Posts from the ‘Machine learning’ Category

Jan 2

Top 10 fastest-growing segments from Gartner’s latest information security forecast Q4 2024

Gartner’s latest information security forecast reflects the optimism of most CISOs about their budgets increasing in 2025. Ninety percent of security and risk management leaders, including CISOs, told Forrester they expect a budget increase this year.

According to Gartner’s latest Q4 2024 forecast, end-user spending will surge from $183.7 billion in 2024 to $293.9 billion in 2028, reaching a 12.47% compound annual growth rate.

Information security spending will grow rapidly, driven by increasing investments in areas such as cloud security (25.9% CAGR) and managed security services (15.0% CAGR) as more enterprises face the many challenges of securing hybrid cloud environments.

Key segments, including infrastructure protection and professional services, underscore the urgency nearly all organizations have in securing their critical systems against increasingly lethal AI and generative AI (gen AI) attacks.

Below is a visual representation of the top 10 fastest-growing segments shaping the cybersecurity landscape.

Please click on the graphic below to expand it for easier reading.

The 10 fastest-growing information security market segments going into 2025

Infrastructure Protection

With spending projected to grow from $31.3 billion in 2024 to $51.2 billion in 2028 (CAGR: 13.1%), infrastructure protection leads the information security market. Securing infrastructure that will increasingly be used to manage model data, LLMs, and AI apps is one of the core drivers in this segment going into 2025. The latest Gartner forecast reflects the growing demand for infrastructure true protection as more organizations go all in on AI.

Security Professional Services

Spending on professional security services is expected to grow from $27.3 billion in 2024 to $42.3 billion in 2028, attaining a CAGR of 11.6%. These services are critical for implementing zero-trust policies and conducting proactive security assessments.

Managed Security Services

Managed security services spending will rise from $24.1 billion in 2024 to $42.1 billion in 2028, reflecting a CAGR of 15.0%. Outsourcing security to external providers has become essential as companies face a more lethal, AI-dominated threatscape while grappling with talent shortages.

Network Security Equipment

Spending on network security equipment will increase from $21.7 billion in 2024 to $32.8 billion in 2028, attaining a CAGR of 10.9%. This reflects the growing need to secure hybrid and multi-cloud networks as organizations expand their digital perimeters.

Security Consulting Services

Spending on security consulting services will grow from $23.0 billion in 2024 to $32.6 billion in 2028, delivering a CAGR of 9.1%. More organizations are looking outside for in-depth expert advice as they attempt to implement advanced security frameworks. Getting compliance right and ensuring consistency when reporting material events to the Security and Exchange Commission (SEC) are also drivers of this segment’s forecast.

Identity Access Management (IAM)

IAM spending will rise from $17.7 billion in 2024 to $25.4 billion in 2028, achieving a CAGR of 9.4% according to Gartner forecast. A key subsegment, Privileged Access Management (PAM), is projected to reach $2.9 billion by 2025 as growing regulatory compliance requirements on a global scale are expected to drive adoption.

Cloud Security

Cloud security spending will grow from $9.0 billion in 2024 to $22.6 billion in 2028, achieving a CAGR of 25.9%. As cloud environments become more complex, investments in Cloud Security Posture Management (CSPM) and Cloud Workload Protection Platforms (CWPP) will continue to accelerate growth.

Other Security Software

Spending on niche and innovative security software solutions will grow from $9.0 billion in 2024 to $14.7 billion in 2028, attaining a CAGR of 13.0%. This category includes specialized tools and apps used for combating advanced social engineering and adversarial AI-based attacks.

Data Security and Privacy

Spending on data security and privacy will increase from $6.1 billion in 2024 to $10.3 billion in 2028, reflecting a CAGR of 14.0%. Stringent data protection regulations and growing cyber threats are driving investments in this segment.

Application Security

Application security spending is forecasted to rise from $6.3 billion in 2024 to $10.1 billion in 2028, driving a CAGR of 12.7%. This segment addresses vulnerabilities in software applications, which remain a primary target for attackers.

Conclusion

Organizations are prioritizing agility and the ability to anticipate new threats while doubling down on cloud security. Predicted to grow at a 25.9% CAGR, cloud security is the fastest-growing segment in the forecast.

Spending on new tools to detect emerging threats is projected to jump from $9 billion in 2024 to $14.7 billion in 2028, further indicating that organizations are willing to invest in new technologies to stop emerging threats.

Ultimately, cybersecurity has become a more crucial business decision than ever before. While other organization budgets are being slashed going into 2025, cybersecurity continues to see gains and is increasingly seen as an investment in business resiliency.

Sep 17

Top Seven Takeaways from Gartner’s 2024 CIO GenAI Survey

For 87% of CIOs, generative AI (GenAI) represents more than a technological advancement—it’s a career-defining opportunity.

Gartner’s 2024 CIO Generative AI Survey finds that GenAI is gaining momentum with CIOs, with 95% believing in the technology’s significant potential to improve their organizations. A significant obstacle: a gap between CIOs and their C-suite peers — tempers their optimism.

While CIOs recognize AI’s potential to unleash productivity gains and improve customer experiences, only a fraction of the C-suite sees it as an urgent priority. Closing that gap underscores CIOs’ essential role in championing GenAI by committing to excel at learning every aspect of the new technology and how it can deliver long-term value to their organizations.

The top seven takeaways from Gartner’s survey provide CIOs with a roadmap on how to take a practical, pragmatic approach to bridge the gaps across the C-suite and help their organizations get results from their GenAI strategies.

Strategic Insights from Gartner’s 2024 CIO GenAI Survey

Gartner’s latest CIO survey on GenAI provides insights into how IT leaders can capitalize on the technology’s significant impact, from career growth and expertise development to helping CIOs achieve more support across the C-suite. Each takeaway focuses on how CIOs can leverage AI to drive success for themselves and their organizations.

Here are the survey’s seven most insightful takeaways:

More CIOs are starting to view GenAI as a career-enhancing opportunity. Eighty-seven percent of CIOs see GenAI as a pivotal career advancement opportunity, with 44% of those proficient in AI strongly affirming this view. GenAI’s rapid adoption in organizations is proving itself a technology capable of delivering productivity gains and is increasingly becoming a skill and expertise essential for career advancement. For CIOs leading AI initiatives, it’s not just about technology—it’s about positioning themselves as visionary leaders qualified to step into more senior positions. To maximize this opportunity, CIOs need to prioritize the development of AI strategies that demonstrate clear, measurable business outcomes. Continuous learning and certification programs are given to any IT professional, especially CIOs, who want to maintain a competitive edge and have their careers capitalize on GenAI’s growth trajectory.

CIOs are more focused than ever on increasing their acumen about GenAI. CIOs are rapidly becoming the in-house experts on AI, with 52% now rating themselves as proficient or advanced, up from 38% nine months ago. This growing expertise is crucial, as 67% of CIOs are tasked with leading AI initiatives, often sharing this responsibility with other C-suite members. Gartner recommends that CIOs deepen their AI knowledge further and foster a culture of AI literacy across their teams to capitalize on this trend. Providing targeted training for IT and business leaders to ensure that AI strategies are fully integrated into broader business goals is quickly becoming table stakes.

Source: Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc.

Disconnect between CIO optimism and C-suite prioritization. Despite 95% of CIOs believing in the potential for GenAI to deliver value, the survey reveals a disconnect with the C-suite—only 21% of CIOs who consider themselves highly knowledgeable about AI believe their C-suite sees it as a high priority. This gap suggests a need for more effective communication and strategic alignment. CIOs need to focus on translating AI’s potential into language that resonates with the C-suite. Regular briefings and ROI-focused presentations can help bridge this gap and elevate AI as a top priority for all executive leaders.

CIOs are leading the charge in AI implementations. CIOs are increasingly in charge of GenAI initiatives, with 48% of CIOs responding to the survey indicating that they are the main executives responsible for these initiatives. Another 28% are part of the team responsible for developing AI strategy. This central role places CIOs at the forefront of digital transformation, requiring them to be strategic leaders and hands-on practitioners. CIOs need to establish clear governance frameworks and metrics for AI initiatives to ensure success and alignment with broader organizational goals. Additionally, partnering with other C-suite members, such as the CFO and CMO, can help secure the necessary resources and support for AI projects.

Focus on productivity gains. GenAI is proving effective in streamlining operations and improving efficiencies organization-wide, with 74% of CIOs citing productivity as its top business value. AI also improves customer experience (49%) and helps streamline digital transformation (31%). These priorities demonstrate AI’s multifaceted role in modern businesses. Gartner recommends that CIOs integrate AI into crucial or core organizational areas, ensuring that AI initiatives align with organizational objectives and are designed to deliver measurable, scalable outcomes.

Concerns over AI hallucinations. Although GenAI holds great potential, there are significant risks. According to 59% of CIOs, the biggest worry is “hallucinations” or misleading or incorrect outputs. In close succession, 44% and 48% of CIOs express concern about privacy violations and false information spread by malicious attackers. These dangers highlight the importance of solid governance, ongoing oversight, and continued investments in cybersecurity. According to Gartner, CIOs need to prioritize creating AI ethics guidelines and investing in auditing tools. Gartner also notes that reducing these risks will require cultivating a culture of accountability and transparency.

C-Suite engagement in AI is growing but still lags. The survey shows that while C-suite engagement with AI is growing, 42% of CIOs note increased investment in understanding AI, and 53% still consider their peers novices, highlighting a critical need for further education and alignment. CIOs need to take the lead and champion targeted AI education and strategy sessions to close this gap, ensuring AI initiatives are fully supported and integrated into the organization’s strategic goals.

