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How machine identities are the key to successful identity management

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Securing digital identities is a problem for many organizations. In fact, according to the Identity Defined Security Alliance (IDSA), 84% of organizations have experienced an identity-related breach. 

Part of the challenge of identity management is the identities that organizations need to manage aren’t just human, but machine-based. 

Today, enterprise identity security provider SailPoint Technologies Holdings, Inc., released a new research report surveying 300 global cybersecurity executives and revealing that machine identities make up 43% of all identities within the average enterprise, followed by customers (31%) and employees (16%). 

This highlights that organizations need to have a solution for managing digital machine identities in real time if they want to secure their environments against the latest threats. 

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The identity threat landscape 

Over the past few years, the identity sprawl created by the adoption of new technologies and cloud-based apps have increased complexity for security teams dramatically. Today, machine identities now outweigh human identities by a factor of 45 times on average, with the average employee having over 30 digital identities

SailPoint’s research also anticipates that this sprawl will continue, estimating that the total number of identities will grow by 14% over the next 3-5 years. 

Yet, many organizations still have a long way to go before they’re ready to confront the identity threat landscape. 

“Our report shows that 45% of companies are still at the beginning of their identity journey. This means they have the unique opportunity to take advantage of today’s technology to build a comprehensive, [artificial intelligence] AI-enabled approach to identity security from the ground up,” said Matt Mills, SailPoint president of worldwide field operations. 

“As enterprise identity needs move beyond human capacity, this approach has quickly become table stakes. Not only that, but identity security has risen to the top as business-essential to securing today’s enterprise,” Mills said. 

In practice, an artificial intelligence (AI) and machine learning (ML) centric approach is essential for detecting digital identities in real time. This appears to be recognized by organizations, with 50% of respondents indicating they’ve implemented AI/ML models to boost their capabilities. 

SailPoint’s own identity security cloud platform leverages AI and ML to automatically identify user and machine identities throughout enterprise environments, so that security teams can more effectively manage and secure them. This approach serves to increase visibility over user access risks. 

The identity management market 

SailPoint falls within the global identity and access management market, which researchers estimate will grow from a value of $13.4 billion in 2021 to $34.5 billion in 2028. 

The vendor remains a significant player in the market, in August reporting that it has raised annual recurring revenue (ARR) of $429.5 million.  

It’s competing against a number of other key players in the market including Okta, an identity platform provider which offers a unified IAM solution with lifecycle management to automatically onboard and offboard employees and contractors, and a single sign-on solution that IT teams can use to monitor and manage user access. 

Okta recently announced raising $383 million in total revenue for during the fourth fiscal quarter of 2022. 

Another competitor is CyberArk, a vendor that provides an enterprise privileged access management solution for automatically discovering and onboarding credentials and secrets used by human and machine identities. It also offers automated password rotation and the ability to authenticate user access through a single web portal.

Earlier this year, CyberArk announced raising ARR of $427 million. 

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AI

How to leverage AI to boost care management success

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Sixty percent of American adults live with at least one chronic condition, and 12% with five or more. They spend exponentially more on healthcare than those without any chronic conditions. For instance, 32% of adults with five or more chronic conditions make at least one ER visit each year. On top of that, 24% have at least one inpatient stay, in addition to an average of 20 outpatient visits — up to 10 times more than those without chronic conditions. In fact, 90% of America’s $4 trillion healthcare expenditures are for people with chronic and mental health conditions, according to the Centers for Disease Control and Prevention (CDC).

The fundamental way healthcare organizations reduce these costs, improve patient experience and ensure better population health is through care management. 

In short, care management refers to the collection of services and activities that help patients with chronic conditions manage their health. Care managers proactively reach out to patients under their care and offer preventative interventions to reduce hospital ER admissions. Despite their best efforts, many of these initiatives provide suboptimal outcomes.

Why current care management initiatives are ineffective

Much of care management today is performed based on past data

For instance, care managers identify patients with the highest costs over the previous year and begin their outreach programs with them. The biggest challenge with this approach, according to our internal research, is nearly 50-60% of high-cost patients were low-cost in the previous year. Without appropriate outreach, a large number of at-risk patients are left unattended with the reactive care management approach. 

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The risk stratification that the care management team uses today is a national model

These models are not localized, so understanding the social determinants of individual locations is not considered.

