With a growing number of firms turning to artificial intelligence to cut costs and ramp up productivity, the business world is in danger of overlooking the need to up-skill today's workforce for the labour of tomorrow. According to a new report, while many firms find that technology creates more work than it replaces, it is work that can only be fulfilled by workers with a higher level of training than many currently have. The continued march of innovative new Artificial Intelligence (AI) has brought with it the potential for improved efficiency of resources, heightened productivity in manufacturing, and even the potential for low-cost medical care in the developing world. However, not all the effects of the new AI revolution are thought of in positive lights – it is set to spark a major realignment of the labour market, and it is unlikely to be as black and white as being a simple increase of decrease of jobs being available. While some outlying analysis suggests that at current growth rates, AI will only create as few as 19% of the jobs lost due to its implementation, AI is often forecast to necessitate more new jobs than the old ones it replaces.

Robotic process automation (RPA) is a hot topic right now. Many organizations, primarily large ones, are using RPA to process data more quickly and to shorten the time spent by employees on repetitive tasks. With RPA on the rise, it's important to keep up, whether you're a developer or in IT operations. Find out how IT4IT value streams can help in this Webinar. The co-founder of Symphony Ventures, which was acquired by Sykes in November 2018, Barkin is a leader and innovator in digital operations, RPA, intelligent automation, and AI.

Bottom Line: The leading growth strategy for manufacturers in 2019 is improving shop floor productivity by investing in machine learning platforms that deliver the insights needed to improve product quality and production yields. Using machine learning to streamline every phase of production, starting with inbound supplier quality through manufacturing scheduling to fulfillment is now a priority in manufacturing. According to a recent survey by Deloitte, machine learning is reducing unplanned machinery downtime between 15 – 30%, increasing production throughput by 20%, reducing maintenance costs 30% and delivering up to a 35% increase in quality. Accenture, Manufacturing The Future, Artificial intelligence will fuel the next wave of growth for industrial equipment companies (PDF, 20 pp., no opt-in)

Of all the technologies that drive digital transformation in the enterprise, people often tout artificial intelligence (AI) as perhaps the most disruptive of all. As automation becomes increasingly sophisticated, there's no question that AI is in the process of disrupting people's day-to-day jobs. As a result, the buzz has largely focused on whether AI will put people out of work vs. whether it will shift work to more productive tasks, as automation takes the grunt work off of everybody's plate. While such discussions are clearly important, they miss the larger transformative story. Digital transformation, after all, takes place at the organizational or even the industry level.

While AI attack surfaces are only just emerging, business leaders' security strategies should account for adversarial AI, with an emphasis on engineering resilient modelling structures and strengthening critical models against attempts to introduce adversarial examples. Step 1 – Conduct an inventory to determine which business processes leverage AI, and where systems operate as black boxes. Step 3 – Prioritize plans for highly critical and highly exposed models, using information you acquired in step 2, and create a plan for strengthening models that support critical processes and are at high risk of attack. Business leaders need to combine multiple approaches to ensure robust, secure AI.

As surely as steam powered the Industrial Revolution, the ample flow of accurate data will drive artificial intelligence systems. Leading business innovators recognize data's central role in harnessing AI's value, though many feel constrained by the relatively meager flows of trusted information they can currently draw upon to feed their systems. That's why the leading technology firms have invested billions to acquire data-focused companies and their capabilities. The power of AI depends on the access to, magnitude, and quality of the data it can process. So, while AI redefines the systems of business engagement, blockchain is recalibrating the systems of record.

In the fall of 2018, the New York Times published a piece about an experiment in which they used an algorithm to produce Halloween costume ideas. The results were amusing: "baseball clown", "cat witch", "king dog." The algorithm combined random letters to make words, which it then compared to the set of real words in its training data. If it found a match, it kept the word and paired it with another one. In this example--and in many more serious ones like it--the algorithm was given pre-existing patterns and taught to replicate them.

Ready to make an impact on the global automation revolution? McKinsey Global Institute ranked "automation of knowledge" work as #2 in their top 12 disruptive technologies that will transform life, business, and the global economy. Automation Anywhere is the market leader in driving the adoption of robotic process automation technology across leading Fortune 1000 and other companies across more than 90 countries. At Automation Anywhere, we are passionate in our belief that Digital Colleagues will free people to create, think, discover, and ultimately build great companies. Our partners are the world's leading consulting firms, service providers and automation service specialists.

Millions of dollars in venture capital or acquisition costs come at a price: Executives need artificial intelligence to begin delivering on its promises of increased worker efficiency and product innovation. But if checks are signed too quickly, companies may not find the ROI they would expect waiting for them at the end of the process. PwC analysts recommend AI technologies must first be evaluated by a diverse team of experts who can attest to the technical backbone of the company, asses if the needed data sources can be improved over time, and determine if AI systems can applied to a company's unique business model. "Moreover, integration plans should consider responsible AI principles and practices to extract a deal's value well after it closes," the report states. A laser-tight focus on business objectives can help maximize ROI for companies when it comes to AI initiatives.