Over the past year, 66% of organizations experienced at least one ransomware attack, with many suffering repeated breaches. According to Deloitte’s Annual Cyber Threat Trends report, ransomware, identity-based attacks, and sophisticated attack methods like zero-day exploits and AI-driven cyber espionage dominate a rapidly changing threat landscape.
Ransomware attackers specialize in making chaos pay
Attackers are using ransomware as a smash-and-grab strategy, often to finance other illegal operations. Cybercrime gangs, including those that are state-funded, rely on ransomware as a primary source of revenue as well.
Ransomware attackers aim to create widespread chaos across supply chains, amplifying the impact of their attacks. For example, United Healthcare paid a $22 million ransom in Bitcoin, demonstrating how greater disruption often leads to higher payouts.
“Sophisticated ransomware operators are increasingly using zero-day exploits as their initial access vector, with 36 percent of victims ransomed in this way. Valid credential compromise was the second most common entry point for ransomware attacks,” says Deloitte in the report.
“Phishing, remote attacks on public-facing infrastructure, and unauthorized remote desktop connections continue to be the primary sources of infiltration for ransomware,” writes Paul Furtado, Gartner vice president analyst, in a recent research report, How to Prepare for Ransomware Attacks.
Furtado notes that “bad actors are mining exfiltrated data to identify other potential sources of revenue,” further increasing the urgency to harden cyberdefenses against ransomware attacks. The following is a typical ransomware attack pattern as defined in the Gartner report.
Source: Gartner, How to Prepare for Ransomware Attacks, 16 April 2024
CrowdStrike’s threat intelligence teams regularly monitor every known ransomware variant. “RaaS kits are easy to find on the dark web, where they are advertised in the same way that goods are advertised on the legitimate web,” writes Kurt Baker in a blog post explaining RaaS. The post continues, “a RaaS kit may include 24/7 support, bundled offers, user reviews, forums, and other features identical to those offered by legitimate SaaS providers.”
The 2024 Annual Threat Assessment of the U.S. Intelligence Community found that “transnational organized criminals involved in ransomware operations are improving their attacks, extorting funds, disrupting critical services, and exposing sensitive data. Important U.S. services and critical infrastructure such as health care, schools, and manufacturing continue to experience ransomware attacks.”
Adversarial AI’s growing tradecraft
Deloitte’s research uncovered the growing use of adversarial AI for cyber espionage, finding it’s driving new forms of tradecraft in influence operations, social engineering, underground services, and collaboration.
Adversarial AI’s goal is to deliberately mislead AI and machine learning (ML) systems so they are ineffective for the use cases they’re being designed for. Adversarial AI refers to “the use of artificial intelligence techniques to manipulate or deceive AI systems. It’s like a cunning chess player who exploits the vulnerabilities of their opponent. These intelligent adversaries can bypass traditional cyber defense systems, using sophisticated algorithms and techniques to evade detection and launch targeted attacks.”
Influence operations are the most active threat vector of the three Deloitte is tracking. AI image deception and deepfake accuracy are accelerating faster than many existing detection technologies can keep up with.
Telesign’s2024 Trust Index found just how wide the trust gap is becoming due to deep fakes and broader influence operations. 87% of Americans hold businesses accountable for digital privacy, yet only 34% trust them to use AI effectively to protect against fraud. Deepfakes and misinformation are driving a wedge of distrust between companies, the customers they serve, and citizens participating in elections this year.
Deloitte found that social engineering-based attacks are becoming more challenging to identify and stop. Nation-states are weaponizing LLMs and using genAI to improve their ability to launch large-scale social engineering attacks aimed at harvesting privileged access credentials and gaining control of thousands of identities in an enterprise at once.
The rapid growth of Voice Cloning-as-a-Service (VCaaS) tools powered by AI, which is used for vishing attacks to clone voices for financial fraud and unauthorized access, continues to defy easy detection. Cybercriminals and nation-state adversaries are quick to invest in new technologies that yield tradecraft that existing cybersecurity systems can’t decipher, and deepfakes are among the most undetectable today.
Preventing a ransomware attack
Start with a zero-trust mindset. Any trust-based connections in a network are a liability—a ransomware attack waiting to happen. Furtado advises, “Build and execute on a zero-trust strategy that reduces the risk of attackers abusing implicit trust in environments to achieve lateral movement, employ available exploits, and gain privilege escalation to deploy ransomware.”
Furtado’s recommendations reflect a strong zero-trust mindset that seeks to eliminate lateral movement, enforce least privilege access, and monitor all network activity while hardening identity and access management (IAM) security. In short, he’s advising having as strong of a zero-trust framework as possible in place to withstand a ransomware attack.
One of the core concepts of zero trust is to assume an attack has already penetrated the network. Furtado’s key takeaways from his recent report on ransomware include the following:
Have a complete preincident prevention strategy that includes workspace and endpoint protection, data protection, immutable backup, asset management, end-user awareness training, and strong identity and access management.
Implement a reliable asset management process to identify what needs to be protected and who is responsible, paying particular attention to legacy systems.
Establish a risk-based vulnerability management process that includes threat intelligence (TI) to address unpatched systems.
Implement both macro and micro network segmentation to minimize the blast radius of ransomware attacks.
Build and execute a zero-trust strategy to reduce the risk of attackers abusing implicit trust in environments.
Remove local administrative privileges on endpoints and limit access to sensitive applications, including email, to prevent account compromise.
Prevent access to the command prompt and block the execution of PowerShell scripts on all user endpoints.
Implement strong authentication for privileged users, such as database and infrastructure administrators and service accounts, and log and monitor their activity.
75% of CEOs used ChatGPT in the first half of 2023, with 44% incorporating it into their jobs.
Gartner’s 2024 CEO survey finds that CEOs are on board with AI to a much greater extent than previously believed. 87% of CEOs agree that AI’s benefits to their business outweigh its risks. “Digitalization, in general, and AI, in particular, will be core innovative elements in revised business strategies, as will environmental-sustainability-based growth ideas,” writes Gartner in the report.
