Entering the Era of Actual AI deployments
Are investors good for anything other than pattern recognition?
After what has felt like an eternity (two years since ChatGPT mania), we are finally seeing enterprises begin to adopt Gen AI solutions in meaningful ways to solve real business problems.
First came content marketing, then came customer support, then came code generation… but in the last quarter or so, we’ve finally begun to see the dam break within Fortune 500 companies as buyers are now willing to make big bets on AI startups catering to the enterprise.
We’ve begun to notice a few patterns in what helps to get AI startups across the chasm of test and trial pilots, and actually get deployed (and used!) within Enterprises. While many startups today are still getting lost in internal politics and endless sales cycles, there is increasingly looking like there’s a light at the end of the tunnel.
Who’s breaking through
Before we get into tactics, there are a few particular profiles / flavors of AI startups that we’re seeing have the most success in breaking into the enterprise. The below list is not exhaustive, but some of the patterns we’ve seen include:
Functional offerings that have a very clear use case / wedge
E.g., Enterprise Search, procurement, recruiting, outbound sales
The key here is to have a killer app: the customer has to know what to use you for, and why you’re better than the current workflow
Offerings that provide clear and simple to track ROI, particularly when mapped against headcount
E.g., for companies that are struggling to hire or retain top talent, can they offload some of the work to AI instead?
This is particularly true in departments that have high turnover (e.g., customer support, or even software development)
Internal facing offerings continue to see much stronger adoption than external facing offerings
Enterprises are much more willing to adopt solutions that are rough around the edges for internal tasks, but less willing to expose those products to their end customers (where polish is more important)
Offerings that solve hair-on-fire problems for enterprises
Buyers are much more likely to take a risk on something new and rough around the edges when they’re struggling to solve the problem themselves. If something is going okay internally, they’re not going to take a risk on a startup
There are exceptions to every rule, but in general, we’ve seen a strong correlation with the above characteristics, and companies that are beginning to take off.
How they’re breaking through
More interesting than just the profiles of companies, are the tactics the successful ones are taking. Below are a few patterns that we’re seeing (again, not an exhaustive list):
Case studies, case studies, and case studies. There is no more effective tool in an Enterprise GTM motion today than by convincing your customers that their competitors are already adopting AI tools, and they’re getting left behind
FOMO has proven to be one of the most valuable tools to get skeptical early adopters to take the leap, as it is one of the few weapons that consistently creates urgency in an enterprise sales process
The more detail (and emphatic quotes!) you can share, the better
Top-down sales. In the world of Enterprise AI, the PLG sales motion appears to be mostly dead (for now). To get organizations ready to make real changes to the status quo and get real buy-in, the best AI companies are going top down
C-suite executives know that they need to push their teams to invest in AI today, and we’ve consistently seen them be willing to be major internal champions and design partners to get solutions deployed
Many enterprises today are actually blocking / banning the use of small solutions, so it’s not easy to enter via the back door
Consultative selling. Longer, consultative sales motions are resonating with buyers. Many executives know that they should be adopting AI, but they don’t know exactly how
Startups that are investing in deeply educating their AE’s, hiring sales engineers, and even leveraging forward deployed engineers are seeing huge payoffs within Fortune 500 customers
Sometimes “custom integrations” are seen as a dirty team in startupland, but if it helps you land massive (and sticky!) contracts, they’re usually worth it
The promised land of land-and-expand. While converting from pilots into real contracts has been slow, same customer expansion has been quick to follow for many AI startups as they earn the trust of their customers (we’re consistently seeing 120%+ NDR, but some companies are currently well above 200%)
Customer success resources have been incredibly valuable when expanding across the enterprise, even moreso in the AI era
Once a providers has earned the mark of approval / trust of an organization, we’re seeing many adjacent departments clamor to get access
Competing on more than just efficacy. As leaderboards and benchmarks move around so much on a week-to-week basis, the best AI startups are differentiating themselves in other, more durable ways (e.g., product velocity)
While model performance will always be an important metric to track, we’re seeing that it’s more important to be “in the ballpark” than to be the outright leader
Catering to non-technical audiences. OpenAI has really targeted engineers and developers as their core audience, but there are millions of non-technical users out there who want to adopt AI. By making their lives easier, you stand to have a stickier customer base
Specifically, prioritizing UI/UX, documentation, customer support, and a willingness to hold the end users hands has shown major payoffs in 2024
These are just a few points that are top of mind for us as we’ve seen AI companies break out in 2024, but we’d love to hear from others who are on the front lines.