The care management team’s primary focus is chiefly on transition of care and avoiding readmissions

Our experience while working with different clients also points to the fact that readmissions contribute only 10-15% of total admission. The focus on proactive care management and avoiding future avoidable emergency room and hospital admission is lacking. This is key to success in value-based care models.

In any given year, high-cost patients can become low-cost

Without such granular understanding, outreach efforts can be ineffective in curbing the cost of care.

How AI can boost care management success

Advanced analytics and artificial intelligence (AI) open up a significant opportunity for care management. Health risks are complex, driven by a wide range of factors well beyond just one’s physical or mental health. For example, a person with diabetes is at higher risk if they also have low-income and limited access to medical services. Therefore, identifying at-risk patients’ needs to consider additional factors to encompass those most in need of care.

Machine learning (ML) algorithms can evaluate a complex range of variables such as patient history, past hospital/ER admissions, medications, social determinants of health, and external data to identify at-risk patients accurately. It can stratify and prioritize patients based on their risk scores, enabling care managers to design their outreach to be effective for those who need it most. 

At an individual level, an AI-enabled care management platform can offer a holistic view of each patient, including their past care, current medication, risks, and accurate recommendations for their future course of action. For the patient in the example above, AI can equip care managers with HbA1C readings, medication possession ratio, and predictive risk scores to deliver proper care at the right time. It can also guide the care manager regarding the number of times they should reach out to each patient for maximum impact.

Unlike traditional risk stratification mechanisms, modern AI-enabled care management systems are self-learning. When care managers enter new information about the patient — such as latest hospital visit, change in medication, new habits, etc. — AI adapts its risk stratification and recommendations engine for more effective outcomes. This means that the ongoing care for every patient improves over time.

Why payers and providers are reluctant to embrace AI in care management

In theory, the impact of AI in care management is significant — both governments and the private sector are bullish on the possibilities. Yet, in practice, especially among those who use the technology every day, i.e., care managers, there appears to be reluctance. With good reason.

Lack of localized models

For starters, many of today’s AI-based care management solutions aren’t patient-centric. Nationalized models are ineffective for most local populations, throwing predictions off by a considerable margin. Without accurate predictions, care managers lack reliable tools, creating further skepticism. Carefully designed localized models are fundamental to the success of any AI-based care management solution.

Not driven by the care manager’s needs

On the other hand, AI today is not ‘care manager-driven’ either. A ‘risk score’ or the number indicating the risk of any patient gives little to the care manager. AI solutions need to speak the user’s language, so they become comfortable with the suggestions. 

Healthcare delivery is too complex and critical to be left to the black box of an ML algorithm. It needs to be transparent about why each decision was made — there must be explainability that is accessible to the end-user. 

Inability to demonstrate ROI

At the healthcare organizational level, AI solutions must also demonstrate ROI. They must impact the business by moving the needle on its key performance indicators (KPIs). This could include reducing the cost of care, easing the care manager’s burden, minimizing ER visits, and other benefits. These solutions must provide healthcare leaders with the visibility they need into hospital operations as well as delivery metrics.

What is the future of AI in care management?

Despite current challenges and failures in some early AI projects, what the industry is experiencing is merely teething troubles. As a rapidly evolving technology, AI is adapting itself to the needs of the healthcare industry at an unprecedented pace. With ongoing innovation and receptiveness to feedback, AI can become the superpower in the armor of healthcare organizations.

Especially in proactive care management, AI can play a significant role. It can help identify at-risk patients and offer care that prevents complications or emergencies. It can enable care managers to monitor progress and give ongoing support without patients ever visiting a hospital to receive it. This will, in turn, significantly reduce the cost of care for providers. It will empower patients to lead healthy lives over the long term and promote overall population health.

Pradeep Kumar Jain is the chief product officer at HealthEM AI.

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Responsible AI is a top management concern, so why aren’t organizations deploying it? 

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Even though responsible artificial intelligence (AI) is considered a top management concern, a newly released report from Boston Consulting Group and MIT Sloan Management Review finds that few leaders are prioritizing initiatives to make it happen. 

Of the 84% of respondents who believe that responsible AI should be a top management priority, only 56% said that it is, in fact, a top priority — with only 25% of those reporting their organizations has a fully mature program in place, according to the research. 

Further, only 52% of organizations reported they have a responsible AI program in place – and 79% of those programs are limited in scale and scope, the BCG/MIT Sloan report said. With less than half of organizations viewing responsible AI as a top strategic priority, among them, only 19% confirmed they have a fully implemented responsible program in place.  