Conclusion

In Gartner’s 2024 CIO Generative AI Survey, GenAI is more than a technological advancement—it’s a strategic imperative for CIOs seeking business transformation and career advancement. GenAI is rapidly becoming a cornerstone of modern enterprise strategy, with 87% of CIOs seeing it as a career-enhancing tool and 95% as a business value driver.

With only 21% of CIOs seeing AI as a high priority for their executive peers, the journey is difficult. 74% of CIOs are focused on productivity gains, but the C-suite is cautious. CIOs must gain AI expertise and lead the way in aligning AI initiatives with organizational goals to mitigate risks like AI hallucinations through robust governance. CIOs can use GenAI to achieve business success and career growth by strategically navigating these dynamics to cement their role as digital visionaries.

Bibliography:

Struckman, C. (2024). Key findings from the 2024 Gartner CIO generative AI survey (ID G00820936). Gartner, Inc. https://www.gartner.com/document/820936 (Client access required).

Sep 15

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

Eighty percent of enterprises have increased their investment in GenAI over the past year, with nearly one-quarter (24%) now integrating the technology into their operations, up from just 6% in the previous year.

Capgemini Research Institute’s recent report, Harnessing the value of generative AI: 2nd edition: Top use cases across sectors, highlights enterprises’ accelerating pace of GenAI adoption and growing importance across their operations and industries.

“Generative AI is not just a technological innovation; it’s a catalyst for transformative change across multiple sectors, driving productivity gains, operational efficiency, and strategic shifts in business models,” write the report’s authors. Enterprise leaders’ sentiment underscores the increasing recognition of GenAI as a critical technology for staying competitive in an increasingly turbulent economic environment.

According to the report, GenAI’s rapid adoption across IT and marketing indicates that companies are actively integrating it into their core operations to drive tangible, measurable benefits. Capgemini’s findings highlight the need for a strong data governance framework, strategic talent development, and vigilant cybersecurity to maximize GenAI’s potential as companies scale their initiatives.

How enterprises are maximizing GenAI’s value

Capgemini found ten key ways enterprises are positioning themselves to maximize GenAI’s potential. These strategies demonstrate how all companies can potentially invest in and integrate GenAI to boost growth, efficiency, and innovation across departments and industries.

Investment surge reflects growing confidence in GenAI. 89% of large businesses with annual revenues over $20 billion are leading this investment surge, highlighting GenAI’s significance for future growth. Additionally, 73% of companies with revenues between $1 billion and $5 billion have significantly increased their GenAI budgets, showing that this trend is not limited to the largest companies. This investment trend indicates that many companies believe GenAI can drive enterprise evolution and deliver substantial returns, with many expecting double-digit productivity and customer engagement growth.

Capgemini report finds top 10 ways enterprises are harnessing the value of GenAI

GenAI maturity grows steadily across industries. GenAI implementations have continued to mature across industry sectors over the past year. Up from 6% in 2023, 18% of organizations will fully integrate GenAI into most or all functions in 2024. With 64% and 53% of companies enabling GenAI, high-tech and financial services lead. Retail grew 17%–40% and industrial manufacturing 14%–35%. With 53% and 47% of telecom and energy/utilities adopting GenAI, respectively, progress has been made.

GenAI’s integration across organizational functions is growing. In one year, GenAI IT adoption rose from 4% to 27% across organizational functions. GenAI is improving enterprise productivity and innovation through this broad integration across sales, marketing, operations, and R&D. Capgemini also found that GenAI is transforming operations and creating value across all business areas.

Productivity and customer engagement gains. Over the last year, organizations that have implemented GenAI have reported a 7.8% increase in productivity and a 6.7% increase in customer engagement. These tangible benefits demonstrate GenAI’s ability to provide real, measurable value to enterprises. Early adopters report significant improvements in key performance metrics, highlighting the strategic importance of incorporating GenAI into business operations.

Small Language Models (SLM) are gaining momentum. 24% of organizations have implemented SLMs, and 56% plan to do so within three years. These models are cheaper and less computationally intensive than larger AI models, so many companies are piloting and eventually moving them into production. SLMs excel in industry-specific applications, allowing businesses to harness AI’s potential without the infrastructure and resource demands of larger models. SLMs are becoming a good option for companies trying to compete in an AI-driven market as they seek efficient and scalable AI solutions.

GenAI is enabling enterprises to advance from chatbots to autonomous multi-agent systems. GenAI is helping 62% of organizations upgrade from chatbots to AI agents that autonomously manage complex goals. 48% of users use multi-agent systems, where AI agents operate independently in changing environments. As businesses automate and optimize complex processes with these systems, decision-making and operational efficiency improve across industries. AI has evolved from simple user interactions to complex, agentic use cases, as shown in the image.

GenAI agents are accelerating the shift to autonomous operations. GenAI agents are increasingly used in enterprise automation, with 82% of companies planning to implement them in 1–3 years. These agents are evolving from supportive tools to autonomous entities that can perform complex tasks without interaction. This shift is significant, with 71% of organizations expecting AI agents to automate workflows and 64% expecting customer service and productivity improvements. AI agents are not just efficient; they are a radical shift toward fully autonomous, AI-driven operations that will transform enterprise productivity and strategic decision-making.

GenAI is forcing major business strategy shifts. 54% of companies expect GenAI to improve their strategies, up from 39% in 2023. 40% of companies are revising their business models to stay competitive as GenAI becomes more important. As GenAI becomes more important, 74% of businesses believe they must use it to grow revenue and stay ahead of the competition.

Strengthening data foundations is crucial for GenAI’s success. More than 60% of companies realize GenAI’s potential depends on solid data foundations. Only 51% have documented data integration processes and 46% have AI data management policies. Even enterprises that have adopted GenAI still struggle to make the most of all their external data sources. Capgemini makes it clear that for GenAI initiatives to succeed, companies need scalable, secure data infrastructure.

Tighten AI controls or risk trust and compliance. Ethics in AI deployment is a priority for 57% of organizations, which recognize the need for control mechanisms that can flex and adapt as their business goals change. While 46% have clear AI governance frameworks, 73% agree that human oversight is necessary to validate AI-driven decisions. Without strong governance, bias, and accountability issues could counteract GenAI’s benefits, so organizations must act now.

Conclusion

With 80% of organizations increasing their investment and almost a quarter already including it in their operations, GenAI is fast changing how businesses run. This general acceptance emphasizes GenAI’s importance as a main engine of efficiency and creativity, providing real advantages in customer interaction and output.

Organizations are not only embracing GenAI as AI agents and Small Language Models (SLMs) acquire traction; they are also including GenAI in their basic strategies. Those who match GenAI with their business models, make investments in solid data foundations, and develop the knowledge required to maximize its possibilities will inherit the future. They will lead in the era of artificial intelligence by doing this, establishing new benchmarks for operational excellence and creativity.

Jul 7

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Forrester’s top ten trends defining identity and access management in 2024

Stolen identity and privileged access credentials now account for 61% of all data breaches. This figure continues to increase as nation-state attackers, cybercrime groups, and rogue attackers integrate AI into their attack tradecraft.

Adversarial AI is taking aim at identities

80% or more of breach attempts aim first at identities and the systems that manage them. CrowdStrike’s 2024 Global Threat Report found that identity-based and social engineering attacks are reaching a new level of intensity. CrowdStrike found that attackers are using AI to launch advanced phishing attacks to impersonate legitimate users and infiltrate secure accounts. Attackers have long sought account credentials, but in 2023, their goals centered on authentication tools and systems, including API keys and OTPs.

“What we’re seeing is that the threat actors have really been focused on identity, taking a legitimate identity. logging in as a legitimate user. And then laying low, staying under the radar by living off the land by using legitimate tools,” Adam Meyers, senior vice president counter adversary operations at CrowdStrike, told VentureBeat in an interview early this year. Two of the most infamous Russian nation-state attackers, Fancy Bear and Cozy Bear, led these efforts, with the former exploiting a Microsoft Outlook vulnerability (CVE-2023-23397) for unauthorized server access.

Top ten trends defining identity and access management (IAM) in 2024

Forrester’s recent report, The Top Trends Shaping Identity And Access Management In 2024, provides an insightful view into the future of Identity and Access Management (IAM) and Privileged Identity Management (PIM). The report predicts that threat detection and remediation will improve with the help of A.I. Forrester also predicts that FIDO passkey authentication will go mainstream. In contrast, biometric authentication will slow down due to concerns regarding deepfakes.

Leading IAM providers include AWS Identity and Access Management, CrowdStrike, Delinea, Cradlepoint, ForgeRock, Ivanti, Google Cloud Identity, IBM Cloud Identity, Microsoft Azure Active Directory, Palo Alto Networks, and Zscaler.

Here is a summary of the top ten trends Forrester believes will shape IAM in 2024:

Trend 1: AI Will Improve Identity-Based Threat Detection and Remediation. Generative AI (genAI) is helping to redefine the future of IAM by improving outlier behavior analysis, increasing alerts’ accuracy, and streamlining administrative tasks while guarding against new threats.

98% of security professionals believe AI and machine learning (ML) will be beneficial in fighting identity-based breaches and see it as a pivotal technology in unifying their many identity frameworks. The majority, 63%, predict AI’s leading use case will be greater accuracy in identifying outlier behavior. 56% believe AI will help improve the accuracy of alerts, and 52% believe AI will help streamline administrative tasks.

