The global workforce is in crisis. The shortage of talent, demands of four unique generations, virtual teams and continuous changes in legislation – all of these forces have created a perfect storm as organizations look to build a productive and engaged workforce. All this is occurring at a time when companies themselves are facing massive upheaval due to digital disruption permeating every corner of the business world.

Job seekers are also facing disruption, with fears of redundancy and displacement due to AI, ever-changing skill set requirements and increasing numbers turning to gig work, all of which are influencing workforces to become disparate, disconnected and increasingly unproductive. Further, the power dynamic is shifting in the job market, with digitally skilled (and often young) workers in the driving seat. In what is now a seller’s market for talent, organizations need to not only source, recruit and onboard talent quickly, but also display an attractive and enticing culture for would-be associates.

At the center of this storm is HR. HR now has a seat at the table, but with the eyes of the C-suite firmly upon it, is HR up to the challenge in its current guise? Many would say not. The data indicates that HR is still bogged down in administrative and transactional activities that suck the lifeblood out of the function, and for the workers having to deal with HR, the frustration is apparent, with the majority rating HR performance poorly.

A New Age for HR

So what is going to pull HR out of the dark ages and enable it to provide an engaged, inspired and fluid workforce that organizations so desperately require? Is the stereotypical image of the HR “green screen” still a reality?

Well, as we enter the second machine age, the answer seems obvious. HR needs to be augmented by digital tools that empower HR professionals to focus on the strategic aspects of talent acquisition and management. Organizations today need to focus just as much on digitally enabling their workforce as they do their customers. HR departments need to be able to deliver a standardized user experience that is personalized, transparent and available anytime anywhere.

The toolsets to do this are available in abundance (think of the usual suspects of SAP SuccessFactors and Workday), but HR technology is going further and is now moving beyond social, mobile, analytics and cloud technologies (the SMAC stack). Artificial intelligence, the Internet of Things, predictive/prescriptive analytics and augmented reality are all beginning to make their presence felt in the HR value chain.

Fundamentally, the solution cannot be purely technology focused. HR now needs to rethink and adapt its processes and culture to fit with not only this new breed of HR tech but also by changing company cultures and workers.

The Complete Picture

As companies get to grips with a limited talent supply and more complex business environments (due to the impact of digital), it is paramount to attract, retain and increase the productivity and longevity of talent. The failure to do so will leave businesses vulnerable to competitive forces in the market and ultimately stifle the revenue and margin gains made possible by digital. The question is, to what extent do organizations see the value in cutting-edge HR technologies and new processes? To what degree are they adopting these new tools and approaches? Are they seen as gimmicks, or are they viewed as a critical component to delivering on business objectives through effective talent management?

Organizations need to think about how these objectives can be met through the application of digital HR. By combining connected employees through technology with HR processes that facilitate the modern workforce, organizations will become optimized for collaboration, innovation, productivity and employee well-being. These outcomes will ultimately define HR and cement its place in the C-suite.

In an experiment designed to teach a bot how to negotiate with humans, Facebook’s AI researchers found that haggling bots quickly discovered lying as a useful tactic in bargaining to sway results in their favor. In fact, these chabots eventually developed their own language and learned to lie to win negotiations. Researchers were quick to declare that “this behavior was not programmed by the researchers but was discovered by the bot as a method for trying to achieve its goals.” So a lying bot’s deceptive behavior emerges on its own to maximize the reward. Isn't it an irony that we build them, but we don't really understand them? Unfortunately, a machine that thinks won’t always think in the ways we want it to, and I‘m not sure we’re ready for the ramifications of that.

We are increasingly viewing and treating machines as humans, which is undermining our own biological abilities. Just because machines exhibit some characteristics of thinking (the ability to drive a car, approve/ reject our loans, enable our doctors to diagnose what ails us, to name few) that doesn’t make them human beings. Humans are complex, emotional, and relationship-driven. We have a fear/ respect for society and make irrational decisions (to err is human), whereas machines are trained to utilize massive quantities of data and they’re perfect (almost) at picking up on the subtle patterns these data contain. I doubt many of us would be okay if a robot did irrational things that we never dreamed of. Would you be pleased to find out your trusty robot was actually a liar?

Humans lie for several reasons: to avoid punishment or embarrassment, to gain advantage, to help others, to protect political secrets, and the list goes on. Robots; however, do not worry about shame, praise or fear. They are programmed to WIN at any cost, a feature that is creating an increasing sense of unpredictability. The reality is that while we continue to make machines more like humans, we lack the ability to really understand how they’re producing the behavior we observe. This can be a serious problem, especially where the world of business is concerned.

Knowingly or unknowingly, we are teaching machines to lie and this raises important technological, social and ethical considerations: What would you call a stock-trading bot when it maximizes profits by breaking the law? How will we ensure machines that lie still have a respect for human feelings? And, whose interest are they meant to protect— the people who made them or the people using them? These emerging ethical questions are forcing us to seriously think about how to deal with machines that learn to lie. As AI spreads to even more parts of society, the consequent ethical challenges will become even more diverse.

