The Robots-
We’ve all seen the sensational headlines: The robots are coming, and they’ll take our jobs! AI can do your job faster and more accurately than you can!
The Reality-
Human jobs won’t go away, but they will change. Roles will be more creative and specialized as AI is integrated into the workday. Better data leads to better math leads to better predictions, so people using AI can automate the tedious work and take action on the insights.
AI and the Near Term
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working.
But how do you separate hype from reality? How can your company apply AI to solve real business problems in 2017?
In September 2016, Luminary Labs convened 30 executives in healthcare, machine learning, and analytics for a grounded discussion on these questions with machine learning expert Hilary Mason, founder and CEO of Fast Forward Labs, and Sandy Allerheiligen, VP of data science and predictive and economic modeling at Merck.
Here’s a synopsis of what we discussed, and what AI learnings your business should keep in mind for 2017.
In the short/long term-
AI does the math faster, saving money by automating normally complex processes. It makes your life easier even now, behind the scenes. This is what it looks like today.
The Nest thermostat remembers what temperatures you like and adjusts automatically, like turning the temperature down when you’re away and turning it up when you’re on your way home. This saves users time, energy, and money.
We’ve all seen the sensational headlines: The robots are coming, and they’ll take our jobs! AI can do your job faster and more accurately than you can!
The Reality-
Human jobs won’t go away, but they will change. Roles will be more creative and specialized as AI is integrated into the workday. Better data leads to better math leads to better predictions, so people using AI can automate the tedious work and take action on the insights.
AI and the Near Term
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working.
But how do you separate hype from reality? How can your company apply AI to solve real business problems in 2017?
In September 2016, Luminary Labs convened 30 executives in healthcare, machine learning, and analytics for a grounded discussion on these questions with machine learning expert Hilary Mason, founder and CEO of Fast Forward Labs, and Sandy Allerheiligen, VP of data science and predictive and economic modeling at Merck.
Here’s a synopsis of what we discussed, and what AI learnings your business should keep in mind for 2017.
In the short/long term-
AI does the math faster, saving money by automating normally complex processes. It makes your life easier even now, behind the scenes. This is what it looks like today.
The Nest thermostat remembers what temperatures you like and adjusts automatically, like turning the temperature down when you’re away and turning it up when you’re on your way home. This saves users time, energy, and money.
Netflix’s predictive analytics recommend what you might want to watch next—and what studios should create next—based on viewer data. Amazon, iTunes, Pandora, and other companies use predictive analytics to make better recommendations.
Sales force Einstein applies natural language processing to analyze text from e-mails exchanged with customers to estimate the likelihood that a user will buy, detect deals a team is at risk of losing, and recommend actions to improve sales.
One of AI’s promises is to make self-driving cars safer. Everyday driving decisions, such as whether to stop abruptly or swerve to avoid hitting an obstacle, will be powered by AI.
AI will help redesign the entire shopping experience, optimizing everything with more and better data. Retailers will seamlessly stock the precise number of goods needed on shelves at any given time, and know which product at which price should be highlighted to a specific customer as they navigate a store.
Where do you start?
Five ways to look past the shiny-object phase and into practical AI planning in 2017.
1. Don’t fear the robots.
The idea is to augment, not replace, work. AI can absorb cognitive drudgery, like turning data points into visual charts, calculating complex math formulas, or summarizing the financial news of the day into a single report. This frees up people to focus on acting on the insights
2. Start with the problem, not the solution.
Before launching an AI program, identify concrete business problems, then consider if AI can help. For example, rather than ask, “What can we use AI for?”, think, “Where could we make our operations more efficient?” or “What decisions are we making without data?”
3. Emphasize empathy.
The more machines we employ, the more people skills we need. Leaders must build empathy across the organization to help employees see impact. Focus on how AI can help workers add more human value, rather than replace them. For example, McDonald’s added robots to their franchises, but doesn’t plan to cut human jobs.
4. Engage the skeptics.
Understand what they fear and start there. Fast Forward Labs’ Hilary Mason shared an example of winning buy-in by demonstrating how machine learning could solve a problem for an overburdened regulatory team.
5. Remember: It’s not magic.
If a vendor can’t explain their AI product or service in terms you understand, don’t buy it. Much of what’s called AI today (“AI personal assistants,” anyone?) is actually humans wrangling a trove of data behind the scenes. If it doesn’t make sense, it might not be real.
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