Forrester asserts that AI will help short-staffed security teams triage alerts and automate time-consuming, mundane aspects of their jobs. Forrester also envisions genAI being used to query, “Which five applications are the riskiest from an identity entitlement perspective?” CrowdStrike announced at RSAC 2024 that Charlotte AI, CrowdStrike’s Generative AI security analyst, can automatically correlate all related contexts into a single incident and generate an LLM-powered incident summary for security analysts.

Trend 2: IAM Platforms Face Increased Scrutiny Of Their Underlying Security. High-profile breaches that began with impersonation leading to identity theft, including MGM and Okta, reflect how social engineering can still bypass IAM safeguards. CISOs are pushing back on their IAM vendors to improve operational processes and security practices and prioritize security for cloud-based SaaS applications and multi-cloud configurations. Forrester writes that their clients running IAM systems expect their vendors to comply with standards like SOC 2, FedRAMP, ISO 27002, and PCI. CISOs and security teams are also asking to vet a vendor’s workforce, including both employees and contractors and understand how the vendor communicates about and addresses security issues.

Forrester’s advice to security and risk management professionals is to “Demand multifactor authentication for all workforce business and admin users, without exception. Prioritize IAM vendors that embrace secure-by-design and secure-by-default principles and value continuous two-way customer engagement to improve their overall cybersecurity posture.”

Trend 3: IAM And Non-IAM Vendors Respond To Identity-Centric Threats. More CISOs and their security teams are taking a zero trust mindset to breaches. They see them as inevitable, and as part of their zero trust frameworks, they’re looking to shut down lateral movement after an intrusion. Forrester observes that “both IAM vendors and non-IAM cybersecurity vendors keep making advances in identity threat detection and response (ITDR). As a result of organic development and acquisitions, ITDR capabilities are being incorporated in platforms from privileged identity management (PIM) vendors like ARCON, BeyondTrust, CyberArk, and Delinea, as well as XDR vendors, such as Cisco, CrowdStrike, Proofpoint, and SentinelOne.”

Trend 4: FIDO Passkey Authentication Goes Mainstream For Workforce And B2C Uses. Forrester notes that a large number of customer-facing sites, including H&R Block, PayPal, and Verizon, are moving to passwordless authentication. At the same time, smaller financial institutions like coinbase.com offer optional fast identity online (FIDO) Authentication and FIDO passkey-based authentication. The research firm expects 30% of B2C websites and apps to offer FIDO passkeys by the end of 2024.

Trend 5: Biometric Adoption Slows Due To Concerns Around Deepfakes. Despite biometric authentication being a security standard on smartphones, CISOs and consumers alike are becoming more concerned about deepfakes. Designing liveness detection and other advanced features for facial and fingerprint recognition systems reduces the threat of spoofing generated by deepfake technology.

As multiple breach attempts have proven, voice biometrics are more susceptible to attack. Forrester notes that in response, the FTC set a Voice Cloning Challenge to “encourage the development of multidisciplinary solutions—from products to procedures—aimed at protecting consumers from artificial intelligence-enabled voice cloning harms, such as fraud and the broader misuse of biometric data and creative content.” Vendors will add additional deepfake detection to their solutions in 2024, resulting in a rebound in biometrics adoption in 2025.

Trend 6: IMG And PIM Vendors Expand Coverage Of Cloud Administrator Identities. Getting multicloud and hybrid cloud security right is getting more challenging and complex to achieve at scale due to configuration complexity. Forrester notes that “zero trust in the cloud starts with understanding the data access entitlements of identities like cloud infrastructure administrators, SaaS administrators, and business users.” Security and risk management professionals need to review cloud administrators’ entitlements that grant access to sensitive data assets and, when necessary, cancel them. Forrester writes, “While tools offer detection and remediation automation, they are no substitute for documented and consistent identity governance processes.”

Trend 7: Government-Issued Digital Identities Continue To Spread. Forrester believes acceptance of government-issued decentralized digital identities (DDIDs) beyond government use cases will grow in 2024. Mobile digital identities, including driver’s licenses, are now available in the US states of Arizona, California, Florida, and Iowa. Jurisdictions that have or will soon issue mobile driver’s licenses include the European Union (based on the eIDAS 2.0 approved set of standards), Estonia, Hungary, and Sweden. Nigeria and the Philippines have digital identities active today. .

Trend 8: B2B IAM Becomes A Differentiating Feature. Security teams and CISOs running them who are operating without an extended IAM ecosystem for partners like contractors, suppliers, and resellers face more severe security risks. B2B IAM involves managing joiner, mover, and leaver (JML) processes differently than internal employees. Forrester predicts that in 2024, IAM vendors will enhance platforms with features like simplified federation onboarding, verifiable credentials for ID verification, and improved access review processes for the extended enterprise.

Trend 9: Commercial and homegrown IAM Solutions Face Growing Demand For Upgrades. Maintaining on-premises IAM systems is becoming more costly and inefficient, making it more attractive to move to a cloud-based platform. Forrester is finding that the brittle, less secure nature of on-premise legacy systems also makes them more difficult to upgrade. Demand is so high for replacing legacy systems that a recent Forrester survey found that the intention to replace homegrown solutions jumped from 4% in 2022 to 18% in 2023.

Trend 10: The Fine-Grained Authorization Market Heats Up. As digital platforms and business app creation continue to proliferate, the need for dynamic and fine-grained access controls is extending beyond security. Forrester says that the IAM market is moving toward centralized and external authorization patterns because of B2B2E and B2B2C relationships and the possibility that genAI could make it easier to create and manage authorization policies.

Feb 17

IT And Marketing Show Strongest Interest In Adopting Gen AI First

Currently, 16% of organizations have implemented generative AI in production, while 44% are piloting it for potential applications.
Interest in deploying generative AI for production applications saw a fivefold increase from the first to the fourth quarter of 2023.
Healthcare, manufacturing, and education are the three industries most actively pursuing generative AI adoption.
A majority of organizations, 63%, deem CRM data critical to their generative AI initiatives.

These and many other insights are from Dresner Advisory Services‘ recent Generative AI Report. The advisory firm surveyed its research community of over 8,000 organizations and vendors’ customer communities. The study is global in scope, with 50% of respondents from North America, 26% from EMEA, 19% from Asia/Pacific and 6% from Latin America. Dresner’s report stands out for its in-depth and nuanced analysis of gen AI adoption across global organizations.

News about generative AI has captivated technology leaders. Demand for gen AI-related news and insights dominates many organization leaders’ time. 29% are following gen AI news updates constantly, and 30% say they often check in and see what’s new in gen AI, 24% regularly check the news. Overall, 72% of analytics and business intelligence (BI) professionals have made gen AI news a priority. North American respondents are the most diligent with constantly checking gen AI news, reflecting the region having the highest production use of gen AI.

Key takeaways from the report include the following:

Professionals in IT and marketing report plans to be the first adopters of generative AI, with 44% of IT and 36% of marketing professionals saying adopting gen AI is a primary focus. Operations/ production, sales, and C-level executives also show significant interest in adopting gen AI early. Dresner’s report states that “finance and human resources least often indicate overall interest, exceeding a majority only when aggregating their primary, secondary, and tertiary responses.”

63% of organizations consider CRM data as critical or very important to generative AI. Finance and accounting data is considered the next most important, followed by call center and supply chain data. Dresner’s analysis found that respondents least often expect generative AI to leverage workforce (HR) data. Organizations are wary of using HR data due to privacy concerns combined with the stringent standards and safeguards on data security and its use across regulated industries today.

Gen AI adoption across organizations accelerated rapidly in 2023. From 1Q23 to 4Q23, production use increased nearly fivefold, experimenting increased by 70%, and planned use in 12 months increased by 157%. Dresner’s research results reflect a major shift in generative AI prioritization last year. The report’s authors contend that implementation activity and funding were primarily from autonomous, decentralized sources, not from C-level mandates or sponsors, as it occurred late in fiscal years and into annual budget cycles.

Consumer services lead gen AI production levels at 43%. Technology, business services, and healthcare have the next three highest levels of gen AI production in use today. The education industry reports the highest experimentation rate at 67%, closely followed by healthcare at 62%, while government trails at 50%. The report notes that the government also reports the highest levels for planned use beyond 12 months and no planned use.

40% of organizations consider it critical to achieve productivity and efficiency gains from gen AI. One in three (30%) say improving customer experience and personalization is the next most critical priority, followed by improved search quality and decision-making (26%).

Data privacy concerns are considered critical to 46% of organizations pursuing gen AI initiatives today. Legal and regulatory compliance, the potential for unintended consequences, and ethics and bias concerns are also significant. Less than half of respondents—46% and 43%, respectively—consider costs and organizational policy important to generative AI adoption.

Apr 19

FinancialForce Unleashes Spring ’23 Release, Strengthening Opportunity-to-Renewal

Finding new ways to improve opportunity-to-renewal is core to any services business’s growth.

FinancialForce has long bet its business on the belief that it could streamline opportunity-to-renewal for people- and software-centered businesses better than any other vendor. In delivering their Spring ’23 release, they’re proving how adept they are at delivering new features on a faster release cadence of three major releases a year. Out of its workforce of 1,000 people, FinancialForce has 400 full-time employees in DevOps, engineering, product management, and quality and nearly 100 outside resources in R&D.

