Last few years have been revolutionary for organisations who were reluctant to accept technology as the way of doing business. As technology solidified its position in any company's economic value generation, these organizations have realized that it's imperative for them to harness the potential of new-age technologies. For example, Machine Learning (ML) can be of significant value for business who learn the right way of approaching it. One can only imagine the type of assistance ML can provide if it is made accessible to anybody in the organization who needs to make decisions based on truths that are delivered by numbers. Businesses are gradually leveraging ML, and this is supported by an IDC study which predicts that by 2024, 40% of large enterprises will expand the use of AI/ML across all business-critical horizontal functions like marketing, legal, HR, procurement, and supply chain logistics.

With the potential to democratize AI and ML, AutoML is the answer many enterprises across industry verticals have been seeking to take AI projects from pilots to scaled deployments. Adopting Artificial Intelligence (AI) is no longer just to gain competitive advantage; it has become table stakes for mere business survival. However, today's acute shortage of data scientists combined with the continuous effort to automate laborious tasks is posing unprecedented challenges for enterprises. Automated machine learning (AutoML) is poised to help. Why? Traditional machine learning (ML) is a time-consuming, arduous, and iterative task that involves data cleansing and preparation, algorithm training, validation, etc., to imitate the way that humans learn to make predictions or decisions without being explicitly programmed to do so.

It's been two years since Mckinsey invented the term analytics translator, called it the'new must-have role' and predicted we'd need around 5 million of them. For the past ten years, we've struggled with the ambiguous title'data scientist', then'citizen data scientist'. Although I've seen many'data scientists' change their Linkedin titles to'analytics translator', the problem remains that no one knows what'analytics translator' really means. Mckinsey seems to have slipped this term into a Harvard Business Review article, and it has somehow taken root. What's more, people seem truly excited by the term.

This is an article from DZone's 2022 Enterprise AI Trend Report. Today, many large organizations are deploying artificial intelligence (AI) models with an event-driven platform in order to solve two common challenges of leveraging enterprise AI. First, to meet their data needs, enterprises often require a variety of model types that are built on different machine learning (ML), deep learning, and AI languages, frameworks, tools, and systems. These models are tied to various ways of deployment, using tools such as PyTorch, scikit-learn, XGBoost, DJL.AI, spaCy, TensorFlow, ONNX, PMML, Apache MXNet, and H2O. As a result, developers and data engineers need to deploy their models in diverse deployment environments with varying characteristics and restrictions, which makes accessing and managing the models complicated.

When software providers talk about the technologies they say "democratize" AI, they also talk a lot about "guardrails." That's because the rapidly evolving world of AI tools is still more like a republic governed by the machine-learning elite. Although no-code and low-code AI tools promise to give everyone a chance to build business analytics models or simple applications that use AI to complete tedious tasks, the amateurs whom no-code AI companies refer to as "citizen data scientists" are often required to play with the bumper rails up. That's because toolmakers and management are worried about the risks inherent in allowing just anyone to create sophisticated AI systems. "As you go into low-code and actually more the no-code environment, then there are guardrails as to what you can and can't do," said Ed Abbo, president and chief technology officer at C3 AI, which provides software designed to help people with zero coding experience build machine learning models.

This is the promise made by enterprise AI company C3 AI in splashy web ads for its Ex Machina software. Its competitor Dataiku says its own low-code and no-code software "elevates" business experts to use AI. DataRobot calls customers using its no-code software to make AI-based apps "AI heroes." They're among a growing group of tech companies declaring that the days of elitist AI are over. They say with software that requires little to no coding at all, even the lowly marketing associate -- now the "citizen data scientist" -- has the power to create and use data-fueled machine-learning algorithms.

The pace of technological change increased in 2021, and if history is any guide, will continue to accelerate in 2022. At the leading edge of high tech are data science and artificial intelligence, two disciplines that promise to keep the pace of change at a high level. Interest in AI, machine learning, and data science is extremely high, if the number of predictions on these topics is any indication. We start this batch of predictions with DataKitchen CEO Chris Bergh, who notes that the global AI market is projected to grow at a compound annual growth rate (CAGR) of 33% through 2027. But that significant growth comes with a hidden risk: reputational harm due to bias and a lack of accountability in AI processes.

In the past, data teams and other jobs dealing with data, we're still pretty much in the wild west, meaning all of it is new territory & is yet to be explored. Certain best practices have been uncovered in recent times, but for the most part, there's not any one proven method to follow and the fact that the job titles of data professionals' (and the roles they play) differ widely is another evidence of this. One of the forks in the path for the future of how data teams will evolve, roles in data, and even the field of artificial intelligence (AI) in general is how AI ought to be inclusive (that includes the various types of people with different roles, working together towards an end objective) as well as exclusive (siloed to particular and specific teams in order to get the job accomplished more precisely and effectively). Which direction AI veers will be able to alter the core structure of companies and even individual career paths. So, what is the future -- inclusive or exclusive?

Most business executives believe they need to harness artificial intelligence (AI) to stay ahead of the pack and grow their business, but they often fail to scale up their AI initiatives across their organisations, according to an Accenture expert. Citing a global study by Accenture, Lee Joon Seong, managing director for applied intelligence in ASEAN at the consulting firm, noted that while 88% of global executives believed they needed AI for their business to survive, the same proportion also struggled to scale AI initiatives beyond the pilot stage. "A lot of people understand the potential of AI and have embarked on AI initiatives, but not many have fully realised their full potential," said Lee. The ability to scale is seen as a barometer of success in AI adoption, given the time, talent and resources involved in AI projects. Here's what organisations can do to scale up their AI initiatives: Earmarking AI as part of your business strategy sounds obvious, but many organisations still struggle to get that right.

Organizations are becoming more analytically inclined, automation is rampant, and business users are empowered to accomplish more at a greater scale than they previously could. Nonetheless, there's another side to the pervasive deployment of cognitive computing technologies throughout the data ecosystem, particularly in terms of the mounting ease, accessibility, and utility of advanced analytics. The increasing demand for predictive insight--and the data required to facilitate it--has very real repercussions in terms of data privacy and regulatory compliance which, if not properly addressed, can restrict AI's use for organizations. Many firms are attempting to balance the data demands for AI with what Privacera SVP of Marketing Piet Loubser termed the "let's stay out of trouble side of things. As much as we think externally of regulations from on top, the majority of organizations have much more stringent things going on inside their four walls."