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AI

A CIO weighs in on how AI can benefit non-technical roles, particularly HR

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Artificial intelligence is transforming how people work by boosting efficiency and productivity. Human resources departments are using AI to create a more adaptive, flexible, and fluid workplace, one where staffers can develop training, streamline onboarding, identify and evaluate candidates during recruiting, process feedback, respond efficiently to service requests, and manage projects. HR is notoriously manual; information is often kept in silos and answering questions can be a labor-intensive process. Whether it is creating workforce experiences personalized to each employee, or sifting through large amounts of information looking for valuable intelligence, HR professionals benefit by incorporating AI into their processes.

Jeff Gregory, chief information officer of global service provider Thirdera, explained to VentureBeat how AI can impact non-technical roles in an organization, especially in the realm of human resources.

This interview has been edited for clarity and brevity.

VentureBeat: AI makes sense as a tool for data scientists, engineers, and IT pros . How can non-technical roles/organizations, such as sales, marketing, HR, and finance, use it?

Jeff Gregory: It’s a misconception that AI is only a tool for engineers and the like. Sales and HR are two examples of business functions where AI is having a huge impact. In sales, reps in many organizations are using AI to improve forecast accuracy, including the timing, value and likelihood a deal will close. AI is also helping sales determine when and when not to contact customers and prospects, and which pipeline activities to focus on based on the probability of success. This is a huge advantage for time-starved reps, which is basically all of them.

Likewise, AI for HR has the ability to offer an entirely new set of insights and self-service benefits. For example, it can help HR reps understand what truly motivates employees, what creates enthusiasm, what info they need to be successful. AI can also make information easier to find. For example, chatbots can provide instant access to pertinent data on benefits and payroll and offer up suggestions based on past results and insights, allowing HR staff and employees to get answers they need, even if they’re not asking the questions correctly.

VentureBeat: What about HR in particular lends itself to AI? What are some issues HR pros face on a daily basis that would be helped if more HR departments had access to AI technology?

Gregory: HR is the steward of a company. Its reps need to have their pulse on the health and development of employees, and this has everything to do with making sure employees can quickly and easily get answers to questions they have. Presenting the “right” information to employees in a timely fashion is a huge challenge for most organizations. In many cases, employees don’t ask for the right info, leading to follow-up questions and conversations that can delay essential tasks, such as onboarding, training, or benefits. AI and bots provide an incredible opportunity for an organization to get the right information to employees 24/7. Bots also have the ability to “learn” from typical questions and follow-ups, enabling more precise and timely responses. Delivering correct info, links and other resources to employees in an efficient manner can save HR and employees hours of time and improve job satisfaction.

VentureBeat: As a CIO, what are some recommendations you have for HR leaders as they consider if, how, and when to implement AI? Please be specific.

Gregory: The best advice is to start small, then learn and grow. AI is amazing with the insights it can produce. However, it is best to start with a few simple tasks. Take time to fully understand the value and how to utilize it within your organization. A chatbot focused on general HR questions is a great example. This is a learning tool that will provide access to the most important information for the employee. Additionally, this will free the HR team from time-consuming requests, allowing them to focus on other pressing items.

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AI

Cloud investments slow to deliver ‘substantial’ benefit for many companies, study finds

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Business leaders across the C-suite see the cloud‘s role in helping to achieve their company’s growth ambitions — and they have high expectations. But few organizations are positioned for the cloud to fully deliver on its promise, according to research firm PricewaterhouseCoopers. PwC’s inaugural U.S. Cloud Business Survey of over 500 executives found that more than half (53%) of companies aren’t realizing substantial value from cloud investments. That’s despite the fact that 56% view the cloud as a platform for innovation and growth.

The PwC report suggests that a new digital talent divide is emerging, affecting both technology and business roles. Forty-seven percent of respondents see the lack of upskilling as a barrier to cloud value. This agrees with a recent survey conducted by 451 Research, which found that 90% of organizations are experiencing shortages of cloud-related skills.

“After a decade of cloud experience, organizations are facing a talent shortage for all cloud-related skills,” Forrester said in a March 2020 report. “Although legacy skill sets translate well to new cloud technologies, the cultural leap to evaluate, select, and operate for productivity, system-level efficiency, and workload-specific problem solving is proving to be a challenge. Enterprise attempts to hire and train talent are constantly plagued with poaching by the cloud vendors themselves.”

Other barriers stand in the way of successful cloud technology implementations. According to PwC, trust-related considerations like a cloud’s impact on customer commitments or regulatory compliance are considered either too late or not all. Only 17% of risk management leaders responding to the firm’s survey said they’re involved at the start of cloud projects. And 55% of chief human resource officers see changes to processes and ways of working as significant issues when it comes to the cloud.