FinancialForce’s overarching goal with the Spring ’23 release is to strengthen the customer’s ability to excel at opportunity-to-renewal. The feature refresh for Spring ’23 includes 18 different areas of their platform, with the most, eight, being in Services CPQ. Dan Brown, Chief Product, and Strategy Officer at FinancialForce, says, “Opportunity-to-renewal is core to companies that deliver services. It’s an area that has been dramatically underserved by classic vendors in this space. Most are fairly product-centric, and that tends to hold companies that are service-oriented back.”

Services-as-a-Business is gaining traction

FinancialForce’s Spring ’23 release shows how Services-as-a-Business is closing gaps and improving the opportunity-to-renewal process. Tight labor markets, spiraling costs and prices due to inflation, and blind spots in opportunity-to-renewal cycles continually jeopardize services revenue. As a result, professional services and software companies relying on service revenue risk losing Annual Recurring Revenue (ARR) and seeing reduced Customer Lifetime Value for every account. The Spring ’23 release provides a more granular, 360-degree view across eight core areas of the opportunity-to-renewal process to help services businesses meet new growth challenges.

“Our new Spring ’23 release is designed to give organizations the kind of certainty they need in these very uncertain economic times,” said Scott Brown, President, and Chief Executive Officer at FinancialForce. “Given the pace at which market and business conditions change, services businesses need confidence in their ability to manage estimates, skills and resources, and solve complex problems. This new release gives organizations a complete, customer-centric view of their business to turn continuous disruption into a competitive edge.”

FinancialForce’s Spring ’23 release doubles down in the areas of Service CPQ and Resource Management, which are the areas where the majority of new features have been added in the Spring ’23 release.

Improving Services CPQ process performance protects margins

FinancialForce is prioritizing Services CPQ, first introduced in the Winter ’22 release, to help customers get more in control of their margins and time management. The number and depth of new features in this area and Dan Brown’s insights into how popular Services CPQ has become with enterprise accounts demonstrate that prioritization. FinancialForce’s enterprise accounts are adopting Services CPQ to save time during sales cycles by providing their prospects with the visibility to identify resources available for quoting work, their billable rate, skills, and previous experience.

Dan Brown said that “in (quote) estimation, you now can reach into your PSA (Professional Services Automation) system and identify the resource that you’re going to quote, what’s their billable rate, what’s their skills, what’s their capabilities. A big issue our customers have is that the As Quoted versus the As Delivered are almost always materially very different.”

He continued, emphasizing, “And that’s where you end up with margin erosion, that’s where you end up with revenue leakage for our customers. Now with Services CPQ, the As Quoted and As Delivered features are tightly linked together. And that has driven enormous improvements.”

Scott Brown added, “When I was a customer, this was a big pain point. For me, the capability to connect your pre-sales activities to your post-sale delivery is a real game changer for us.”

Underscoring how vital Services CPQ is to FinancialForce’s opportunity-to-renewal strategy, the Spring ‘23 Customer Overview notes that “with usability improvements in Services CPQ, support for additional pricing and costing scenarios, and streamlined estimate export for correct Statements of Work, services teams will be able to create accurate and competitive proposals faster, leading to higher win rates on projects, with much lower risk profiles.”

Among the many enhancements to Services CPQ are usability enhancements to the Estimate Builder, helping to reduce errors in As Quoted and As Delivered Results.

New features to optimize resources and projects

Additional goals of the spring ’23 release are to provide customers with improved workflows for optimizing resources and streamlining project management. Given how every professional services firm and software company today is under pressure to continually find new ways to optimize resources and be more done with less, the timing of Resource Optimizer Enhancements and introducing Resource Manager Work Planner is excellent. FinancialForce allows assigning multiple resources to project enhancements, integrating with MS Outlook and Google Calendar, as well as mass deletion of pass utilization results. FinancialForce also delivers task-based scheduling of held resource requests.

The Spring ’23 release is designed to help enterprises optimize resources from small-scale to multi-location projects by adding Resource Work Planner and Enhanced Skills Maintenance that can scale across multiple global locations.

How FinancialForce’s Spring ’23 Release Strengthens Opportunity-to-Renewal

“This new release gives organizations a complete, customer-centric view of their business to turn continuous disruption into a competitive edge,” remarked Scott Brown during a recent briefing. FinancialForce aims to help services businesses more efficiently monetize their time and resources by concentrating their development efforts across opportunity-to-renewal.

The release shows how services companies are looking to real-time financial analytics, including new risk management features, as guardrails to keep their businesses on track to margin and profit goals. The Spring ’23 release shows FinancialForce’s view of the opportunity-to-renewal process and what strengths it can offer customers, from a new Scheduling Risk Dashboard that provides early intervention and project course corrections in real time, to streamlined estimate exports for accurate Statements of Work (SOWs).

The following table uses the opportunity-to-renewal process as a framework to put the new release into context. It compares each phase of the opportunity-to-order process, how FinancialForce defines their role, how the Spring ’23 release strengthens each area, what the people and software-oriented benefits are, along with their leading customer references. You can also download a copy of the Opportunity-to-Renewal Process comparison here.

Apr 8

Five Ways AI Can Help Create New Smart Manufacturing Startups

AI and machine learning’s potential to drive greater visibility, control, and insight across shop floors while monitoring machines and processes in real-time continue to attract venture capital. $62 billion is now invested in 5,396 startups concentrating on the intersection of AI, machine learning, manufacturing, and Industry 4.0, according to Crunchbase.

PwC’s broader tech sector analysis shows a 30% year-over-year growth in funding rounds that reached $293.2 billion in 2021. Smart manufacturing startups are financed by seed rounds at 52%, followed by early-stage venture funding at 33%. The median last funding amount was $1.6 million, with the average being $9.93 million.

Abundant AI startup opportunities in smart manufacturing and industry 4.0

According to Gartner, “The underlying concept of Industry 4.0 is to connect embedded systems and smart production facilities to generate a digital convergence between industry, business, and internal functions and processes.” As a result, Industry 4.0 is predicted to grow from $84.59 billion in 2020 to $334.18 billion by 2028. AI and machine learning adoption in manufacturing are growing in five core fields: smart production, products and services improvements, business operations and management, supply chain, and business model decision-making. Deloitte’s survey on AI adoption in manufacturing found that 93% of companies believe AI will be a key technology to drive growth and innovation.

Machine intelligence (MI) is one of the primary catalysts driving increased venture capital investment in smart manufacturing. Startup CEOs and their customers want AI and machine learning models based on actual data, and machine intelligence is helping to make that happen. An article by McKinsey & Company provides valuable insights into market gaps for new ventures. McKinsey’s compelling data point is that those leading companies using MI achieve 3X to 4X the impact of their peers. However, 92% of leaders also have a process to track incomplete or inaccurate data – which is another market gap startups need to fill.

McKinsey and the Massachusetts Institute of Technology (MIT) collaborated on a survey to identify machine intelligence leaders’ KPI gains relative to their peers. They found that leaders achieve efficiency, cost, revenue, service, and time-to-market advantages. Source: Toward smart production: Machine intelligence in business operations, McKinsey & Company. February 1, 2022.

Based on the uplift MI creates for new smart manufacturing startup funding and the pervasive need manufacturers have to improve visibility & control across shop floors, startups have many potential opportunities. The following are five that AI and machine learning is helping to create:

AI-enabled Configure, Price, and Quote (CPQ) systems that can factor in supply chain volatility on product costs are needed. Several startups are already using AI and machine learning in CPQ workflows, and they compete with the largest enterprise software providers in the industry, including Salesforce, SAP, Microsoft, and others. However, no one has taken on the challenge of using AI to factor in how supply chain volatility changes standard and actual costs in real-time. For example, knowing the impact of pricing changes based on an allocation, how does that impact standard costs per unit on each order? Right now, an analyst needs to spend time doing that. AI and machine learning could take on that task so analysts could get to the larger, more complex, and costly supply chain problems impacting CPQ close rates and revenue.
Using AI-enabled real-time data capture techniques to identify anomalies in throughput as an indicator of machine health. The aggregated data manufacturing operations produced every day holds clues regarding each machine’s health on the shop floor. Automated data capture can identify scrap rates, yield rates and track actual costs. However, none of them can analyze the slight variations in process flow product outputs to warn of possible machine or supply chain issues. Each process manufacturing machine runs at its cadence or speed, and having an AI-based sensor system track and analyze why speeds are off could save thousands of dollars in maintenance costs and keep the line running. In addition, adding insight and intelligence to the machine’s real-time data feeds frees quality engineers to concentrate on more complex problems.
Industrial Internet of Things (IIoT) and edge computing data can be used for fine-tuning finite scheduling in real-time. Finite scheduling is part of the broader manufacturing systems organizations rely on to optimize shop floor schedules, machinery, and staff scheduling. It can be either manually intensive or automated to provide operators with valuable insights. A potential smart manufacturing opportunity is a finite scheduler that relies on AI and machine learning to keep schedules on track and make trade-offs to ensure resources are used efficiently. Finite schedulers also need greater accuracy in factoring in frequent changes to delivery dates. AI and machine learning could drive greater on-time delivery performance when integrated across all the shop floors a manufacturer relies on.
Automated visual inspections and quality analysis to improve yield rates and reduce scrap. Using visual sensors to capture data in real-time and then analyze them for anomalies is in its nascent stages of deployment and growth. However, this is an area where captured data sets can provide machine learning algorithms with enough accuracy to identify potential quality problems on products before they leave the factory. Convolutional neural networks are an effective machine learning technique for identifying patterns and anomalies in images. They’re perfect for the use case of streamlining visual inspection and in-line quality checks in discrete, batch, and process manufacturing.
Coordinated robotics (Cobots) to handle assemble-to-order product assembly. The latest cobots can be programmed to stay in sync with each other and perform pick, pack, ship, and place materials in warehouses. What’s needed are advanced cobots that can handle simple product assembly at a more competitive cost as manufacturers continue to face chronic labor shortages and often run a shift with less than half the teams they need.

