Where do AI and ethics meet?
AI poses as many challenges as opportunities. Here are a few common characteristics we’ve seen among businesses that have realized success with data and AI.
Most senior leaders would agree that adapting in real time to customer and market needs is vital for achieving their visions and goals. And to develop this capability, they’d also likely agree they need data and artificial intelligence (AI).
The examples are all around us. The city of Chicago uses 12 variables, including high daily temperatures, to prioritize which of the city’s 7,000 “high-risk” restaurants it should send its 35 food inspectors to. The AI solution found violations a week earlier than they otherwise would have.
We helped an insurer create an AI system that provides underwriters with more precise estimates of risk, as well as the likelihood applicants will accept a specific price quote. The insurer can adjust how the AI environment makes its approval and pricing recommendations to ensure compliance with changing corporate priorities around risk vs. revenue.
We’ve also seen AI help predict customers’ emerging needs as markets change. If the economy goes into a recession, informed analysis could help organizations not only cut costs but also provide the products and services customers might need in a downturn.
And, of course, there are the businesses that have used data and AI to create new revenue streams and business models that drive lasting competitive advantage. For game changers like Uber, data is at the core of the company and constitutes value, not cost.
Facing Up to AI Realities
But there’s another hard fact that would draw consensus from many business leaders today: the unprecedented effort required to move an enterprise in an AI direction. The fact is, AI-enabled business change requires as much alteration to corporate culture, organizational structures and processes, and workforce roles and skills as it does new technology.
For example, too many organizations still treat data as an expense and a security risk. Technical or organizational siloes make it difficult to pool information in flexible data lakes. Many businesses also lack the skills to manage AI-enabled analytics amid rising privacy and ethical concerns. Others are unable to provide audit trails on how AI decisions were made or deliver AI-enabled analytics quickly enough.
AI Success Factors
Here are a few common characteristics we’ve seen among businesses that have realized success with data and AI:
Our final recommendation: Start now. AI can be difficult and complex, and it’s hard to catch up once you’ve fallen behind. Even modest successes will teach you a lot, and the real danger in digital is being a laggard.
Originally posted here.
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