Getting data to power AI models is easy. Using that data responsibly is a lot harder. That's why enterprises need to implement a framework for AI governance. "With great data comes great responsibility," said Monark Vyas, managing director of applied intelligence strategy at Accenture, alluding to the proverb made popular by comic book hero Spider-Man. Speaking during a panel at the AI Summit Silicon Valley conference, Vyas noted just how easy it is for companies to mishandle data.
"You won't see many people with my background talking about ethics," said Beena Ammanath, executive director of the Global Deloitte AI Institute and head of Trustworthy AI and Ethical Tech at the global consulting company. A computer scientist who worked as a database and SQL developer and held data science- and AI-related technology roles at Bank of America, GE and Hewlett Packard before joining Deloitte in 2019, Ammanath wasn't always gung-ho to talk AI ethics. Then she decided to write a book about it. "There has arguably never been a more exciting time in AI," she wrote in her book "Trustworthy AI." "Alongside the arrival of so much promise and potential, however, the attention placed on AI ethics has been relatively slight." Protocol spoke with Ammanath about why ethical AI practices should be part of every employee's training, the limitations of providing internal guidance inside a sprawling consultancy and why she finally gave in and joined the AI ethics conversation.
To many leaders, it comes as a surprise to learn that the investment needed to develop AI solutions cannot realize a return through the deployment of single, disconnected use cases, or even a handful.1 This is why it's so important to have an AI strategy that is connected and coordinated across the enterprise, in tight alignment with the overarching business strategy. All too often, however, business leaders get the planning process out of order, focusing too much on use cases or abdicating leadership of the AI strategy to IT or data sciences. This can be a slippery slope, diminishing the organization's ability to use AI to create new ways of competing for customers, launching products, accelerating time-to-market, securing supply chains, and beyond. The strongest AI strategies tend to begin without ever mentioning AI.
Undetected machine failures are the most expensive ones. That is why many manufacturing companies are looking for solutions that automate and reduce maintenance costs. Traditional vibrodiagnostic methods can be too late in many cases. Taking readings in the presence of a diagnostician occasionally may not detect a fault in advance. The benefits of predictive maintenance are dependent on the industry or the specific processes that it is applied to. However, Deloitte analyses at that time have already concluded that material cost savings amount to 5 to 10% on average.
Some bad news: when it comes to deploying AI across your organization, you probably haven't scratched the surface. Some good news: you probably haven't scratched the surface. While many companies have started to embrace the technology, few are on the right path to realizing its full potential. AI will unlock value, improve processes, and strengthen organization culture. All of us will benefit.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. New York-headquartered Vic.ai, a startup that provides a platform to automate accounting and financial processes for enterprises, today announced an AI-powered tool for enterprise-grade cost optimization. Officially dubbed Spend Intelligence, the offering comes as the latest addition to Vic's flagship intelligence product designed to provide business and operational insights to help companies improve existing processes. According to the company, the feature will further expand the product, enabling enterprise finance departments to make better, data-backed choices about their costs and eventually improve profits. "With Spend Intelligence, we continue to push the boundaries of what AI can do for finance," Alexander Hagerup, CEO of Vic.ai, said.
The digital transformation in manufacturing is accelerating. This means that companies that make things must move faster to innovate their products and transform the way those products are delivered from design to procurement to manufacturing to sustainment. They don't have to go at it alone. A 2020 Deloitte and MAPI Study finds ecosystems can create a competitive edge for manufacturers facing ongoing disruption.1 Today, we'll dive into a component of this ecosystem, Computer Vision. Computer Vision is one of the many Artificial Intelligence solutions that will continue to transform manufacturing.
While some surveys show that people prefer to talk to a human as opposed to a chatbot, whether they're shopping online or dealing with a customer service issue, that hasn't dissuaded companies from adopting them. A 2019 Salesforce report found that 53% of service organizations expected to use chatbots within 18 months. According to Statista, the size of the global chatbot market could surpass $1.25 billion by 2025, a steep climb from $190 million in 2016. A customer's satisfaction -- or lack thereof -- with a chatbot ultimately depends on the scenario and the capabilities of the chatbot in question. Obviously, a chatbot that fails to answer basic questions will lead to frustration.
AI Forum, a business that was acquired by special economic zone company Euler Digital SEZC in February 2022, has released a global study on Enterprise AI in research partnership with Ernst & Young in the United States. The report describes key trends regarding AI adoption, expenditure and digital transformation. It also highlights the challenges related to governance, scalability and skills. Ian Gilmour, Euler Digital managing director and chair of the AI Forum Advisory Board, said, "After analysing data from more than 1,700 AI expert practitioners, we estimate that less than 20% of organizations had AI awareness training in place. As a result, it is clear that AI is not as established as may generally be thought."