Published on April 14th, 2020 | by Emergent Enterprise0
A Radical Solution to Scale AI Technology
When it comes to emergent technology initiatives, companies tend to overplan. This is reflected in this post by Athina Kanioura and Fernando Lucini at Harvard Business Review regarding proof of concept (POC) efforts. As companies become so focused on the POC the final product never sees the light of day. As HBR shows, launching a small scale pilot into the real world can be much more effective than a highly-controlled POC. The reason this works better is that actual users get to use the product and the best lessons are learned there. When businesses stop thinking they know best, good things happen
Illustration: Israel G. Vargas
Most C-suite executives know they need to integrate AI capabilities to stay competitive, but too many of them fail to move beyond the proof of concept stage. They get stuck focusing on the wrong details or building a model to prove a point rather than solve a problem. That’s concerning because, according to our research, three out of four executives believe that if they don’t scale AI in the next five years, they risk going out of business entirely. To fix this, we offer a radical solution: Kill the proof of concept. Go right to scale.
We came to this solution after surveying 1,500 C-suite executives across 16 industries in 12 countries. We discovered that while 84% know they need to scale AI across their businesses to achieve their strategic growth objectives, only 16% of them have actually moved beyond experimenting with AI. The companies in our research that were successfully implementing full-scale AI had all done one thing: they had abandoned proof of concepts.
The companies that did this attempted scaling twice as often, succeeding at their scaling initiatives twice as often, and — because they were structured correctly and could incorporate what they learned along the way — ended up not only completing scaling projects more quickly, but spending less money on pilots and fully scaled deployments. The result? They achieved nearly three times the return on their AI investments when compared to their lower-performing counterparts. When you consider that the average company in our study spent $215 million on AI in the past three years, the 54% difference represents a $115-million gap in missed returns from AI. And it’s not just about the money. Successful scalers report significant benefits in customer service and satisfaction to workforce productivity to how efficiently the companies utilize their assets.
Why Proof of Concept Doesn’t Work
Here’s how proof of concept can be a promise of failure, using a fairly common example. Let’s say an organization sets aside six months to build a customer experience optimization platform as a proof of concept to improve customer service. They get it up and running, confirm (as many have before) that it works, and then move it to production. Here’s the mistake: they proved that a concept could technically work without spending one hour thinking about what was needed to put it into production, the model risks, data bias, data privacy, or ethical considerations. The result? They’ve just put their organization into a technical debt because they never built it for scale from the beginning.
Consider how one company we worked with did this differently. Nordea, the largest banking group in the Nordics, needed a chatbot to help with customer service so that their call center staff would have more time to work on complex customer issues. Nordea had a structure for testing and development already in place — including the right data foundation, talent, organizational design and ethical frameworks — and so they skipped the proof of concept and went right to scale. They got their data right, built a minimum viable product, gave it an avatar, and waited to see how the customers interacted with it.