This indicates that responsible AI lags behind strategic AI priorities, according to the report.

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Factors working against the adoption of responsible AI include a lack of agreement on what “responsible AI” means along with a lack of talent, prioritization and funding. 

Meanwhile, AI systems across industries are susceptible to failures, with nearly a quarter of respondents stating that their organization has experienced issues ranging from mere lapses in technical performance to outcomes that put individuals and communities at risk, according to the research. 

Why responsible AI isn’t happening and why it matters

Responsible AI is not being prioritized because of the competition for management’s attention, Steve Mills, chief AI ethics officer and managing director and partner at BCG, told VentureBeat. 

“Responsible AI is fundamentally about a cultural transformation and this requires support from everyone within an organization, from the top down,” Mills said. “But today, many issues compete for management’s attention — evolving ways of working, global economic conditions, lingering supply chain challenges — all of which can down-prioritize responsible AI.”

There is also an uncertain regulatory environment even with AI-specific laws emerging in jurisdictions around the world, he said.

“On the surface, this should accelerate [the] adoption of responsible AI, but many regulations remain in draft form and specific requirements are still emerging. Until companies have a clear view of the requirements, they may hesitate to act,” Mills said.

He stressed that companies need to move quickly. Less than half of respondents reported feeling prepared to address emerging regulatory requirements — even among responsible AI leaders, only 51% reported feeling prepared.

“At the same time, our results show that it takes companies three years on average to fully mature responsible AI,” he said. “Companies cannot wait for regulations to settle before getting started.” 

There is also a perception challenge.

“Much of the hesitation and skepticism regarding responsible AI revolves around a common misconception that it slows down innovation due to the need for additional checklists, reviews and expert engagement,’’ Mills said. “In fact, we see that the opposite is true. Nearly half of responsible AI leaders report that their responsible AI efforts already result in accelerated innovation.”

Responsible AI can be difficult to deploy

Mills acknowledged that responsible AI can be hard to implement, but said, “the payoff is real.”

Once leaders prioritize and give attention to responsible AI, they still need to provide appropriate funding and resources and build awareness, he said. “Even once those early issues are resolved, access to responsible AI talent and training present lingering challenges.”   

Yet, Mills makes the case for companies to overcome these challenges, saying there are “clear rewards. Responsible AI yields products that are more trusted and better at meeting customer needs, producing powerful business benefits,” he said.

Having a leading responsible AI program in place reduces the risk of scaling AI, according to Mills.

“Companies that have leading responsible AI programs and mature AI report 30% fewer AI system failures than those with mature AI alone,” he said.

This makes sense, intuitively, Mills said, because as companies scale AI, more systems are deployed and the risk of failures increases. 

A leading responsible AI program offsets that risk, reducing the number of failures and identifying them earlier, minimizing their impact. 

Additionally, companies with mature AI and leading responsible AI programs report over twice the business benefits as those with mature AI, alone, Mills said.

“The human-centered approaches that are core to responsible AI lead to stronger customer engagement, trust and better-designed products and services,” he said.  

“More importantly,” Mills added, “it’s simply the right thing to do and is a key element of corporate social responsibility.”

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AI

Transforming the supply chain with unified data management

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Many organizations lack the technology and architecture required to automate decision-making and create intelligent responses across the supply chain, as has been shown by the past few years’ supply chain disruptions. However, these critical breakdowns can no longer be blamed solely on the COVID-19 pandemic. Rather, they can be blamed on businesses’ slow adoption of automated supply chain decision-making, which has resulted in inventory backlogs, price inflation, shortages and more. Further contributing to backlogs is continued single sourcing to one region rather than leveraging distributed regional capabilities. These factors have added to the complexity of systems and the disadvantages of lack of automation and the pandemic brought these existing critical breakdowns into stark relief.

This brings us to today and how this inability to effectively manage data streams is proving debilitating to many companies. In a Gartner study of more than 400 organizations, 84% of chief supply chain officers reported that they could service their customers better with data-driven insights. An equal number of respondents stated that they needed more accurate data in order to predict future conditions and make better decisions.

The challenge here is that companies are managing their supply chains with a series of disparate and disconnected tools and datasets. Instead of residing in a centralized location, critical information may be scattered across the supply chain, kept in functional siloes and tied to individual technology solutions and operating teams, limiting transparency and optimization. 

