There's so much buzz about AI in marketing I need a bee keeper's suit just to keep the bullshit off of me. The marketing world is swarming with articles, opinions, podcasts and videos about how AI's going to change world completely. Most of the publicity is positive, touting the time savings and efficiency that AI tools will provide. But there are also plenty of Chicken Littles who are saying I'm bound to lose my job any day now. That kind of fear is a familiar refrain for those of us who know the history of marketing. Way back in the 50s when television was widely adopted, everyone said radio was dead. They said it again in 1981 when MTV came out… "Who would want to just listen to music when you can watch music videos."

This article was published as a part of the Data Science Blogathon. Machine learning projects can be extremely challenging in the IT industry. Several factors can make them difficult, including the volume of data that needs to be processed, the complexity of the algorithms involved, and the need to ensure that the systems are accurate and reliable. In addition, machine learning projects can be time-consuming and expensive to develop and deploy. The challenges of machine learning projects in the IT industry can be daunting but also very rewarding.

The most immediate benefit of artificial intelligence (AI) for business is increasingly clear: it's a huge opportunity for increased productivity. Gartner recently calculated that In 2021, AI augmentation will create €2.6 trillion of business value and save 6.2 billion man-hours globally and a survey by McKinsey has estimated that AI analytics could add around $13trn, or 16%, to annual global GDP by 2030. The easiest and fastest way to implement business AI is to add machine learning to existing business processes. Automation brings the most value when it's applied to narrow, repetitive business decisions that are made thousands of times a day, replacing the more boring aspects of knowledge work. For example, machine learning has proved successful at automating repetitive finance tasks such as the automatic matching of invoices and payments, increasing rates from 70% to 94% in just a few weeks--resulting in massive savings in time and effort.

… of of Vijay Gullapalli, vice president, artificial intelligence/machine learning and data science, and Pete Ball, technology licensing leader.

The next step in new technology adoption certainly is appealing to IT leaders and enterprise executives. They can try new and different things. Yet success with concepts such as artificial intelligence (AI) and machine learning is less about technology and more about people. AIOps, observability, continuous intelligence, cloud and remote work require a rethinking of the IT organization, new degrees of trust in data, and dramatic changes in the workforce. This ain't plug and play.

The most immediate benefit of artificial intelligence (AI) for business is increasingly clear: it's a huge opportunity for increased productivity. Gartner recently calculated that In 2021, AI augmentation will create €2.6 trillion of business value and save 6.2 billion man-hours globally and a survey by McKinsey has estimated that AI analytics could add around $13trn, or 16%, to annual global GDP by 2030. The easiest and fastest way to implement business AI is to add machine learning to existing business processes. Automation brings the most value when it's applied to narrow, repetitive business decisions that are made thousands of times a day, replacing the more boring aspects of knowledge work. For example, machine learning has proved successful at automating repetitive finance tasks such as the automatic matching of invoices and payments, increasing rates from 70% to 94% in just a few weeks--resulting in massive savings in time and effort.

In our dealings with customers, one of the most challenging aspects of launching intelligent process automation (IPA) projects is a seemingly simple one: where to start. Whether it's a company that has hit the limits of what robotic process automation can do or is starting from scratch with IPA, in this post we offer three actionable steps to get your project underway. Intelligent process automation, in a nutshell, helps companies automate workflows and processes that involve unstructured content, including text and images found in documents of various formats (PDF, Word, etc.). It enables companies to automate more workflows than they can address with RPA alone, which can only deal with structured content -- like information found in a database. IPA achieves this by applying AI technologies such as machine learning and natural language processing, bringing powerful capabilities to bear.

In June, TechCrunch Ethicist in Residence Greg M. Epstein attended EmTech Next, a conference organized by the MIT Technology Review. The conference, which took place at MIT's famous Media Lab, examined how AI and robotics are changing the future of work. Greg's essay, Will the Future of Work Be Ethical? reflects on his experiences at the conference, which produced what he calls "a religious crisis, despite the fact that I am not just a confirmed atheist but a professional one as well." In it, Greg explores themes of inequality, inclusion and what it means to work in technology ethically, within a capitalist system and market economy. Accompanying the story for Extra Crunch are a series of in-depth interviews Greg conducted around the conference, with scholars, journalists, founders and attendees. Below he speaks to two key organizers: Gideon Lichfield, the editor in chief of the MIT Technology Review, and Karen Hao, its artificial intelligence reporter.

Tom Davenport has literally written the book on analytics. Actually, several of them, to be precise. Over the course of his career, Tom has established himself as the authority on analytics and how their role in the modern organization has evolved in recent years. Tom is a distinguished professor at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior advisor at Deloitte Analytics. The discussion was timely as Tom had just written an article about a financial services company that had trained its employees on human-centered design so that they could ensure any use of AI would be customer-driven and valuable. "If you survey organizations and ask them, 'Does your company have a data-driven culture?' they almost always say no. Surveys even show a kind of negative movement over recent years in that regard. And it's because nobody really addresses that issue. They only address the technology side." Eventually, I think some fraction of [AI and analytics solutions] get used and are moderately effective, but there is not nearly enough focus on this. A lot of analytics people think their job is to create models, and whether anybody uses it or not is not their responsibility…We don't have enough people who make it their jobs to do that sort of thing. I think we need this new specialist, like a data ethnographer, who could sort of understand much more how people interact with data and applications, and how many ways they get screwed up.--Tom I don't know how you inculcate it or teach it in schools, but I think we all need curiosity about how technology can make us work more effectively. It clearly takes some investment, and time, and effort to do it.-- TD Wealth's goal was to get [its employees] to experientially understand what data, analytics, technology, and AI are all about, and then to think a lot about how it related to their customers.

In the era of disruption, the largest, incumbent organisations need to innovate and take advantage of the growing global partner ecosystem. Instead of running away from the innovators, the traditional enterprise must turn and fight, by taking advantage of the different technologies in the marketplace. In one way, this can be achieved by building out a solid technology or IT team, whose work is stitched into the fabric of the business. But, every business needs a partner -- it's unwise to go it alone, especially if you're not an expert in a specific field. Below, Information Age identifies in three parts, three Silicon Valley AI startups that can help your business seize the advantage of technology and avoid disruption in the digital era.