Talent remains an area of need

Manufacturers’ CEOs and COOs say that recruiting and retaining enough talent to run all the production shifts they need is the most persistent issue. In addition, those manufacturers located in remote regions of the world are turning to robotics to fulfill orders, which opens up opportunities for integrating AI and machine learning to enable cobots to complete assemble-to-order tasks. The unknown impact of how fast supply chain conditions change needs work from startups, too, especially in tracking actual cost performance. These are just a few opportunities for startups looking to apply AI and machine learnings’ innate strengths to solve complex supply chain, manufacturing, quality management, and compliance challenges.

Apr 6

LinkedIn Best Companies To Work For In 2022 Dominated Again By Tech

Amazon’s Sunnyvale, CA Campus (source: Istockphoto)

Tech leaders are six of LinkedIn’s top ten companies to grow your career in 2022.
Amazon is the again highest rated company, followed by Alphabet (2^nd), IBM (6^th), AT&T (7^th), Apple (9^th), and Comcast (10^th).
19 of the 50 top companies in the U.S. are in the tech industry, including Dell, Intel, Oracle, Salesforce, Cisco, and others.
LinkedIn identified four key trends in their analysis, with flexible work is becoming table stakes for recruiting and retaining employees.

These and many other insights are from LinkedIn Top Companies 2022: The 50 best workplaces to grow your career in the U.S., published today. All 50 companies are currently hiring and have over 530,000 jobs open across the U.S, with over 70,000 being remote positions. The LinkedIn analysis of the best companies to grow your career spans 35 global markets, including the U.S., Canada, Mexico, Brazil, Argentina, Colombia, Chile, Ireland, France, Switzerland, Austria, Germany, Israel, Italy, Spain, the U.K., Sweden, Belgium, Denmark, the Netherlands, Portugal, India, Japan, Singapore, Philippines, Malaysia, Indonesia, Australia, New Zealand, UAE, Egypt, Saudi Arabia, South Africa, Nigeria, and Kenya.

LinkedIn’s Top Companies 2022 spotlights the organizations investing in employee success and career development. LinkedIn’s methodology and internal analysis ranked companies based on seven pillars that display career progression: ability to advance, skills growth, company stability, external opportunity, company affinity, gender diversity, and educational background.

The 19 Best Tech Companies To Grow Your Career In 2022

The following are profiles of the top 19 tech companies hiring in the U.S. today with links to available positions accessible via LinkedIn:

Amazon

Amazon is the parent company of Whole Foods Market, Zappos, Twitch, PillPack, and others.

Global headcount: 1,600,000 (with 1,100,000 in the U.S.) | Top U.S. locations: Seattle, San Francisco Bay Area, New York City | Most notable skills: Warehouse Operations, Data Entry, AWS Lambda | Most common job titles: Software Engineer, Fulfillment Associate, Warehouse Associate | Largest job functions: Operations, Engineering, Program and Project Management | What you should know: Even as the country’s second-largest private employer, Amazon continues to compete in recruiting and retaining top talent amid a competitive labor market. The company recently announced that it’s doubling its maximum base salary for corporate and tech workers, and it raised average wages for warehouse workers late last year, increasing pay for more than half a million of its employees. But the e-commerce giant is going beyond compensation, too: investing $1.2 billion over the next three years to expand its education and skills training initiatives. Amazon now pays 100% of college tuition for frontline employees as part of its Career Choice program and covers high school diploma programs, GEDs, and English proficiency certifications.

See jobs at Amazon

Alphabet

Alphabet is the parent company of Google, YouTube, Fitbit, Waymo, Verily, and others.

Global headcount: 156,000 | Top U.S. locations: San Francisco Bay Area, New York City, Seattle | Most notable skills: Video Editing and Production, Google Cloud Platform (GCP), C++ | Most common job titles: Software Engineer, Program Manager, Product Manager | Largest job function: Engineering, Information Technology, Program and Project Management | What you should know: It’s been a big year for Alphabet: The company onboarded nearly 6,500 employees last quarter and saw significant growth across Google’s Cloud service and YouTube (whose revenues are now growing at a faster rate than Netflix). For those interested in flexibility, the tech giant has a robust offering. In addition to adopting a hybrid work model, the company told LinkedIn that Alphabet offers four ‘work from anywhere’ weeks per year, sabbaticals for long-term employees, and ‘no meeting’ days. But Alphabet has also worked to maintain a collaborative culture and support career growth while working remotely. Employees can take advantage of resource groups like Women@Google and its Googler-to-Googler training, which lets its workers get first-hand knowledge across different fields from other employees.

See jobs at Alphabet

IBM

IBM is the parent company of Red Hat, SoftLayer Technologies, Truven Health Analytics, and others.

Global headcount: 250,000 | Top U.S. locations: New York City; Raleigh-Durham, N.C.; San Francisco Bay Area | Most special skills: Kubernetes, Openshift, Hybrid Cloud | Most common job titles: Software Engineer, Project Manager, Data Scientist | Largest job functions: Engineering, Information Technology, Sales | What you should know: The perennial IT giant has re-upped its benefits offerings amid the Great Reshuffle, IBM told LinkedIn. The new initiatives are increased paid time off, more promotion and pay reviews, backup dependent care, virtual tutoring, and ‘compassionate leave’ for parents who experience stillbirth or miscarriage. In addition, as the company moves forward with a hybrid working model that allows employees to decide how often they want to be onsite, IBM has also transformed its onboarding process with “a focus on empathy and engagement” to help remote new hires feel more connected.

See jobs at IBM

AT&T

AT&T is the parent company of DIRECTV, WarnerMedia, Cricket Wireless, and others.

Global full-time headcount: 202,600 | Top U.S. locations: Atlanta, Dallas, New York City | Most notable skills: Design Thinking, Customer Experience, Futurism | Most common job titles: Retail Sales Consultant, Client Solutions Executive, Customer Service Representative | Largest job functions: Sales, Information Technology, Engineering | What you should know: Just three years after the acquisition of Time Warner, AT&T is changing course. The company agreed to a deal last year that will combine WarnerMedia’s assets with Discovery’s to create a new, separate global entertainment giant. Once the spinoff is completed (likely mid-2022), the telecom company will be focused on its core business — expanding access to broadband internet. For its employees, AT&T offers several advancement opportunities. For example, it invests $2 million annually in ‘AT&T University,’ an internal training program to help its workers upskill, and has partnered with groups like Udacity and Coursera to offer advanced online courses.

See jobs at AT&T

Apple

Apple is the parent company of AuthenTec, NeXT Software, Shazam, and others.

Global headcount: 154,000 | Top U.S. locations: San Francisco Bay Area; Austin, Texas; New York City | Most notable skills: Apple Software and Hardware, Technical Learning, iOS | Most common job titles: Software Engineer, Technical Specialist, Mac Genius | Largest job functions: Engineering, Information Technology, Sales | What you should know: Apple is increasing benefits and pay for retail workers to attract and retain employees at its 270 retail stores across the U.S. — including doubling sick days for both full-time and part-time employees and granting more vacation days. Its retail employees are also eligible for paid parental leave and can access discounted emergency childcare. In addition, after being one of the first companies to tell its corporate employees to work remotely in March 2020, Apple is now asking that they return to the office three days a week.

See jobs at Apple

Comcast

Comcast is the parent company of NBCUniversal, Sky, DreamWorks Animation, and others.

Global headcount: 189,000 (with 130,000 in the U.S.) | Top U.S. locations: Philadelphia, New York City, Los Angeles | Most notable skills: Media Production, Cable Modems, Broadcast Television | Most common job titles: Software Engineer, Communications Technician, Salesperson | Largest job functions: Engineering, Sales, Information Technology | What you should know: Comcast prioritizes career growth and development among its employees through various benefits — including mentorship programs, department rotations and tuition assistance for continuing education and skills development. As a part of its commitment to wellbeing, it also pays for 78% of its employees’ health care costs. Want an in? Comcast says the #1 skill it looks for in new hires is authenticity. “We believe that by being yourself, you are empowered to do your best work,” the company told LinkedIn.

See jobs at Comcast

Meta

Meta is the parent company of Onavo, WhatsApp, Instagram, and others.

Global headcount: 71,900 | Top U.S. locations: San Francisco Bay Area, Seattle, New York City | Most notable skills: PHP, Program Management, Social Media Marketing | Most common job titles: Software Engineer, Technical Recruiter, Data Scientist | Largest job functions: Engineering, Information Technology, Human Resources

See jobs at Meta

Dell Technologies

Dell Technologies is the parent company of Dell EMC, SecureWorks, and others.

Global headcount: 133,000 | Top U.S. locations: Austin, Texas; Boston; San Francisco Bay Area | Most notable skills: Software as a Service (SaaS), Kubernetes, Salesforce | Most common job titles: Account Executive, Software Engineer, Inside Sales Representative | Largest job functions: Sales, Information Technology, Engineering

See jobs at Dell Technologies

Accenture

Accenture is the parent company of Karmarama, The Monkeys, Fjord, and others.

