now smallness is.
The next 24 months will be a strange cycle in Indian B2B, and the cost of doing nothing has never been higher.
The argument is directional about relative speed; it does not claim every small business will execute an AI rebuild successfully.
For brands planning a campaign
A small-business campaign can make agility the core selling point for AI adoption, targeting 5- to 50-person companies with a message about moving faster than larger, more constrained competitors.
Anish Modi describes enterprise SaaS as a former moat: a 200 crore company would buy a 1 crore CRM, customize it for six months, and create a system smaller players could not afford.
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That math has flipped, they argue.
A founder with Claude, a Supabase database, and a weekend can build a CRM that fits the business better than Salesforce for 25,000 rupees instead of 1 crore.
The advantage is no longer who has the biggest tool; it is who knows what the business actually needs.
Enterprise software protected incumbents from competition, and AI has dismantled that protection.
For brands planning a campaign
An AI software or consulting partner can use this cost-disruption and build-versus-buy angle for Indian SMB decision-makers. It also gives Anish Modi technical credibility for content about custom tools and enterprise alternatives.
Anish Modi built an outbound system that processed 5,000 leads in 90 days.
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The scrapers, enrichment, and drafting held up; the first thing to break was the definition of a good lead.
Nearly half landed in a bucket labeled MISC: not manufacturing, not D2C, not services, but unclear.
The machine did not fail, they write; it held up a mirror.
A fuzzy targeting thesis printed itself 5,000 times.
Scale does not fix a weak assumption; it exposes it at volume, with receipts.
Built and ran an outbound system with scrapers, enrichment, and drafting, then reviewed 5,000 leads over 90 days.
The technology worked; the failure was in the definition of a qualified lead.
For brands planning a campaign
This offers a concrete implementation case study for a campaign about AI-assisted outbound, lead qualification, or operational scale. Anish Modi’s experience can anchor a discussion of diagnosing targeting and data definitions before adding automation.
Follow one argument across different business settings in Anish Modi’s posts: AI creates value when a tool fits a specific problem, not when it’s tested as a general chatbot.
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Start with the AI adoption pieces on the generic-output trap and the “abhi nahi” delay, then move to operating examples: a 5,000-lead outbound system, custom software undercutting enterprise vendors, and a warning that creator-economy advice can distract operators.
The tags group these posts around AI adoption, Indian SMBs, outbound sales, custom software and founder advice.
For adjacent reading, Tom B. is the closest match on AI and outbound systems; Yash Patel and Ronit Thakur apply a similar short-form, one-thesis-per-post style to other parts of Indian business.
Both use short posts to explain an Indian market shift through a business example, then draw a strategic lesson.
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Yash Patel’s Uber safety post argues that India is moving from an access economy to a trust economy; the IndianOil post says companies can grow by selling more to existing customers. Anish Modi applies the same one-example approach to AI adoption and custom software.
Yash Patel covers consumer trust and asset reuse across travel, fuel and retail. Anish Modi focuses on AI implementation, outbound systems and software choices for small businesses.
Both write contrarian LinkedIn analyses of Indian companies, comparing business models and spending to steer founders toward a different priority.
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Ronit Thakur contrasts Sugar’s distribution-led losses with Kay Beauty’s profitable leverage, and frames Zepto’s marketing spend as buying default behaviour.
Ronit Thakur’s work focuses on D2C beauty and quick-commerce brand economics. Anish Modi covers AI adoption, custom software and B2B outbound for SMBs.
Both explain how AI changes practical B2B outbound: tools can run parts of the process, but someone still has to choose targets, signals, offers and tests. Both also write about finding prospects who match an existing best client rather than sending volume to a loose list.
Tom B. focuses on cold-email infrastructure, agency prospecting and AI as a control layer for outbound campaigns. Anish Modi focuses on Indian SMB AI adoption, diagnosis before tools, and custom builds that undercut enterprise software.
Anish Modi had 9,032 LinkedIn followers at the profile snapshot of 9 October 2026.
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Their writing is aimed at Indian small-business owners weighing AI adoption: they address founders directly, ask them questions and use Hinglish lines such as "Haan haan, AI use karna hai.