Perhaps unsurprisingly given the roadblocks, just 52% of chief financial officers say that they’re confident they can measure cloud return on investment (ROI). Those with this confidence are at a significant advantage. According to a Unisys Corporation survey, organizations that conduct a thorough ROI analysis before embarking on cloud migrations are 44% more successful in realizing cost-savings expectations than those that don’t.

“Without question, we find ourselves in the midst of an accelerated cloud sea-change on the heels of a pandemic, which brought a new awareness that any position of strength can be fragile, and organizations that operate with a greater degree of resiliency and agility can thrive in the future. Our recently conducted [survey] confirmed this,” PwC U.S. deputy advisory leader Jenny Koehler told VentureBeat via email. “Some of the greatest areas of promise include improved resiliency and agility, improved decision making given enhanced data and analytical capabilities, and the ability to innovate products and services. Despite this widespread adoption, however, there is a substantial value gap that persists.”

Realizing returns

Despite setbacks in embracing the cloud, executives responding to PwC’s survey say that they’re prioritizing cloud capabilities into the next year. Companies are specifically investing in cybersecurity (48%); AI and machine learning (39%); hybrid cloud (39%); analytics (37%); and enterprise apps (28%). Beyond this, 33% of executives say that their companies are using cloud to advance environmental, social, and governance strategy, such as automating reporting and progressing green goals.

The rise of the pandemic defined 2020 for nearly every industry, and cloud computing is no exception to the rule. Gartner estimates that $257.9 billion will be spent on public cloud services in 2020, up 6.3% from 2019. And according to Statista, the worldwide public cloud computing market will reach an estimated $397 billion in 2022.

“In reflecting upon these [survey] results, [the] value gap may be symptomatic of the fact that many companies have not fundamentally aligned their cloud investments to their underlying business strategy, or in certain cases, have defined business value in too abstract, or broad, of terms, such as ‘revenue growth’ or ‘cost cutting,’” Koehler continued. “In addition to that, the cloud itself fundamentally introduces new capabilities to an enterprise. And, as is the case with any new capability, it must be nurtured in the context of new operating models and a mindset of continuous improvement, customer centricity and innovation, all enabled by end users that have been up-skilled not only on the IT side, but also on the business.”

It comes as no surprise that members of the C-Suite are more involved than before in cloud adoption efforts, given the amount of capital at stake. Over 70% of respondents told PwC that they’re helping to make cloud strategy decisions as well as cloud-related talent and upskilling decisions.

“Steps can be taken to address [challenges], including the introduction of holistic digital upskilling programs, mentorship programs in areas where greater cloud depth is required, as well as partnering with external third parties for long-term success,” Koehler said. “Even in the midst of this value gap, at this moment, we [at PricewaterhouseCoopers] remain optimistic that it can be closed, with the right alignment to underlying business strategy, and shared responsibility among the entirety of the C-suite.”

Cloud providers are reaping the windfall benefits. In its most recent earnings report, Google said that its cloud division brought in $4.047 billion in sales for the first quarter of 2021, up 46% from the year prior. Amazon’s Amazon Web Services (AWS) posted a record $13.5 billion in profits for 2020. And Azure, Microsoft’s cloud business, notched third quarter 2021 revenue growth of 50% year-over-year, beating analyst expectations.

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AI

How AIOps can benefit businesses

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“AIOps,” which stands for “AI for IT operations,” refers to the way data and information from a dev environment is managed by an IT team — in this case, using AI. AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions with proactive and personal insights, enabling the use of multiple data sources and data collection methods. In theory, AIOps can provide faster resolutions to outages and other performance problems, in the process decreasing the costs associated with IT challenges.

The benefits of AIOps are driving enterprise adoption. Eighty-seven percent of respondents to a recent OpsRamp survey agree that AIOps tools are improving their data-driven collaboration, and Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023.

But when deploying an AIOps solution, businesses without a clear idea of potential blockers can run into challenges. That’s why it’s important to have a holistic understanding of AIOps before formulating a strategy.

What is AIOps?

AIOps platforms collect data from various IT operations tools in order to automatically spot issues while providing historical analytics. They typically have two components — big data and machine learning — and require a move away from siloed IT data in order to aggregate observational data alongside the engagement data in ticket, incident, and event recording.

