Rethinking the KPIs of Corporate Success in GenAI

There are common ways to track a product’s success. But when it comes to GenAI, they don’t measure up.

June 9, 2023

Rethinking the KPIs of Corporate Success in GenAI

Generative AI has the potential to totally disrupt industries. From the capacity to optimize business processes, to overall increasing employee efficiency, it’s no wonder that corporations are in a mad rush to create and launch their own AI-based products. 

However, the technology is still fresh, and it may not be prudent for companies to jump the gun. The algorithms are constantly changing, and new LLMs are being released on a frequent basis. Companies that dive head first into the market and invest huge sums into building full blown GenAI products will not necessarily be successful in the usual sense. They may launch a product that is no longer relevant due to a different company’s tech leapfrogging theirs. It could be that more efficient code is released that would have made their product much less costly. The possibilities for a snafu on the road to traditional success are endless, and that’s why it’s key that corporations rethink what that success actually looks like. Such an innovative technology requires a complete overhaul on expectations and KPIs. 

Adopt an experimentation mindset

While we usually consider product traction or strong revenue streams as hallmarks of a successful product, GenAI requires you to zoom further out when trying to understand if you’ve found success. With such a volatile product, and technology that is ever-evolving, corporations should shift their perspective entirely. Rather than focus on creating a full blown product for launch, teams should view these products as experiments. By adopting an experimentation mindset, organizations set themselves up to better understand the value that comes with attempting to develop in this industry. Each step in the experimentation process is one step closer to your organization becoming a leader in the GenAI space. 

This perspective opens up new skills streams for your employees in the AI space. In an experiment, many teams will be included in the process, creating learning opportunities and chances to gain/refine the basic skills necessary for building successful products in the future. Similarly, lowering the stakes to an experiment gives teams the chance to develop multiple products simultaneously. 

Focus on internal products first

Ilya Venger, Principal Product Manager of Industry AI at Microsoft shared, “Though it may seem counterintuitive, products that spend more time between internal teams before becoming customer facing are the ultimate learning experience for your corporation. In fact, smaller low investment prototypes should be taken advantage of for this exact reason.”

When corporations build a bunch of smaller products internally, their teams are exposed to invaluable experience. Business stakeholders can gain a better understanding of GenAI as they continually brush with these technologies, seeing first hand their capabilities and limitations. The technical side can hone their parsing skills, brush up on command prompts, and ensure that they’re writing the highest levels of code for even the most basic functionalities. This process also promotes technical agility, while allowing developers to take advantage of each GenAI iteration that hits the market. In this case, corporations aren’t building for the market – they’re building to promote a competitive edge for future opportunities. 

Rethinking KPIs 

It can be difficult to parse out measurable performance indicators when dealing with such a new perspective.  However, there are a couple of takes a corporation can use to view the journey as one that’s worthwhile. Practically, corporations can register how many people contributed to building the product, which basic inputs were relevant vs. can be disregarded, how many experiments were run and how they played out, etc. By taking the perspective of “internal test run,” companies both assign and derive value from the development process. Another beneficial KPI – the skill sets that your internal teams gained while developing. This is arguable the most important indicator, as it sets your teams up to better iterate in the future. 

The bottom line 

GenAI is growing exponentially, but it is key that corporations don’t rush to capitalize. We want to prevent the bubble – both metaphorical and economic – from bursting. By taking the time to build something that is stronger and more foundational, corporations can maximally leverage the capital that has been poured into the industry to create a high-quality and competitive product. And though it may seem funny, the process of such a product originates as internal learning opportunities, inter-team communication, and even a few product failures along the way. 

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