Video · 2026-08-13 · 44m · 12 moments

Lighthouse or Landgrab? How to Pick Your AI Sales Strategy

✦ AI generated

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Definition◆

Enterprise AI sales strategies can be mapped on a 2x2 matrix of buyer exposure (risk of making a wrong purchase) versus proof mobility (whether social proof travels in the market).

Joe Schmidt introduces a 2x2 framework with buyer exposure on the Y axis and proof mobility on the X axis, where the top-right quadrant (high exposure, proof travels) is a lighthouse market and the bottom-left (low exposure, proof doesn't travel) is a land grab market.

transcript

Joe Schmidt: We really were thinking about, okay, what are the axes that we should be kind of mapping opportunities against? And so, we decided upon like the Y axis is really what we called like the buyer's exposure. And it's intentionally called exposure because there's the exposure of, you know, making a mistake with the solution that you buy. There's also the exposure of the solution inside of your company. Like, does this product that I'm selling to my customer end up, you know, being is it shown to their end customers, right? Which is actually an important distinction. And really just kind of the overall risk associated with buying this piece of software. So, that was like kind of the Y axis, and it goes from like high to low. And then the X axis is whether or not proof travels in any given market. And so, you know, if you think about like the top right would be proof travels in this market. And it's high buyer exposure and high, you know, high buyer risk. And that's a lighthouse market, right? This is to be clear. And the bottom left would be, you know, low proof traveling, but also low buyer exposure. And that would be a land grab market.

gives example · 1

Definition◆

The best sales playbook depends on whether proof travels in your market and how much buyer exposure/risk the sale carries.

Joe Schmidt explains the 2x2 framework he developed for evaluating whether founders should pursue a lighthouse or land grab sales strategy, with axes measuring buyer exposure and whether proof travels in the market.

transcript

Joe Schmidt: And so, we really were thinking about, 'Okay, what are the axes that we should be kind of mapping opportunities against?' And so, we decided upon like the Y axis is really what we called like the buyer's exposure. Um and it's it's intentionally called exposure because there's the exposure of, you know, making a mistake with the solution that you buy. There's also the the exposure of the solution inside of your company. Like, does this does this product that I'm selling to my customer end up you know, being is it shown to their end customers, right? Which is actually an important distinction. Um and really just kind of the overall risk associated with buying this piece of software. So, that was like kind of the Y axis, and it goes from like high to low. Um and then the the X axis is whether or not proof travels in any given market. And so, you know, if you think about like the top right would be proof travels in this market. Um and it's it's high buyer exposure and high you know, high buyer risk. Um and the bottom left would and and that's a lighthouse market, right? This is to be clear. Um and the bottom left would be, you know, low low you know, low proof traveling, but also low buyer exposure. Uh and that would be a land grab market.

gives example · 1

Claim

The key distinction between lighthouse and land grab markets is proof versus math: lighthouse markets require proving you're safe to buy from (proof travels), while land grab markets let you simply show superior ROI (math).

Joe explains that in lighthouse markets (regulated industries with few logos), the buyer risk is so high that social proof of safety is essential; in land grab markets, established budgets exist and buyers can be convinced purely through ROI math.

transcript

Joe Schmidt: And so I think those are actually quite different and if you think about the standard markets that fit into the lighthouse model, it's, you know, regulated industries. Often times there's a more constricted number of logos. If you're wrong in the industry, like if the buyer buys the wrong piece of software and it ends up doing the wrong thing, it can lead to very bad things happening for your firm, including, you know, potentially getting in trouble with the regulator, even doing something illegal. That's very bad, of course. And then on the flip side, when you're looking at the more of a land grab market, there's an established budget. People have very, you know, used to and accustomed to paying for a type of service, and you can kind of come in there and show like the end buyer the math of like, hey, my solution is better than whatever solution you're using today, whether that's a software driven solution or human driven solution. So, the kind of distinction that we drew was between like proof on the top right of the quadrant and math on the bottom left of the quadrant.

explains mechanism · 1extends · 1gives example · 1

Anecdote◆

Samsara's success was partly due to timing—they entered the market during the ELD mandate that forced an entire industry to adopt electronic logging devices.