CEOs experimenting with synthetic video
Almost a third of CEOs have considered making and using a synthetic video of themselves. Gartner notes that Estelle Brachlianoff, CEO of the European utility services company Veolia, has posted an AI-augmented video of herself on LinkedIn and X appearing to speak in multiple languages.
Driving AI adoption
CEOs who adopt new technologies immediately drive their adoption enterprise-wide because everyone immediately sees those technologies as critical to their jobs. Seasoned CEOs know the quickest way to get a new enterprise app’s adoption rate to go up is to use it themselves and demonstrate their mastery quickly. What’s happening with AI’s adoption is faster than many CEOs expected.
Key takeaways from Gartner’s 2024 CEO survey include the following:
Growth dominates CEO agendas, reaching a new record in Gartner’s annual survey. “CEOs’ top business priority of growth is up 25% and is at the highest level since 2014,’ writes Financial considerations increased by 25%, cost management by 11%, and customer priorities grew by 22%. The survey points towards CEOs being more focused on profitability and margins, two signs of internal process gains to reduce operating costs and improve efficiency. The survey results point to more CEOs looking at how to get greater returns from the most expensive assets their businesses operate.
Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches
CEOs mentioning AI as one of their top two technology priorities jumped from 4% in 2023 to 24% in 2024. Technology innovation also increased from 7% to 11%, and the use of digital transformation for growth increased from 9% to 11%. It’s interesting to see how CEOs are focusing on how to improve, integrate, and modernize their strategic use of technology. That category jumps from 1% in 2023 to 5% in 2024. “AI is explicitly mentioned a lot more in 2024 than it was in the 2023 survey. At the same time, mentions of “digitalization” have declined significantly, as have mentions of e-commerce and omnichannel,” writes Gartner.
Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches
34% of CEOs say that the next business transformation their enterprises will pursue after digital is AI. CEO’s intentions to pursue AI as their next business transformation are nearly four times greater than their interest in operations efficiency and agility. Sustainability and ESG are a distant third priority. Just 5% of CEOs say customer experience/centricity will be a priority.
Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches
59% say AI is the technology that will most impact their industry. AI has a four-year track record of being the top category, starting in 2020, with the percentage of CEOs mentioning it ranging between 18% to 29%. Gartner mentions in the survey results that in 15 years of asking this question and comparable ones to it, there’s never been a category that emerges as dominant as AI has. In the past, CEOs believed cloud and big data technologies would be the most impactful. Previous technologies have had nowhere near the extent of impact that AI does today. “Eighty-six percent of CEOs expect AI will help maintain or grow their revenue in 2024-2025, and when asked exactly how that would happen, the top answer category was an improvement to customer experience and relationships,” writes Gartner.
Source: Gartner 2024 CEO Survey — The Year of Strategy Relaunches
The top 10% of enterprises have one or more GenAI applications in production at scale across their organizations. 44% of these top-performing organizations are realizing significant value from scaled predictive AI cases. 70% of top performers explicitly tailor their GenAI projects to create measurable value.
Boston Consulting Group (BCG) estimates that an organization with $20 billion in revenue can achieve gains of $500 million to $1 billion in profit using GenAI, with nearly a third of those gains coming in the first 18 months. Their recent analysis of what sets GenAI’s top performers apart, What GenAI’s Top Performers Do Differently, looks at the factors that most differentiate enterprises excelling with GenAI today.
What further differentiates these top performers from others is how they’re looking to use GenAI to redefine the functional areas of their organizations. They’re far more likely to have a solid foundation in predictive AI and four times more likely to increase their investment in AI and digital-first strategies and technologies.
Half of the enterprise leaders BCG interviewed say their organizations are testing GenAI in pilot projects today but have not achieved full-scale implementation. The remaining 40% haven’t taken any action on GenAI yet.
Source: Boston Consulting Group, What GenAI’s Top Performers Do Differently.
What Sets The Top 10% Apart
Two-thirds of GenAI’s top-performing enterprises aren’t digital natives like Amazon or Google but instead leaders in biopharma, energy, and insurance. BCG found that a U.S.-based energy company launched a GenAI-driven conversational platform to assist frontline technicians, increasing productivity by 7%. A biopharma company is reimagining its R&D function with GenAI and reducing drug discovery timelines by 25%.
Top GenAI performers have their greatest lead over peers across five main capabilities. These capabilities include having a clear link to business performance, modern technology infrastructure, strong data capabilities, leadership support, and a grounding in responsible AI. The steep curves shown in the graphic below suggest how these five most differentiated capabilities are essential for successful GenAI adoption at scale.
Source: Boston Consulting Group, What GenAI’s Top Performers Do Differently.
Key takeaways from BCG’s analysis of GenAI top performers
Top performers excel at creating strong links between GenAI initiatives and business value. Seven in ten enterprises who are high achievers know how to build a business case for their GenAI projects and pilots. They’re focused on measuring results and quantifying value. BCG found that in a typical GenAI portfolio, 60% of the initiatives are focused on reducing costs and 40% on increasing revenue.
An all-in mindset when it comes to maintaining and growing a modern technology infrastructure. GenAI top-performing enterprises are three times more likely to already have a modular, modern IT tech stack and supporting infrastructure in place. They’re focused on being prepared to develop new, GenAI-powered services on their current and future AI models while supporting DevOps. BCG says top performers are 1.5 times more likely to focus on building the GenAI stack internally over the coming three years, underscoring their desire to make the technology a core capability for the organizations.
Are pursuing and advanced data strategy that includes unstructured data. GenAI top performers are two times more likely to have data pipelines and data management practices in place to streamline data sourcing and storage. They’re also more likely to have unstructured data expertise. BCG observes that an advanced data strategy “is a critical element of GenAI, given that models are only as strong as the data on which they’re trained.” Organizations have found success with less mature skills in these areas, although it may take longer as they need to address infrastructure and data strategy gaps or shortcomings.
