# Omprakash Karuppanan

Omprakash Karuppanan runs ABM execution programs for enterprise SaaS and IT services companies. Karuppanan's work focuses on improving enterprise deal progression, including diagnosing stalled deals, mapping buying committees, and using account signals to guide sales and marketing action. Karuppanan says they run Fuego Systems.

- Profile: https://b2binfluencer.marketing/creators/omprakash-karuppanan/
- LinkedIn: https://www.linkedin.com/in/omprakashmarketing
- LinkedIn followers: 15,808
- Topics: artificial-intelligence, sales, account-based-marketing, enterprise-sales, sales-and-marketing-alignment
- Based on 12 dated posts, latest 2026-10-08

## Summary

Omprakash Karuppanan is most useful for making ABM operational in enterprise SaaS and IT services pipeline decisions.

- Best for: Enterprise B2B SaaS or IT services revenue teams wanting faster pipeline and a runnable ABM playbook.
- Not for: Martech vendors selling AI-assisted outbound personalization and top-of-funnel lead generation campaigns.
- Check first: Confirm whether they offer sponsorship or paid partnership formats before contacting them.

### Takeaways

- ABM is a go-to-market rebuild, not a targeting upgrade.
- Stalled enterprise deals follow a repeatable seven-signal pattern.
- Buying committees, not single contacts, are the unit of enterprise deals.

## Main ideas

- **ABM is a go-to-market rebuild, not a targeting upgrade**: Omprakash Karuppanan argues that describing ABM as better targeting plus personalization plus AI is why many programs fail. In their account, real ABM changes internal operations: the ideal customer profile gets challenged; not every large logo is worth pursuing; sales loses solo control of accounts; marketing influences deal progression mid-funnel rather than only at handoff; content is built for the buying committee and its deal stage instead of a generic persona; and success shifts from marketing-qualified leads to deal movement and pipeline velocity. The best ABM teams, they write, ask how to move one specific deal forward this week rather than how to target better in general.
- **The top of the funnel is saturated; the mid-funnel is where AI earns leverage**: They argue that AI has made personalization cheap and fast, so every team runs the same outbound playbook and nothing stands out. The real edge is in the middle of the funnel, where deals quietly stall. Most stacks already hold the data teams need: sales call recordings, webinar question-and-answer and chat logs, email threads between account executives and the buying committee, demos where a question went unanswered, and internal pipeline review transcripts where a new stakeholder was named but never tracked. They list four uses of AI there: catching likely stalls early by reading every conversation in a deal, finding buying-committee gaps, aggregating objections across accounts, and replacing activity counts with deal-health signals such as a champion whose reply time slipped from two hours to a week.
- **Stalled enterprise deals follow a repeatable seven-signal pattern**: Omprakash Karuppanan treats stalled deals as diagnosable rather than mysterious. Their pipeline audit checks seven signals: single-threaded engagement on a deal over $50K annual contract value; no buying-committee activity for 60 days; the same stage held for 1.5 times average velocity; recent activity that is entirely outbound with no replies for 21 or more days; no compelling event such as a renewal, compliance deadline, or board mandate; a champion quiet for 30 or more days with no replacement built; and a close date pushed three or more times. Three signals stacked together mean the deal is almost dead. The hard part, they add, is the conversation with representatives that follows; the ones who fight hardest usually have the strongest dead-deal exposure, and the pipeline number will drop in week one.
- **Buying committees, not single contacts, are the unit of enterprise deals**: Omprakash Karuppanan repeatedly returns to buying-committee coverage. They say enterprise decisions involve five to nine stakeholders, committees run six to ten people, and most stalled deals are single-threaded into one contact. Names mentioned in calls, webinars, and emails often never reach the CRM. Champions go quiet because they lose internal support, get reassigned, or leave; a follow-up email fixes none of those problems.
- **HubSpot and CRM setups fail when they automate activity instead of account signals**: They name six recurring failures in HubSpot-run ABM: workflows used as standalone nurture drips rather than plays triggered by account-level changes; static target account lists that miss new committee members, departed champions, and funding rounds; lead scoring on individuals rather than accounts; marketing owning HubSpot alone while sales never touches the workflows or activity history; no executive view of pipeline velocity, buying-committee coverage, or quiet Tier-1 accounts; and automation overload that floods sales development representatives with alerts until they stop trusting them. The platform is infrastructure, they argue; the program is the buying-committee map, the trigger logic, and the shared sales and marketing cadence.
- **AI is useful where judgment is repeatable and useless where it is contextual**: Omprakash Karuppanan draws a clear boundary. AI helps with transcript extraction, signal scoring, account research, and pattern detection across thousands of conversations. It does not help with contextual judgments such as which stalled account to reactivate first, whether a champion is fighting for the project or quietly letting it die, or whether to push for signature this quarter or wait out a budget freeze.

