Business interest in artificial intelligence (AI) has rocketed in recent years — spending could reach $15.7 trillion by 2030, according to PwC. But there remain lingering concerns that businesses are failing to realize the full value from their investments.
Ever since their emergence, AI, machine learning (ML), and data science have all been surrounded by hype. We’ve been promised technology that will solve our most complex challenges for us and automatically optimize everything from internal processes to customer experiences.
Advances are being made every day that promise to transform virtually every aspect of our lives. But, for most businesses, the reality of the AI and ML use cases they’re deploying looks very different. They’re delivering business value, but they’re typically far narrower, and more targeted in their scope than the headline examples that shape how the public perceives AI.
The AI and ML applications we’re seeing businesses deploy — especially those available in the mass market — are frequently simple automation solutions, turning routine tasks or simple processes into automated workflows.
That’s not necessarily a bad thing, as automation has a role to play in the operation and optimization of almost every business process. But there’s a very big difference between the automation of tasks and the augmentation of human intelligence that many organizations want to achieve.
If business leaders want to realize AI’s full potential and bring truly game-changing use cases that augment human skills to life, a fresh approach to such projects can help: one that emphasizes breaking out of the perpetual hype cycle, asking different questions of their data and capabilities, and challenging supposed ‘best practice’.
Here are three pieces of advice, learned from successful ML projects at industry-leading clients, that can help businesses of all sizes reframe how they think about machine learning, develop higher-value use cases, and see stronger results.