Global headcount: 674,000 | Top U.S. locations: Washington D.C., New York City, Chicago | Most notable skills: Amazon Web Services (AWS), Management Consulting, Software Development Life Cycle (SDLC) | Most common job titles: Managing Director, Management Consultant, Business Integration Manager | Largest job functions: Information Technology, Business Development, Engineering

See jobs at Accenture

Verizon

Verizon is the parent company of GTE Corporation, MCI Communications Corporation, and others.

Global headcount: 119,400 (with 105,800 in the U.S.) | Top U.S. locations: New York City, Dallas, Washington D.C. | Most notable skills: Quotas, Wireless Technologies, Solution Selling | Most common job titles: Solutions Specialist, Customer Service Representative, Business Account Manager | Largest job functions: Sales, Engineering, Information Technology

See jobs at Verizon

Intel

Intel is the parent company of Mobileye, Data Center Group, and others.

Global headcount: 121,000 (with 55,700 in the U.S.) | Top U.S. locations: Portland, Ore.; Phoenix; San Francisco Bay Area | Most notable skills: JMP, System on a Chip (SoC), Statistical Process Control (SPC) | Most common job titles: Software Engineer, Process Engineer, System-on-Chip Design Engineer | Largest job functions: Engineering, Operations, Information Technology

See jobs at Intel

Oracle

Oracle is the parent company of MICROS Systems, NetSuite, Peoplesoft, BEA Systems, and others.

Global headcount: 133,000 (46,600 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Boston, Denver | Most notable skills: Oracle Cloud, NetSuite, OCI | Most common job titles: Software Engineer, Business Development Consultant, Application Sales Manager | Largest job functions: Engineering, Sales, Information Technology

See jobs at Oracle

Salesforce

Salesforce is the parent company of Slack, Mulesoft, Buddy Media, Tableau, and others.

Global headcount: 74,300 (41,000 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Seattle, New York City | Most notable skills: Salesforce.com Administration, Salesforce Sales Cloud, Slack | Most common job titles: Account Executive, Software Engineer, Solutions Engineer | Largest job functions: Sales, Engineering, Information Technology

See jobs at Salesforce

Cisco

Cisco is the parent company of Duo Security and others.

Global headcount: 81,800 (38,800 in the U.S.) | Top U.S. locations: San Francisco Bay Area; Raleigh-Durham, N.C.; Dallas | Most notable skills: Software as a Service (SaaS), Kubernetes, Network Engineering | Most common job titles: Software Engineer, Account Manager, Program Manager | Largest job functions: Engineering, Information Technology, Sales

See jobs at Cisco

Cognizant

Global headcount: 330,600 (34,680 in the U.S.) | Top U.S. locations: New York City, Dallas, Chicago | Most notable skills: Amazon Web Services (AWS), Software Development Life Cycle (SDLC), Agile & Waterfall Methodologies | Most common job titles: Project Manager, Software Engineer, Technical Lead | Largest job functions: Engineering, Information Technology, Program and Project Management

See jobs at Cognizant | See people you may know at Cognizant

Siemens

Siemens is the parent company of Mendix and others.

Global headcount: 303,000 (with 40,000 in the U.S.) | Top U.S. locations: New York City, Philadelphia, Atlanta | Most notable skills: Building Automation, HVAC Controls, Electrical Troubleshooting | Most common job titles: Project Manager, Software Engineer, Senior Sales Executive | Largest job functions: Engineering, Sales, Operations

See jobs at Siemens

Juniper Networks

Global headcount: 10,400 (with 4,400 in the U.S.) | Top U.S. locations: San Francisco Bay Area, Boston, Washington D.C. | Most notable skills: Junos, Kubernetes, Border Gateway Protocol (BGP) | Most common job titles: Software Engineer, System Engineer, Technical Support Engineer | Largest job functions: Engineering, Sales, Information Technology

See jobs at Juniper Networks

Viasat

Viasat is the parent company of RigNet and others.

Global headcount: 5,800 | Top U.S. locations: San Diego, Denver, Atlanta | Most notable skills: RF Test, Amazon Web Services (AWS), Satellite Communications (SATCOM) | Most common job titles: Software Engineer, Program Manager, System Engineer | Largest job functions: Engineering, Information Technology, Operations

See jobs at Viasat

MathWorks

Global headcount: 5,000 (with 3,000 in the U.S.) | Top U.S. locations: Boston, Detroit, Los Angeles | Most notable skills: MATLAB, Simulink, Deep Learning | Most common job titles: Software Engineer, Application Support Engineer, Principal Software Engineer | Largest job function: Engineering, Information Technology, Sales

See jobs at MathWorks

LinkedIn’s Key Trends Of 2022

Flexible work is becoming table stakes for recruiting and retaining employees. With job seekers and employees in the driver’s seat and able to ask for the work-life balance they need, flexible work has become required to attract and retain top talent. Most companies on this year’s list offer some form of work-from-anywhere flexibility, with more than 70,000 remote jobs open now across the top 50 companies. Many companies also allow employees to set their schedules and work custom “on” hours through asynchronous work. Some, like Amazon (#1), Raytheon Technologies (#21), and General Motors (#44), are encouraging work-life balance with company-wide days off, while others offer unlimited paid vacation and sabbaticals. In addition, many companies are testing out new flexible offerings – employees at Cisco (#30) have adopted a four-day workweek through the company’s Interim Reduced Workweek program, IBM (#6) has set mandatory “off” hours, Cognizant (#33) offers the option to work a compressed week through its WorkFlex program, Realogy (#40) has a no meetings policy on “Focus Fridays,” Publicis Groupe (#41) allows employees the freedom to work from anywhere they like for up to six weeks per year and PwC (#32) allows employees to step away from work for up to six months while paid through its new Leave of Absence program.
Top companies offer stability in an unstable world. While many companies across the U.S. have faced challenges and disruptions over the last year, the Top Companies offer stability and upskilling opportunities that employees can count on – from tuition assistance and PTO for professional development to mentorship programs and job shadowing. Many organizations instituted new programs to retain employees. For example, Deloitte (#11) introduced a new Talent Experience Office focused on employee sentiments and preferences to help inform company choices, EY (#22) offers a Pathway to Purpose virtual program to help employees discover and live their personal purpose and vision, and Kimley-Horn (#31) offers job rotations, so employees learn from different roles and departments. Amazon (#1) is investing $1.2 billion to expand its education and skills training initiatives, Walmart (#5) gives field-based associates access to a no-cost college degree through its Live Better U program, and Verizon (#18) offers an apprenticeship program for those facing employment loss due to automation in technology to prepare them for the jobs of the future. PwC (#32) invested $3 billion in a “New World. New Skills” commitment to equip employees with digital training and awarded a “thank you” bonus of one-week extra pay. Bank of America (#8) provided an additional $1 billion in compensation stock awards to employees globally, and Northrop Grumman (#38) enhanced their annual bonus plan in addition to their ongoing stay interviews.
Mental health care is going mainstream across hiring and talent management. To keep employees healthy and happy at work, almost all of this year’s honorees now provide services that address mental health and well-being. Companies like Intel (#23), Salesforce (#28), and Juniper Networks (#46) provide dedicated mental health days, with many – including FedEx (#47) and Blackstone (#43) – offering company-paid mental health benefits. In addition, EY (#22) has expanded its no-cost counseling and mental health coaching sessions to 25 per year for employees and family. Deloitte (#11) provides a $1,000 well-being subsidy in addition to individualized psychological health resources. Unitedhealth Group (#13) provides complimentary access to wellness apps offering coaching, talk therapy, and more.
Authenticity, compassion, and curiosity are must-have skills. Most of the Top Companies do not require college degrees and instead look for soft skills that can translate across departments and roles. For example, the #1 skill Comcast (#10) seeks in new hires is authenticity, HCA Healthcare (#37) wants new hires to possess compassion, and Dell Technologies (#14) looks for people who thrive in an environment with a diversity of people and ideas. Accenture (#17), Oracle (#27), and Lockheed Martin (#29) value candidates with curiosity and eagerness to learn and grow. Alphabet (#2) looks for problem-solving skills and a growth mindset.

LinkedIn’s Top 50 Companies In The U.S., 2022

Amazon
Alphabet
Wells Fargo
JPMorgan Chase & Co.
Walmart
IBM
AT&T
Bank of America
Apple
Comcast
Deloitte
Meta
UnitedHealth Group
Dell Technologies
CVS Health
The Walt Disney Company
Accenture
Verizon
GE
Boeing
Raytheon Technologies
EY
Intel
Keller Williams
Kaiser Permanente
Target
Oracle
Salesforce
Lockheed Martin
Cisco
Kimley-Horn
PwC
Cognizant
Citi
Citadel
Johnson & Johnson
HCA Healthcare
Northrop Grumman
Siemens
Realogy
Publicis Groupe
Whiting-Turner
Blackstone
General Motors
Capital One
Juniper Networks
FedEx
Ford Motor Company
Viasat
MathWorks

Oct 17

1 Comment

2021 State Of The Machine Learning Market: Enterprise Adoption Is Strong

data science, machine learning, enterprise software, AI, artificial Intelligence

59% of all large enterprises are deploying data science (DS) and machine learning (ML) today.

Nearly 50% of all organizations have up to 25 or more ML models in use today.

29% of enterprises are refreshing their data science and machine learning models every day.

The higher the data literacy an enterprise can achieve before launching Data Science & Machine Learning initiatives, the higher the probability of success.

