Artificial intelligence can appear to be many different things--a whole host of programs with seemingly little common ground. Sometimes AI is a conversation partner, an illustrator, a math tutor, a facial-recognition tool. But in every incarnation, it is always, always a machine, demanding almost unfathomable amounts of data and energy to function. AI systems such as ChatGPT operate out of buildings stuffed with silicon computer chips. To build bigger machines--as Microsoft, Google, Meta, Amazon, and other tech companies would like to do--you need more resources.

Data is the lifeblood of business – it drives innovation and enhances competitiveness. However, its importance was brought to the fore by the pandemic as lockdowns and social distancing drove digital transformation like never before. Forward-thinking businesses have started to grasp the importance of their data; they understand the consequences of not fully mobilizing it, but many are sat at the start of their journey. Even the best organizations are failing to extract the maximum benefits from their data while keeping it safe. This is where artificial intelligence (AI) comes into play – it can benefit enterprises with their data in three fundamental ways.

Artificial intelligence (AI) initiatives are supposed to enable us to reinvent how we do business, and, one day, transform society at large. However, the truth is that most organizations are still barely scratching the surface of AI's potential. The biggest barrier to the success of AI continues to be an inability for many organizations to take advantage of the data they collect. Data is the lifeblood of AI; yet, according to an HBR survey, 69 percent of companies have not yet created a data-driven organization, and 52 percent aren't even treating data as a business asset. The bottom line is most companies haven't figured out how to break down their data silos and gain insight across all their data.

The Wall Street Journal referred to deposits as "the lifeblood of banks and a key factor in profitability." Yet overall deposit growth in the U.S. has slowed down dramatically over the past few years, and many banks are seeing flat or declining deposits. Download this ebook to find out what banks can do to protect the continued flow of their "lifeblood" deposits, win back their own customers and earn the trust of new ones while turning them into savers.

Breaking down the workforce options for AI developers to structure raw data for machine learning. While it may seem like artificial intelligence (AI) has hit the big time, a lot of work needs to be done before its potential really come to life. In our modern take on the 20th-century space race, AI developers are hard at work on the next big breakthrough that will solve a problem and establish their expertise in the market. It takes a lot of hard work for innovators to deliver on their vision for AI, and it's the data that serves as the lifeblood for advancement. One of the biggest challenges AI developers face today is how to process all the data that feeds into machine learning systems, a process that requires a reliable workforce with relevant domain expertise and high standards for quality.

As global firms with deep pockets invest in machine learning and launch new AI-backed products, services and processes, how should mid-sized organisations with less time and technical expertise respond? Up to now most have chosen to wait and see. But many who have invested have achieved results which show that across industries and company sizes machine learning solutions can slice cost from internal business processes and inspire new product and service possibilities. We've seen voice and image recognition come of age, mapping platforms identify traffic delays and recommend new routes in near real-time and e-commerce platforms find and cluster similar products helping customer find what they need and what they didn't know they needed. For mid-sized organisations, machine learning can lower costs or opens up new markets by offsetting the scale advantages available to larger organisations, and much more.

When it comes to artificial intelligence (AI), there is no such thing as data overload. Because AI systems have the ability to process enormous amounts of data, and their accuracy increases along with data volume, the demand for data continues to grow. Consider, for example, an AI program designed to identify the cause of defective medical devices produced during the manufacturing process. As with any AI application, the software looks for patterns in the data using algorithms developed by data scientists. To try to solve this problem, suppose that the AI program receives and sorts through production data from different days of the week, times of day, machines and operators.

Much has been written about the rise of autonomous vehicles with testing being conducted globally and the increasing number of consumers who are already enjoying the benefits. One of my colleagues, who juggles his time between our office in Cupertino and the vineyard and olive orchard he runs in Sonoma County (Trattore Farms), tells me he's taken full advantage of his Tesla's autopilot feature for his drives. It's not just the hands off driving potential that impresses him though; it's the way that his car, as part of a network of cloud-connected vehicles, is learning as the car documents data points and uploads them to the cloud in real time. So for example, if several Teslas log information at the same GPS point where their driver taps the brakes if their car approaches a dip in the road too fast, the algorithm directing autopilot through that location will automatically update and all the Teslas using autopilot at that location will automatically slow down. I've not had the chance to experience the Tesla autopilot for myself but I'm also seeing the power of data sharing and the IoT with my drone flying.

Virtually every Fortune Global 500 company is on the path to digital transformation, but each is in a different phase and many need to clear the final hurdles to truly unlock its true potential. The journey toward transformation is no doubt difficult, and large firms will sometimes need to tolerate high failure rates to push through. One of the primary reasons companies struggle with digital transformation is they lack enterprise-wide intelligence and a culture of teamwork to harmonize their efforts. In short, they lack a "digital brain," the nexus for continuous, automated learning from data across all business units, departments, product lines and services, that gives the organization higher cognition. That's why every company needs a digital brain to make their digital transformation come alive, else they risk incremental, short-sighted and superficial change.

Singapore: Cognizant Technology Solutions Corp., a US-based information technology (IT) firm with most of its employees working out of India, expects its business growth in the Asia-Pacific region to outpace the company average this year, maintaining the trend seen in recent years, Jayajyoti Sengupta, vice-president and Asia-Pacific head, said in an interview. Automation, which includes robots, machine learning and artificial intelligence, will be among the new frontiers for Cognizant, as rote and repetitive processes become "digital, instrumented, analyzed and intelligent", he said. Cognizant has said it expects its revenue growth to slow to between 10% and 14.3% for the calendar year 2016. How do you see the situation in the Asia-Pacific? It would be pertinent to note that Cognizant's growth of 21% in calendar 2015 included revenues from the acquisition of TriZetto.