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Oracle Artificial Intelligence Blog

5 Artificial Intelligence Myths: The Realities Will Make You Think Twice

Emma Hitzke
Senior Product Marketing Director, Emerging Tech

When it comes to artificial intelligence (AI) in the enterprise, we’re pretty much at the peak of the hype cycle. It’s true that AI will be the most disruptive technology we’ll see for decades, but presently there are a lot of myths coming down from executive quarters.

 

As the hype is slowing down, management’s expectations are accelerating. So let’s separate myth from reality. Here’s our list of the top 5 enterprise AI myths and the reality behind them based on a presentation at Oracle Open World by Clive Swan, Senior VP of Applications Development for Oracle Adaptive Intelligent Apps:

Myth #1: Enterprise AI requires a build-it-yourself approach.

Reality: Enterprise AI requires both build and buy. It’s tempting to believe that your business needs are unique. But think about it: How many of those needs are in fact common across your industry? While you may start with a few DIY pilot programs, the real trick to achieving value from AI is deploying it across your entire enterprise. Smart companies realize that it’s more effective to focus their data teams’ efforts on creating value through differentiation while deploying ready-to-go AI solutions for more common business problems. Oracle’s prebuilt AI applications span operations (finance and supply chain), customer experience (sales and marketing) and human resources management.

Myth #2: AI will deliver magical results…immediately.

Reality: AI is not magic! The path to AI success is hard and takes time, and not just because of the technology. You also need a strategic framework and an iterative approach to avoid delivering a random set of disconnected AI solutions. The temptation is to go for moonshots to deliver on the magic, but such projects often fail to live up to expectations because you still don’t have the less glamorous basics down pat.

                                             

Myth #3: Enterprise AI doesn’t require people.

Reality: Enterprise AI and people need each other. No, the robots aren’t about to take over. AI is at its most valuable when it augments people’s capabilities. It can remove the grunt work, freeing people up for more strategic activities. That has the added benefit of making people more motivated, productive, and loyal. Enterprise AI also relies on people to feed it the right data and work with it the right way. Often, AI doesn’t provide conclusive answers to issues, but rather highly informed recommendations that an actual human can weigh to make the final decision. A recent study showed that 64% of people trust a robot more than their manager. But even where we want autonomous AI decision-making, there will always be circumstances when a recommendation falls below an acceptable confidence level, so the solution directs the decision to a person for a decision.

Myth #4: The more data the better.

Reality: Enterprise AI needs smart data. This myth is pervasive. Sure, data is the fuel for any AI solution. But what’s most important is that the data is high quality, relevant, up-to-date, and enriched. Unified enterprise data models are a good starting point because they ensure integrity of your data, but not the quality. And data lakes are essential, but they don’t—by themselves—address integrity or quality issues. In short, you need smart data, which is high-quality, comprehensive, and current.

What’s needed is data domain expertise combined with AI applications.  Oracle DataFox Cloud Service has a modern data engine that leverages AI to not only automate data collection but also enrich data. It uses a combination of natural language processing (NLP), machine learning, and human-in-the-loop techniques to scan the web and create trusted B2B company data and signals.

Myth #5: Enterprise AI needs only data and models to succeed.

Reality: Data and models are a start; you need scalable AI solutions.

To date, most enterprise AI solutions have been hand-crafted by data scientists, requiring extensive manual setup and ongoing manual maintenance. The problem? These approaches don’t scale. Oracle’s strategy is to industrialize the AI solutions, for example  by using machine learning to automate many of the technical maintenance tasks that those expensive data scientists would otherwise be doing manually. In this way, we can scale to hundreds of AI models being delivered in our enterprise suite across thousands of customers. 

 

At Oracle, we believe that enterprises must embrace AI to thrive—and perhaps even to survive. But doing so requires a clear-headed approach, not wishful thinking. We want to make AI as accessible and consumable as possible for any enterprise, so we give you the freedom to figure out your enterprise AI as you move forward with ready-to-go AI-powered cloud applications; a ready-to-build AI platform and our ready-to-work Autonomous Database that automates security patching, backups, and optimization so your team can focus on initiatives that create value.

To learn more about Oracle AI, visit oracle.com/artificial-intelligence.

 

 

 

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Comments ( 1 )
  • burak görsev Thursday, October 31, 2019
    excellent blog, simple but yet explanatory, clearing the doubts in AI. thank you
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