We are clearly facing an ethical dilemma with machines that lie. With this in mind, there are some important questions we must consider: How will we enforce accountability on AI’s that lie and cheat? Can we fine a machine that lies? How will a machine be punished if it is caught cheating? While it is clear that AI needs governance, currently there is no central body to conduct such a task. Moreover, ethics is probably the last thing that innovators want to think about. However, if ethical considerations are continually overlooked, AI could have a catastrophic effect on companies’ brands, reputations, and finances—and I’m referring to consequences we haven’t yet foreseen.

It is only a matter of time before we begin programming other human tendencies into machines. We need to program morality into thinking machines as well. The people who are creating and managing new machines need to be trained and re-trained about the importance of ethics. In fact, human ethics must become a key performance indicator for people building new machines. In preparation for these next advancements in AI, we need to establish more open and honest conversations about the ethical implications of AI and how we can best prepare ourselves for the exciting times ahead.

We as humans, not machines, we have the opportunity to determine our future. Yet, with great power comes great responsibility. And in this case, that responsibility is to create a world we want to live in. Machines have the potential to make our world a faster, more effective place to live, but they also come with certain unwanted risks. As we continue to endow our new creations with an ever-increasing amount of human characteristics, we need to consider what each our human characteristics will teach them and how they may one day use it to their advantage.

Until the time that you may have to face off against a lying bot, why not work on an extra skill to help spot robots that lie. This TED talk from Pamela Meyer, author of Liespotting, will help you hone that extra skill.

So, do you still think an AI that lies is by accident?

"Even if you have the best in class product if you're number three in launching your product then you've just lost 50% of the potential value of that product." - Peter Stevenson, VP and General Manager, Pfizer

Market changes that once took decades now transpire in weeks and months. The unstoppable rise of automation, analytics and artificial intelligence (AI) is accelerating the unprecedented levels of speed of doing business, generating value, making decisions, meeting customer expectations, and getting products and services to market before the competition does. Very simply, speed-to-everything determines whether you disrupt or are disrupted because not being first means being last in this fast pace machine age. It’s not a coincidence that many shareholders’ reports of S&P 500 companies are littered with “speed,” “fast” and their synonyms today.

Ten years ago, companies had to learn to compete at “the China price” as globalization ruthlessly drove unit costs down. Today, your business needs to make another change by adapting to “the Google price” as well as to “Google speed.” Entry barriers to any market have come down significantly as tech entrepreneurs leverage digital platforms and quickly build billion-dollar fortunes, thereby challenging traditional business models and industries. Consider these points:

• In our Work Ahead survey of 500 IT managers, the number one issue reported was that their businesses were too slow to effectively capitalize digital.
• A new study confirms that many retailers have been too slow to invest in the areas that create competitive differentiation and new revenue streams, putting them at risk of being outperformed by faster moving, more innovative retail ventures.
• Data is the new oil, but companies are awash in data. You cannot match the speed of the game if your decision-making cycle takes months even for mission-critical projects. Companies that use advanced analytics and machine learning are twice as likely to be top-quartile financial performers and three times more likely to execute effective decisions.

Some companies already are competing on the speed mandate. They are transforming industry business models, challenging the status quo, taking actions and risks, and changing the rules of the game forever. Examples include:

• The success of Reliance Jio in India reached 100 million subscribers in just 170 days, or roughly seven users per second per day, forcing the competition to lower its prices.
• For Adidas, the speed imperative revolves around “significantly improving time-to-market and keeping pace with customers.” To meet this objective, Adidas is completely reshaping its business model, from range planning to product creation, sourcing, supply chain, go-to-market, and sales. Its goal is to derive 50% of its sales from the reshaped business by 2020
• ANZ Bank is leveraging AI for back-office automation to reduce time-to-market for the approval of unsecured and personal loans. According to the bank’s CTO, 1,000 hours of back-office activity have been eliminated due to the increased automation.
• Telefónica, a Spanish telecom giant, is changing its business DNA to create an all-digital, data-driven identity. The company overhauled its core business processes and systems globally, with the goal of moving to a real-time business model capable of reacting to rapidly changing business circumstances.
• Dutch bank ING has set up a transformation “war room” to get a complete overview of the status of all projects and to quickly solve issues. The objective is to speed up communication and decision-making.

Faster time to market is a competitive necessity, and this pressure isn’t disappearing any time soon. The speed of business metabolism needs to increase. When banks noticed that there was no point fighting against FinTech startups, they collaborated with them. Today, many large banks are pumping millions of dollars into startups.

So how can organizations gear up to the speed they need to win in this new, bold world? You don’t have to figure it out all at once. You just have to be willing to start and to make the organizational changes required for success. It’s far more desirable to be successful at a series of smaller tasks than to fail spectacularly upon attempting to tackle a transformation initiative as a big-bang effort. Set the direction, draw a line in the sand, and understand that line will move. There is nothing wrong with taking time early on to prepare to speed up later. The speed at which a business can successfully move depends on its starting state, namely how change-ready it is and its speed tolerance.

Bottom line: Speed-to-everything has become a strategic imperative, and most successful companies in the years ahead will be those that move the fastest. Not every company, however, can move at the same pace because each firm has its own ambitions and priorities in the new machine age. You have to find your true north, and for that you can refer to our speed framework we have developed to help leaders find the current speed of their business and goals they need to set to accelerate the pace.

With so much at stake, companies can’t afford to take their foot off the pedal.