A lot of ink—and keyboard strokes—have been dedicated to how the pandemic has accelerated the move to the cloud and the application of AI in a vast array of business contexts. AI is transforming every industry you can think of as businesses figure out ways to support remote work, automate business processes, and deliver customer value without requiring in-person interaction. In one sense, AI tools have been democratized; resources and tool-kits are readily available to any company looking to innovate. But as much as it may seem obvious that organizations need to apply AI to their business, execution isn’t so easy. Unfortunately, many emerging startups don’t have the capital to hire expertise in-house or pay a third party to tackle their analytics wish list.
However, the angel investors, venture capital firms, private equity outfits, and other investment institutions that have placed bets on them often do, and these financiers have a great opportunity and incentive to share their experience applying AI to their own processes in order to actively steer their bets to a bigger payoff. Yes, the capital, management knowledge, and connections they provide already help their portfolio companies a great bit. However, institutional investors can do more by making their analytics foundation available, helping their portfolio companies select the right AI tools from a sea of publicly available offerings, and working with them to tailor AI to their unique business needs, accordingly.
It’s the applied AI knowledge, not the technology that makes a difference
In this new model, institutional investment firms not only make their proprietary analytics platforms and development teams available to all of their startups, many of which are still in the seven-figure revenue bracket, they add operational value by helping them use tools like predictive analytics, machine learning, and natural language processing to build workflows that create efficiencies, increase agility, and expand insight into their core businesses.
With only so much time, money, and manpower, emerging scale-ups have always had to be judicious in allocating resources toward strategic and operational initiatives. Now, the blue ribbon in each of these hotly contested spaces is likely to go to the resourced-constrained startups that discover the most effective application of applied AI to their back-end processes and client-facing services. This should incentivize their respective investment firms to be more hands-on in helping them apply AI to innovate in the respective vertical markets they serve. If they supply the platform, developers, and knowledge to bring big-ticket marketing, sales, and business process initiatives to life, the odds of a successful exit increase dramatically.
Today, a growing number of startups are using their investors’ resources to make this vision a reality. For example:
- Sales software company Conversica was able to use its investors’ shared applied-AI resources to develop a “champion tracker” program that automatically alerted executives when primary contacts at client organizations move to new companies. This application helped generate 16 high-quality leads with an average deal size of $55,000 and an expected closed won deal rate of 20 percent.
- Fulcrum Spatial Networks, in collaboration with its investor firm, developed a way to automatically identify and up-sell potential Big-Whale customers within its base of hundreds of clients—Global 1,000 companies that originally signed up on a self-serve basis to try its offerings on a small scale.
- A cloud communications platform company drastically reduced the time it took to identify potential acquisition targets by leveraging its investors’ analytics resources and expertise in the research stage.
SaaS pufferfish: helping bootstrapped startups act like bigger entities
Although each of these case studies shows AI’s power in boosting bootstrapped companies’ sales and operations, collectively they illustrate the impact active institutional investors can make by providing tools, staff, and know-how in tailoring AI to their portfolio companies’ fast-moving industries—an exercise several institutional investors have performed on their own operations. Few would disagree with the notion that the future victors in each market will be the ones that do more with less and make the most capital-efficient decisions in the early going. However, investors may have to do more than infuse a large pile of cash, sit on boards, and open up their Rolodexes. They will have to not only provide the analytics tools but help them derive business value from them in the form of streamlined workflows, efficient use of resources, or offerings that boost top-line growth.
Wholly owned and operated analytics platforms are about to become a new institution in technology investing.
About the Authors
Javier Rojas is a founder and managing partner of growth equity firm Savant Growth. He has spent 30 years helping founder-entrepreneurs effectively navigate uncharted waters to drive growth and build market-leading companies. He has spent his career coaching founders of bootstrapped businesses on how to achieve high value outcomes.
Eric Filipek is a founder and managing partner at Savant Growth. He is a primary architect of the firm’s vision of invsting in bootstrapped, founder-led, B2B SaaS and tech-enabled services businesses. Prior to founding Savant Growth, Eric served as a managing director at Kennet Partners for past 20 years. He earned his undergrad business degree at Wharton.
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