Ultimately, this impacts the overall results of supply chain digitalization. Human analysts, as well as advanced technology engines, may have trouble accessing data that is relevant, current and reliable. Data may be segregated across functions, resulting in a lack of end-to-end transparency. Lag times can significantly impact an organization’s ability to sense and respond immediately to disruptions or new information.

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End-to-end connectivity across the supply chain

The supply and demand disruptions in 2020 and 2021 clearly demonstrated the need for digital transformation and end-to-end visibility and orchestration. And the availability of new digital capabilities like artificial intelligence (AI), machine learning (ML), data science and advanced analytics has been nothing short of a game-changer for connecting the world’s supply chains. To keep pace with manufacturers’ and retailers’ demand surges, supply chains must evolve to become real-time, adaptive ecosystems.

Whenever an exception or a disruptive event occurs anywhere in the ecosystem, it can be recognized and addressed autonomously in a synchronized and collaborative manner. No matter how geographically distributed the value network is and how many suppliers it includes, today even the most complex global supply chain can be digitally connected via intelligent solutions in near real-time. 

The advanced technology that enables near real-time monitoring and communication depends on data for its success. Across the value chain, each supplier is digitally contributing information regarding costs, timing, inventory levels, availability and other key metrics — offering the opportunity for key partners to gain and offer feedback in real time, thus gaining key insights into the evolution of demand. 

But that is just the beginning. Today’s forecasting, business planning and execution optimization engines also depend on enormous volumes of third-party data — including news, weather and even social media — that impact end-to-end supply chain performance. Enabled by new, advanced capabilities such as AI, ML and predictive analytics, these new cognitive engines are incredibly powerful and accurate at translating huge amounts of raw data into strategic, actionable recommendations, often autonomously, allowing supply chain teams to shift focus from firefighting to strategic improvements.  

Leveraging partners to build a supply chain ecosystem 

Digital platforms can bring together these disparate data sources and functions to enable faster decisions and greater collaboration. Unified data management makes companies more agile and flexible in responding to changes. Through a best-of-breed network of partners and internal developers, companies can share data and ideas across teams, enabling real-time response and cognitive planning across stakeholders. However, to deliver a synchronized response across the global supply network, traditional walls will have to be overcome with advanced technology that supports real-time, end-to-end orchestration. 

Breaking down these traditional walls requires a partner- and developer-friendly platform, fully integrated across the network, to help democratize data access, streamline data management and encourage self-learning and continuous improvement. Through a digital command center, information can be shared across the supply chain to generate cognitive insights, identify disruptions and opportunities, and recommend strategic actions. These partnerships can transform data into a competitive edge by unifying the entire supply chain around a holistic, truly integrated technology ecosystem. 

And as data is aggregated and made accessible to every stakeholder, companies can make intelligent, strategic decisions based on a single set of real-time insights. The supply chain is a robust ecosystem fed by data, and it requires scalability, security, data integrity, real-time responsiveness and exceptional processing speeds. Think about the massive amounts of data from customers, partners and suppliers consumed by companies. Millions of bits of information inundate every network touchpoint. Without collaboration, users will find themselves siloed by their disparate data-driven workflows, making decisions based on slow, incomplete and disconnected data. 

To truly harness this vast amount of data, companies should be looking to solutions that support self-learning. Democratized supply chains are not created overnight. They require every partner and function to have equal access to data and optimization engines that take into consideration every outcome and priority — ingesting data and making decisions more rapidly than ever before. Such ecosystems result in supply chains that are strategic, functional and built to withstand today’s fluctuations and obstacles. 

Jim Beveridge is Senior Director of Product Marketing at Blue Yonder

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AI

Can NIST move ‘trustworthy AI’ forward with new draft of AI risk management framework?

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Is your AI trustworthy or not? As the adoption of AI solutions increases across the board, consumers and regulators alike expect greater transparency over how these systems work. 

Today’s organizations not only need to be able to identify how AI systems process data and make decisions to ensure they are ethical and bias-free, but they also need to measure the level of risk posed by these solutions. The problem is that there is no universal standard for creating trustworthy or ethical AI. 

However, last week the National Institute of Standards and Technology (NIST) released an expanded draft for its AI risk management framework (RMF) which aims to “address risks in the design, development, use, and evaluation of AI products, services, and systems.” 