## Work to read

- [What is Account-Based Marketing? (A.K.A) ABM](https://www.linkedin.com/posts/omprakashmarketing_what-is-account-based-marketing-aka-activity-7513586123911294978--oCA)
- [How Can I Identify a Stalled Enterprise Sales Deal?](https://www.linkedin.com/posts/omprakashmarketing_how-can-i-identify-a-stalled-enterprise-sales-activity-7508875456750936064-W5fx)
- [AI made B2B outbound worse, not better](https://www.linkedin.com/posts/omprakashmarketing_ai-made-b2b-outbound-worse-not-better-activity-7510691231140724736-Q_Hu)
- [What Most Teams Get Wrong in HubSpot for ABM](https://www.linkedin.com/posts/omprakashmarketing_what-most-teams-get-wrong-in-hubspot-for-activity-7505975729705340928-C0GT)
- [I've built 10 AI Skills for ABM this year, and I want to list my top 5](https://www.linkedin.com/posts/omprakashmarketing_ive-built-10-ai-skills-for-abm-this-year-activity-7511053161944551424-xOk4)

## Brand relationships

- **Fuego Systems** (owned_business)
- **HubSpot** (mention)
- **Claude** (mention)

## Partnerships

Omprakash Karuppanan's posts do not say whether they offer sponsorship or paid-partnership formats. All five posts close with a direct invitation to contact Karuppanan by LinkedIn message, and each one frames that invitation around a service relationship rather than a sponsored placement: "If you run ABM on HubSpot and want to see what one of these outputs looks like, DM me and we can share notes," and closings such as "we should talk" or "DM me" after descriptions of ABM pilots, stalled-deal reactivation, and HubSpot-based execution for enterprise SaaS and IT services teams. One post offers a sample output from the AI skills they use for ABM on request. No rate card, media kit, sponsor policy, deliverable list, or published audience specification appears in the posts here, and no post discloses a sponsored or paid brand collaboration. The practical consequence for a buyer is that terms would have to be negotiated one-to-one from a direct message, with deliverables, pricing, disclosure language, approval and review timelines, and usage rights all settled in that conversation rather than read off a published page. The posts here do not show whether Karuppanan accepts sponsorships; they simply do not show an offer, its price, or its operating requirements.

## Alternatives

- Heather Schuck: Choose Heather Schuck when the blocker is the cadence of reporting rather than the mechanics of individual deals. The two posts argue that finance can afford to look backward while revenue leaders cannot, and that the fix is a real-time feedback loop built on sales stage velocity: a baseline calibrated to the company's own product, price, and people. Schuck then shows how to read it — deals stalling at discovery point to a qualification problem, slowing at proposal to pricing or stakeholder alignment, and sitting in negotiation to a competing internal priority taking the decision-maker's attention or budget — and makes pipeline coverage a calculation a reader can run: 4x coverage with 30% of deals stale for 60 or more days is 2.8x real coverage. The second post points to a six-part playbook on where to cut without killing growth, with a calculator in every section and questions covering whether the problem is activity or efficiency, whether the pipeline is real, where deals are dying, and which investments to protect. Pick this alternative when the campaign audience is revenue leaders and the asset needs the finance-versus-revenue framing, a coverage recalculation, or a self-serve calculator.
- Brigitta Ruha: Choose Brigitta Ruha when the need is to build GTM capacity stage by stage, not repair a specific stalled pipeline. One post lays out 62 GTM rules split by ARR band, from $0–1M founder-led selling through $25M+ optimisation. It names what to do at each stage, which tools teams tend to use (HubSpot, Attio, Clay, Instantly, Common Room, 6sense, HockeyStack, Gong) and what commonly goes wrong: hiring SDRs before the founder knows how to sell, buying more tools when the process is the problem, and starting ABM without GTM and RevOps support. The second post is a build guide for AI-assisted research: pick buying signals first, on the reasoning that only about 5% of an ICP is ready to buy; require two or more signals together; encode the process as a Claude Code skill with CRM access and ICP definitions attached; and teach the writing skill the team’s own voice using real sent messages and words to avoid. Take this option when the deliverable is a repeatable research and outbound system, or when the campaign needs a stage-by-stage investment framework the audience can check its own numbers against.
- Jani Vrancsik: Choose Jani Vrancsik when buyers want a team to build and operate a GTM engine rather than an in-house framework. One post documents the system behind a stated $1.5M ARR agency and 7-figure pipeline with three AI agents: LinkedIn content across more than seven profiles as the dominant pipeline source, SEO supported by free mini-tools, cold outbound personalised from HubSpot and Clay enrichment data, deanonymised website visitors, and monthly partner webinars. It also publishes numbers buyers rarely get, including monthly impressions, the finding that most closed customers never engage with posts, and that a share of closed losses return after trying cheaper lead generation help. The second post describes ATLAS, a GTM playbook assembled by six Claude skills covering market research, ICP hypothesis, a signal library of 25+ buying signals, battlecards, a messaging matrix and one consolidated deliverable. It was produced in about two days, with sales editing the last portion. Take this alternative when the campaign promises an installed operating system with channel-level evidence and reusable AI skill files, or targets founders and GTM leads who want the build done for them.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/