Lekin abhi nahi."
They also report speaking with founders in hospitality, fashion, manufacturing and marketing.
For a campaign aimed at Indian SMB founders or owners, the content-to-audience match is plausible.
Before committing to a speaker or content partnership, request current follower demographics (location, seniority, industry and company size); impressions, engagement and click data for recent posts on AI adoption for small businesses and the outbound-system topic; and any audience survey documenting readers’ professional profiles.
If the campaign targets a specific job title or industry, ask for recent results for comparable topics, depth and formats.
LinkedIn followers
9,032
LinkedIn profile follower count at snapshot · 2026-10-09
Anish Modi’s seven published posts offer advice and analysis for founders and operators, not sponsorship offers, rate cards, media kits or brand-deal announcements.
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None names a sponsor, describes a paid collaboration, states a bookable format or provides an inquiry route for commercial requests.
Two posts describe work a buyer could ask about: an automated outbound system that processed 5,000 leads in 90 days, presented as a lesson about fuzzy targeting rather than a service for sale; and repeated conversations with founders across hospitality, fashion, manufacturing and marketing about where AI adoption stalls.
These are accounts of first-hand experience, not purchasable packages.
The posts do not say whether Modi accepts sponsorships; ask for a current sponsorship or press page, media kit, published rates and deliverables, or a disclosed collaboration naming the brand and work delivered.
A B2B AI consulting firm or software vendor selling AI implementation to Indian small and midsize businesses — roughly 5 to 200 employees — that wants a speaker, workshop host, or co-authored content partner on AI adoption. The goal is credible education that leads to scoping conversations, not mass reach.
Reasons
Anish Modi's recurring argument is that buying decisions should start with the business problem, not the tool. After speaking with founders in hospitality, fashion, manufacturing, and marketing, they report hearing the same thing: businesses know they should use AI, do not know where to start, and have already tried two tools that did not stick. An implementation seller can build a talk or content series around that diagnosis without turning it into a product demo.
The "I tried ChatGPT and it did not work" piece gives the session a recognisable opening. Founders who have already given up on AI will see themselves in it, and the argument explains why a general chatbot produced generic output rather than why the buyer was foolish — a more useful starting point than a general "AI for business" introduction.
The "abhi nahi" post offers a hook that is easy to promote and easy for an audience to identify with. It names a trigger for starting — work that is boring, repetitive, and taking time from work that matters — that a consultant can turn into a qualifying question.
The outbound-system account is the only example of Anish Modi describing building and running something and saying plainly what broke: 5,000 leads in 90 days, scrapers, enrichment, and drafting all holding up, and nearly half the leads landing in an unclear bucket because the targeting definition was fuzzy. First-hand delivery stories let a speaker answer "have you done this?" rather than only comment on other people's work.
The economics post gives budget holders a short, quotable commercial frame — a custom build fitted to the business versus a large off-the-shelf platform — while the small-business-speed post identifies a specific target segment rather than "SMBs" in general.
Caveats
No sponsorship, speaking, or content-partnership format or commercial inquiry route is documented in the material reviewed. Since Anish Modi has not publicly priced or packaged this work, confirm availability, format, and terms before proceeding.
Anish Modi has 9,032 LinkedIn followers in a profile snapshot dated 9 October 2026. No demographics, job titles, industries, or company sizes are available, so the posts do not support a reach-based commitment.
The figures in the posts — 15 hours a week saved, ₹25,000 versus ₹1 crore, 18 months late — are Anish Modi's framing from founder conversations, not measured benchmarks. Present them as a point of view, not verified industry data.
When to choose a specialist
Poor fit.
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Brief
An established enterprise software vendor selling a flagship CRM or ERP platform to mid-to-large Indian companies, buying creator content to promote the product and drive demo requests, with success judged on reach, engagement, and follower growth.
Reasons
Anish Modi argues that enterprise software once protected incumbents from competition and that AI dismantled that protection, using the large-platform-versus-₹25,000-build comparison as the illustration. A brief whose purpose is to sell the flagship platform cuts against the argument the audience comes to hear.