The second draft builds on its initial March 2022 version of the RMF and a December 2021 concept paper. Comments on the draft are due by September 29. 

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The RMF defines trustworthy AI as being “valid and reliable, safe, fair and bias is managed, secure and resilient, accountable and transparent, explainable and interpretable, and privacy-enhanced.”

NIST’s move toward ‘trustworthy AI’ 

The new voluntary NIST framework provides organizations with parameters they can use to assess the trustworthiness of the AI solutions they use daily. 

The importance of this can’t be understated, particularly when regulations like the EU’s General Data Protection Regulation (GDPR) give data subjects the right to inquire why an organization made a particular decision. Failure to do so could result in a hefty fine. 

While the RMF doesn’t mandate best practices for managing the risks of AI, it does begin to codify how an organization can begin to measure the risk of AI deployment. 

The AI risk management framework provides a blueprint for conducting this risk assessment, said Rick Holland, CISO at digital risk protection provider, Digital Shadows.

“Security leaders can also leverage the six characteristics of trustworthy AI to evaluate purchases and build them into Request for Proposal (RFP) templates,” Holland said, adding that the model could “help defenders better understand what has historically been a ‘black box‘ approach.” 

Holland notes that Appendix B of the NIST framework, which is titled, “How AI Risks Differ from Traditional Software Risks,” provides risk management professionals with actionable advice on how to conduct these AI risk assessments. 

The RMF’s limitations 

While the risk management framework is a welcome addition to support the enterprise’s internal controls, there is a long way to go before the concept of risk in AI is universally understood. 

“This AI risk framework is useful, but it’s only a scratch on the surface of truly managing the AI data project,” said Chuck Everette, director of cybersecurity advocacy at Deep Instinct. “The recommendations in here are that of a very basic framework that any experienced data scientist, engineers and architects would already be familiar with. It is a good baseline for those just getting into AI model building and data collection.”

In this sense, organizations that use the framework should have realistic expectations about what the framework can and cannot achieve. At its core, it is a tool to identify what AI systems are being deployed, how they work, and the level of risk they present (i.e., whether they’re trustworthy or not). 

“The guidelines (and playbook) in the NIST RMF will help CISOs determine what they should look for, and what they should question, about vendor solutions that rely on AI,” said Sohrob Jazerounian, AI research lead at cybersecurity provider, Vectra.

The drafted RMF includes guidance on suggested actions, references and documentation which will enable stakeholders to fulfill the ‘map’ and ‘govern’ functions of the AI RMF. The finalized version will include information about the remaining two RMF functions — ‘measure’ and ‘manage’ — will be released in January 2023.

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AI

Evisort embeds AI into contract management software, raises $100M

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For lawyers and the organizations that employ them, time is quite literally money. The business of contract management software is all about helping to optimize that process, reducing the time and money it takes to understand and manage contracts. 

As it turns out, there is big money in the market for contract management software as well. An increasingly integral part of the business is the use of AI-powered automation. To that end, today contract management vendor Evisort announced that it raised $100 million in a series C round of funding, bringing total funding to date up to $155.6 million. 

Evisort was founded in 2016 and raised a $15 million series A back in 2019. The company was founded by a team of Harvard Law and MIT researchers and discovered early on that there was a market opportunity for using AI to help improve workflow for contracts within organizations.

“If you think about it, every time a company sells something, buys something or hires somebody, there’s a contract,” Evisort cofounder and CEO Jerry Ting told VentureBeat. “Contract data really is everywhere.”

Contract management is a growing market

Evisort fits squarely into a market that analysts often refer to as contract lifecycle management (CLM). Gartner Peer Insights lists at least twenty vendors in the space, which includes both startups and more established vendors.

Among the large vendors in the space is DocuSign, which entered the market in a big way in 2020 with its $188 million acquisition of AI contract discovery startup Seal Software. Startups are also making headway, with SirionLabs announcing this week that it has raised $85 million to help add more AI and automation to its contract management platform.

The overall market for contract lifecycle management is set to grow significantly in the coming years, according to multiple reports. According to Future Market Insights, the global market for CLM in 2021 generated $936 million in revenue and is expected to reach $2.4 billion by 2029. MarketsandMarkets provides a more considerable number, with the CLM market forecast to grow to $2.9 billion by 2024.

