Quantum sensing, computing, and communications require different expertise, and an agency may not need experts in all three areas. Similarly, a single quantum scientist won't be equally suited to roles across--or even within--the three technology clusters. For example, quantum communications encompasses a wide variety of efforts from building a quantum internet to transitioning to post-quantum cryptography. These quantum-enabled and quantum-safe innovations leverage different skills and may be best supported by different subject matter experts. With early support from trusted advisors, organizations can identify which quantum skillsets correspond to their mission-critical use cases and ensure access to the right talent for research and prototyping.

Reaching out to you with a request for Career related advice / input. I am a professional with about 18 years of experience in advisory services and implementation of SAP solutions across industries and geographies, presently working with a consulting firm in the U.S. I am a Chartered Accountant from India by education. From 2018, I started focusing on ML/AI as my parallel interest / career in addition to SAP. For the last four years, I started putting efforts to learn fundamentals of the ML/AI domain. I read multiple blogs, books, completed courses as well as completed PoC for a few Embedded Machine Learning solutions of SAP.

You won't see many people with my background talking about ethics,

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.