At the risk of oversimplifying, I think of artificial intelligence as what becomes possible after you’ve fully embraced analytics and you’re starting to get bolder about how to use it. Your models are getting better, your predictions are more accurate, your results are stronger and over all, confidence grows in the decisions that are being made with the analytic models you’ve built.
And model building is rarely once-and-done. It’s iterative like any other scientific or mathematical process. It starts with a hypothesis, or a business need. Then you develop and apply a construct, you test and tweak, and then you iterate until you get it right. At some point, it makes sense to automate your process – possibly starting with that iterative part where the emphasis is on learning in order to get to the best outcomes.
Once you’ve built that learning into your process and you automate it, guess what? You have artificial intelligence. At its core, SAS defines AI as having both of those elements:
Artificial intelligence is the science of
training systems to emulate human tasks
through learning and automation.
Some people think that AI is being over hyped right now, and I can appreciate why it may seem that way since AI as we think of it today has been around since the 1950s after the rise of the programmable digital computer. The reason that AI seems to be coming of age after 60 years is partly explained by broad-based digitization of processes and things, greater interconnectedness met by improvements in computing, cheaper data storage, and advancements in analytics.
The bottom line is that the world is awash in data and we now have the ability to analyze it, apply learning to it and automate it like never before. What’s promising is that momentum is building for AI and models for success are starting to emerge from early adopters’ experiences. A recent global study conducted by Forbes Insights for SAS, Intel and Accenture Applied Intelligence pointed to an encouraging global trend that AI adoption is growing. It also uncovered several other developments worth noting, including these six findings:
- AI is working
Survey respondents report that they are encountering real success with AI in many areas of the organization. - Leaders in AI see a strong connection between AI and analytics
Those who have successfully deployed AI were more likely than others to view AI as being strongly connected to analytics. - Full deployment levels signal healthy momentum
Many leaders have moved beyond the experimental phase of AI deployment into more widespread applications. - AI oversight is not optional
These leaders are putting processes in place for reviewing the outputs of AI-enabled systems, overriding questionable results and more – pointing to new levels of AI maturity. - Ethical use of AI is top of mind
Most respondents report having ethics-focused processes already in place. - Employee concerns about AI shouldn’t be ignored
Consistent with many reports suggesting that the threat of job loss from AI may be greatly overestimated, these respondents do not believe large-scale job loss is likely. Some, however, are concerned about employee perceptions of the impact of AI.
It remains to be seen exactly how AI plays out across different industries, especially in the 5 areas where AI deployment was the lowest – where some of the greatest opportunities for AI may yet be realized. No matter what, it seems that the moment is right to take the plunge. And those six findings can point the way for organizations looking to chart their own course toward reaping the benefits of AI.
Download the full report: AI Momentum, Maturity and Models for Success.I promise it’s well-worth your time. Let me know what you think in the comments below or find me on Twitter and comment there.
