#6: "Disagree and fume silently" đŻď¸ On scaling enterprise AI, decision-making, and more!
Ashwini Asokan on tackling the unknowns of enterprise AI, Suhail Doshi on âdisagree and commitâ and other decision-making conundrums, and Tyson Quick on persevering towards PMF.
Hi there,
Welcome to the sixth edition of The Baton. A fortnightly newsletter that brings you three, select pieces of advice drawn from the thoughtful founder-to-founder exchanges and interviews taking place on Relay and the interwebz. So, stay tuned!
In this edition, youâll find instructive and inspiring pickings from the brains of Vue.aiâs co-founder Ashwini Asokan, Mixpanelâs co-founder Suhail Doshi, and Instapageâs founder Tyson Quick.
But before we dive into it, a brief announcement: Geckoboardâs founder and CEO, Paul Joyce will be joining us for a founders-only AMA on Relay next Thursday (October 8th). Over the past decade or so of leading Geckoboard, Paul has acquired thoughtful and wide-ranging perspectives on most central SaaS challenges. You can ask him all about: âChoosing and implementing KPIs, metric tracking + all of the confusion, uncertainty and hassle associated with thisâŚthe dangers of startup bs and how we can all do something about it, building a business that I wanted to work in, the changing face of SaaS,â and more. So, if you arenât already on Relay, reply to this email for an invite to the session! âď¸
All yours now!
#1. Ashwini Asokan, co-founder and CEO of Vue.ai, offers a brilliantly thorough dissection of the challenges involved in building enterprise AI and the inventive ways with which theyâve countered them; a must read for anyone attempting to crack the unknowns of a new category. (Source: Relay)
Everything about AI has largely been unknown. Thereâs been so much hype and noise about AI. Very low signal:noise ratio honestly. And while there have been a lot of new businesses coming up in the space, we rarely actually hear about the ones solving real problems in verticals. Here are some of the challenges Iâve seen:
Lack of an actual problem to solve. AI is often a hammer looking for a nail. A lot of companies Iâve seen in this space, fail because they fail to use AI to solve a real issue.
The category is non-existent and the job of the company is to create the need and change human behaviour. This is a gargantuan task. And one that Iâm deeply familiar with because weâve been going at this for years at Vue.ai. Iâll get back to this in a moment. There is a huge component here that is entirely about educating the audience. It took us years of educating, engaging with the audience before we could crack them on scale. And we were acutely aware that the industry we were dealing with was largely kitted out with legacy tech.
Helping humans who are doing these jobs get over the fear that their job will not exist in a few years. This is a very real issue. People in companies using these systems are very suspicious of AI systems and very unforgiving, as well. Often with good reason but itâs a challenge, regardless.
Lack of understanding on RoI & emotional impact of AI on users - Our users often said things like âya the machine failed this once when I really needed it, so we might as well do this manuallyâ. This meant they overlooked the fact that productivity and throughput tripled when compared to non-AI based systems or manual labor but focused on the one time that they looked bad in front of their senior management.
Here are some of the things weâve done over the years to counter some of these:
PRODUCT MARKETING FTW: We invested heavily in product marketing from day 0. This was perhaps one of the best things we did. Whether it was demos, FAQs, use cases, new ideas - our goal was to constantly demonstrate all the ways in which our systems could help people reimagine their work, reimagine new workflows with a 10x / order of magnitude growth in either revenue or savings on the expenses/cost side.
CUSTOMER MARKETING FTW: We invested heavily in customer marketing from the time we were at $1M ARR. A big portion of category creation is showing that the category is being adopted by the industry, saying stories about that behaviour, RoI and more. People constantly want to know what everyone else is doing. Youâd be surprised but the #1 question we get on sales calls is: âWhat are other retailers in this space doingâ. A lot of this process had to do with normalizing, familiarizing the category and making it less âotherâ, alien to their daily work.
TEACHING THE AUDIENCE ABOUT RoI of AI: This one was huge because it helped us show people we were not there to put them out of their jobs but also help them produce 10x better work. The language took us a while to iterate and learn but eventually we realized it was not about how AI was the star of the show, it was about how the AI made those users the star of the show.
HELPING THE TEAM REIMAGINE THEMSELVES AS EDUCATORS NOT COOL AI PEOPLE: The early years of a startupâs journey are exciting, filled with adrenaline rush and a lot of ups and downs and emotional outbursts. I can tell you when we crossed our 3 year mark, for me it was all about helping the team undo, unlearn, rethink, remake and rebuild themselves not as people building bleeding edge tech but about helping them think of themselves as people who have a responsibility to help the world around them become AI-Natives. I can tell you this is one of the best things thatâs happened to the org as a whole, maybe we should have done it sooner. But the responsibility that comes with enabling people with AI is a very real one and that can help counter the fear and the high threshold for change.
