Collaborating Authors

Three tips for crafting an AI strategy


Artificial intelligence (AI) is expected to provide enterprises with the knowledge they need to create new revenues, streamline business processes and deliver superior customer experiences. While there is a great deal of debate over where to begin and which use case is more critical to profitability, operational issues are often handled at the end of the planning process. Machine learning (ML) models need to work efficiently to generate meaningful insights and the only way to make sure this happens is to tackle production issues from the beginning. Algorithms are required to process large volumes of data efficiently to generate timely insights. But often models fail to execute as intended in production, because of data bottlenecks and architectural complexities that were not foreseen in the early planning stages.

Seven AI implementation challenges for businesses


Technological innovations like artificial intelligence, machine learning and deep learning are increasingly becoming the driving force for various industries. And with the world dealing with the current pandemic, AI is playing a considerable role in tackling the rapidly spreading COVID-19 pandemic, right from delivery of services, diagnosing the risk of the outbreak to drug discovery. Utilizing AI technology and advanced conversational tools, several brands, across the world, have enabled their remote workforce to work from home and yet meet the modern-day requirements of their customers. However, in this rapidly challenging environment, few companies are still struggling to make their business resilient. "In 2019, nearly 37% of enterprises implemented AI – depicting an increase of around 270% in the past four years."

NGA Human Resources Launches Range of Digital HR Innovations at SuccessConnect London 2017


NGA Human Resources today announced a range of Digital HR innovations for businesses running SAP SuccessFactors, including four new XtendHR SAP Cloud Platform Apps, the integration of machine learning into NGA Case Management Central, and a FastTrack deployment solution for SAP SuccessFactors Employee Central Compensate and Variable Pay. Each Digital HR innovation extends the value of cloud technology investments, improves the employee experience, and accelerates the time to performance for businesses operating SAP SuccessFactors. Commenting on the news, Simon Porter, WW VP Sales Digital HR Services at NGA Human Resources, explains: "The relationship between HR and technology has never been closer. Cloud technology makes it possible to solve organizational challenges, better engage with employees, and improve business outcomes faster and at lower cost. The digital HR solutions developed by NGA HR provide employees and managers with the modern, single touch experience they expect, and business leaders with the workforce data they need to make smarter decisions."

Deep Learning stands to benefit from data analytics and High Performance Computing (HPC) expertise


As I noted in a February blog post, many enterprises today need solutions that couple high-performance computing with data analytics. This convergence of technologies is blurring the boundaries between HPC and big data, and clearing the way forward for the advent of high-performance data analytics (HPDA). In a parallel trend, enterprises increasingly need solutions that merge technologies for machine learning and deep learning -- a need I will explore more deeply in today's post. Machine learning was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks. Researchers interested in artificial intelligence (AI) wanted to see if computers could learn from data and the process of iterative training on new data sets.

How intelligent workload management tools can help IT admins cut through cloud complexity


The pace of digital transformation has notably picked up in the past decade, as enterprises invest in technology to retain their competitive edge and avoid having their market share eroded by disruptive newcomers. Organisations' ability to out-innovate their competitors in this way often requires a full-scale modernisation of the IT infrastructure stack underpinning their operations so they are better positioned to respond to the changing needs of their customers. For many enterprises, this process of modernisation has seen them look to invest in making their private, virtualised datacentres and server rooms more agile, responsive and easier to manage by investing in software-defined networking (SDN) technologies and automation tools. Such investments can help enterprises make better and more efficient use of their existing compute capacity, but that alone may not be enough to stave off competitive threats, prompting some IT leaders to weigh up a move to the public cloud. The benefits of such an approach are well-documented and proven, with the public cloud offering enterprises ready access to an almost infinite supply of cloud-based compute resources that can be set to auto-scale in line with peaks and troughs in demand, meaning enterprises only pay for what they use.