What learning trends are in store for 2023? The coming year will bear witness to some significant changes across the entire business world. Recession poses an ever-constant threat so it's important that businesses equip themselves for the future. On top of it all, businesses are having trouble finding new recruits due to a labor shortage. So 2023 will force businesses to strengthen their current ranks to protect against a shallow recruitment pool.

AI adoption is rapidly moving from an experiment to an essential part of business practices and planning. Across every industry, more use cases are being developed for AI to drive business efficiencies, optimize data, improve the customer experience and support business goals and initiatives. Companies looking to mature their AI programs should keep an eye out for these important conversations around the adoption of AI tools and technology. Machine learning is a valuable tool that has benefited businesses for decades but only became widely popular recently. In this new era of digital acceleration, companies are looking for ways to drive efficiency in their companies--and for many, the answer lies in automation.

Martin Birch, CEO and president of ibml, has 20 years of experience as a global leader in the intelligent information management industry. AI adoption is rapidly moving from an experiment to an essential part of business practices and planning. Across every industry, more use cases are being developed for AI to drive business efficiencies, optimize data, improve the customer experience and support business goals and initiatives. Companies looking to mature their AI programs should keep an eye out for these important conversations around the adoption of AI tools and technology. Machine learning is a valuable tool that has benefited businesses for decades but only became widely popular recently.

This year at Microsoft Ignite we explore how organizations can activate AI and automation directly in their business workflows and empower developers to use those same intelligent building blocks to deliver their own differentiated experiences. The global pandemic has created unprecedented levels of uncertainty, as well as the need to sense and reshape our physical and digital environments, sometimes in completely new ways. Leaders across industries recognize innovation as the only path forward. Critically, we've seen a shift from "innovation for innovation's sake" toward a desire to lower operating costs, anticipate trends, reduce carbon footprints, and improve customer and employee experiences. Automation and AI are key ingredients for digital perseverance and helping organizations drive efficiency.

Let's say you have identified a use case in your organization that you would like to handle via a chatbot. You familiarized yourself with Amazon Lex, built a prototype, and did a few trial interactions with the bot. You liked the overall experience and now want to deploy the bot in your production environment, but aren't sure about best practices for Amazon Lex. In this post, we review the best practices for developing and deploying Amazon Lex bots, enabling you to streamline the end-to-end bot lifecycle and optimize your operations. We have covered the planning, design, and configuration phases in previous blog posts.

Today, nearly everyone accepts that in order to slow the damage we are doing to our planet and environment, humans must transition away from the use of fossil fuels. This has led to many science and business innovations as we search for new sustainable or renewable alternatives to coal, oil, and gas. Although it would be nice to think everyone wants to do their part in order to save the world, there are strong financial incentives too. The value of the renewable energy market is set to grow from $880 billion to nearly $2 trillion by 2030. And the growing awareness of the importance of environmental and social governance (ESG) issues means there are tremendous political incentives, too.

Since the beginning of the year, there has been a significant uptick across health plans, healthcare providers, and analytics firms utilizing AI to change how healthcare is delivered and how patients can be more engaged in their care. AI applications in healthcare are all over the map today. Data from my firm's digital health intelligence database, DamoIntelTM, has identified a significant rise in the launch of AI use cases across clinical and administrative areas in 2020. An analysis of AI/ML applications deployed by the top 50 health systems across the United States identifies that AI-enabled solutions fall into multiple technology categories: machine learning, natural language processing (NLP), conversational interfaces such as chatbots, and robotic process automation (RPA). COVID-related use cases in clinical and administrative areas contributed to the growth in adoption of newer technologies such as chatbots in healthcare.

RPA is fast becoming the destination for efficient and more productive resources, what do you think is the future of human intelligence? Processes that are easily defined, repeatable and rules-based will all ultimately be automated. As to what happens to human intelligence – that's a great question with no short answer. As AI and cognitive computing continue to improve and expand, there will inevitably come an inflexion point, when the super-smart machine will surpass our mere mortal intelligence. The question I have is whether intelligence and knowledge may soon be ubiquitous and hence commoditized?

Machine learning has moved from the stuff of science fiction to a staple of modern business, as organizations across nearly every industry vertical implement ML technologies. Doctors are using machine learning to more accurately diagnosis and treat their patients, retailers are using ML to get the right merchandise to the right stores at the right time, and researchers are utilizing the technology to develop effective new medicines. That is just a sliver of the use cases emerging, as all sectors -- from energy and utilities, to travel and hospitality, to manufacturing to logistics -- and the various functions within any given organization increasingly put machine learning to work. Machine learning is a subset of artificial intelligence, where computers use algorithms to learn from data, allowing the machines to identify patterns -- a capability that organizations can put to use in multiple ways. Experts said machine learning enables organizations to perform tasks on a scale and scope previously impossible to achieve.

A global asset manager who recently consolidated technology teams to support Fixed Income and Equities collectively, was looking for ways to streamline costs while leveraging best practices across asset classes.