Strong leadership support for innovation, including the willingness to champion GenAI. Senior executives’ support and prioritizing an innovative culture are the most differentiating factors in defining GenAI’s high performers. Gen AI high performers who are scaling use cases are three times more likely than no-action companies to have leaders who prioritize innovation and actively support GenAI. BCG notes that these leaders often have a deep understanding of the technology’s potential impact on their industry, and they are publicly committed to ensuring that the organization capitalizes on it in new ways that generate value. “Visible support and commitment from our leadership team has been crucial, as it provided the freedom to experiment and deal with failures along the way,” said the head of data and analytics at a global media company referenced in BCG’s report.
Have responsible AI guidelines, guardrails and processes in place. Top-performing enterprises are more likely to have responsible AI frameworks, guidelines, and guardrails in place. BCG observes that a common trait top-performing enterprises have is ensuring their AI systems and workflows put humans in the loop and use only factual data. “Our research shows that leading companies are far more likely to have developed guardrails, guidelines, and policies to ensure that they follow the principles of responsible AI. In the findings, the share of scaling companies that are cautious about the potential misuse of GenAI and taking proactive measures to address these risks is 20 percentage points higher than the share of companies taking no action in this area,” write BCG’s researchers.
AI continues to become more weaponized with nation-state attackers and cybercrime gangs experimenting with LLMs and gen AI-based attack tradecraft. The age of weaponized LLMs is here.
At the same time, multi-cloud-based infrastructures more businesses rely on are coming under attack. Exfiltrating any identity data available from endpoints and then traversing a network to gain more access by collecting more credential data is often the goal.
Cyberattacks that combine AI and social engineering are just beginning
Attackers have a version of human-in-the-middle, too, but their goal is to unleash AI’s offensive attack capabilities within social engineering campaigns. Last year’s social engineering-based attacks on MGM, Comcast, Shield Healthcare Group, and others serve as a case in point.
CrowdStrike’s2024 Global Threat Report finds that cloud intrusions jumped 75% last year. There was a 76% increase in data theft victims named on data leak sites and a 60% increase in interactive intrusion campaigns. Worse, 75% of attacks were malware-free, making them difficult to identify and stop. There was also a 110% YoY increase in cloud-conscious cases.
PwC’s 2024 Digital Trust Insights Report finds that 97% of senior management teams have gaps in their cloud risk management plans. 47% say cloud attacks are their most urgent threat. One in three senior management teams is prioritizing cloud security as their top investment this year.
Gartner sees a more complex threatscape driving growth
Nation-state attackers are picking up the pace of their stealthy AI arms race. They’re looking to score offensive first victories on an increasingly active digital battlefield. Gartner predicts that in 2027, 17% of the total cyberattacks and data leaks will involve generative AI.
Another key assumption driving Gartner’s latest forecast is that by 2025, user efficiency improvements will drive at least 35% of security vendors to offer large language model (LLM)-driven chat capabilities for users to interact with their applications and data, up from 1% in 2022.
Gartner has also factored in the surge in cloud attacks and the continued growth of hybrid workforces. One of their key assumptions driving the forecast is that “by the end of 2026, the democratization of technology, digitization, and automation of work will increase the total available market of fully remote and hybrid workers to 64% of all employees, up from 52% in 2021.”
Source: Gartner, Forecast Analysis: Information Security and Risk Management, Worldwide, Published February 29, 2024
Market subsegments predicted to see the most significant growth through 2027 include the following:
Gartner has high expectations for Zero Trust Network Access (ZTNA) growth, stating the worldwide market was worth $575.7 million in 2021 and predicting it will soar to $3.99 billion in 2027, attaining a 31.6% CAGR in the forecast period.
Identity Access Management (IAM) is predicted to grow from $4 billion in 2021 to $11.1 billion in 2027, attaining a 17.6% CAGR. Identity Governance and Administration software is predicted to grow from $2.8 billion in 2021 to $5.77 billion in 2027, attaining a 12.8% CAGR.
Endpoint Protection Platforms (EPP) are predicted to grow from $9.8 billion in 2021 to $26.9 billion in 2027, achieving a 17.2% CAGR.
Threat Intelligence software is predicted to grow from $1.1 billion in 2021 to $2.79 billion in 2027, growing at a 15.6% CAGR through the forecast period.
Cloud Access Security Brokers (CASB) is predicted to grow from $928M in 2021 to $4.75 billion in 2027, attaining a CAGR of 30.2%. Gartner believes that the market share of cloud-native solutions will continue to grow. They are predicting that the combined market for cloud access security brokers (CASBs) and cloud workload protection platforms (CWPPs) will reach $12.8 billion in constant currency by 2027, up from $4.6 billion in 2022. Gartner continues to also see strong demand for cloud-based detection and response solutions that include endpoint detection and response (EDR) and managed detection and response (MDR).
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.
Over 90% of enterprise AI decision-makers have concrete plans to adopt generative AI (gen AI) for internal and customer-facing use cases. Nearly half, 47%, say productivity gains are the primary goal. Those anticipated gains are closely followed by greater innovation (44%) and cost efficiency (41%). One in three, or 34%, expect AI to deliver greater revenue growth.
Productivity gains are happening faster than enterprises can track because Shadow AI is growing quicker than many IT and security teams anticipated. OpenAI says their enterprise adoption is soaring, with over 80% of Fortune 500 companies’ workers and departments having accounts.
Enterprise workers are achieving a 40% performance boost thanks to ChatGPT based on a recent Harvard study. A second study from MIT discovered that ChatGPT reduced skill inequalities and accelerated document creation times while enabling enterprise workers to be more efficient with their time. Seventy percent haven’t told their bosses about it.
Forrester: A Wave Of Disruption Is Coming
Forrester’s recent report, The State of Generative AI, 2024, warns enterprises not to discount the impact of generative AI on their operations and to start planning for greater experimentation, governance, and security now.
Get ready for the challenges of defining and managing bring your own AI (BYOAI) as workers are inventing and adopting gen AI tools and apps faster than IT or security can keep up. Forrester advises that enterprises need to have forward-thinking governance and security guardrails in place before launching their AI-based apps.