Andy recounts how Samsara benefited from the Electronic Logging Device mandate that required trucking companies to replace manual logbooks with technology, creating budget and demand across the industry.

transcript

Andy: And and so you know, over a 2-year period, that was basically implemented between 2016 and 2019 with various phases of of sort of compliance. But what it did is it it provided this huge tailwind for anybody making these electronic logging devices. And we just happened to be one of the newer companies doing it. And there were some very very established players, right? AT&T had a solution, Verizon had a solution. There were a number of companies that were in sort of a you know, hundreds of millions, half a billion in revenue already doing this. And you know, rising tide floats all boats. It helped everybody. But if you were a new entrant into the market, like we were at Samsara, it really helped because basically the entire industry all of a sudden had to find budget to go out and and buy these things. And a certain percentage of them, you know, clearly would say, 'Hey, let's check out what's new out there. Are there any new entrants into the field?' And so it really helped kind of, you know, give us a a boost.

Anecdote◆

Regulatory mandates create massive market tailwinds that benefit all players, and Samsara's entry into the electronic logging device market was propelled by the 2016-2019 ELD mandate forcing the entire trucking industry to adopt the technology simultaneously.

Andy recounts how the ELD mandate between 2016-2019 forced every trucking company to purchase electronic logging devices, creating a rising tide that helped Samsara as a new entrant compete against established players like AT&T and Verizon.

transcript

Andy: In the US, roundabout 2016, they implemented this ELD mandate, right? And stands for electronic logging devices. And the idea there was, hey, we can use technology to actually track when the vehicle's moving and when it isn't and are they taking enough breaks and so forth, right? Take the human element out of it. And so over a 2-year period, that was basically implemented between 2016 and 2019 with various phases of compliance. But what it did is it provided this huge tailwind for anybody making these electronic logging devices. And we just happened to be one of the newer companies doing it. And there were some very, very established players, right? AT&T had a solution, Verizon had a solution. There were a number of companies that were in sort of hundreds of millions, half a billion in revenue already doing this. And rising tide floats all boats. It helped everybody. But if you were a new entrant into the market, like we were at Samsara, it really helped because basically the entire industry all of a sudden had to find budget to go out and buy these things.

extends · 1

Example

Stut demonstrates the land grab playbook by entering the accounts receivable market with AI that replaces manual collections workflows, proving superior ROI to mid-market buyers through math rather than social proof.

Joe explains how Stut targets the accounts receivable market by showing mid-market buyers the math: their AI can collect more effectively than human teams, improving working capital and saving money, without needing marquee logos first.

transcript

Joe Schmidt: Stut, this amazing business founded by two incredible entrepreneurs Tarc and Ben. And what they are going after is the accounts receivable market. For listeners who maybe have never thought about like AR, accounts receivable basically this is when someone owes you money in an enterprise context and you have to go collect the money from them. This is not glamorous. But however there has been a mechanism to do this historically. Right? There are collections teams and there's big pieces of software. There are big companies that do lots of AR that sell in this market. But it's been very manual. Right? These human teams have to interact with this piece of software and they have to go out there and collect. And so what Stut said was hey AI is actually quite good at having conversations with people. It's very good at looking at information internally and basically doing this process end to end. And so we could reimagine this historic way of doing collections and instead of having humans do it we can have humans plus AI do this and do it even more effectively. And what they basically went out and showed all of the early stage buyers was in doing this we have the math to prove it. We'll be more effective than your current solution and your current human teams at collecting. And this will improve working capital by a tremendous amount. It'll save you money. It'll actually make you more money. So, they were able to go out to the mid-market and just basically show the math and be like, would you like to have this solution, yes or no?

explains mechanism · 1provides context · 2

Example

Harvey exemplifies the lighthouse playbook by winning critical law firm accounts first in the high-risk legal AI market, where social proof from marquee clients travels to reduce buyer hesitation.