Ting commented that while every organization has contracts, in this view many organizations still do not handle contracts with a digital system and rely on spreadsheets and email. That’s one of the key reasons why he expects to see significant growth in the CLM space as organizations realize there is a better way to handle contracts.

Integrating AI to advance the state of contract management

Evisort’s flagship platform uses AI to read contracts that users then upload into the software-as-a-service (SaaS)-based platform.

Ting explained that his company developed its own algorithm to help improve natural language processing and classification of important areas in contracts. Those areas could include terms of a deal, such as deadlines, rates and other conditions of importance for a lawyer who is analyzing a contract. Going a step further, Evisort’s AI will now also analyze the legal clauses in an agreement.

“We can actually pull the pertinent data out of a contract, instead of having a human have to type it into a different system,” Ting said. 

Once all the contract data is understood and classified, the next challenge that faces organizations is what to do with all the data. That’s where the other key part of Evisort’s platform comes into play, with a no-code workflow service. The basic idea with the workflow service is to help organizations collaborate on contract activities, including analysis and approvals.

What $100M of investment into AI will do for Evisort

With the new funding, Ting said that his company will continue to expand its go-to market and sales efforts. Evisort will also be investing in new AI capabilities that Ting hopes will democratize access to AI for contract management.

To date, he explained that Evisort’s AI works somewhat autonomously based on definitions that Evisort creates. With future releases of the platform, Ting wants to enable users to take Evisort’s AI and adjust and train the algorithm for specific and customized needs. The plan is to pair Evisort’s no-code capabilities into the future feature, in an approach that will make it easy for regular users and not just data scientists, to build AI capabilities to better understand and manage contracts.

“I think the 100 million dollar mark tells the market, hey, this company is a serious player and they’re here to stay,” Ting said. “It’s a scale-up, not a startup.”

The new funding round was led by TCV with participation from Breyer Capital as well as existing investors Vertex Ventures, Amity Ventures, General Atlantics and Microsoft’s venture capital fund M12.

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AI

AI will soon oversee its own data management

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AI thrives on data. The more data it can access, and the more accurate and contextual that data is, the better the results will be.

The problem is that the data volumes currently being generated by the global digital footprint are so vast that it would take literally millions, if not billions, of data scientists to crunch it all — and it still would not happen fast enough to make a meaningful impact on AI-driven processes.

AI helping AI

This is why many organizations are turning to AI to help scrub the data that is needed by AI to function properly.

According to Dell’s 2021 Global Data Protection Index, the average enterprise is now managing ten times more data compared to five years ago, with the global load skyrocketing from “just” 1.45 petabytes in 2016 to 14.6 petabytes today. With data being generated in the datacenter, the cloud, the edge, and on connected devices around the world, we can expect this upward trend to continue well into the future.

In this environment, any organization that isn’t leveraging data to its full potential is literally throwing money out the window. So going forward, the question is not whether to integrate AI into data management solutions, but how.

AI brings unique capabilities to each step of the data management process, not just by virtue of its capability to sift through massive volumes looking for salient bits and bytes, but by the way it can adapt to changing environments and shifting data flows. For instance, according to David Mariani, founder of, and chief technology officer at AtScale, just in the area of data preparation, AI can automate key functions like matching, tagging, joining, and annotating. From there, it is adept at checking data quality and improving integrity before scanning volumes to identify trends and patterns that otherwise would go unnoticed. All of this is particularly useful when the data is unstructured.

One of the most data-intensive industries is health care, with medical research generating a good share of the load. Small wonder, then, that clinical research organizations (CROs) are at the forefront of AI-driven data management, according to Anju Life Sciences Software. For one thing, it’s important that data sets are not overlooked or simply discarded, since doing so can throw off the results of extremely important research.

Machine learning is already proving its worth in optimizing data collection and management, often preserving the validity of data sets that would normally be rejected due to collection errors or faulty documentation. This, in turn, produces greater insight into the results of trial efforts and drives greater ROI for the entire process.

Mastering the data

Still, many organizations are just getting their new master data management (MDM) suites up and running, making it unlikely they will replace them with new intelligent versions any time soon. Fortunately, they don’t have to. According to Open Logic Systems, new classes of intelligent MDM boosters are hitting the channel, giving organizations the ability to integrate AI into existing platforms to support everything from data creation and analysis to process automation, rules enforcement, and workflow integration. Many of these tasks are trivial and repetitive, which frees up data managers’ time for higher-level analysis and interpretation.