With nearly every enterprise app and platform integrating gen AI into its feature set, it’s up to IT, security, and senior management to have an AI plan that can adapt and flex fast to the fast-growing feature set gen AI is delivering.
Enterprise AI leaders also need to define where they stand on the controversial and complex state of model training data. Forrester says that questions regarding the quality of training data using copyright material, model and data bias, and frequent “hallucinations” by models.
“In 2024, as organizations embrace the generative AI (genAI) imperative, governance and accountability will be a critical component to ensure that AI usage is ethical and does not violate regulatory requirements,” writes Forrester in their cybersecurity predictions late last year. “This will enable organizations to safely transition from experimentation to implementation of new AI-based technologies,” the report continues.
Top 10 Insights From Forrester’s State of Generative AI
The ten most valuable insights from Forrester’s State of Generative AI report provide a comprehensive overview of the current and future landscape of generative AI. Noteworthy for its balance between opportunities and risks, the report explains the hurdles in front of enterprises looking to gain value from gen AI now and in the future.
Here are the top 10 insights from the report:
Gen AI shows strong potential to improve and scale enterprise operations. Forrester is optimistic regarding the potential of gen AI to increase productivity and deliver measurable business value. “Forrester expects that gen AI will add convenience to and remove friction from a variety of experiences, reshape jobs in ways we are only beginning to contemplate and disrupt organizations and industries,” writes Forrester’s research team in the report. The report explains that there are three broad tiers of generative AI suppliers, further supporting the market’s expansion and growth.
Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024
Large Language Models will continue to dominate the gen AI narrative. Large language models (LLMs), including Anthropic, Google, Meta, and OpenAI’s GPT series, will continue to dominate the gen AI landscape. Forrester notes that significant data and infrastructure requirements make the task of creating and maintaining an LLM difficult for companies considering competing in the market. Open-source LLMs are redefining the market, a point Forrester touches on briefly.
Gen AI is becoming part of planning cycles in enterprises: Forrester found that over 90% of global enterprise AI decision-makers have definite plans to implement generative AI. Internal use cases are dominating the planning cycles of enterprise AI leaders today.
Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024
Overcoming technical skills shortages and integration challenges stand in the way of achieving benefits. A third of enterprise AI leaders say that lack of technical skills in their organizations is the single greatest roadblock to their gaining the benefits they’re looking for from gen AI. Twenty-eight percent say they’re having difficulty integrating gen AI into their existing tech stacks and infrastructure. The potential to gain significant benefits from gen AI motivates enterprise AI leaders to look for new ways to overcome technical skills shortages and find new ways to integrate gen AI into their infrastructure.
Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024
Ethical and safe gen AI use will require new cybersecurity and governance approaches. Enterprises need to decide how they approach the most challenging and controversial aspects of LLMs, gen AI, and the future of AI at scale in their companies now rather than later. The core message of Forrester’s report is for enterprises not to procrastinate but to start making plans now for their stance on the issues of model bias, data security, regulatory compliance, and ethics of model training.
Weaponized LLMs are a fact of life for every enterprise looking to adopt these technologies today. There’s also the growing threat of intellectual property and confidential data being accidentally shared with LLMs via ChatGPT and comparable chatbots. The intensity cybersecurity providers are putting behind this problem makes it one of the fastest evolving areas in the industry today.
While not mentioned by Forrester in their report, these vendors are defining the state of the art when it comes to protecting confidential data being entered into LLMs. Cisco, Ericom Security by Cradlepoint’s Generative AI isolation, Menlo Security, Nightfall AI, Wiz, and Zscaler have all developed and launched solutions to reduce the threat of confidential data making it into LLMs via chatbots.
The use of a virtual browser that is separate from an organization’s network environment in the Ericom Cloud distinguishes Ericom’s Generative AI Isolation. Data loss protection, sharing, and access policy controls are applied in the cloud to prevent confidential data, PII, or other sensitive information from being submitted to the LLM and potentially exposed.
“Generative AI websites provide unparalleled productivity enhancements, but organizations must be proactive in addressing the associated risks,” said Gerry Grealish, Vice President of Marketing Ericom Cybersecurity Unit of Cradlepoint. “Our Generative AI Isolation solution empowers businesses to attain the perfect balance, harnessing the potential of generative AI while safeguarding against data loss, malware threats, and legal and compliance challenges.”
Early adopters are implementing gen AI in a wide variety of use cases, ranging from operations to customer engagement and product development. Forrester notes that they’re seeing organizations use knowledge management bots to accelerate workflows, automate tasks, generate new ideas, and drive innovation across a broad base of use cases. Early adopters are also piloting gen AI for diverse use cases across operations, customer engagement, and product development. Forester emphasizes that for external use cases, they’re seeing enterprises adopt a main-in-the-middle workflow to strengthen model training with human intelligence – and avert potential errors in model response.
Source: Forrester, The State of Generative AI, 2024 report. January 26, 2024
Internal use cases precede external use cases as enterprises look to gain expertise in controlled environments. Companies will initially focus on internal use cases for generative AI to refine their models before slowly expanding to customer-facing applications, employing heavy human-in-the-loop management. Forrester is seeing gen AI being used internally to drive employee productivity and workflow optimization gains first. The top three use cases of employee productivity, knowledge management, and software development are all focused on internal improvements.
Legal and intellectual property uncertainty will continue to surround gen AI. Forrester implies there’s going to be an increasingly complex, controversial legal landscape regarding the data models are trained on, especially when it comes to copyrighted content.
Privacy and regulatory concerns are going to continue influencing adoption. Forrester is seeing enterprises in heavily regulated industries exercising extreme caution to protect company and customer data, with some even banning tools like ChatGPT over concerns about data protection and regulatory backlash.
Enterprises need to get a sense of urgency about preparing for gen AI governance and experimentation. Having a clear vision of how they’re going to adopt gen AI is essential if enterprises are going to succeed in governing these new technologies at scale. The combination of growing and recruiting in-house skills, having a solid plan for BYOAI, and defining guardrails for internal use cases are all needed to avoid being blindsided by risks.