The post on founder advice says most advice founders follow was written for people whose product is advice, that operators were taught to optimise for attention when they should optimise for margin, and that reach is not revenue. A brief measured on impressions or follower growth asks Anish Modi to do what the content criticises. The post's closing question — what have you been doing for reach that has never once brought a paying customer? — is a fair preview of how the collaboration would read.
The targeting conflicts. Anish Modi's segment claim is that five-person firms have nothing to defend and can rebuild workflows quickly, while fifty-person firms have procedures, an IT vendor, managers protecting their patch, and a recent CRM purchase, so change takes a quarter. A mid-market enterprise pitch targets the slower-moving group the content describes as the harder sale, without explaining how to reach them.
The content is short opinion and analysis. The posts show neither a demo-led nor a product-comparison format, and the only build story ends with a failure in the lead definition rather than a product result. A vendor-style product walkthrough is not supported by the material.
With no follower demographics, post-level engagement, or click data published, a campaign built around reach has no data to plan against.
A worked campaign example
Angle
"Abhi nahi" — the excuse, the cost, and the one workflow worth fixing. Lead with the sentence founders actually say, not the technology, and end with a diagnostic attendees complete on the spot.
Assets
An 800-word companion piece built on the "abhi nahi" argument, keeping the cost-of-waiting framing concrete and leaving out follower-growth talk.
A blank version of the diagnostic worksheet that stands alone: someone who never attends the session can fill it in and decide which workflow to fix first.
A short slide or pull-quote set drawn from the custom-build economics post and the small-business-speed post, for the session's segment-reasoning section.
Brief
The good-fit brief: a B2B AI consulting firm wants a session and companion content that turns Indian SMB owners from stalled AI dabblers into scoping conversations.
Risks
Anish Modi has no published speaking or sponsorship format or inquiry route. Confirm availability, format, fee, and rights before promoting the session.
The source claims are directional. Present the 15-hours-a-week and 18-months-late figures as Anish Modi's read from founder conversations, not measured outcomes.
The workshop only works if the firm can respond to the diagnostics it collects. If consultants cannot turn a completed worksheet into a next step, the session produces goodwill but no pipeline.
Steps
Open with the pattern from the cross-industry conversations: businesses know they should use AI, do not know where to start, everyone is selling a different tool, and they have tried two that did not stick. Present it as the firm's view of the market, drawn from founder conversations, not as a survey result.
Explain the ChatGPT trap in two minutes: generic output from a tool with no company data, no memory, and no workflow that ends in a result is the expected outcome, not proof that AI does not work. This clears the ground before any pitch.
Use the 5,000-lead outbound story to show why the order matters. The scrapers, enrichment, and drafting held up; the definition of a good lead did not. At scale, that fuzziness was printed 5,000 times. The lesson: fix the assumption before buying the tool.
Give the audience one decision rule: start where the work is boring, repetitive, and stealing time from work that matters. Have each attendee write down one workflow that meets all three tests.
Hand out a one-page diagnostic worksheet covering the chosen workflow, the data it touches, who currently does it, and what a finished result would look like. Attendees complete it in the room; consultants review the sheets and turn the strongest ones into short scoping conversations.
Close with the budget frame: who ends up better served by a fitted build versus a large platform. Use that comparison to make the case for acting now rather than after the dust settles.
Success measures
Number of completed diagnostic worksheets collected in the room.
Number of completed worksheets that lead to a scoping call within 30 days.
Qualitative check: whether attendees describe their problem in workflow terms afterwards, rather than asking which AI tool to buy.
Do not use impressions, follower growth, or engagement rate as the headline measure; the brief and the content both point away from it.
Choose Tom B. when the engagement is about outbound pipeline rather than AI adoption in general: a session or content series that ends with a target list and a message, not a workflow audit.
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The two posts describe an outbound stack moving toward an AI control layer: lead data, enrichment, a sending platform and CRM operated through a conversation instead of someone clicking between six tools.
The argument is that operating the tools matters less than knowing who to target, which buying signals count, what offer will land and how to read results.