This trend toward deploying AI to manage the data it needs to perform other duties in the digital enterprise will change the nature of work for data scientists and other knowledge workers. People will no longer be tasked with doing the work they do now and instead will focus on monitoring the results of AI-driven processes and then making changes should they veer from defined objectives.

More than anything, however, AI-driven data management will speed up the pace of business dramatically. Data is king in the digital universe, and kings don’t like to wait.

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Computing

Apple Launches Business Essentials For Mac IT Management

Apple is now targeting small businesses with a new subscription following the company’s success with its Apple One service for individuals and families. Like Apple One, the new Apple Business Essentials subscription includes iCloud storage, but the business-oriented offering swaps out consumer services — like Apple TV+, Apple Fitness+, and Apple News+ — for device management and onboarding services. as well as access to Apple Support.

Apple’s Business Essentials plan covers all Apple hardware, including the iPhone, iPad, and Mac, and it includes setup, onboarding, backup, security, repairs, and updates. Apple is positioning its Business Essentials service as an IT service for small and midsize businesses (SMBs).

The Cupertino, California, maker of the Mac and iPhone is targeting Apple Business Essentials for small and medium businesses with up to 500 employees. The service launches today in the United States and will be available to SMBs for free while it is still in beta. When it exits beta in spring 2022, pricing will range from $3 per seat per month to $13 per seat per month. The pricing range takes into account the number of devices a user has as well as the amount of iCloud storage for the plan. Up to 2TB of iCloud storage is available on the highest-tier plan.

With its mobile device management service, Apple Business Essentials has a section called Collections within the app that employees can use to download apps that are required for their workflow, including Microsoft Office, Cisco Webex, and more.

“When employees sign in to their corporate or personally owned device with their work credentials, Collections automatically pushes settings such as VPN configurations and Wi-Fi passwords,” Apple detailed of its new service. “In addition, Collections will install the new Apple Business Essentials app on each employee’s home screen, where they can download corporate apps assigned to them, such as Cisco Webex or Microsoft Word.”

If an employee leaves, the Business Essentials service also makes it easy for SMB owners to reassign old devices to new users or issue new devices to new users.

Business Essentials is notable in that it will bring a more managed IT experience that employees at larger corporations have relied upon to the world of small businesses. In addition to device management and hardware support, Apple will also allow small businesses to bundle its AppleCare+ optional extended warranty services to Business Essentials starting in the spring. Employees will have access to two device repairs per year on their plan, and the repairs can be initiated directly within the Apple Business Essentials app. Apple will also offer on-site repair services in addition to mail-in repairs with its small business offering, and technicians can arrive on site in as little as four hours.

Apple has been making an aggressive push into its services business, which includes Apple Music. In its most recent earnings report, the company announced that its net sales from the services business grew by more than 27% year-over-year for the fiscal year ended September 25, 2021. Apple reported that its services businesses generated $68.4 billion, compared to $297.4 billion for products.

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Game

Pokimane is starting a talent management company for streamers

It can be difficult to ‘make it’ as a full-time streamer, but Pokimane (aka Imane Anys) thinks there’s a better way to nurture budding internet broadcasters. The well-known Twitch personality is co-founding RTS, a talent management and brand consulting company that plans to fix “what is broken” for both game streamers and esports. The firm will rethink management to help creators run a “stable business” that survives in the long run, and to support game developers and other brands wanting to make a significant impact.

Pokimane saw RTS tackling the problems she and others faced getting started. There are plenty of talented streamers who are “spinning their wheels on basic stuff” and forging partnerships that don’t work for either side, she said. Ideally, her new company will reflect her experience and give rookies the support they need to avoid many of those early headaches.

The startup will include a slew of game industry veterans, not to mention some major customers. Twitch and Endeavor veteran Stuart Saw will serve as CEO, while the remaining executives include alumni from Twitch, Blizzard and PAX. The board includes Twitch co-founder Kevin Lin, PUBG Corp’s Americas head Brian Corrigan and Endeavor Executive VP Karen Brodkin. RTS will work with Pokimane as well as Epic Games and Facebook. It will own and co-manage the Evo fighting game tournament.

It could take a while before it’s clear how well RTS fares compared to existing online talent management outfits. However, it’s notable that Pokimane and her team are focused on growing small-time streamers rather than courting big names can already fetch major deals. While this certainly won’t guarantee fame, it might lead to more people pursuing streaming as a job (if not a full-fledged career) instead of a hobby.

