# Felix Markman

Felix Markman writes about improving revenue operations: finding waste in SaaS stacks, measuring ROI, and adopting AI and software in ways that work in production.

- Profile: https://b2binfluencer.marketing/creators/felix-markman/
- LinkedIn: https://www.linkedin.com/in/felixmarkman
- LinkedIn followers: 3,393
- Topics: artificial-intelligence, revenue-operations
- Based on 11 dated posts, latest 2026-09-28

## Summary

Felix Markman is most useful for skeptical revenue operations diagnostics, SaaS stack waste, and AI production readiness.

- Best for: A B2B SaaS company seeking cost cuts and better revenue operations efficiency.
- Not for: A campaign asking them to claim an unproven AI interface is production ready.
- Check first: Confirm recent post results and audience data before scaling a campaign.

### Takeaways

- Most teams look for savings in vendor discounts, not idle seats and overlapping tools.
- AI features must be judged by adoption, data quality, governance, and user trust.
- Markman cites 36% of SaaS licenses go unused and an illustrative $40K monthly stack exposure.

## Main ideas

- **ROI is the deliverable, and small teams can outrun bigger ones**: Markman defines their mission as producing and measuring ROI for revenue operations, and sees the gap between AI hype and measured results as the problem to solve. They argue that technology now favours small operators: large organisations route decisions through many human approvers and move more slowly. A solo operator with the right tools can outrun a team of five, and a pair can outrun ten or twenty. Markman points to economic data showing that the productivity boom predicted by early generative-AI hype has not fully arrived.
- **AI features earn their place in production, not in a keynote**: Writing after Dreamforce, Markman challenged the claim that AI replaces the user interface. They predict that a year later, well under 20% of users will be able to avoid logging into Salesforce, and that the pendulum will swing back within two or three years towards features that work only if users log in. Their reasoning: demos look finished, but adoption is the real barrier, and the hardest business problems require KPIs, clean data and permissions—not a new product line. First-mover advantage is real in markets, they argue, but rarely in unpriced, half-available platform bets. Their advice is to let others take the first patch, study the results, then move decisively.
- **The money leaks in stack hygiene, not in vendor negotiation**: Markman argues that most teams hunt for savings in the wrong places: pushing vendors for 5% off at renewal or arguing over a $30/month tool while larger sums quietly go. They identify six leaks to look for: idle seats, two tools in the same category run by different owners, untracked auto-renew notice windows, brittle integrations held together by someone's weekly CSV cleanup, duplicate or stale CRM and marketing-automation data, and AI copilots with no owner or ROI map. They say fixing these issues requires no new tool or big project—just looking in the right place first.
- **Start from the whole picture before judging one improvement**: Markman describes themself as a stickler for the big picture and says no question or orthodoxy is beyond questioning. They use AI to compile a helicopter view of the landscape first, arguing that it makes little sense to study one tree when you can see the forest. Their posts start with a market-level question and work down to a specific action, rather than starting with a single tool.

## Work to read

- [I want to be the guy who actually delivers ROI.](https://www.linkedin.com/posts/felixmarkman_i-want-to-be-the-guy-who-actually-delivers-activity-7508480974641422337-QHMm)
- [Hot take after Dreamforce: “AI replaces the UI” is a keynote line.](https://www.linkedin.com/posts/felixmarkman_df26-aiforce-salesforce-activity-7507755843258499072-bPYq)
- [What if 1 in 3 of your software licenses is sitting unused?](https://www.linkedin.com/posts/felixmarkman_saas-saasmanagement-revops-activity-7510324421358714881-OSsG)

## Brand relationships

- **Salesforce** (mention)
- **Zylo** (mention)

## Partnerships

Felix Markman’s posts contain no documented sponsorship or partnership offer. They cover revenue operations, SaaS waste, and AI adoption; none discloses a sponsored format, media kit, rate card, or route for brands to enquire. The only commercial call to action is their Revenue Stack Savings Guide, promoted through a link in the comments of a post about unused SaaS licences—a self-published resource, not a paid placement or brand collaboration. For a buyer planning a campaign, there is nothing published to book yet. Any partnership would have to be scoped directly with them, and any budget line would rest on assumptions rather than a stated offer.

## Alternatives

- Vernon Keenan: Choose Vernon Keenan when the cost-and-efficiency question turns on platform architecture, agent governance, or a build-versus-buy decision at the CRM and enterprise-application layer. Their posts analyse ServiceNow's acquisition of Sweep as a “Delivery Digital Twin” — a metadata graph that shows AI agents the objects, fields, automations, permissions, code, and dependencies inside a Salesforce org — and argue that ServiceNow is pursuing “control without data gravity.” A separate post reads Salesforce's AIforce launch as “a control story wearing a UI costume,” with the governed context underneath as the real product and a trust harness not expected until 2027. For buyers weighing platform consolidation, governing agents from outside the CRM, or where release automation and metadata visibility fit into a SaaS cost problem, that analytical frame can be more useful than a savings list. The post includes a five-act timeline and a delivery scorecard for testing a vendor claim.
- Mike Coutts: Choose Mike Coutts when the decision is about quantifying the cost of delay, revenue leakage from manual commercial processes, or making the case for a revenue-operations fix before a diligence or value-creation review. He writes about companies that model upfront cost, maintenance, labour, interest, and headcount but never put a number on waiting. The pattern shows up in quote turnaround, forecasts nobody trusts enough to plan production against, and deals that quietly stop closing. One example: a manufacturer running quote-to-cash across an ERP, a CRM the sales team works around, and about 40 spreadsheets. Coutts argues that every quarter of delay brings unmeasured revenue leakage and gives a competitor time to get faster; the bill often comes due during diligence or a value-creation review, when there is no runway left. A separate post on Dreamforce says the first AI use case has to be small and measurable, and that placing an agent on top of manual work does not remove the “Manual Tax” — it automates it. This is a strong fit when buyers need to argue “fix the process now” rather than “cut a licence.”
- Shubham Goel: Choose Shubham Goel when the buyer's cost-and-efficiency problem is really about enterprise-scale AI governance, integration between CRM and ERP, or organisational readiness rather than licence rationalisation. Goel relays lessons from CIOs at large banks, telecom, and technology companies. Recurring points include the ungoverned tools and AI-risk ownership questions raised by the age of the citizen developer; some CIOs' proposal for an independent “Agentic AI Oversight Board”; the possibility that a future “Agentic AI Manager” might run a team of 20 agents; lost knowledge and poor documentation that limit what agents can do, prompting incentives to add knowledge to the company wiki; and the hard roadblock posed by integration between systems such as CRM and ERP, which requires sales, enterprise apps, Salesforce developers, and many others to align. A separate Dreamforce post argues that AI adoption is uneven, Salesforce is rebranding as an AI company, and the product is no longer the UI, leaving open whether underlying data, integration, agent management, and security problems will be fixed. For buyers blocked by cross-system alignment, agent oversight, or knowledge loss, this is closer to the problem than a licence-savings playbook.

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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/