For a firm selling AI to sales teams, that is a sharper promise than a general AI-adoption talk.
One post ends with a published, low-friction offer — send the name of your best client, get back three companies like them, who to contact and what to say, no call required — that a buyer could copy as an inquiry mechanic.
Tom B. writes about sales systems, not the wider question at the heart of Anish Modi's material: where a small business should start with AI.
Neither post touches operations, hiring or industries beyond outbound, so a cross-industry AI adoption session would miss the diagnostic angle needed for a campaign aimed at CFOs.
There is also a direct conflict: the posts market Tom B.'s outbound service, including an offer aimed at the same agency and consulting buyers an AI firm would want to reach.
The figures — three good matches beating three hundred loose ones, 400 companies, and buying signals in the last 30 days — are Tom B.'s framing, not measured results.
Ask for audience size, engagement data and speaking format; only two posts are available, so the range of formats is unclear.
Choose Ronit Thakur when the campaign is about Indian consumer categories rather than AI adoption among small businesses — for example, a session or piece on why distribution without brand loyalty becomes expensive shelf space, or what it costs to buy the default app in a young category.
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The two posts put numbers alongside familiar brand stories: a beauty brand with 45,000 retail touchpoints, 200 stores and 550 cities whose revenue fell from ₹505 crore to ₹380 crore while losses doubled to ₹134 crore, set against a competitor doing ₹132 crore revenue, ₹11 crore net profit and ₹16.6 crore of marketing; and a quick-commerce brand spending ₹950 crore on marketing, read as buying habit while the category is still forming. A buyer whose audience is D2C founders or consumer marketers gets category-level reasoning with figures attached — a useful alternative to a talk about which workflow to automate first.
The subject matter does not overlap with a campaign aimed at small-business owners considering AI adoption.
Both posts assess other companies' financial outcomes from public reporting; neither shows Ronit Thakur building something, running an implementation or working with small businesses.
There is no first-hand delivery story for an audience member asking whether the speaker has done the work.
The numbers are Ronit Thakur's reading, not audited benchmarks, and one post's conclusion — that hustle scales until it does not and leverage scales forever — is an argument to debate rather than a method to apply.
The audience described is consumer-brand founders and marketers, not the 5 to 200 employee SMB owners suited to this campaign.
Ask for audience size, engagement data, speaking format and a commercial inquiry route; only two posts are available.
Choose Yash Patel when the session needs a market-level frame on Indian consumer behaviour rather than an AI implementation agenda — for instance, how safety, fraud protection and authenticity have become competitive products, or how a company that already owns the customer and the location can sell more to that base.
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The two posts make those arguments: a ride-hailing app's safety features signal that India is moving from an access economy to a trust economy, with payments apps competing on fraud protection, marketplaces on authentic products and banks on scam detection; and an oil company launching packaged water as a retail-platform move built on 43,603 fuel outlets rather than a water business. A buyer seeking a talk on how mature markets shift the basis of competition, or on incumbent cross-sell strategy, gets a clear structural argument with a worked example in each post.
Neither post concerns AI, software selection or how a small business decides what to build, so this alternative does not serve a campaign about those needs.
The analysis stays at the level of large public companies and their strategic moves; Yash Patel's posts do not show work with small or midsize businesses or a practical audience exercise, which this kind of campaign relies on.
The trust-economy argument and retail-platform argument are readings of public announcements, offered as a point of view rather than verified data.
Ask for audience size, engagement data, speaking format and a commercial inquiry route; only two posts are available.
Anish Modi's seven LinkedIn posts cover AI adoption among Indian small businesses, the cost of delaying AI work, custom software versus enterprise platforms, a 5,000-lead outbound system, and advice for founders.
The posts do not give publication dates, engagement data, follower demographics, client work, or information about other channels.
A LinkedIn profile snapshot dated 9 October 2026 recorded 9,032 followers.
Figures in the posts, such as 15 hours a week saved or ₹25,000 versus ₹1 crore, are Anish Modi's own framing from founder conversations and system accounts, not independently verified benchmarks.
The posts here do not describe any sponsorship, speaking, or content-partnership format.
