Why does HR Need Decision Intelligence? 

HR is a dynamic and strategic function in today’s business landscape. The decisions in HR significantly influence the future of business organizations, implying the importance of thoughtful decision-making. With the right support from decision intelligence, HR decisions can increase operational efficiencies for organizations on multiple levels. 

Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle. AI Marketplaces, Reinforcement Learning, Decision Intelligence, AI Cloud Services, Data Labeling, and Annotation Services, and Knowledge Graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.

"New graduates in the business development field have a wealth of career paths available to them. Some industries, like tech, are always booming, and job seekers may naturally gravitate toward those areas. However, there are plenty of other industries that can provide incredible work opportunities and growth potential for recent grads.

"We asked members of Forbes Business Development Council about the hottest industries for new business development professionals to explore right now."

". . . [B]uilding a true AI is likely to require investing years of work into machines that won’t solve a single business problem. Search engines and image recognizers make a lot of money but don’t think like people. Any true AI is likely to spend years stumbling around and making mistakes before it does anything useful, just like we do. Remember, training a human intelligence (the best kind we know about) takes about twenty years per installation, and that’s with millions of years of evolutionary advantage baked in. This means that for the machine learning problems that really count, money is likely to stay tight.

"So, you might wonder, if all this is true, why get excited about the upcoming AI winter? Simple. This is when all that expertise we’ve built up during the current boom will start filtering out into other fields. The dirty secret of the current AI boom is that not everyone is trying to build a self-driving car. In fact, for most businesses, the problems they need to solve are far more pragmatic. Often, companies need to make sense of the data they’re already sitting on and those datasets are usually too small and too messy for deep learning to be much use. Or they need to figure out how to take dodgy intermittent data from new sources like IoT sensors and make it reliable."

"Our whole infrastructure needs to be tuned up with security and risk management in mind.  Fortunately I think AI will help with that...we need a human/AI cooperation / collaboration...the challenges of data [will only increase]...you need the compute infrastructure that can handle all of that data...data becomes part of the value and the currency that a company or a society has that helps enable it to make better decisions but it becomes a burden in and of itself."

"When people say 'AI software', what do they really mean? This is a critically important question to understanding the impact that AI can have on the retail industry (on any industry, really), and a significant source of the gap that exists between AI hype and reality.

"While McKinsey focuses on AI types of classification, prediction, and generation, I’ve found it more useful to look at natural language processing, computer vision, and prediction. But each of these are umbrella terms for lots of what amount to 'micro-capabilities', which is an important limitation when thinking about AI. Artificial General Intelligence (AGI, but sometimes referred to as Human-Level Artificial Intelligence) is at least 20 years off, according to experts in the field, and possibly 50 years. And both ends of the estimate acknowledge that some kind of breakthrough that we have not currently achieved would have to happen before we can get there – there is no current known path to achieving AGI."

"For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

"89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example."

"Given that companies have broader needs that go beyond the application area of deep learning, you have to consider the technical and organizational complexity being imposed on your enterprise users by having to adopt yet another set of tools."

"If we wait for the hidden biases and other flaws hiding in our data to emerge out of AI-based decision systems, it will be too late.

"We are moving artificial intelligence (AI) systems from the lab, where we can control for a limited set of variables, to potentially massive-scale implementations, where variables will propagate and multiply. We must have an AI engineering discipline that can help predict and adjust for those variables.

As enterprise companies embrace AI, the potential for harm is greater than it is for consumer applications. Here are some examples and potential implications.