Novel vs. Good Enough: Focus on Results, Not the Technology
There is an undeniable allure to adopting cutting-edge technology. Here at Georgian, we are big fans of new technologies because they offer very real competitive advantages when applied appropriately. That certainly doesn’t mean you should adopt new technologies blindly. So what is the best way to decide when to adopt a new technology vs. using a ‘tried and true’ approach?
In our recent podcast, I spoke with Adam Drake, White House Presidential Innovation Fellow and IEEE senior member. Adam also works as a growth stage advisor to a range of start-ups.
In their discussion they compare the pursuit of novel technological solutions to more established, but potentially less glamorous solutions and explain why focusing on business value should be the priority.
Do Companies Need the Technically Interesting, or Good Enough and Practical?
Adam shares that in his experience many startups can be distracted by cutting-edge research emerging from academic papers. He suggests that in general, businesses should instead focus on finding the fastest, most pragmatic solution to a problem. While we’ve worked on plenty of cutting edge research problems ourselves, when it comes to getting the basics right first we couldn’t agree with Adam more. In fact, Principle 6 of our 10 Principles of Applied Artificial Intelligence is “Start with Proven Modeling Techniques”.
A Case in Point: Kaggle Competitions
Adam points to Kaggle competitions to show that the most interesting technology solution is not necessarily what is most useful or appropriate for a business problem. Kaggle is a platform where companies seek out solutions to data science problems by eliciting responses from teams who compete against each other in return for a reward. Usually, the intention is that the top Kaggle solution will be implemented by the sponsoring business. However, according to Adam, often it isn’t the winning solution that ends up being implemented. His point is that just because a solution is novel or wins a data science competition doesn’t mean that it will generate business value or even be practical for a business to implement.
Business Value Must Be Front and Center
Adam suggests that businesses should look for effective and practical solutions using existing approaches where possible. This approach finds viable solutions that can be implemented now.
Adam reminds us that even state of the art tech can quickly fade into the background. Voice assistants including Siri and Alexa use AI, but they are now part of our everyday lives. The risk of pursuing the novel is that it can be commoditized relatively quickly. Instead, he recommends incorporating existing tech into solutions that can also achieve large gains from automation.
“There’s a big difference between no automation and mediocre automation, and comparatively zero difference between mediocre automation and better-than-mediocre automation,” Adam writes in a blog post on the topic.
The Tech vs. Value Debate Has a Long History
Of course, technology for technology’s sake is nothing new. In the 1970s George Heilmeier proposed the Heilmeier Catechism – a simple set of questions that helps guide decision making around new research and product development projects to help leaders avoid this. The questions are:
- What are you trying to do? Articulate your objectives using absolutely no jargon.
- How is it done today, and what are the limits of current practice?
- What is new in your approach and why do you think it will be successful?
- Who cares? If you are successful, what difference will it make?
- What are the risks?
- How much will it cost?
- How long will it take?
- What are the midterm and final “exams” to check for success?
Considering AI vs IA
Artificial intelligence can, without doubt, bring the leaps in automation that Adam describes, but it can be hard to cut through the hype. Adam suggests that CEOs should forget about general artificial intelligence and instead focus on intelligence amplification (IA). This means that rather than opting for a tech-driven solution, businesses should examine how technology can augment already successful ways of working. In other words, boost the intelligence of their customers’ people and processes.
Leadership teams can stay focused by always asking themselves what business problem they are trying to solve. This will prevent them from falling into the trap of finding technical solutions and looking for a problem. Then they should ask what the most naive solution could be to that business problem. If they haven’t tried that – that should be where they start.
Listen to the full podcast episode to find out more, including:
- How you can identify opportunities to use AI/ML in your business
- Why you should start with proven modeling techniques
- Getting valuable products out with customers, rather than stuck in R&D
- How to develop an AI BS detector
Source: “The Heilmeier Catechism” on DARPA