No one could have predicted the challenges brought on by the coronavirus pandemic, but there have been best practices in place for successfully planning ahead and innovating frugally for years. Karim Lakhani and Navi Radjou both provide guidance for how companies can compete in their respective books — whether that’s leveraging emerging technologies like artificial intelligence or figuring out ways to get a larger return on smaller investments.
During a keynote session at InnoLead’s Impact event, the authors shared how a company can position itself to succeed — even during uncertain times.
Do Better With Less
This year has been an exercise in adapting to changing customer needs. Not only do innovation teams need to prove their worth to their company, they also need to prove their ability to understand their customers. This is where Radjou’s first frugal best practice comes in: “Meet customers where they are.”
Instead of trying to convince customers to buy expensive products above their means, now is the time for companies to simplify their offerings and appeal to the frugal consumer. Radjou brings up the French automobile manufacturer, Renault, as an example. Back in 2004, the company launched a simple, no-frills model called the Logan. According to Radjou, the company expected the affordable product to flop. Instead, it became a best seller and led to the creation of a series of similarly affordable cars. Their R&D spending was also exponentially lower on these projects. This year is an opportunity for companies to embrace Renault’s strategy, says Radjou.
Radjou’s six principles of frugal innovation:
- Engage and iterate
- Flex all your resources
- Co-create regenerative solutions
- Shape customer behavior
- Co-create value with prosumers, or prospective customers who help inform the development process
- Hyper-collaborate
Solve Challenges With Machine Learning
When people picture what artificial intelligence looks like, they tend to think of what Karim Lakhani called “strong AI” — the type of futuristic machines that appear in science fiction movies. But the technology that many companies are already leveraging today is called “weak AI” — and this type of machine learning is much more cost-effective, he explains.
An example of weak AI in action can be seen at JD.com, the largest retailer in China. When tasked by the Chinese government to create financial products for farmers, JD.com leveraged a number of algorithms and existing facial recognition technology to create a program that would count livestock. This removed a time-consuming, expensive, and potentially dangerous manual process and replaced it with a more efficient system — and with it discovered an entire new market for the company.
“Now remember, these guys aren’t going to go to market by selling pig facial recognition software. They’re not going to get rich based on that,” Lakhani says. “But they’ll use this AI to open up a brand new market for JD [and]…serve both their customers and do it in a very different way.”
Lakhani emphasizes that a lot of the technology necessary for creating smarter solutions already exists; it just needs to be reimagined.
Lakhani also discusses a paper he co-authored on how crowdsourced innovation was used to develop an AI solution to improve radiation therapy. The solution: A series of algorithms that are just as effective at identifying lung cancer tumors as the average oncologist, thus allowing more scans to be completed in a shorter amount of time, and for less money. And the source of the solution? Pre-existing code from self-driving automobile technology that was applied to lung cancer.
While AI is not without its problems, its widespread use is inevitable, according to Lakhani.
“I can scale the benefits of AI to lots of companies and lots of people. I can also scale the harm of AI, through bias, through cybersecurity issues, privacy issues, transparency issues, inclusiveness, issues, and so forth,” he says. “This for us is the C-suite level discussion. It’s a discussion for executives or managers to get really serious about the responsibilities that are now in front of us when we start thinking about AI.”
