Crunchbase recently introduced an AI startup evaluation tool to help investors gauge whether a startup can successfully raise funds. Yet, soon after the release, the question arose whether the tool would genuinely be helpful or if they missed the opportunity. The initial consensus is that their success metric does not match how most startup investors view success. Yet, this discussion should lead to a more profound question on why it is critical to determine what makes an AI project successful in business and whether a working AI tool is genuinely beneficial.
Why You Need The Right Metric?
For most businesses, finding the right metric for success is critical to setting the right course. It’s the difference between steering your ship through treacherous waters with a compass or hoping for the wind to get you to any destination. As such, the right metrics provide you with the destination, the goal, and the blueprint to align your decisions.
Yet, not all metrics are created equal. In my startup world, profitability is the overreaching long-term metric. The closer you get to a self-sustaining company, the more likely it is to generate a successful exit.
Nevertheless, the time horizon to reach profitability necessitates intermittent goals and targets to measure the success of individual products.
The right metric can thus drive a company to reach its long-term goals. Thus, it is more than a number on a dashboard or a sales statement. It is a call to action. When everyone in the company knows the target, they can prioritize decisions, align plans with this goal, and focus on activities that propel the organization toward success.
Moreover, the right metric is a universal language between the company and its investors. Investors must buy into the corporate vision and believe the team can execute it to make the most out of the network. By focusing on meaningful metrics, leaders speak the investor’s language and demonstrate that they understand the levers of business growth.
Thus, the right metric is about creating success just as much as it is about measuring and showcasing it.
What Is The Right Goal For An AI?
Thus, for AI and any company, asking what service you provide and what the customer wants to achieve is the first step towards building successful metrics. In some cases, the two don’t have to align fully. Your service can be a subservice of the overreaching goal.
Let us return to the Crunchbase example. Most early-stage investors invest in startups to make money and make an impact with their portfolios. Few invest in startups to invest in startups. As such, Crunchbase’s metric of a successful startup doesn’t align with its customer base’s idea of success.
Yet, their product could serve as an essential stepping stone for many investors in that it can sieve out startups that will never be successful in closing a round. Thus, they can save investors a significant amount of time. In my portfolio, only 10% of the companies that pitched me ever finalized their round. While there is an inevitable overlap between the ones I decide to pursue and those that close their rounds, there are also enough that never reach their goal. It would be a great time saver for investments outside my focus area to predetermine whether a company will close its round.
Similarly, most companies should analyze whether their current offering matches customers’ needs, expectations, and metrics. Otherwise, it would be prudent to bring these three critical components into harmony.
AI The Cause Of Bad Metrics
Ultimately, the drive for many companies to show their investors that they are “AI Companies” has led to multiple irrelevant and wrongly designed products. From proclaimers that “We Are Now Using AI!” to faulty, hidden agendas, the rise of AI has pushed companies into a frenzy to release products that were not ready for the market. Unless we correct course and create products that match our customers’ expectations, we will face significant disappointment and disillusionment with our offerings.