We have a tendency to blame technology when things go wrong. I’m the first to admit that after years of working in the technology industry I’ve become more and more annoyed with the technology I use.
As artificial intelligence (AI) capabilities have emerged in my smart phone keeping me on schedule, telling me how to get somewhere, or generally keeping me in line, I’ve gotten conditioned to technology just working. Except when it doesn’t. That’s when I want to throw that phone, espresso machine, laptop or home security pad into a blender. (Yes, it was a rough morning.)
AI pioneers have provided us with a glimpse of and conditioned us to ambient AI making it hard to break up with each other. They have also set a very high bar on our expectations of what AI should do for our businesses. But, let’s understand, Google was able to do this after two decades of research, curating collections and observing our every move. Apple too has tracked our app usage, music preferences, and daily lives through its iCloud. And Facebook sees our public and private conversations, what we share, and our personal opinions. Creepy, yes, but that is another conversation.
The point is that enterprises embarking on AI need to radically shift their approach to technology adoption and analytics. This is not a plug-and-play and bolt-on strategy. It takes work to go from POC to a capability that comes close to our expectations of AI based on our consumer experience.
AI begins its education through observation, not instructions. It needs vast amounts of data to establish domain expertise. Even if you purchase an AI solution that promises that domain expertise upfront so the data and training is reduced, it still needs YOUR DATA and YOUR CONTEXT to do the job. You aren’t programming a robot, you are building a relationship.
And so, it is not that AI is immature for enterprise use or value, necessarily. That was already proven by Google, Apple, and Facebook. The issue is that we, the enterprise, are the problem in the relationship for two reasons:
- We ignore the Observation Principle. AI pioneers put in listening posts in their solutions long before they turned on intelligent services. They took the time to understand intent, behavior, personalities, and expectations. Projects to recognize cats was the stepping stone for expanding use and interaction with image content. Companies like Crowdflower built intelligent data quality capabilities by watching humans augment and fix data on a gig worker platform. We need to orient AI implementation around what the AI system till do and allow it to watch and learn.
- We are really bad at data. Start any analytic project and the first question is what data sources are needed. The next question is what do I need to do to prepare the data. Where we get the data in some ways is less important than what data we get. If AI needs to observe, raw data without context is a really bad school book. The other issue is that most organizations are still in the throws of executing on data strategies. Forrester’s Data Management Playbook Benchmark study showed companies gave themselves low scores across the data management board. When they embark on AI, the deficiency in information competency becomes more evident.
These are not insurmountable challenges. They just require companies to:
- Don’t stop training. As humans, we learn through additional experience but also by continuously sourcing information from blogs, articles, books, and conversation. Continuously add and expose your AI system to new scenarios, new insight, and allow it to collaborate more widely with other bots and people. This keeps your AI system a relevant virtual worker.
(This post originally appeared on the Forrester Research blog, which can be viewed here).
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