50 percent of respondents to a recent McKinsey survey reported that their companies adopted artificial intelligence (AI) in at least one business function in 2020. As interest and investment in AI and machine learning (ML) continue to grow across different business functions, is Procurement keeping pace with its business unit counterparts?

Procurement value generation is heavily dependent on fast access to accurate data; while other business functions are automating decisioning using AI, in many organizations today Procurement is still working manually just to collect and clean source data before even getting to the decisioning stage.

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Using artificial intelligence to streamline their service desk operations is something that 93 percent of businesses are planning according to a new survey.

The study conducted by IDG for Freshworks shows 61 percent of IT managers have already deployed AI at some level and 32 percent are exploring the possibilities.

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Recommendations based on AI are something we encounter all the time. From shopping sites, streaming services and social media we're constantly shown stuff that the AI thinks we'll like.

But how easy would it be for an attacker to manipulate these recommendations to promote conspiracy theories or spread disinformation?

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We've all seen the headlines suggesting that AI is racist and sexist. However, many people overlook one important fact -- that AI is simply a tool, incapable of being inherently biased. That’s not to say AI isn’t capable of producing biased outcomes -- as the headlines show, it certainly is. But it can only ever be as biased as the data upon which it relies.

So how can developers and marketers avoid deploying biased AI? Unfortunately, there is no magic one-size-fits-all solution. As with any successful technology deployment within a business, it requires a thorough understanding of the datasets you are working with, and the outcomes AI can produce with said data. The first step is knowing what to look for.

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Traditional cybersecurity isn't necessarily bad at detecting attacks, the trouble is it often does so after they have occurred.

A better approach is to spot potential attacks and block them before they can do any damage. One possible way of doing this is via 'deep learning' allowing technology to identify the difference between good and bad.

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We know technology has had an impact on every area of our lives. We can manage almost our entire lives through our smartphones, from arranging appointments to paying bills. Technological innovations have also found a way into the workplace, completely revolutionizing the way we work.

As technology is embedded into workplaces, it can unsettle workers. With technologies including artificial intelligence and automation able to replicate elements of our jobs, it’s easy to see why. Half of UK workers believe they may be replaced by automation, AI, or robots in the next decade. A further 61 percent are concerned about AI.

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Artificial intelligence (AI) and machine learning (ML) have been two of the most disruptive technological advancements of the past several years.

Gartner predicts that by 2024, 75 percent of enterprises have shifted from AI pilot stage implementation to operationalization. Their effects have been wide-ranging and promise to continue into the foreseeable future. Businesses will gain a substantial competitive advantage by capitalizing on the benefits of AI and ML.

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The Federal Trade Commission (FTC) recently published a blog entitled "Aiming for truth, fairness, and equity in your company's use of AI" that should serve as a shot across the bow for the large number of companies regulated by the FTC. 

Signaling a stronger regulatory stance on deployed algorithms, the FTC highlights some of the issues with AI bias and unfair treatment and states that existing FTC regulations -- such as the Fair Credit Reporting Act, the Equal Credit Opportunity Act, and the FTC ACT -- all still apply and will be enforced with algorithmic decision-making.

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As we move further into the digital age, technologies need to evolve quickly enough to support the constantly changing needs of the modern enterprise. While cloud computing has surged in popularity in recent years, most organizations must simultaneously continue to rely on their legacy systems for many of their core functions.

Despite the cloud’s ability to minimize IT infrastructure costs and adjust resources to meet fluctuating and unpredictable demand while getting applications up and running faster, 71 percent of the Fortune 500 and more than 90 percent of the world’s largest 100 banks, 10 largest insurance companies and 25 largest retailers in the U.S. all continue to depend on outdated systems to power their mission-critical applications.

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The debate about the energy greediness of large AI models is raging. Recently, an AI ethics researcher at Google was dismissed because she had pinpointed the upward spiral of exploding training data sets. The fact is that the numbers make one’s head swim. In 2018, the BERT model made the headlines by achieving best-in-class NLP performance with a training dataset of 3 billion words.

Two years later, AI researchers were not working with billions of parameters anymore, but with hundreds of billions: in 2020, OpenAI presented GPT-3 -- acclaimed as the largest AI model ever built, with a data set of 500 billion words!

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Can artificial intelligence be deployed to slow down global warming, or is AI one of the greatest climate sinners ever? That is the interesting debate that finds (not surprisingly) representatives from the AI industry and academia on opposite sides of the issue.

While PwC and Microsoft published a report concluding that using AI could reduce world-wide greenhouse gas emissions by 4 percent in 2030, researchers from the University of Amherst Massachusetts have calculated that  training a single AI model can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car. Who is right?

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Artificial Intelligence (AI) has quickly emerged as a game-changing technology, often helping researchers uncover insights much faster than they otherwise could.

Here are five impressive ways that researchers have already capitalized on AI this year.

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The popularity of enterprise AI continues to grow but practices and maturity are stagnant as organizations run into challenges implementing AI within their organizations.

The annual AI Adoption in the Enterprise survey from learning platform O'Reilly finds a lack of skilled people and difficulty hiring topped the list of challenges to adopting AI, cited by 19 percent of respondents. This compares to last year when 22 percent named company culture as the major barrier.

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Every contact center needs to track its compliance with laws and regulations, yet so many make it tougher on themselves to do so. Reviewers screen call after call, checking off compliance steps and ensuring agents meet expectations. But with potentially millions of calls to screen, manual compliance tracking becomes an impossible task -- and workaround solutions can leave centers open to damaging compliance failures, potentially costing companies millions of dollars in fines.

The cost of human error is too high, but manual processes are too overwhelming, time-consuming and expensive. Automating compliance monitoring can remove much of the burden from compliance managers and officers and decrease their chances of human error. Technology like Interaction Analytics helps reduce the traditional pain-points and limitations in reviewing contact center compliance while also unlocking insights on larger trends and ways to improve contact center operations.

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Human error and poor security decisions are among the leading causes of data breaches, but it can be hard for security teams to know where to invest resources to address these risks and provide help to employees who need it most.

Tessian is introducing what it calls the Human Layer Risk Hub -- a solution that offers organizations full visibility into employees' risk levels and drivers on email, enabling security and risk management leaders to take a more tailored approach to employee security.

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