There are a record number of 9,977 machine learning startups and companies in Crunchbase today, an 8.2% increase over the 9,216 startups listed in 2020 and a 14.6% increase over the 8,705 listed in 2019.
Artificial Intelligence (A.I.) and machine learning (ML)-related companies received a record $27.6 billion in funding in 2020, according to Crunchbase.
Of those A.I. and machine learning startups receiving funding since January 1, 2020, 62% are seed rounds, 31% early-stage venture rounds and 6.7% late-stage venture capital-funded rounds.
A.I. and machine learning startups’ median funding round was $4.4 million and the average was $29.8 million in 2020, according to Crunchbase.
Throughout 2020, venture capital firms continued expanding into new global markets, with London, New York, Tel Aviv, Toronto, Boston, Seattle and Singapore startups receiving increased funding. Out of the 79 most popular A.I. & ML startup locations, 15 are in the San Francisco Bay Area, making that region home to 19% of startups who received funding in the last year. Israel’s Tel Aviv region has 37 startups who received venture funding over the last year, including those launched in Herzliya, a region of the city known for its robust startup and entrepreneurial culture.
The following graphic compares the top 10 most popular locations for A.I. & ML startups globally based on Crunchbase data as of today:
Top 20 Machine Learning Startups To Watch In 2021
Augury – Augury combines real-time monitoring data from production machinery with AI and machine learning algorithms to determine machine health, asset performance management (APM) and predictive maintenance (PdM) to provide manufacturing companies with new insights into their operations. The digital machine health technology that the company offers can listen to the machine, analyze the data and catch any malfunctions before they arise. This enables customers to adjust their maintenance and manufacturing processes based on actual machine conditions. The platform is in use with HVAC, industrial factories and commercial facilities.
Alation – Alation is credited with pioneering the data catalog market and is well-respected in the financial services community for its use of A.I. to interpret and present data for analysis. Alation has also set a quick pace to evolving its platform to include data search & discovery, data governance, data stewardship, analytics and digital transformation. With its Behavioral Analysis Engine, inbuilt collaboration capabilities and open interfaces, Alation combines machine learning with human insight to successfully tackle data and metadata management challenges. More than 200 enterprises are using Alation’s platform today, including AbbVie, American Family Insurance, Cisco, Exelon, Finnair, Munich Re, New Balance, Pfizer, Scandinavian Airlines and U.S. Foods. Headquartered in Silicon Valley, Alation is backed by leading venture capitalists including Costanoa, Data Collective, Icon, Sapphire and Salesforce Ventures.
Algorithmia – Algorithmia’s expertise is in machine learning operations (MLOps) and helping customers deliver ML models to production with enterprise-grade security and governance. Algorithmia automates ML deployment, provides tooling flexibility, enables collaboration between operations and development and leverages existing SDLC and CI/CD practices. Over 110,000 engineers and data scientists have used Algorithmia’s platform to date, including the United Nations, government intelligence agencies and Fortune 500 companies.
Avora – Avora is noteworthy for its augmented analytics platform, making in-depth data analysis intuitively as easy as performing web searches. The company’s unique technology hides complexity, empowering non-technical users to run and share their reports easily. By eliminating the limitations of existing analytics, reducing data preparation and discovery time by 50-80% and accelerating time to insight, Avora uses ML to streamline business decision-making. Headquartered in London with offices in New York and Romania, Avora helps accelerate decision making and productivity for customers across various industries and markets, including Retail, Financial Services, Advertising, Supply Chain and Media and Entertainment.
Boast.ai – Focused on helping companies in the U.S. and Canada recover their R&D costs from respective federal governments, Boast.ai enables engineers and accountants to gain tax credits using AI-based tools. Some of the tax programs Boast.ai works with include US R&D Tax Credits, Scientific Research and Experimental Development (SR&ED) and Interactive Digital Media Tax Credits (IDMTC). The startup has offices in San Francisco, Vancouver and Calgary.
ClosedLoop.ai – An Austin, Texas-based startup, ClosedLoop.ai has created one of the healthcare industry’s first data science platforms that streamline patient experiences while improving healthcare providers’ profitability. Their machine learning automation platform and a catalog of pre-built predictive and prescriptive models can be customized and extended based on a healthcare provider’s unique population or client base needs. Examples of their technology applications include predicting admissions/readmissions, predicting total utilization & total risk, reducing out-of-network utilization, avoiding appointment no-shows, predicting chronic disease onset or progression and improving clinical documentation and reimbursement. The Harvard Business School, through its Kraft Precision Medicine Accelerator, recently named ClosedLoop.ai as one of the fastest accelerating companies in its Real World Data Analytics Landscapes report.
Databand – A Tel Aviv-based startup that provides a software platform for agile machine learning development, Databand was founded in 2018 by Evgeny Shulman, Joshua Benamram and Victor Shafran. Data engineering teams are responsible for managing a wide suite of powerful tools but lack the utilities they need to ensure their ops are running properly. Databand fills this gap with a solution that enables teams to gain a global view of their data flows, make sure pipelines complete successfully and monitor resource consumption and costs. Databand fits natively in the modern data stack, plugging seamlessly into tools like Apache Airflow, Spark, Kubernetes and various ML offerings from the major cloud providers.
DataVisor – DataVisor’s approach to using AI for increasing fraud detection accuracy on a platform level is noteworthy. Using proprietary unsupervised machine learning algorithms, DataVisor enables organizations to detect and act on fast-evolving fraud patterns and prevent future attacks before they happen. Combining advanced analytics and an intelligence network of more than 4.2B global user accounts, DataVisor protects against financial and reputational damage across various industries, including financial services, marketplaces, e-commerce and social platforms. They’re one of the more fascinating cybersecurity startups using AI today.
