It’s 10 a.m. on a Monday, and Aman, one of the developers of a new artificial intelligence tool, is excited about the technology launching that day. Leaders of Duke University Hospital’s intensive care unit had asked Aman and his colleagues to develop an AI tool to help prevent overcrowding in their unit. Research had shown that patients coming to the hospital with a particular type of heart attack did not require hospitalization in the c and its leaders hoped that an AI tool would help emergency room clinicians identify these patients and refer them to noncritical care. This would both improve quality of care for patients and reduce unnecessary costs.
A man and his team of cardiologists, data scientists, computer scientists, and project managers had developed an AI tool that made it easy for clinicians to identify these patients. It also inserted language into the patients’ electronic medical records to explain why they did not need to be transferred to the ICU. Finally, after a year of work, the tool was ready for action.
Fast-forward three weeks. The launch of the tool had failed. One ER doctor’s comment that “we don’t need a tool to tell us how to do our job” is typical of front-line employees’ reactions to the introduction of AI decision support tools. Busy clinicians in the fast-paced ER environment objected to the extra work of inputting data into a system outside of their regular workflow — and they resented the intrusion on their domain of expertise by outsiders who they felt had little understanding of ER operations.
Bu hikaye MIT Sloan Management Review dergisinin Summer 2022 sayısından alınmıştır.
Start your 7-day Magzter GOLD free trial to access thousands of curated premium stories, and 9,000+ magazines and newspapers.
Already a subscriber ? Giriş Yap
Bu hikaye MIT Sloan Management Review dergisinin Summer 2022 sayısından alınmıştır.
Start your 7-day Magzter GOLD free trial to access thousands of curated premium stories, and 9,000+ magazines and newspapers.
Already a subscriber? Giriş Yap
Avoiding Harm in Technology Innovation
To capitalize on emerging technologies while mitigating unanticipated consequences, innovation managers need to establish a systematic review process.
Make a Stronger Business Case for Sustainability
When greener products and processes add costs, managers can shift other levers to maintain profitability.
How to Turn Professional Services Into Products
Product-based business models can help services firms achieve greater scale and profitability. But the transformation can be challenging.
Do You Really Need a Chief AI Officer?
The right answer depends on the strategic importance and maturity of AI in your company.
Where To Next? Opportunity on the Edge
Doing business in regions considered less stable or developed can pay off for companies. But they must invest in working with local communities.
Make Smarter Investments in Resilient Supply Chains
Many companies invest in resilience only after a disruption. Applying the concept of real options can help decision makers fortify supply chain capabilities no matter the crisis.
The Three Traps That Stymie Reinvention
Organizational identity, architecture, and collaboration can be either assets or liabilities to pursuing growth in new sectors.
What Makes Companies Do the Right Thing?
Vaccine makers varied widely in their engagement with global public health efforts to broaden access to COVID-19 immunizations. Ethically motivated leadership was a dominant factor.
Build the Right C-Suite Team for Your Strategy
CEOs can foster a more effective leadership team by understanding when to tap senior executives' competitive instincts and when to encourage collaboration.
A Better Way to Unlock Innovation and Drive Change
A strengths-based approach to building teams can win employee commitment to change and foster an inclusive, agile culture.