#2. Suhail Doshi, co-founder of Mixpanel, reviews the fruitless, back-and-forths that most decisions can inevitably turn into as a company grows in size and complexity, and lays out penetrating, practical considerations for founders hoping to do better. (Source: Medium)
One of the things that gets harder as the company grows is the speed at which the company can make decisions. At some point, itâs not possible for you & the person responsible to come to a decision. You must grapple with the spectrum of reaching consensus vs deciding yourself.
The conflict is twofold while making decisions as you scale: (1) if *you* make it, you risk lack of support from the team & possibly move slower because people are reticent or (2) you require support from everyone but risk moving slowly if not everyone agrees w/ the direction.
To further exacerbate this problem: (1) you need to decide what % accuracy of data you need to make the decision, (2) is this a decision that needs to be decided quickly (urgent but room for error) vs thoughtfully (avoid irrevocable consequences)?, (3) who do you include?
To deal with this contention, some companies opt for & overuse the Amazon model: âDisagree & commit.â The truth is, if you use it too often, it becomes âDisagree & fume silentlyâ beginning the descent towards a negative culture. Another solution is to simply empower your team & impose a set of reviews to body check the decision. The risk here is that you put the team in review hell & require them to wait on you & your schedule creating a slower feedback loop. Often this disempowers the team.
The last common solution Iâll write about is to simply empower the team. This risks misaligned priorities & goals if the team doesnât have the breadth about whatâs going on in the business. Itâs ideal if they knew all the details required to make the decision but not practical.
One approach I took to dealing with a part of the problem was the idea of reaching âreasonable consensus.â Meaning, you only need to convince 80% of the people in the room that the solution is close to correct. Framing decisions as an experiment vs an absolute can help the dissenters overcome their fear or concerns.
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If youâre CEO & the team disagrees with you, just recognize they are willing to go toe-to-toe with you & bet their reputation (and possibly career) because they passionately believe in the solution. Thatâs worth taking into account. Have an open mind to seek their truth.
Donât use lack of data to further delay a decision that you happen to disagree with. Deal with the conflict head on. Itâs okay to admit youâre not convinced & need time to think. The team may want your support!
Itâs important to build a company that can bear making the wrong decisions. The fear is that you will lose w/o enough right decisions. Thatâs true. Too few & you die. However, youâll also die if you build a company that must always make the right ones. Find a balance.
#3. Tyson Quick, founder and CEO of Instapage, recounts their five-year-long journey to product-market fit; the scrappy beginnings, the unforeseen trouble with serving obvious personas, and the central insight that eventually turned things around. (Source: GrowthHackers)
In 2012 when I started the business, we had minimal resources (~$75,000) left, and we needed to be incredibly scrappy. We needed to get to revenue as soon as possible. Doing so required us to go after the lowest hanging fruit. This meant low value, but easy to acquire customers. These customers are also the most forgiving of MVP products. Thus we priced our product at only $5 a page, which is a far cry from where we're at today.
This initial strategy allowed us to start generating cash and helped us get to a point where we could at least pay our own bills. Once we got to his point, we started planning the next phase. We increased prices and eliminated the per page model as it wasn't going to be sustainable in the long term. At this point, the product was still rough around the edges, and we still hadn't figured out our ideal customer persona and absolute best use case. Regardless the product had gotten a bit better, and so we introduced our first monthly pricing tiers. I believe we started at $17/month with some other plans that went up to $97/month.
At this point we started making a bit more money and were able to pass the $1,000,000 mark, however, we still didn't have solid product-market fit and still wouldn't for a few years. The reason; when I first went to market with Instapage v.1, I assumed that every marketer who had a marketing campaign would need landing pages and thus the market potential had to be enormous.
The problem with this was to create a highly valuable solution in this space, we had to build many products that would work together to solve the end-to-end workflow of deploying and optimizing landing pagesâŚWithout any one of these products, the solution could not provide a complete solution to the problem.
Fast forward a few years to 2016, and we started to realize that there were two types of customers: Builders - Are looking for a fast way to build a few pages for a temporary campaign or test. Converters - Are looking for a way to increase advertising conversion.
Our mistake was that we had marketed the value of building more than the value of converting more of your advertising traffic. For the builders, the value is becoming a commodity; however, for the converters, the value is quantified by how much ROI they can get out of their advertising investment. We began to realize that we needed to focus entirely on the converters to establish long-term product-market fit and thus launched our enterprise offering and built a product and marketing roadmap that would transition to this entirely over the next two years.
So here we are in 2019, and we have done precisely that. The majority of our new revenue growth is from Enterprise and those enterprise customers have a 112% annual revenue retention (they expand). They are using our product based on its true value. It took us over five years to establish product-market fit, but we persevered and now have a fast growing enterprise software company which is defining a new category we call "Post-Click".
Hope you enjoyed reading this edition of The Baton. We are trying to bring you the most nuanced insights from SaaS founders across geographies, so if you think we can do better or have any suggestions, let us know. We are all ears! :)
Until next time,
Astha and Akash