These and many other insights defining the state of the data science and machine learning market in 2021 are from Dresner Advisory Services’ 2021 Data Science and Machine Learning Market Study. The 7th annual report is noteworthy for its depth of analysis and insight into how data science and machine learning adoption is growing stronger in enterprises. In addition, the study explains which factors drive adoption and determine the key success factors that matter the most when deploying data science and machine learning techniques. The methodology uses crowdsourcing techniques to recruit respondents from over 6,000 organizations and vendors’ customer communities. As a result, 52% of respondents are from North America and 34% from EMEA, with the balance from Asia-Pacific and Latin America.

“The perceived importance of data science and machine learning correlates with organizational success with BI, with users that self-report as completely successful with BI almost twice as likely to rate data science as critical,” said Jim Ericson, vice president, and research director at Dresner Advisory. “The perceived level of data literacy also correlates directly and positively with the current or likely future use of data science and machine learning in 2021.”

Key insights from the study include the following:

59% of large enterprises are deploying data science and machine learning in production today. Enterprises with 10K employees or more lead all others in adopting and using DS and ML techniques, most often in R&D and Business Intelligence Competency Center (BICC)-related work. Large-scale enterprises often rely on DS and ML to identify how internal processes and workflows can be streamlined and made more cost-efficient. For example, the CEO of a manufacturing company explained on a recent conference call that DS and ML pilots bring much-needed visibility and control across multiple plants and help troubleshoot inventory management and supply chain allocation problems.

The importance of data science and ML to enterprises has doubled in eight years, jumping from 25% in 2014 to 70% in 2021. The Dresner study notes that a record level of enterprises sees data science and ML as critically important to their business in 2021. Furthermore, 90% of enterprises consider these technologies essential to their operations, rating them critically important or very important. Successful projects in Business Intelligence Competency Centers (BICC) and R&D helped data science and ML gain broad adoption across all organizations. Larger-scale enterprises with over 10K employees are successfully scaling data science and ML to improve visibility, control, and profitability in organizations today.

Enterprises dominate the recruiting and retention of data science and machine learning talent. Large-scale enterprises with over 10K employees are the most likely to have BI experts and data scientists/statisticians on staff. In addition, large-scale enterprises lead hiring and retention in seven of the nine roles included in the survey. It’s understandable how the Business Intelligence (BI) expertise of professionals in these roles is helping remove the roadblocks to getting more business value from data science and machine learning. Enterprises are learning how to scale data science and ML models to take on problems that were too complex to solve with analytics or BI alone.

80% of DS and ML respondents most want model lifecycle management, model performance monitoring, model version control, and model lineage and history at a minimum. Keeping track of the state of each model, including version control, is a challenge for nearly all organizations adopting ML today. Enterprises reach ML scale when they can manage ML models across their lifecycles using an automated system. The next four most popular features of model rollback, searchable model repository, collaborative, model co-creation tools, and model registration and certification are consistent with the feedback from Data Science teams on what they need most in an ML platform.

Financial Services prioritize model lifecycle management and model performance monitoring to achieve greater scale from the tens of thousands of models they’re using today. Consistent with other research that tracks ML adoption by industry, the Dresner study found that Financial Services leads all other industries in their need for the two most valuable features of ML platforms, model lifecycle management and model performance monitoring. Retail and Wholesale are reinventing their business models in real-time to become more virtual while also providing greater real-time visibility across supply chains. ML models in these two industries need automated model version control, model lineage and history, model rollback, collaborative, model co-creation tools, and model registration and certification. In addition, retailers and Wholesalers are doubling down on data science and machine learning to support new digital businesses, improve supply chain performance and increase productivity.

Enterprises need support for their expanding range of regression models, text analytics functions, and ensemble learning. Over the last seven years, text analytics functions and sentiment analysis’ popularity has continually grown. Martech vendors and the marketing technologists driving the market are increasing sentiment analysis’ practicality and importance. Recommendation engines and geospatial analysis are also experiencing greater adoption due to martech changing the nature of customer- and market-driven analysis and predictive modeling.

R, TensorFlow, and PyTorch are considered the three most critical open-source statistical and machine learning frameworks in 2021. Nearly 70% of respondents consider R important to getting work done in data science and ML. The R language has established itself as an industry standard and is well-respected across DevOps, and IT teams in financial services, professional services, consulting, process, and discrete manufacturing. Tensorflow and Pytorch are considered important by the majority of organizations Dresner’s research team interviewed. They’re also among the most in-demand ML frameworks today, with new applicants having experience in all three being recruited actively today.

Data literacy predicts DS and ML program success rates. 64% of organizations say they have extremely high literacy rates, implying that DS and ML have reached mainstream adoption thanks partly to BI literacy rates in the past. Enterprises that prioritize data literacy by providing training, certification, and ongoing education increase success odds with ML. A bonus is that employees will have a chance to learn marketable skills they can use in their current and future positions. Investing in training to improve data literacy is a win/win.

On-database analytics and in-memory analytics (both 91%), and multi-tenant cloud services (88%) are the three most popular technologies enterprises rely on for greater scalability. Dresner’s research team observes that the scalability of data science and machine learning often involves multiple, different requirements to address high data volumes, large numbers of users, data variety while supporting analytic throughput. Apache Spark support continues to grow in enterprises and is the fourth-most relied-on industry support for ML scalability.

Jun 21

1 Comment

2021 Roundup Of AI And Machine Learning Market Forecasts Show Strong Growth

Forrester forecasts that the size of the AI software market will approach $37 billion by 2025.

IDC predicts the worldwide artificial intelligence (AI) market, including software, hardware, and services, will grow from $327.5 B in 2021 to $554.3B in 2024 with a five-year compound annual growth rate (CAGR) of 17.5%.

By 2024, 70% of enterprises will use cloud and cloud-based AI infrastructure to operationalize AI, thereby significantly alleviating concerns about integration and upscaling, according to Gartner’s data and analytics predictions for 2021.

By 2025, 50% of enterprises will have developed AI orchestration platforms to operationalize AI — up from fewer than 10% in 2020, according to a recent study by Gartner.

Demand for TensorFlow expertise is one of the leading indicators of machine learning and AI adoption globally. Kaggle’s State of Data Science and Machine Learning 2020 Survey found that TensorFlow is the second most used machine learning framework today, with 50.5% of respondents currently using it.

TensorFlow expertise continues to be one of the most marketable machine learning and AI skills in 2021, making it a reliable leading indicator of technology adoption. In 2020, there were on average 4,134 LinkedIn open positions that required TensorFlow expertise soaring to 8,414 open LinkedIn positions this year in the U.S. alone. Globally, demand for TensorFlow expertise has doubled from 12,172 open positions in 2020 to 26,958 available jobs on LinkedIn today.

Demand for machine learning expertise, as reflected in LinkedIn open positions, also shows strong growth. Increasing from 44,864 available jobs in 2020 to 78,372 in 2021 in the U.S. alone, organizations continue to staff up to support new initiatives quickly. Globally, LinkedIn’s open positions requiring machine-learning expertise grew from 98,371 in 2020 to 191,749 in 2021.

Market forecasts and projections also reflect strong growth for AI and machine learning spending globally for the long term. The following are key takeaways from the machine learning market forecasts from the last year include the following:

Forrester says the AI market will be defined and grow within four software segments, with AI maker platforms growing the fastest, reaching $13 billion by 2025, helping drive the market to $37 billion by 2025. Forrester is defining the four AI software segments as follows: AI maker platforms for general-purpose AI algorithms and data sets; AI facilitator platforms for specific AI functions like computer vision; AI-centric applications and middleware tools built around AI for specialized tasks like medical diagnosis; and AI-infused applications and middleware tools that differentiate through advanced use of AI in an existing app or tool category. New AI-centric apps built on AI functions such as medical diagnosis and risk detection solutions will be the second-largest market, valued at nearly $10 billion by 2025. Source: Sizing The AI Software Market: Not As Big As Investors Expect But Still $37 Billion By 2025, December 10, 2020.

IDC predicts worldwide revenues for the artificial intelligence (AI) market, including software, hardware, and services, will grow from $327.5 billion in 2021 to $554.3 billion in 2024, attaining a five-year compound annual growth rate (CAGR) of 17.5%. IDC further predicts that the AI Software Platforms market will be the strongest, with a five-year CAGR of 32.7%. The slowest will be AI System Infrastructure Software, with a five-year CAGR of 13.7% while accounting for roughly 36% of AI software revenues. IDC found that among the three technology categories, software represented 88% of the total AI market revenues in 2020. It’s the slowest growing category with a five-year CAGR of 17.3%. AI Applications took the largest share of revenue within the AI software category at 50% in 2020. Source: IDC Forecasts Improved Growth for Global AI Market in 2021, February 23, 2021

AI projects continued to accelerate in 2020 in the healthcare, bioscience, manufacturing, financial services, and supply chain sectors despite economic & social uncertainty. Two dominant themes emerge from the combination of 30 diverse AI technologies in this year’s Hype Cycle. The first theme is the democratization or broader adoption of AI across organizations. The greater the democratization of AI, the greater the importance of developers and DevOps to create enterprise-grade applications. The second theme is the industrialization of AI platforms. Reusability, scalability, safety, and responsible use of AI and AI governance are the catalysts contributing to the second theme. The Gartner Hype Cycle for Artificial Intelligence, 2020, is shown below: Source: Software Strategies Blog, What’s New In Gartner’s Hype Cycle For AI, 2020, October 20, 2020.

