I’ve spent my entire career looking at data and the world through a scientific lens. Perhaps that’s why when observing the vaccine rollout, I discovered some interesting connections between the challenges governments and scientists have overcome and those most enterprise companies face.
When considering this comparison, I discovered four tech takeaways for enterprise companies, in terms of becoming data driven and innovating with big data and AI:
1. Train your employees to trust the science
When we think about the extensive campaigns and mass education that helped build confidence in the science behind the vaccine, the same needs to happen within the enterprise. There is a tendency for employees to dismiss AI on anecdotal evidence. They lean towards their own biases instead of allowing AI and predictive modeling to do its job.
We see this often among sales reps. They see that their AI tech was off once or twice and dismiss the science completely. Sadly, this can interfere or override the enterprise go-to-market strategy entirely. So, what enterprise companies should do is educate their workforce on how to work with the technology and the data, not against it.
Employees should learn how to look at AI in a scientific way, analyzing its risk-reward effectiveness according to benchmarks and overall pipeline metrics, seeing how AI is impacting their business as a whole instead of on a case-by-case basis.
2. AI can still be powerful in a world where you have a limited sample size
As in the process of developing the vaccine, enterprise companies are also limited to a small data set and quick timeline. They don’t have the luxury to run multiple tests or put in years of research and trials. Neither the Covid-19 virus nor enterprise customers have that kind of patience, unfortunately.
I work in the B2B world and that industry has a fraction of the amount of data compared to B2C. In the case where companies only have a few tens of clients, they would like to use AI to find more. Given that they are using the right methods – selecting the right benchmarks, accurately A/B testing and bringing in additional data from outside their organization — AI can be just as powerful when dealing with a small data set and short timeline.
3. Be aggressive with your timeline
No matter the industry, every company I’ve ever worked with has considered their goal as “high stakes”. Although not as high stakes as developing a vaccine, these businesses still have millions on the line and are solving high stake business problems. So my suggestion to them is to be aggressive.
During the pandemic, I worked closely with a company whose demand increased by 10X because of the nature of their business and our world’s current needs. Before the pandemic, they were using manual solutions, but with such “high stakes” and massive opportunity, they couldn’t afford not to bring on sophisticated AI technology.
On top of making the switch so quickly, they were aggressive in their deployment as well. Every minute was crucial to their sales team, so in most cases, they followed the 80/20 rule of thumb — if 80% of the problem is solved after running AI, then it’s time to go live!
Which brings me to my last point.
4. AI isn’t 100% guaranteed
AI will never be 100% right, which means you need to start with the lower risk and higher gains first, and keep monitoring the performance for potential risks. We saw this with vaccinating the front liners first. In business we do this by focusing on those who need AI to help them make decisions most — usually sales and marketing — and they become our front liners. From there, we apply AI to the remaining departments that will benefit from it.
Now, as a data technologist, it’s natural for me to “trust the science.” I take all of this information — be that around the vaccine or enterprise data — and churn it into statistics and predictions while living quite well with the uncertainty. What the vaccine rollout has done is create a moment in time for the scientific perspective to sink in throughout the world. And when the enterprise joins in on this new wave of scientific thinking, it will drastically change the way AI, big data and technology impact business.
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