Exceed.ai – What makes Exceed.ai noteworthy is how their AI-powered sales assistant platform automatically communicates the lead’s context and enables sales and marketing teams to scale their lead engagement and qualification efforts accordingly. Exceed.ai follows up with every lead and qualifies them quickly through two-way, automated conversations with prospects using natural language over chat and email. Sales reps are freed from performing error-prone and repetitive tasks, allowing them to focus on revenue-generating activities such as phone calls and demos with potential customers.
Indico – Indico is a Boston-based startup specializing in solving the formidable challenge of how dependent businesses are on unstructured content yet lack the frameworks, systems and tools to manage it effectively. Indico provides an enterprise-ready A.I. platform that organizes unstructured content while streamlining and automating back-office tasks. Indico is noteworthy given its track record of helping organizations automate manual, labor-intensive, document-based workflows. Its breakthrough in solving these challenges is an approach known as transfer learning, which allows users to train machine learning models with orders of magnitude fewer data than required by traditional rule-based techniques. Indico enables enterprises to deploy A.I. to unstructured content challenges more effectively while eliminating many common barriers to A.I. & ML adoption.
LeadGenius – LeadGenius is noteworthy for its use of AI to provide personalized and actionable B2B lead information that helps its clients attain their global revenue growth goals. LeadGenius’s worldwide team of researchers uses proprietary technologies, including AI and ML-based techniques, to deliver customized lead generation, lead enrichment and data hygiene services in the format, methods and frequency defined by the customer. Their mission is to enable B2B sales and marketing organizations to connect with their prospects via unique and personalized data sets.
Netra – Netra is a Boston-based startup that began as part of MIT CSAIL research and has multiple issued and pending patents on its technology today. Netra is noteworthy for how advanced its video imagery scanning and text metadata interpretation are, ensuring safety and contextual awareness. Netra’s patented A.I. technology analyzes videos in real-time for contextual references to unsafe content, including deepfakes and potential cybersecurity threats.
Particle – Particle is an end-to-end IoT platform that combines software including A.I., hardware and connectivity to provide a wide range of organizations, from startups to enterprises, with the framework they need to launch IoT systems and networks successfully. Particle customers include Jacuzzi, Continental Tires, Watsco, Shifted Energy, Anderson EV, Opti and others. Particle is venture-backed and has offices in San Francisco, Shenzhen, Las Vegas, Minneapolis and Boston. Particle’s developer community includes over 200,000 developers and engineers in more than 170 countries today.
RideVision – RideVision was founded in 2018 by motorcycle enthusiasts Uri Lavi and Lior Cohen. The company is revolutionizing the motorcycle-safety industry by harnessing the strength of artificial intelligence and image-recognition technology, ultimately providing riders with a much broader awareness of their surroundings, preventing collisions and enabling bikers to ride with full confidence that they are safe. RideVision’s latest round was $7 million in November of last year, bringing their total funding to $10 million in addition to a partnership with Continental AG.
Savvie – Savvie is an Oslo-based startup specializing in translating large volumes of data into concrete actions that bakery and café owners can utilize to improve their bottom line every day. In doing so, we help food businesses make the right decisions to optimize their operations and increase profitability while reducing waste at its source. What’s noteworthy about this startup is how adept they are at fine-tuning ML algorithms to provide their clients with customized recommendations and real-time insights about their food and catering businesses. Their ML-driven insights are especially valuable given how bakery and café owners are pivoting their business models in response to the pandemic.
SECURITI.ai – One of the most innovative startups in cybersecurity, combining AI and ML to secure sensitive data in multi-cloud and mixed platform environments, SECURITI.ai is a machine learning company to watch in 2021, especially if you are interested in cybersecurity. Their AI-powered platform and systems enable organizations to discover potential breach risk areas across multi-cloud, SaaS and on-premise environments, protect it and automate all private systems, networks and infrastructure functions.
SkyHive – SkyHive is an artificial intelligence-based SaaS platform that aims to reskill enterprise workforces and communities. It develops and commercializes a methodology, Quantum Labor Analysis, to deliver real-time, skill-level insights into internal workforces and external labor markets, identify future and emerging skills and facilitate individual-and company-level reskilling. SkyHive is industry-agnostic and supporting enterprise and government customers globally with a mission to reduce unemployment and underemployment. Sean Hinton founded the technology company in Vancouver, British Columbia, in 2017.
Stravito – Stravito is an A.I. startup that’s combining machine learning, Natural Language Processing (NLP) and Search to help organizations find and get more value out of the many market research reports, competitive, industry, market share, financial analysis and market projection analyses they have by making them searchable. Thor Olof Philogène and Sarah Lee founded the company in 2017, who identified an opportunity to help companies be more productive, getting greater value from their market research investments. Thor Olof Philogène and Andreas Lee were co-founders of NORM, a research agency where both worked for 15 years serving multinational brands, eventually selling the company to IPSOS. While at NORM, Anders and Andreas were receiving repeated calls from global clients that had bought research from them but could not find it internally and ended up calling them asking for a copy. Today the startup has Carlsberg, Comcast, Colruyt Group, Danone, Electrolux, Pepsi Lipton and others. Stravito has offices in Stockholm (H.Q.), Malmö and Amsterdam.
Verta.ai – Verta is a startup dedicated to solving the complex problems of managing machine learning model versions and providing a platform to launch models into production. Founded by Dr. Manasi Vartak, Ph.D., a graduate of MIT, who led a team of graduate and undergraduate students at MIT CSAIL to build ModelDB, Verta is based on their work define the first open-source system for managing machine learning models. Her dissertation, Infrastructure for model management and model diagnosis, proposes ModelDB, a system to track ML-based workflows’ provenance and performance. In August of this year, Verta received a $10 million Series A round led by Intel Capital and General Catalyst, who also led its $1.7 million seed round. For additional details on Verta.ai, please see How Startup Verta Helps Enterprises Get Machine Learning Right. The Verta MLOps platform launch webinar provides a comprehensive overview of the platform and how it’s been designed to streamline machine learning models into production:
V7 – V7 allows vision-based A.I. systems to learn continuously from training data with minimal human supervision. The London-based startup emerged out of stealth in August 2018 to reveal V7 Darwin, an image labeling platform to create training data for computer vision projects with little or no human involvement necessary. V7 specializes in healthcare, life sciences, manufacturing, autonomous driving, agri-tech, sporting clients like Merck, GE Healthcare and Toyota. V7 Darwin launched at CVPR 2019 in Long Beach, CA. Within its first year, it has semi-automatically annotated over 1,000 image and video segmentation datasets. V7 Neurons is a series of pre-trained image recognition applications for industry use. The following video explains how V7 Darwin works:
Bottom Line: Using AI to measure and predict revenue, costs, and margin across all Professional Services (PS) channels leads to greater accuracy in predicting payment risks, project overruns, and service forecasts, reducing revenue leakage in the process.