Capgemini finds that Life Sciences, Retail, Consumer Products, and Automotive industries lead in the percentage of successfully deployed AI use cases today. Life Sciences leads all interviewed industries to AI maturity, with 27% of companies saying they have deployed use cases in production and at scale. Retail is also above the industry average of 13% of companies that have deployed AI in production at scale, with 21% of companies in the industry has adopted AI successfully. 17% of companies in the Consumer Products and Automotive industries now have AI in production, running at scale. Source: Capgemini, Making AI Work For You, (The AI-powered enterprise: Unlocking the potential of AI at scale) 2021

Between 2018 and 2020, there’s been a 76% increase in sales professionals using AI-based apps and tools. Salesforce’s latest State of Sales survey found that 57% of high-performance sales organizations use AI today. High-performing sales organizations are 2.8x more likely to use AI than their peers. High-performing sales organizations rely on AI to gain new insights into customer needs, improve forecast accuracy, gain more significant visibility of rep activity, improve competitive analysis, and more. Source: Salesforce Research, 4^th Edition, State of Sales, June 2020

While 24% of companies are currently using AI for recruitment, that number is expected to grow, with 56% reporting they plan to adopt AI next year. In addition, Sage’s recent survey of 500 senior HR and people leaders finds adoption of AI as an enabling technology for talent management increasing. AI is proving effective for evaluating job candidates for potential, improving virtual recruiting events, and reducing biased language in job descriptions. It’s also proving effective in helping to improve career planning and mobility. Josh Bersin, a noted HR industry analyst, educator, and technologist, recently published an interesting report on this area titled The Rise of the Talent Intelligence Platform. Leaders in the field of Talent Intelligence Platforms include Eightfold.ai. Grounded in Equal Opportunity Algorithms, the Eightfold^® Talent Intelligence Platform uses deep-learning AI to help each person understand their career potential, and each enterprise understands the potential of their workforce.Sources: VentureBeat, 8 ways AI is transforming talent management in 2021, March 25, 2021, and Eightfold.ai.

84% of marketers are using AI-based apps and platforms today, up from 28% in 2018. Salesforce Research’s latest State of Marketing survey finds that high-performing marketers use an average of seven different applications or use cases. The familiarity high-performing marketers have with AI is a primary factor in 52% of them predicting they will increase their use of AI-based apps in the future. Source: Salesforce Research, 6^th Edition, State of Marketing, June 2020

Marketing and Sales lead revenue increases due to AI adoption, yet lag behind other departments on cost savings. 40% of the organizations McKinsey interviewed see between a 6 and 10% increase in revenue from adopting AI in their marketing and sales departments. Adopting Ai to reduce costs delivers the best manufacturing and supply chain management results based on the McKinsey survey results. Revenue increases and cost reductions based on AI adoption are shown in the graphic below. Source: McKinsey & Company, The state of AI in 2020, November 17, 2020

AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service as the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption cases. Just over 52% of small businesses with sales of $25M or less use AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte, and American Marketing Association. (71 pp., PDF, free, no opt-in).

Three out of four companies are fast-tracking automation initiatives, including AI. Bain & Company found that executives would like to use AI to reduce costs and acquire new customers, but they’re uncertain about the ROI and cannot find the talent or solutions they need. Bain research conducted in 2019 found that 90% of tech executives view AI and machine learning as priorities that they should be incorporating into their product lines and businesses. But nearly as many (87%) also said they were not satisfied with their Company’s current approach to AI. Source: Bain & Company, Will the Pandemic Accelerate Adoption of Artificial Intelligence? May 26, 2020

Gartner’s Magic Quadrant for Data Science and Machine Learning Platforms predicts a continued glut of exciting innovations and visionary roadmaps from competing vendors. Competitors in the Data Science and Machine Learning (DSML) market focus on innovation and rapid product innovation over pure execution. Gartner said key areas of differentiation include UI, augmented DSML (AutoML), MLOps, performance and scalability, hybrid and multicloud support, XAI, and cutting-edge use cases and techniques (such as deep learning, large-scale IoT, and reinforcement learning). Please see my recent article on VentureBeat, Gartner’s 2021 Magic Quadrant cites ‘glut of innovation’ in data science and ML, March 14, 2021.

76% of enterprises are prioritizing AI & machine Learning In 2021 IT Budgets. Algorithmia’s survey finds that six in ten (64%) organizations say AI and ML initiatives’ priorities have increased relative to other IT priorities in the last twelve months. Algorithmia’s survey from last summer found that enterprises began doubling down on AI & ML spending last year. The pandemic created a new sense of urgency regarding getting AI and ML projects completed, a key point made by CIOs across the financial services and tech sectors last year during interviews for comparable research studies. Source: Algorithmia’s Third Annual Survey, 2021 Enterprise Trends in Machine Learning.

Technavio predicts the Artificial Intelligence platforms market will grow to $17.29 billion by 2025, attaining a compound annual growth rate (CAGR) of nearly 35%. The research firm cites the increased levels of AI R&D investments globally combined with accelerating adoption for pilot and proof of concept testing across industries. Technavio predicts Alibaba Group Holding Ltd., Alphabet Inc., and Amazon.com Inc. will emerge as top artificial intelligence platforms vendors by 2025. Source: Artificial Intelligence Platforms Market to grow by $ 17.29 Billion at 35% CAGR during 2021-2025. June 21, 2021

Tractica predicts the AI software market will reach $126 billion in worldwide revenue by 2025. The research firm predicts AI will grow fastest in consumer (Internet services), automotive, financial services, telecommunications, and retail industries. As a result, annual global AI software revenue is forecast to grow from $10.1 billion in 2018 to $126.0 billion by 2025. Source: T&D World, AI Software Market to Reach $126.0 Billion in Annual Worldwide Revenue by 2025.

Sources of Market Data on Machine Learning:

Algorithma, 2020 state of enterprise machine learning, Nov. 2019 (PDF, 29 PP., no opt-in)
Accenture, Machine Learning In Insurance (PDF, 14 pp., no opt-in)
Ark Invest Big Ideas 2019, Innovation is the Key To Growth (PDF, 94 pp., no opt-in)
Artificial Intelligence: Emerging Opportunities, Challenges and Implications. U.S. Government Accountability Office, March 2018 (PDF, 100 pp., no opt-in)
Artificial Intelligence in Europe: How 277 Major Companies Benefit from AI Outlook for 2019 and Beyond by Ernst & Young (PDF, 41 pp., no opt-in)
Artificial Intelligence Index, 2018 Annual Report (PDF, 94 pp., no opt-in)
Boston Consulting Group, AI at Scale: The Next Frontier in Digital Transformation
Capgemini, Accelerating Automotive’s AI transformation: How driving AI enterprise-wide can turbo-charge organizational value, March 2019. PDF of the study is available here (PDF, 36 pp.., no opt-in)
Chamakkala, Vipin, Today’s AI Software Infrastructure Landscape (And Trends Shaping The Market) Medium. May 7, 2018
Deloitte, State of AI in the Enterprise, 2nd Edition, Early adopters combine bullish enthusiasm with strategic investments (PDF, 28 pp., no opt-in)
Forbes, 10 Ways Machine Learning Is Revolutionizing Sales, December 26, 2018
Forbes, How China Is Dominating Artificial Intelligence, December 16, 2018
Forbes, How To Improve Supply Chains With Machine Learning: 10 Proven Ways, April 28, 2019
Forbes, Microsoft Leads The AI Patent Race Going Into 2019, January 6, 2019
IDC Worldwide Artificial Intelligence Market Shares, 2018: Steady Growth — POCs Poised to Enter Full-Blown Production
IDC Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast to Reach $77.6 Billion in 2022, According to New IDC Spending Guide.
The Economist, Risks and Rewards, Scenarios around the economic impact of machine learning (PDF, 80 pp., no opt-in)
McKinsey, An Executive’s Guide to AI
McKinsey Global Institute, Tackling Europe’s gap in digital and AI, February 2019 Discussion paper
McKinsey Global Institute, Applying artificial intelligence for social good, November, 20-8 discussion paper
McKinsey Global Institute, Notes from the AI Frontier: Tackling Europe’s Gap In Digital and AI (PDF, 60 pp., no opt-in)
McKinsey Global Institute, Notes from the AI frontier: Applications and value of deep learning, April 2018
McKinsey Global Institute, Visualizing the uses and potential impact of AI and other analytics, April 2018
MIT Sloan Management Review, Artificial Intelligence in Business Gets Real: Pioneering Companies Aim for AI at Scale, September 17, 2018, PDF available here.
Stanford University, Artificial Intelligence Index Report 2019, (PDF, 291 PP., no opt-in)
Statista, In-Depth: Artificial Intelligence 2019, February 2019
Statista, Machine Learning Tops AI Dollars, May 10, 2019.

Tractica, Artificial Intelligence: 10 Predictions for 2019 (PDF, 12 pp., no opt-in)
U.S. Government Accountability Office, AI technology Assessment, Emerging Opportunities, Challenges, and Implications (PDF, 100 pp., no opt-in)
World Economic Forum, How to Prevent Discriminatory Outcomes in Machine Learning (PDF, 30 pp., no opt-in)
Stanford University’s Institute for Human-Centered Artificial Intelligence, the Artificial Intelligence Index Report 2021, (PDF, 222 PP., no opt-in)
The Economist, An understanding of AI’s limitations is starting to sink in, June 13, 2020
PWC, AI Predictions 2021, June 2021

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