Joe explains how Harvey succeeded in legal AI by winning key law firms first, creating social proof that reassured other risk-averse buyers that the AI solution was safe to adopt.

transcript

Joe Schmidt: Another example on the lighthouse side would be we had Harvey in our article, and they just did a fantastic job of winning the right law firms for this very new, very theoretically high-risk initiative where you're augmenting your human workforce with AI capabilities and really automating what junior lawyers would be doing on a day-to-day basis. And so when they won the first few critical lighthouse accounts inside of their market, that proof traveled like big time. And then the buyers that had this tremendous amount of exposure realized, hey, it's actually safe for me to buy this solution.

provides context · 2

Example

Meraki used a land grab strategy by giving away free access points through webinars to let mid-market customers experience the product's simplicity firsthand.

Andy describes how Meraki, facing entrenched competition from Cisco and HP, used free access points to let mid-market customers experience their simpler cloud-managed networking equipment.

transcript

Andy: And so what we do is we'd run these webinars and we'd say, 'Hey, you attend the webinar, we'll send you a free access point. You plug it in, try it out.' And the idea was if they if they try it, the lightbulb goes off and they say, 'Wow, this is just so much easier than what I'm using. Why don't I why don't I use that?' And it was it was very very successful for a long period of time and even as the company matured, you know, we were very very liberal in our trial and eval because you just fundamentally want customers to experience the technology. Um and realize that it's better than the alternative.

provides context · 1

Mechanism

AI trials and proofs of concept risk turning into endless science projects without clear end dates and success criteria defined upfront.

Andy explains the discipline needed in AI sales trials—they must have clear end dates and pre-defined success criteria to avoid becoming open-ended science projects that never convert.

transcript

Andy: And so what you what you have to stay away from, and I think one of the real dangers today is these things turn into like science projects, right? Like I'm going to deploy this, well, can it do this? Can it do this? Can you show me this? Can you show me this? And of course things are advancing every day, so the answer is probably yes, I could, but then you run the risk of these trials or proof of concepts going on forever. Yeah. And so it just takes a lot of discipline, I think, in today's day and age to to really box that in and say, 'Listen, here's what our solution does, and we're going to, you know, define it this way, and we're both going to agree that if it, you know, has done this after 45 days, it's success and you're going to move forward with the purchase.'

Mechanism◆

Companies often start with one strategy (land grab or lighthouse) and transition to the other as they mature and verticalize.

Andy explains that successful companies typically deploy both strategies over time—starting with whichever fits their early stage, then transitioning as they scale and enter new verticals.

transcript

Andy: I I I I'll also say that you know every every small company wants to become a big company. I don't know too many very large successful companies that at some point in time haven't deployed both strategies. You might start off with land grab but then you mature and you have a lighthouse strategy or you start with lighthouse and then you get big enough that you can go broad you know into land grab. So I think for founders when I get this question early on do what makes the most sense for your business right now. What does that mean? Go out and talk to customers. Find out where the early earliest and easiest sales are and pursue that strategy. It doesn't mean you're completely you know punting on the other one. It just means come back to it. And in in both of the last companies that I worked for both Meraki and Samsara we started with land grab but as soon as we matured and started getting up in enterprise then what do you do? Well you verticalize and all of the sudden like great who are the top five you know transportation companies who are the top five warehousing companies who are the top five you know public sector and then you want to go take those down and so you can morph into a land grab strategy.

extends · 2gives example · 1provides context · 1

Claim◆

Founders should allocate roughly 1% of their time to strategy selection and 99% to execution, discovering which playbook works through direct customer engagement rather than analysis.

Andy advises founders to stop agonizing over which playbook to follow and instead get out to talk to customers, figure out who is willing to buy today, and chase that path—strategy refinement can come later.

transcript

Andy: I think the biggest mistake that I see founders make at an early stage, honestly, is just spending too much time trying to figure it out, right? It's like too much time on the strategy. And strategy's important, but you should spend like 1% of your time on the strategy. Pick it and then spend 99% of your time trying to execute. So, rather than sit back and say, well, should we do lighthouse or should we do land grab? Get out, talk to your customers. Figure out which ones are willing to buy your product, the features and the services that it delivers today, and then chase that path, right? There's no bonus points for hard-earned revenue. You don't get extra multipliers on your revenue if you get the big logo or something. Go after the customers you can, and then constantly be improving your product and then you can always reassess your strategy.

gives example · 1supports · 1

Claim

The biggest mistake founders make is spending too much time analyzing which strategy to choose instead of executing and learning from customers.

