Goto

Collaborating Authors

corporate culture


Implementing AI and managing relationships: 5 ideas from MIT Sloan Management Review

#artificialintelligence

As artificial intelligence matures and expands within enterprises, leaders across industries are struggling to get everyone on board. At the same time, they must manage customer and employee relationships amid shifting expectations in an era of digital transformation. The latest ideas from MIT Sloan Management Review consider how to overcome the barriers of AI implementation and go all in on putting AI tools into production. Leaders will also learn how to know what customers want, how to avoid a toxic workplace, and how to run effective brainstorming sessions. AI-powered decision-making tools have the potential to increase efficiency, improve service quality, reduce costs, and boost revenue.


How Artificial Intelligence is Respacing the Corporate Culture?

#artificialintelligence

Artificial intelligence is definitely one of the most divisive technologies of our time. Some people see it as the key to unlocking new levels of human potential, while others view it as a tool for Replacing humans in the workforce. Despite these concerns, businesses have been eagerly adopting AI into their operations. Many experts believe that this is because AI can actually improve corporate culture in some very significant ways. The article discusses how artificial intelligence is reshaping the culture in the corporate world.


How to Improve Corporate Culture with Artificial Intelligence

#artificialintelligence

Contrary to press-propagated blames on rapid industry changes, unforeseen circumstances and uncontrollable crises, most business failures boil down to poor corporate culture. Interestingly, how corporate culture is perceived has changed just as rapidly as industries have evolved in recent times. In the 20th and early 21st centuries, assessment of corporate culture focused almost entirely on how businesses treated their customers. For instance, the dent in Blackberry's culture was caused by the company prioritizing its smartphone technology over customers' needs. Meanwhile, how customers interact with technology was changing. More recently, corporate culture has more to do with how companies manage communication internally than with their public relations.


Toxic Culture Is Driving the Great Resignation

#artificialintelligence

More than 40% of all employees were thinking about leaving their jobs at the beginning of 2021, and as the year went on, workers quit in unprecedented numbers.1 Between April and September 2021, more than 24 million American employees left their jobs, an all-time record.2 As the Great Resignation rolls on, business leaders are struggling to make sense of the factors driving the mass exodus. More importantly, they are looking for ways to hold on to valued employees. To better understand the sources of the Great Resignation and help leaders respond effectively, we analyzed 34 million online employee profiles to identify U.S. workers who left their employer for any reason (including quitting, retiring, or being laid off) between April and September 2021.3 The data, from Revelio Labs, where one of us (Ben) is the CEO, enabled us to estimate company-level attrition rates for the Culture 500, a sample of large, mainly for-profit companies that together employ nearly one-quarter of the private-sector workforce in the United States.4 Monthly research-based updates on what the future of work means for your workplace, teams, and culture. While resignation rates are high on average, they are not uniform across companies.


Market moving language under machine learning microscope

The Japan Times

You could become your own worst enemy. CEOs and other managers are increasingly under the microscope as some investors use artificial intelligence to learn and analyze their language patterns and tone, opening up a new frontier of opportunities to slip up. In late 2020, according to language pattern software specialist Evan Schnidman, some executives in the IT industry were playing down the possibility of semiconductor chip shortages while discussing supply-chain disruptions. All was fine, they said. Yet the tone of their speech showed high levels of uncertainty, according to an algorithmic analysis designed to spot hidden clues in -- ideally unscripted -- spoken words. "We found that IT sector executives' tone was inconsistent with the positive textual sentiment of their remarks," said Schnidman, who advises two fintech companies behind the analysis.


AI can see through you: CEOs' language under machine microscope

#artificialintelligence

Natural language processing (NLP) increasingly popular Investors seek edge in world of'unstructured data' But CEOs are cottoning on, with more speech scripted Investors seek edge in world of'unstructured data' LONDON, Oct 20 (Reuters) - Executives, beware! You could become your own worst enemy. CEOs and other managers are increasingly under the microscope as some investors use artificial intelligence to learn and analyse their language patterns and tone, opening up a new frontier of opportunities to slip up. In late 2020, according to language pattern software specialist Evan Schnidman, some executives in the IT industry were playing down the possibility of semiconductor chip shortages while discussing supply-chain disruptions. All was fine, they said.


AI can see through you: CEOs' language under machine microscope

#artificialintelligence

LONDON (Reuters) - Executives, beware! You could become your own worst enemy. CEOs and other managers are increasingly under the microscope as some investors use artificial intelligence to learn and analyse their language patterns and tone, opening up a new frontier of opportunities to slip up. In late 2020, according to language pattern software specialist Evan Schnidman, some executives in the IT industry were playing down the possibility of semiconductor chip shortages while discussing supply-chain disruptions. All was fine, they said.


Six keys to unlocking upskilling at scale

#artificialintelligence

These elements have a diverse heritage in learning and management theory, and the way they are implemented will vary from one organization to another. Most or all of them are present, we believe, in every successful effort to raise the caliber of digital skills in an organization. When an initiative is designed effectively, the elements complement one another. Together, these elements create an immersive workplace environment that makes it easy to build new habits and learn new skills, continually reminding people of the progress they've made and the learning yet to come. Just as learning a new language is easier if you move to a community where it is constantly spoken, learning digital proficiency is easier if you are surrounded by other people who are fluent with the relevant technologies. But such widespread fluency is not the situation in businesses today. Training Industry, an organization and information source devoted to "the business of learning," estimates that organizations spent more than $362 billion on employee training and education in 2018 alone, reflecting a growth rate of 1.2 percent per year. Yet as Harvard Business School professor Michael Beer had already pointed out in 2016 in Harvard Business Review, organizations "are not getting a good return on their investment.


How to Build a Team in AI Startups

#artificialintelligence

Creating an AI startup team structure is demanding in terms of time and resources needed for building a team. This post will cover 10 top tips on startup team building. Artificial intelligence is a force for businesses to reckon with. It has enough potential to reshape the way businesses approach daily workflows and manage projects. As for consumer-facing AI applications, every existing industry will soon be introduced to projects that involve one or more applications of artificial intelligence.


AI in Biopharma Slowed by Challenges Involving Data, Corporate Culture

#artificialintelligence

Biopharmas are warming up to artificial intelligence (AI), but a series of challenges will need to be addressed before it becomes widely used by drug developers, a panel of industry executives agreed. Speaking at the 2019 Annual Meeting of NewYorkBIO in New York City yesterday, panelists identified those challenges as finding more and better data, integrating data from multiple sources, and creating partnerships to gather and analyze that data. The panel also cited challenges that go beyond data, such as attracting a new generation of professionals capable of applying AI and related technologies such as machine learning--and adapting biopharmas to the new technologies. Those observations are in line with a study released today by The Pistoia Alliance, a global not-for-profit organization of more than 150 members established by executives from AstraZeneca, GlaxoSmithKline (GSK), Novartis, and Pfizer. The Alliance surveyed 190 life sciences professionals in the US and Europe, with 52% citing access to data, and 44% a lack of skills, as the two key barriers of adoption of AI and machine learning.