AI & Machine Learning
On the Road to AI, Don’t Ask “Are We There Yet?”
Businesses should understand that in the long run, the promise of AI is self-limiting.
Businesses should understand that in the long run, the promise of AI is self-limiting.
Free on-demand webinar with MIT SMR authors of “Analytics as a Source of Business Innovation.”
Many organizations need to develop greater expertise at valuing their data assets.
The challenge we face today is not a “world without work” but a world with rapidly changing work.
The 2017 Data & Analytics Report by MIT Sloan Management Review finds that companies that embraced analytics have begun to find new ways to derive strategic benefit from analytics.
Here are the essential elements of a transformative IoT strategy.
How AI affects organizations’ use of and relationship to time — in reacting, managing, and learning — may be a tough adjustment.
To grasp how artificial intelligence will change organizations, understand how it delivers value.
IoT early adopters are reaping rewards in more timely, accurate, detailed, and reliable data.
Managers should start incorporating AI into business processes now.
A new MIT SMR and BCG initiative investigates the challenges and opportunities AI offers business.
AI is expected to be the single most disruptive new capability for companies in the next decade.
Advanced risk identification tools require companies to take a new approach to supply chain resilience.
Automation and robotics could have far-reaching effects on labor — ones we’ve seen before.
The ability to monetize data — not hoard it — can offer competitive advantage in the digital economy.
In this webinar, Thomas H. Davenport and Stephan Kudyba discuss the process of developing a new generation of data products.
Providing up-front structure for data may reduce the need to process it — and limit distortions.
There’s a boom in using analytics for human resource decisions. Tenure decisions should be next.
Subscription e-commerce uses AI to offer personalized, low cost, convenient products. It’s working.
Miscommunications between decision makers and data scientists are common. Enter the data translator.