Both speakers agree that founders should spend minimal time on strategy analysis and maximum time executing—talking to customers, finding early buyers, and adjusting course based on real market feedback.

transcript

Andy: I think I think the biggest mistake that I see founders make in an early stage, honestly, is just spending too much time trying to figure it out, right? It's like too much time on the strategy. And strategy's important, but you should spend like 1% of your time on the strategy. Pick it and then spend 99% of your time trying to execute. So, you know, rather than sit back and say, 'Well, you know, should we do lighthouse or should we do land grab?' Get out, talk to your customers. Figure out which ones, you know, are are willing to, you know, buy your product, the features and the services that it delivers today, and then chase that path, right? There's no bonus points for hard-earned revenue. You know, you don't get extra, you know, multipliers on your revenue if you get the big logo or something. Like go after the customers you can, you know, and then, you know, constantly be improving your product and then, you know, you can always reassess your strategy.

gives example · 1supports · 1

Highlight slides

2x2 Enterprise AI Sales Framework✦ from: Enterprise AI sales strategies can be mapped on a 2x2 matrix of buyer exposure (risk of making a wrong purchase) versus proof mobility (whether social proof travels in the market).The 2x2 Sales Strategy Framework✦ from: The best sales playbook depends on whether proof travels in your market and how much buyer exposure/risk the sale carries.Market Quadrants✦ from: Enterprise AI sales strategies can be mapped on a 2x2 matrix of buyer exposure (risk of making a wrong purchase) versus proof mobility (whether social proof travels in the market).Defining the Axes✦ from: The best sales playbook depends on whether proof travels in your market and how much buyer exposure/risk the sale carries.Quadrant Outcomes✦ from: Enterprise AI sales strategies can be mapped on a 2x2 matrix of buyer exposure (risk of making a wrong purchase) versus proof mobility (whether social proof travels in the market).Quadrant Outcomes✦ from: The best sales playbook depends on whether proof travels in your market and how much buyer exposure/risk the sale carries.Samsara 胜在时机：ELD 法规创造行业推力✦ from: Samsara's success was partly due to timing—they entered the market during the ELD mandate that forced an entire industry to adopt electronic logging devices.法规影响：需求激增与市场竞争格局✦ from: Samsara's success was partly due to timing—they entered the market during the ELD mandate that forced an entire industry to adopt electronic logging devices.Timing Gave Samsara an Edge✦ from: Samsara's success was partly due to timing—they entered the market during the ELD mandate that forced an entire industry to adopt electronic logging devices.ELD Mandate Created Universal Market Tailwind✦ from: Regulatory mandates create massive market tailwinds that benefit all players, and Samsara's entry into the electronic logging device market was propelled by the 2016-2019 ELD mandate forcing the entire trucking industry to adopt the technology simultaneously.New Entrants Benefited Disproportionately✦ from: Regulatory mandates create massive market tailwinds that benefit all players, and Samsara's entry into the electronic logging device market was propelled by the 2016-2019 ELD mandate forcing the entire trucking industry to adopt the technology simultaneously.Strategy Evolution: Land Grab → Lighthouse✦ from: Companies often start with one strategy (land grab or lighthouse) and transition to the other as they mature and verticalize.Founder Guidance: Pick What Fits Now✦ from: Companies often start with one strategy (land grab or lighthouse) and transition to the other as they mature and verticalize.Verticalization: The Maturation Play✦ from: Companies often start with one strategy (land grab or lighthouse) and transition to the other as they mature and verticalize.The 1% vs 99% Rule for Early Founders✦ from: Founders should allocate roughly 1% of their time to strategy selection and 99% to execution, discovering which playbook works through direct customer engagement rather than analysis.Discover Your Playbook Through Action✦ from: Founders should allocate roughly 1% of their time to strategy selection and 99% to execution, discovering which playbook works through direct customer engagement rather than analysis.Revenue Has No Bonus Points✦ from: Founders should allocate roughly 1% of their time to strategy selection and 99% to execution, discovering which playbook works through direct customer engagement rather than analysis.

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