Professional Services’ Revenue Challenges Are Complex
Turning time into revenue and profits is one of the greatest challenges of running a Professional Services (PS) business. What makes it such a challenge is incomplete time tracking data and how quickly revenue leaks spring up, drain margins, and continue unnoticed for months. Examples of revenue leaks across a customers’ life cycles include the following:
Billing errors are caused by the booking and contract process not being in sync with each other leading to valuable time being wasted.
When products are bundled with services, there’s often confusion over recognizing each revenue source, when, and by which PS metric.
Inconsistent, inaccurate project cost estimates and actual activity lead to inaccurate forecasting, delaying the project close and the potential for bad debt write-offs and high Days Sales Outstanding (DSO).
Revenue leakage gains momentum and drains margins when the following happens:
Un-forecasted delays and timescale creep
Reduced utilization rates across each key resource required for the project to be completed
Invoice and billing errors that result in invoice disputes that turn into high DSOs & write-offs
Incorrect pricing versus the costs of sales & service often leads to customer churn.
Revenue leakage gains momentum as each of these factors further drains margin
Adding up all these examples and many more can easily add up to 20-30% of actual lost solution and services margin. In many ways, it’s like death by a thousand small cuts. The following graphic provides examples across the customer lifecycle:
Why Professional Services Are Especially Vulnerable To Revenue Leakage
Selling projects and the promise of their outcomes in the future create a unique series of challenges for PS organizations when it comes to controlling revenue leakage. It often starts with inaccurately scoping a project too aggressively to win the deal, only to determine the complexity of tasks originally budgeted for will take 10 – 30% longer or more. Disconnects on project scope are unfortunately too common, turning small revenue leaks into major ones and the potential of long Days Sales Outstanding (DSO) on invoices. When revenue leaks get ingrained in a project’s structure, they continue to cascade into each subsequent phase, growing and costing more than expected.
The SPI 2021 Professional Services Maturity™ Benchmark Service published by Services Performance Insight, LLC in February of this year provides insights into the hidden costs and prevalence of revenue leakage. The following table illustrates how organizations with high levels of revenue leakage also perform badly against other key metrics, including client referencability. The more revenue leakage an organization experiences, the more billable utilization drops, on-time project deliveries become worse, and executive real-time visibility becomes poorer.
How FinancialForce Is Using AI To Fight Revenue Leakage
It’s noteworthy that FinancialForce is now on its 12th consecutive product release that includes Salesforce Einstein, and many customers, including Five9, are using AI to manage revenue leakage across their PS business. Throughout the pandemic, the FinancialForce DevOps, product management, and software quality teams have been a machine, creating rich new releases on schedule and with improved AI functionality based on Einstein. The 12th release includes prebuilt data models, lenses, dashboards, and reports.
Andy Campbell, Solution Evangelist at FinancialForce, says that “FinancialForce customers have access to best practices to minimize revenue leakage by scoping and selling the right product and services mix to allocating the optimal range and amount of services personnel and finally billing, collecting and recognizing the right amount of revenue for services provided.” Andy continued, saying that recent dashboards have been built for resource managers to automate demand and capacity planning and service revenue forecasting and assist financial analysts in managing deferred revenue and revenue leakage.
By successfully integrating Einstein into their ERP system for PS organizations, FinancialForce helps clients find new ways to reduce revenue leakage and preserve margin. Relying on AI-based insights for each phase of a PS engagement delivered a 20% increase in Customer Lifetime Value according to a FinancialForce customer. And by combining FinancialForce and Salesforce, customers see an increased bid:win ratio of 10% or more. The following graphic illustrates how combining the capabilities of Einstein’s AI platform with FinancialForce delivers results.
Conclusion
FinancialForce’s model building in Einstein is based on ten years of structured and unstructured data, aggregated and anonymized, then used for in-tuning AI models. FinancialForce says these models are used as starting points or templates for AI-based products and workflows, including predict to pay. Salesforce has also done the same for its Sales Cloud Analytics and Service Cloud Analytics. In both cases, Salesforce and FinancialForce customers benefit from best practices and recommendations based on decades of data, which should be particularly interesting considering the “black swan” nature of 2020 data for most of their customers.
43% of enterprises say their AI and Machine Learning (ML) initiatives matter “more than we thought,” with one in four saying AI and ML should have been their top priority sooner.
50% of enterprises plan to spend more on AI and ML this year, with 20% saying they will be significantly increasing their budgets.
56% of all enterprises rank governance, security and auditability issues as their highest-priority concerns today.
In just over a third of enterprises surveyed (38%), data scientists spend more than 50% of their time on model deployment.
Enterprises accelerated their adoption of AI and machine learning in 2020, concentrating on those initiatives that deliver revenue growth and cost reduction. Consistent with many other surveys of enterprises’ AI and machine learning accelerating projects last year, Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning finds enterprises expanding into a wider range of applications starting with process automation and customer experience. Based on interviews with 403 business leaders and practitioners who have insights into their company’s machine learning efforts, the study represents a random sampling of industries across a spectrum of machine learning maturity levels. Algorithmia chose to limit the survey to only those from enterprises with $100M or more in revenue. Please see page 34 of the study for additional details regarding the methodology.
Key insights from the research include the following:
76% of enterprises prioritize AI and machine learning (ML) over other IT initiatives in 2021. Six in ten (64%) 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.