As Seth Paskin, director of operations at BMC Software, writes: “The outcomes IT professionals expect from AIOps can be categorized generally as automation and prediction … Their first expectation from AIOps is that it will allow them to automate what they are currently doing manually and thus increase the speed at which those tasks are performed. Some specific examples I’ve heard include: correlate customer profile information with financial processing applications and infrastructure data to identify transaction duration outliers and highlight performance impacting factors; evaluate unstructured data in service tickets to identify problem automation candidates; categorize workloads for optimal infrastructure placement; and correlate incidents with changes, work logs, and app dev activities to measure production impact of infrastructure and application changes.”

An AIOps platform canvasses data on logs, performance alerts, tickets, and other items using an auto-discovery process that automatically collects data across infrastructure and application domains. The process identifies infrastructure devices, running apps, and business transactions and correlates all the data in a contextual form. Automatic dependency mapping determines the relationships between elements such as the physical and virtual connections at the networking layer by mapping app flows to the supporting infrastructure and between the business transactions and the apps.

AIOps’ automated dependency mapping has another benefit: helping to track relationships between hybrid infrastructure entities. AIOps platforms can create service and app topology maps across technology domains and environments, allowing IT teams to accelerate incident response and quantify the business impact of outages.

To identify patterns and predict future events, like service outages, AIOps employs supervised learning, unsupervised learning, and anomaly detection based on expected behaviors and thresholds. Particularly useful is unsupervised machine learning, which enables AIOps platforms to learn to recognize expected behavior and set thresholds across data and performance metrics. The platforms can analyze event patterns in real time and compare those to expected behavior, alerting IT teams when a sequence of events (or groups of events) demonstrates activity that indicates anomalies are present.

The insights from AIOps platforms can be turned into a range of intelligent actions performed automatically, from expediting service desk requests to end-to-end provisioning to deployment of network, compute, cloud, and applications. In sum, AIOps brings together data from both IT operations management and IT service management, allowing security teams to observe, engage, and act on issues more efficiently than before.

Challenges

Not every AIOps deployment goes as smoothly as planned. Challenges can stand in the way, including poor-quality data and IT team errors. Employees sometimes face difficulty in learning how to use AIOps tools, and handing over control to autonomous systems can pose concerns among the C-Suite. Moreover, adopting new AIOps solutions can be time-consuming — a majority of respondents to the OpsRamp survey said it takes three to six months to implement an AIOps solution, with 25% saying that it takes greater than six months.

Because AIOps platforms rely so heavily on machine learning, challenges in data science can impact the success of AIOps strategies. For example, getting access to quality data to train machine learning systems isn’t easy. According to a 2021 Rackspace Technology survey, poor data quality was the main reason for machine learning R&D failure among 34% of respondents. Thirty-one percent said they lacked production-ready data.

Beyond data challenges, the skills gap also presents a barrier to AIOps adoption. A majority of respondents in a 2021 Juniper report said their organizations were struggling with expanding their workforce to integrate with AI systems. Laments over the AI talent shortage have become a familiar refrain from private industry — O’Reilly’s 2021 AI Adoption in the Enterprise paper found that a lack of skilled people and difficulty hiring topped the list of challenges in AI, with 19% of respondents citing it as a “significant” blocker.

Unrealistic expectations from the C-suite are another top reason for failure in machine learning projects. While 9 in 10 of C Suite survey respondents characterized AI as the “next technological revolution,” according to Edelman, Algorithmia found that a lack of executive buy-in contributes to delays in AI deployment.

Benefits

Successfully adopting AIOps isn’t a sure-fire thing, but many businesses find the benefits worth wrestling with the challenges. AIOps systems reduce the torrent of alerts that inundate IT teams and learn over time which types of alerts should be sent to which teams, reducing redundancy. They can be used to handle routine tasks like backups, server restarts, and low-risk maintenance activities. And they can predict events before they occur, such when network bandwidth is reaching its limit.

As Accenture explains in a recent whitepaper, AIOps ultimately improves an IT organization’s ability to be an effective partner to the business. “An IT operations platform with built-in AIOps capabilities can help IT operations proactively identify potential issues with the services and technology it delivers to the business and correct them before they become problems,” the consultancy wrote. “That’s the value of having a single data model that service and operations management applications can share seamlessly.”

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Categories
AI

AI Weekly: How the power grid can benefit from intelligent software

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Google parent Alphabet’s “moonshot” X lab announced last week at the White House Leaders Summit on Climate that it’s working on a project for the electric grid. Over the past three years, the lab says it has been investigating “new computational tools” designed to bring the grid “out of the industrial age and into the age of the intelligence.” Among other areas, X says it’s experimenting with (1) a real-time virtualization that shows power moving onto and off the grid, (2) tools that simulate what might actually happen on the grid, and (3) a platform to make information about the grid useful to stakeholders.

