Machine learning (ML) is helping companies remain competitive. In fact, many companies' core business today is based on machine learning and image/speech recognition. Google, for example, uses machine learning in image recognition for Google Photos and speech recognition for Google Home and Google Assistant. Millions of people talk to Siri, Apple's virtual assistant. The company extended the application of its virtual assistant through HomePod, a smart home device.

Many companies struggle to reap the benefits of investments in digital transformation, while others see enormous gains. What do successful companies do differently? This article describes the five stages of digital transformation, from the traditional stage, where digital and technology are the province of the IT department, through to the platform stage, where a comprehensive software foundation enables the rapid deployment of AI-based applications. The ideal is the native stage, whose hallmarks are an operating architecture designed to deploy AI at scale across a huge, distributed spectrum of applications; a core of experts; broadly accessible, easy-to-use tools; and investment in training and capability-building among large groups of businesspeople. Over the past decade, Novartis has invested heavily in digital transformation.

When AI is a priority to the C-suite and leadership of the company, it gets dedicated and specialized attention, ensuring its success. The most successful organizations were smart about where to place the technology to bring the most benefit, with all of them deploying machine learning in forecasting, maintenance optimization, and logistics and transportation. Creating strategic partnerships that are intensive, long-lasting and far-reaching was another key to success. Over half of successful companies trained non-IT specialists in the technology, ensuring the whole team understands and benefits from the tech and embedding it into the company culture instead of siloing it. To create the best analytics, successful companies democratized data, often by ensuring cloud access.

At a time when companies are increasingly embracing technologies such as robotic process automation, natural language processing, and artificial intelligence, and as companies' automation efforts mature, findings from our second McKinsey Global Survey on the topic show that the imperatives for automation success are shifting. The online survey was in the field from February 4 to February 14, 2020, and garnered responses from 1,179 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP. Two years ago our survey found that making business-process automation a strategic priority was conducive to success beyond the piloting stage. 2 2. We define business-process automation as the use of general-purpose technologies (for example, bots and algorithms) to perform work that was previously done manually, in order to improve the functionality of a company's underlying systems. In the survey, automation did not include the use of automation that was custom built (for example, Excel macros and custom scripts) for organizations.

Artificial Intelligence in Practice is a practical resource that demystifies how Artificial Intelligence (AI) and machine learning can be used to solve common business challenges and open the door to opportunities that often exceed expectations. The book is filled with insights from some of the most important AI giants including Google, Microsoft, Amazon, Alibaba, and other forward thinking industry leaders. It also presents compelling case studies from traditional businesses and startups, that detail how AI is being applied in the real world of business. Bestselling author and AI expert Bernard Marr offers detailed examinations of 50 companies that have successfully integrated AI into their business practices. He provides an overview of each company, describes the specific problem AI addressed and explains how AI offered a workable solution.

As the world's largest beverage company, Coca-Cola serves more than 1.9 billion drinks every day, across over 500 brands, including Diet Coke, Coke Zero, Fanta, Sprite, Dasani, Powerade, Schweppes and Minute Maid. Big data and artificial intelligence (AI) power everything that the business does – the global director of digital innovation, Greg Chambers, said: "Artificial intelligence is the foundation for everything we do. Artificial intelligence is the kernel that powers that experience." Marketing soft drinks around the world is not a "one-size-fits-all affair". Coca-Cola products are marketed and sold in over 200 countries.

Artificial intelligence (AI) can deliver significant business value. But to maximize the benefits of AI, you need to focus on creating solutions to real business challenges rather than on the technology itself. AI can help you solve business challenges in virtually every industry. At the recent Gartner ITxpo, my colleague Anne-Sophie Lotgering, Orange Business Services CMDO, talked about how the most successful companies will augment human workforces with AI. In fact, Gartner predicts that AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity by 2021. It estimates that 37% of large enterprises have already tipped their toe in the water and using AI to some extent.

A survey of over 1,200 executives has just revealed that despite massive and increasing investments in digital transformation and technologies such as artificial intelligence and big data, companies are struggling to turn those investments into real business results. A survey unveiled today by Deloitte has found that the number of companies investing heavily in digital transformation has almost doubled in the past year. The accounting and services giant questioned 1,200 executives at organizations of at least 500 people with above $250 million in revenue, finding that 19% planned to invest $20 million or more during 2019. When asked the same question at the start of 2018, 10% gave that answer. Despite Massive Investments In AI And Digital Transformation, Survey Finds Poor Results And 7 Enabling Capabilities The term "digital transformation" has come to mean steps that move an organization towards adopting data-driven business models, typically involving artificial intelligence (AI), big data and predictive analytics technology.

The world's most successful companies (Uber, Amazon, Airbnb, and many others) already use dynamic pricing to predict changes in supply and demand, optimizing prices in real-time to maximize revenue. Consumers are already familiar with dynamic pricing: Uber rides, flights, hotels, and even Disneyworld tickets are all examples. The benefits of AI and Dynamic pricing are still far from fully realized. As a part of my series about "Big Ideas That Might Change The World In The Next Few Years" I had the pleasure of interviewing Alex Shartsis. Alex is co-founder and CEO of Perfect Price, an AI company empowering companies to make better decisions about pricing, profitability, and utilization.

As many organizations move to build their automation capabilities, recent survey results suggest that certain best practices will differentiate successful efforts from others. Organizations in every region and industry are automating at least some business processes, yet only a slight majority have succeeded at meeting their targets, according to a new McKinsey Global Survey on the topic.1 1.The online survey was in the field from January 16 to January 26, 2018, and garnered responses from 1,303 participants representing a full range of regions, industries, company sizes, functional specialties, and tenures. Of these respondents, 764 work at organizations that have piloted the automation of, or that have fully automated, business processes in at least one function or business unit. To adjust for differences in response rates, the data are weighted by the contribution of each respondent's nation to global GDP. As advances in artificial intelligence, software robotics, machine learning, and innovative technology platforms enable businesses to redefine processes, workplace automation is expected to provide a significant opportunity for improvements in performance and efficiency.2