There is no surprise that AI and ML have become the key ingredients of modern technology and cyberspace. From wearables to robotics, AI is almost everywhere and in every sector. Most companies extend their hands to AI vendors to adopt AI into their workflow. They spent lots of time, money, and effort to ensure a successful project. However, Gartner estimated that more than 85 percent of AI projects fail and render errors. Another report says that around 70 percent of companies say that implementing AI has minimal or zero impact on overall workflow efficiency.

Start typing'artificial intelligence will change' into a search engine and you will see suggested sentence endings like'the world', 'everything in your lifetime' and'the face of business in the next decade.' Search a little further and it will become clear that AI and machine learning projects are not only driving advancements, but are integral to their success. According to research from Accenture, 85% of executives in capital-intensive industries say they won't achieve their growth objectives unless they scale AI. At the same time, research from MIT Sloan suggests that the gap between organizations successfully gaining value from data science and those struggling to do so is widening. As we know, data science and machine learning are the engine behind AI applications, as it is through processing data that AI learns how to interpret our world and respond as we want it to. If AI is to make a real impact on companies and their customers, companies need a new approach to machine learning.

These points also inform what you need to succeed: a feasible solution that doesn't burden your team, a clear goal, execution and implementation, and forward-thinking management.

Data science has been hot for many years now, attracting attention and talent. There is a persistent thread of commentary, though, that says data science's core skill of statistical modeling is overhyped and that managers and aspiring data scientists should focus on engineering instead. Vicki Boykis' 2019 blog post was the first article I remember along these lines. Don't do a degree in data science, don't do a bootcamp…It's much easier to come into a data science and tech career through the "back door", i.e. starting out as a junior developer, or in DevOps, project management, and, perhaps most relevant, as a data analyst, information manager, or similar… While tuning models, visualization, and analysis make up some component of your time as a data scientist, data science is and has always been primarily about getting clean data in a single place to be used for interpolation. More recently, Gartner's 2020 AI hype cycle report acknowledges the role of data scientists but says: Gartner foresees developers being the major force in AI.

These points also inform what you need to succeed: a feasible solution that doesn't burden your team, a clear goal, execution and implementation, and forward-thinking management.

There's no denying the competitive edge and the value promise that AI has to offer: confident prediction of future demand, faster analysis and insight generation from vast amounts of data, surfacing inherent business process efficiencies, and more. But when push comes to shove, many AI projects either fail to scale, are put on hold or simply never materialize. Gaurav, our VP of Business Development, spends a lot of his time speaking to senior business leaders about their artificial intelligence dreams and how they can best achieve them. It's his job to ensure the project runs smoothly and the foundations are defined around a value generating future. "Typically a business leader will approach us with a complex decision challenge they want to overcome; maybe it's forecasting demand for their products or raw material, maybe it's engine tuning. We'll then go over that problem with a fine-tooth comb, looking over their data and processes to define what the best AI solution could be. "The most important part of my job is to ensure that the right foundations are in place that help our customers generate long term value.

Lots of people are interested in artificial intelligence, and there are plenty of stories about the amazing strides that are being enabled by AI, in business, in medicine, and in any number of other areas. But the stories of wild successes hide a very different part of the AI process -- all the hard work that comes before that success. Justin Nguyen, director of data science and AI and Sense Corp., likens those AI successes to snapshot photos he has taken at the summits of his favorite hikes in some of the world's most beautiful places. One photo shows an amazing view capturing a blue sky with puffy white clouds, miles of ocean coastline and then an entire forest, and hills of rocky outcroppings. "You can see that it was beautiful at the top," he said.

At the end of 2019, it would have been a fairly common occurrence to stumble upon a tech article like this ZDNet one, that used statements such as, "AI jobs are on the upswing, as are the capabilities of AI systems." Fast forward about 6 months and this statement is still accurate in some ways, but highly conditional in others. As COVID-19 continues to act as a threat to the US and global economy, businesses are being forced to reevaluate not only their current and prospective AI projects, but also who they hire and who they retain to help execute these projects. Many business leaders will likely feel pressure to quickly finalize AI projects and bring them to market, but in the process they may succumb to one or more of the major pitfalls outlined below, rendering their results invalid and even potentially harmful to the public. Data should often be the starting point for your AI project, as it will come to represent the underlying fuel for your market offering (platform, solution, etc.) to thrive.

I spoke at a conference recently and one of the talks really resonated with me. The speaker asked the audience, "Who in this room has developed a machine learning or artificial intelligence model for their business?" "Now," he continued, "how many of you have that code in production?" Nearly every hand went down. The demonstration was so simple, yet it was incredibly effective.

Robotic process automation (RPA) can be a great fit for tedious and repetitive processes, but it won't fix a process you don't fully understand or that is otherwise fundamentally broken. That's a basic – and frequent – misstep that commonly leads to an RPA project not achieving its intended goals. Read also: How to explain Robotic Process Automation (RPA) in plain English. "People are trying to apply RPA before they really know how their processes work," says Antony Edwards, COO at Eggplant. "That tends to fail as they are constantly discovering new exceptions and variants."