When Should You Not Invest in AI?
A study was conducted on the business adoption of Artificial Intelligence (AI) in the 1980s. Published in the MIS Quarterly, the study found that enterprises were rushing to invest in AI, and the projected market value was $4 billion.
However, the results were shocking.
The study found that over a five year period, just 33% of AI solutions delivered business value, while the rest were abandoned. Many popular applications of AI were proven to be pure hype and several companies became disillusioned with AI.
Today, the same story is repeating all over again.
Despite decades of progress in AI research and many recent breakthroughs, enterprises continue to struggle with adopting the . A survey by McKinsey found that only 8% of firms had practices that enabled them to adopt and scale AI.
AI has exceptional capabilities but it is not a fit for every situation. Here are five situations where it does not pay to invest in AI.
A majority of business problems can be solved by simple analysis. Even among organizations that use machine learning today, simple regression-based techniques are the most popular. Only a fraction of businesses really need AI. With AI capability getting democratized, it can be tempting to use it for every business problem. But, why use a cannon to swat a fly?
The $1 Million Netflix Prize was a global challenge to improve the accuracy of the Netflix movie recommendation engine by 10%. Netflix found a winner from over 50,000 global teams. They paid them the money, but never used their algorithm! Instead, they deployed a lower-ranked submission. Despite lower accuracy, this simpler solution had lower engineering costs and was more suitable for real-world use.
Analytics techniques need data to discover actionable insights. The more powerful a technique is, the higher the volume of data it needs. AI has a huge data appetite and it needs hundreds of thousands of data points for basic tasks such as detecting pictures. This data must be cleaned and prepared in a specific format to teach AI. Unfortunately, a high volume of quality, labeled data is not a luxury that every organization can afford.
For example, AI can predict your sales for the next 4 weeks. But only if you have many months of granular, historical data. If all you have is the last few weeks’ data, AI will not be able to help. So instead, use a simple forecasting technique like extrapolation. With just a few data points, it can give you credible insights to base your business decisions.
We are seeing spectacular advances in AI research every single day. Today, AI can generate pictures from your captions, or control a swarm of drones. However, there is a big difference between doing stuff in carefully controlled scenarios, and performing tasks in the real world. Many of AI’s impressive achievements are still in the experimental stages.