The work is being led by Audrey Zibelman, former managing director at Australian electricity and gas systems operations firm Australia Energy Market Operator, and it remains in the planning stages. But experts believe the core of this effort — intelligent software — is likely to become increasingly important in the energy sector.

“Hybrid plants and battery energy storage now mean power plants can be controlled and can simulate traditional power plants, and this will require sophisticated IT to integrate forecasting of reusable energy production, along with forecasting prices,” Ric O’Connell, executive director of clean energy consulting firm GridLab, told VentureBeat via email.

The U.S. electrical grid has long been burdened by aging infrastructure. Sixty percent of distribution lines have surpassed their 50-year life expectancy, according to Black and Veatch, while the Brattle Group anticipates $1.5 trillion to $2 trillion in spending by 2030 to modernize the grid and maintain reliability. The latest report from the American Society for Civil Engineers found that current grid investment trends will lead to funding gaps of $42 billion for transmission and $94 billion for distribution by 2025.

Neil Sahota, chief innovation officer at the University of California, Irvine, says intelligent software opens the door to the deployment of AI designed for power grid use cases. Utilities are already employing AI to address the windfalls and fluctuations in energy usage. Precise load forecasting ensures operations aren’t interrupted, thereby preventing blackouts and brownouts. And it can bolster the efficiency of utilities’ internal processes, leading to reduced prices and improved service.

“There are a lot of subtle clues that in aggregate show where and when a natural disaster can occur. To ‘see’ the clues, we need to process a lot of data across a broad spectrum of variables and look for subtle differences,” Sahota told VentureBeat via email. “This is difficult for people to do effectively but is in the wheelhouse of AI. Consider wildfires, where we are using climate information (including wind forecasts), drone surveillance, and satellite images to predict hot spots and how a fire may start and spread. AI can monitor all these millions of data points in real time and constantly generate prediction models.”

For example, startup Autogrid works with more than 50 utilities in 10 countries to deliver AI-informed power usage insights. Its platform makes 10 million predictions every 10 minutes and optimizes over 50 megawatts of power, which is enough to supply the average suburb. Flex, the company’s flagship product, predicts and controls tens of thousands of energy resources from millions of customers by ingesting, storing, and managing petabytes of data from trillions of endpoints. Using a combination of data science, machine learning, and network optimization algorithms, Flex models both physics and customer behavior, automatically anticipating and adjusting for supply and demand patterns.

O’Connell believes that efforts like X’s will face challenges, particularly on the distributed energy resource (DER) side of the equation. DER systems — small-scale power generation or storage technologies that provide an alternative to traditional power systems or enhance those systems — can be difficult to orchestrate because they might span solar panels, electric vehicle charging setups, and even smart thermostats. But if a digital transformation of the power grid succeeds, its long-term benefits could be significant, O’Connell says.

“Currently, when independent system operators want to add a new market participant type, it takes them a year to incorporate those changes. That’s legacy IT systems,” he said. “The IT systems that grid operators will need are going to have to get a serious upgrade from the ’90s technology that they use now.”

For AI coverage, send news tips to Kyle Wiggers — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI channel, The Machine.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

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Tech News

Facebook now says it will benefit from Apple’s privacy changes

For the past few months, Facebook has been stepping up its rhetoric in painting Apple as the enemy of the Internet and the free market. This mostly revolves around the iPhone maker’s App Tracking Transparency or ATT that will be drastically changing the ways advertisers and advertising platforms work on iOS. It seems, however, that Facebook is now changing its strategy and saying that it might actually profit from this but only because Apple will be forcing vendors to all flock to Facebook’s platforms instead.

Apple’s ATT pretty much mirrors the current trend among web browsers these days to block or disable cross-site tracking cookies and turning this protection on by default. On iOS, the implementation means that users will have to explicitly opt into tracking activity through first-use prompts or by diving into the platform’s settings.

Facebook’s main gripe is that this will drastically reduce the effectiveness of advertising platforms and strategies because users are most likely to keep tracking turned off. This, in turn, would result in lower revenues for both Facebook and its advertising customers, potentially causing many of the smaller businesses to lose critical profits they sorely need these days.

Speaking at a Clubhouse chat, CEO Mark Zuckerberg changed the company’s message but as a not-so-subtle jab at Apple. He is now saying that Facebook might actually be in a stronger position due to the ATT because more businesses will conduct their e-commerce activities on Facebook’s and Instagram’s platform since Apple will practically be locking user data in its own platform.

It remains to be seen if this will run afoul of Apple’s well-known policy about apps and services doing business on iOS, especially the requirement to only use its payment systems and taking a cut of the revenue. Needless to say, Facebook is now trying to turn the tables on Apple and painting what it once insisted was a great loss into something that could be a controversial gain in the end.

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