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
83% of enterprises have increased their budgets for AI and machine learning year-over-year from 2019 to 2020. 20% of enterprises increased their budget by over 50% between 2019 and 2020. According to MMC Ventures’ The State of AI Divergence Study, one in ten enterprises now uses ten or more AI applications with chatbots, process optimization and fraud analysis leading all categories. A recent Salesforce Research report, Enterprise Technology Trends, found that 83% of IT leaders say AI & ML is transforming customer engagement and 69% say it is transforming their business. The following compares year-over-year AI and ML budget changes between FY 2018 – 2019 and FY 2019 – 20.
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
Improving customer experiences to drive greater revenue growth and automating processes to reduce costs are the two most popular use cases or application areas for AI and ML in enterprises today. It’s noteworthy that seven of the top 20 use cases are customer-centric, nearly half of all use cases tracked in Algorithmia’s survey. 46% of enterprises are using AI & ML to combat fraud, which will most likely grow given the growth and severity of breaches, including the SolarWinds cyberattack. Capgemini’s recent study of AI adoption in cybersecurity found network, data and endpoint security are the three leading use cases of AI in cybersecurity today, with each predicted to get more funding in 2021, according to CISOs interviewed for the report.
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
AI and ML business cases that provide greater customer revenue growth, reduced costs and greater financial visibility have the highest priority of being funded inside any enterprise today. The combination of improving customer experiences, automating processes (to reduce costs) and generating financial insights (for greater financial visibility) is the ideal combination for getting a proof of concept started for an AI or ML project. The proliferation of AI and ML use cases shown in the graphic below is attributable to how each contributes to enterprises achieving a tangible, positive ROI by combining them to solve specific business problems.
Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning
According to Burning Glass Technologies, the two tech job skills paying the highest salary premiums today and in 2021 are IT Automation ($24,969) and AI & Machine Learning ($14,175).
The average salary premiums for the most in-demand tech skills range from $4,204 to nearly $25,000.
Startups valued at $1 billion or more are 33% more likely to prioritize one or several top ten tech job skills in their new hire plans versus their legacy Fortune 100-based competitors or colleagues.
These and many other fascinating insights are from Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech, Burning Glass Technologies’ latest research study published earlier this month. Their latest study provides pragmatic, useful insights for tech professionals interested in furthering their careers and earning potential. Burning Glass Technologies is a leading job market analytics provider that delivers job market analytics that empowers employers, workers and educators to make data-driven decisions.
Using AI To Find The Most Valuable Job Skills
Using artificial intelligence-based technologies they’ve developed, Burning Glass Technologies analyzed over 17,000 unique skills demanded across their database of over one billion historical job listings. The study aggregates then define disruptive skill clusters as those skill groups projected to grow the fastest, are most undersupplied and provide the highest value. For additional details regarding their methodology, please see page 8 of the report.
The research study is noteworthy because it explains how essential acquiring skills is to translating new technologies’ benefits into business value. They’ve also taken their analysis a step further, providing technical professionals with additional insights they need to plan their personal development and careers.
Key takeaways from their analysis include the following:
IT Automation expertise can earn technical professionals a $24,969 salary premium, the most lucrative of all tech job skills to have in 2021. Burning Glass Technologies defines IT Automation as the skills related to automating and orchestrating digital processes and workflows. Six of the ten job skills are marketable enough to drive technical professionals’ salaries above $10,000 a year. At an average salary uplift of $8,851, proactive security (cybersecurity) job skills’ market value seems low. Future surveys in 2021 will most likely reflect the impact of the SolarWinds breach on demand for this skill set. The following graphic compares the average salary premium by tech job skill area.
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
Software Dev. Methodologies (DevOps) expertise is the most marketable going into 2021, with 634,600 open positions available in North America based on Burning Glass Technologies’ analysis. Employers initiated 1,714,483 job postings requesting at least one disruptive skill area between December 2019 and November 2020. With each skill predicted to grow at least 17%, technical professionals have several lucrative options for their personal and professional development plans. The following graphic compares job openings by skill areas for the time frame of the study:
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
Quantum Computing, Connected Technologies, Fintech and AI & Machine Learning expertise are predicted to be the fastest-growing tech job skills in 2021 and beyond. Demand for technical professionals skilled in building and optimizing quantum computers and their applications will be in high demand for the next five years based on the study’s findings. Connected Technologies refers to skills related to the Internet of Things and connected physical tools and the telecommunications infrastructure needed to enable them. Fintech skills are related to technologies, including blockchain and others, that make financial transactions more efficient and secure. The following graphic compares the top ten tech job skills predicted to grow the fastest in 2021.
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
AI & Machine Learning, Cloud Technologies, Parallel Computing and Proactive Security (Cybersecurity) are the most distributed across industries, translating into more diverse job opportunities for technical professionals with these skills. Professional Services leads all industries in demand for nine of the ten tech job skills, except Parallel Computing, the most in-demand skill in Manufacturing. Factors contributing to Professional Services leading all industries in demand for technical job skills include the following factors. First, their business models need to continue pivoting fast to stabilize during the pandemic. Second, better risk and compliance controls of remote operations are urgently needed. Third, better visibility into services costs across all systems to ensure financial reporting accuracy is a must-have, according to the CFOs I spoke with regarding the survey results. The following graphic compares demand for tech skills by industry sector.
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
Demand for AI and Machine Learning skills is growing at a 71% compound annual growth rate through 2025, with 197,810 open positions today. Technical professionals with job skills in this area see salary premiums of $14,175. Top positions include Data Scientist, Software Developer, Network Engineer, Network Architect, Data Engineer and Senior Data Scientist.
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
Positions requiring IT Automation job skills are predicted to grow 59% over the next five years and have 282,380 positions open today. Besides being the most lucrative job skillset to have, IT Automation job skills lead to positions including Software Developer, DevOps Engineer, Senior Software Developer, Systems Engineer and Java Developer or Engineer.
Skills of Mass Disruption: Pinpointing the 10 Most Disruptive Skills in Tech by Burning Glass Technologies
