Post-pandemic, the demand for AI is surging, as many organizations ascertain the need for AI to keep pace with the current business landscape in the face of a looming recession. AI can help enterprises improve business processes, increase speed and accuracy, and help make predictions to optimize their performance. In 2023, there will be many ways that enterprises can implement AI but for more traditional organizations, we suggest the following trends will play an important role. This includes the need for companies to get their data fabric in place before implementing AI, new and interesting ways to "white-label" AI, and the need to develop a Center of Excellence to ensure the entire company is aligned with an AI strategy. As more enterprises look to implement AI projects in 2023 to increase productivity, gain better insights, and have the ability to make more accurate predictions regarding strategic business decisions, the challenge will be for traditional enterprises to establish a robust data framework that will allow their organizations to leverage data effectively for AI purposes.

Every industry today is both impacted by and also seeking exploit AI technologies already widely used in the'new economy'. Tata Consultancy Services' "Business 4.0" framework guides this'digital transformation' of traditional enterprises by focusing on extreme personalization, developing ecosystems and embracing risk to drive exponential value: Enterprise AI developed with agility and deployed on the cloud form the critical technical enablers for Business 4.0. In his talk, Gautam Shroff guides attendees through how TCS Research is applying AI in traditional enterprises spanning the spectrum from automation to amplification, beginning with its hands-on experience of developing and deploying a deep-learning based semantic system for virtual assistance as well as knowledge synthesis within TCS, at scale, on its internal collaboration platform. From the transformative effects of AI and IOT on manufacturing to supply change management, and the necessity of embracing risk for deploying AI in the field, come explore the intersection of AI and business in this special lecture on June 12th at 4:00 pm in Room 1190 of the Bahen Centre, 40 St. George St., Toronto.

The recent rise of artificial intelligence (AI) can be partly attributed to improvements in graphics processing unit (GPU) processors, mostly deployed in cloud server architectures. GPUs are massively parallel processors that can map well to the large number of vector and matrix multiplication calculations that need to be performed in deep learning. GPUs were originally designed to perform matrix multiplication operations for three-dimensional (3D) computer graphics, but it turns out that deep learning applications have similar requirements, and GPUs have been successful in accelerating the training and inference of AI algorithms. Hyperscalar internet companies, including Google, Facebook, Amazon, and Microsoft, have built massive cloud server farms that can perform industrial-scale training and inference operations for AI, fueled by the troves of consumer data they collect, further improving their AI algorithms. NVIDIA has been the main beneficiary of this trend, as its GPUs power the majority of these cloud-based AI data centers.

Enterprise applications vendor Infor recently showed a concept design for a similar agent, coincidentally also called Max. Project Max … uses intelligent automation across Infor's Sales Suite products to provide timely information, reminders and action prompts to sales reps as they're working out in the field. Now unbundling has reached the field of enterprise applications, due to the combined effect of integrated cloud platforms, smart mobile devices and intelligent agents. Expect to see new models of enterprise organization and teamwork to emerge as new functional bundles of automation enable new ways of working together to achieve business outcomes.

The traditional suite of enterprise applications -- ERP, HCM, CRM and SCM, or more prosaically money, people, sales and spend -- reflects the functional organization of the traditional enterprise. In other words, these applications have their roots in functions that were originally defined by the need to marshall operations using the flow of paper documents around the twentieth-century enterprise. Those functional boundaries are beginning to dissolve in the digital enterprise. Connected technologies, most notably cloud computing and smart mobile devices, make it possible to connect up data and processes across the old demarcations. Where once each separate function managed its own dataset and only occasionally married up data with adjacent operational silos, today's connected digital infrastructure makes it possible to share data in real-time and access it anywhere.

Google is starting to piece together a cloud platform strategy beyond just being a lower-cost option than the competition, but it's a considerable risk, banking on a set of services many enterprises likely won't use for years to come. Machine learning and deep analytics are the latest trend to gain attention in the cloud market, and Google has latched on wholeheartedly -- it sees these tools a way to differentiate in the market, by externalizing what has driven it to be one of the largest corporations in the world. The most full-throated endorsement of this strategy emerged at the GCP Next user conference back in March. Eric Schmidt, chairman of Google parent company Alphabet Inc., talked in broad strokes about creating an internet operating system, adding that in five years, every major IPO will be for companies using machine learning. "The platform is not the end; it's the bottom, and above it is machine learning," Schmidt said.

Google is starting to piece together a cloud platform strategy beyond just being a lower-cost option than the competition, but it's a considerable risk, banking on a set of services many enterprises likely won't use for years to come. Machine learning and deep analytics are the latest trend to gain attention in the cloud market, and Google has latched on wholeheartedly -- it sees these tools a way to differentiate in the market, by externalizing what has driven it to be one of the largest corporations in the world. The most full-throated endorsement of this strategy emerged at the GCP Next user conference back in March. Eric Schmidt, chairman of Google parent company Alphabet Inc., talked in broad strokes about creating an Internet operating system, adding that in five years every major IPO will be for companies using machine learning. "The platform is not the end, it's the bottom and above it is machine learning," Schmidt said.