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Categories
AI

AI-powered spend management platform Yokoy secures $26M

Spend management platform Yokoy today announced that it raised $26 million in series A financing from Left Lane, with participation from prominent European investor Balderton Capital. The Zurich, Switzerland-based company says that it’ll use the new funding — which brings its total raised to $27.1 million — to expand across the U.S., Europe, and other regions around the world while enhancing the technologies underpinning its platform.

The spend management software industry is projected to be worth $3.97 billion by 2027, up from $1.08 billion in 2019, according to Verified Market Research. The Global Business Travel Association pegs the cost to companies of processing a single expense report at $58 each time, while processing a single invoice is estimated to cost between $12 and $32. Indeed, the biggest cost drivers when it comes to spend management are often errors, manual work, and transaction fees. Yokoy asserts that these costs can be cut by half if certain resource-intensive processes are automated.

Founded in Switzerland in 2019, Yokoy provides an AI-powered spend management suite for midsize and enterprise companies. Through a combination of machine learning, automation, and API integrations, the startup offers expense management, supplier invoice management, and corporate credit cards.

CEO Philippe Sahli, and chief technology officer Devis Lussi (who previously worked at CERN) met while working at Ernst & Young’s management consultancy, while chief customer officer Lars Mangelsdorf had been building software-as-a-service (SaaS) products at other startups. Meanwhile, CFO Thomas Inhelder — who’s held accounting roles at KPMG — came into contact with Sahli at a previous startup.

“With Yokoy, we’re building a highly intelligent, highly secure, and highly customizable global spend management platform that empowers our customers to take control of their vast corporate spending processes and fine-tune their workflows,” Lussi said in a statement. “We’re helping them to cut costs, save time and bring clarity to their global operations in a way that fits their ambitions. It’s this that we believe will see us becoming the leading spend management platform in the world.”

AI-powered spend management

With Yokoy’s platform, midsize to enterprise companies can configure and build their own process flows and integrate Yokoy with third-party tools. Lussi claims that the platform is “self-learning,” enabling Yokoy to monitor individual workflows and processes to make them more efficient and scalable over time.

For example, customers can import expense receipts and invoices by snapping pictures of them through Yokoy’s mobile app. The platform’s algorithms enhance the picture quality before extracting the words and numbers via optical character recognition, validating more than 300 data points in a single receipt or invoice. In the case of invoices, Yokoy can also recognize suppliers, match them with data from a company’s enterprise resource planning software, and fill any missing data into the scanned document. With the information it extracts from documents, Yokoy checks relevant policies, gauges the potential for fraud, and validates the data for outliers and rules violations.

Yokoy

Above: Yokoy’s spend management platform.

Image Credit: Yokoy

Any scans that don’t pass Yokoy’s quality assurance benchmarks are set aside for manual review, while the rest are automatically exported to an accounting system.

“Yokoy is able to reconstruct [the] context [of documents] based on various features. Such features can be the relative position of a sequence of characters on the paper — top, bottom, left, right — or the presence of certain keywords. With the help of keywords (Yokoy’s list comprises more than 100,000 entries in many different languages), something can be found out about the type of an expense,” Sahli told VentureBeat via email. “Yokoy has described the relationships between these features within [AI] models. In the fraction of a second [that] it takes the software to digitize and analyze an expense receipt, several such models are processed. The models have been trained with millions of examples, and they are constantly being refined.”

Yokoy competes with Ramp and others in the spend management solutions segment. But in two years, the company has managed to attract 80,000 users across 400 customers including DPD Group, Stadler Rail, Russia’s Sberbank, the Swiss bank Swissquote. Part of the latest investment will be put toward growing the company’s headcount by the end of 2021, Sahli says.

“Spend management includes the processing of supplier invoices, travel expenses, corporate credit card expenses, and all such accounts payable categories. While the travel expenses have decreased during the pandemic, other categories — like online purchases over corporate credit cards — have increased during that time,” Sahli added. “The total spend volume processed over Yokoy per customer during the pandemic has actually increased. Yokoy is a true winner of the pandemic, but for a completely different reason: COVID-19 has truly accelerated the digitization and automation in companies and that’s what has pushed us, especially the new customers number.”

Yokoy employs about 100 people throughout its five offices. It expects to more than double that number to 250 by the end of 2022.

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