Accelerating GenAI Adoption with an Open Source Platform for Seamless AI, LLMOps, and MLOps Development, Deployment, and Resource Management

ClearML, the leading solution for unleashing AI in the enterprise, today announced the launch of its expansive end-to-end AI Platform, designed to streamline AI adoption and the entire development lifecycle. This unified, open source platform supports every phase of AI development, from lab to production, allowing organizations to leverage any model, dataset, or architecture at scale. ClearML’s platform integrates seamlessly with existing tools, frameworks, and infrastructures, offering unmatched flexibility and control for AI builders and DevOps teams building, training, and deploying models at every scale on any AI infrastructure.

With this release, ClearML becomes the most flexible, wholly agnostic, end-to-end AI platform in the marketplace today in that it is:

– Silicon-agnostic: supporting NVIDIA, AMD, Intel, ARM, and other GPUs
– Cloud-agnostic: supporting Azure, AWS, GCP, Genesis Cloud, and others, as well as multi-cloud
– Vendor-agnostic: supporting the most popular AI and machine learning frameworks, libraries, and tools, such as PyTorch, Keras, Jupyter Notebooks, and others
– Completely modular: Customers can use the full platform alone or integrate it with their existing AI/ML frameworks and tools such as Grafana, Slurm, MLflow, Sagemaker, and others to address GenAI, LLMOps, and MLOps use cases and to maximize existing investments.

“ClearML’s end-to-end AI platform is crucial for organizations looking to streamline their AI operations, reduce costs, and enhance innovation – while safeguarding their competitive edge and future-proofing their AI investments by using our completely cloud-, vendor-, and silicon- agnostic platform,” said Moses Guttmann, Co-founder and CEO of ClearML. “By providing a comprehensive, flexible, and secure solution, ClearML empowers teams to build, train, and deploy AI applications more efficiently, ultimately driving better business outcomes and faster time to production at scale.”

The ClearML end-to-end AI Platform encompasses newly expanded capabilities and integrates previous stand-alone products, and includes:

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– A GenAI App Engine, designed to make it easy for AI teams to build and deploy GenAI applications, maximizing the potential and the value of their LLMs.
– An Open Source AI Development Center, which offers collaborative experiment management, powerful orchestration, easy-to-build data stores, and one-click model deployment. Users can develop their ML code and automation with ease, ensuring their work is reproducible and scalable.
– An AI Infrastructure Control Plane, helping customers manage, orchestrate, and schedule GPU compute resources effortlessly, whether on-premise, in the cloud, or in hybrid environments. These new capabilities, which were also introduced today in a separate announcement, maximize GPU utilization and provide fractional GPUs, as well as multi-tenancy and extensive b****** and chargeback capabilities that offer precise cost control, empowering customers to optimize their compute resources efficiently.

ClearML’s AI Platform enables customers to use any type of machine learning, deep learning, or large language model (LLM) with any dataset, in any architecture, at scale. AI Builders can seamlessly develop their ML code and automation, ensuring their work is reproducible and scalable. That’s important, because it addresses several critical challenges faced by organizations in developing, deploying, and managing AI solutions in the most complex and demanding environments. Here’s why it matters:

Unified End-to-end Workflow: ClearML provides a seamless workflow that integrates all stages of AI development, from data ingestion and model training to deployment and monitoring. This unified approach eliminates the need for multiple disjointed tools, simplifying the AI adoption and development process.

Superior Efficiency and ROI: ClearML’s new AI infrastructure orchestration and management capabilities help customers execute 10X more AI and HPC workloads on their existing infrastructure.

Interoperability: The platform is designed to work with any machine learning framework, dataset, or infrastructure, whether on-premise, in the cloud, or in a hybrid environment. This flexibility ensures that organizations can use their preferred tools and avoid vendor lock-in.

Orchestration and Automation: ClearML automates many aspects of AI development, such as data preprocessing, model training, and pipeline management. This ensures full utilization of compute resources for multi-instance GPUs and job scheduling, prioritization, and quotas. ClearML empowers team members to schedule resources on their own with a simple and unified interface, enabling them to self-serve with more automation and greater reproducibility.

Scalable Solutions: The platform supports scalable compute resources, enabling organizations to handle large datasets and complex models efficiently. This scalability is crucial for keeping up with the growing demands of AI applications.

Optimized Resource Utilization: By providing detailed insights and controls over compute resource allocation, ClearML helps organizations maximize their GPU and cloud resource utilization. This optimization leads to significant cost savings and prevents resource wastage.

Budget and Policy Control: ClearML offers tools for managing cloud compute budgets, including autoscalers and spillover features. These tools help organizations predict and control their monthly cloud expenses, ensuring cost-effectiveness, by providing advanced user management for superior quota/over-quota management, priority, and granular control of compute resources allocation policies.

Enterprise-Grade Security: The platform includes robust security features such as role-based access control, SSO authentication, and LDAP integration. These features ensure that data, models, and compute resources are securely managed and accessible only to authorized users.

Real-Time Collaboration: The platform facilitates real-time collaboration among team members, allowing them to share data, models, and insights effectively. This collaborative environment fosters innovation and accelerates the development process.

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Dscout today announced the launch of AI Analysis, an innovative new feature designed to transform how companies conduct and analyze their research.

Dscout, the leading experience research platform, announced the launch of AI Analysis, an innovative new feature designed to transform how companies conduct and analyze their research. This new feature empowers research, design, and product teams to efficiently and effectively uncover deeper insights from their studies, enabling them to make informed decisions faster than ever before.

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“AI Analysis is a feature that comes at a time when research, design, and product teams are under increasing pressure to deliver rapid, high-quality insights,” said Michael Winnick, CEO of Dscout. “Our new AI-powered capabilities allow users to not only keep up with the pace of demand but to stay ahead of it by providing deeper, more actionable findings in less time.”

AI Analysis was created to comb through data from experience research projects on Dscout, gather key takeaways, and highlight critical insights. The feature allows researchers and designers to uncover hidden patterns and gain a better understanding of user behavior. Dscout customers can distill and share these insights with their wider organizations to ultimately help drive innovation and growth.

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With Dscout’s AI Analysis, customers can:

– Generate paragraph recaps of open-ended and video responses in unmoderated studies, allowing them to quickly identify common participant phrases.
– Receive summaries of key points from moderated session transcripts, with small talk removed to focus on the main discussion points.
– Utilize AI-generated themes to categorize topics within responses, aiding in hypothesis confirmation and pattern identification.
– Offload time-consuming tasks like tagging and summarizing.
– Focus on deeper analysis and communicating critical insights.
– Minimize human error and bias while retaining control over the research process.

At its core, AI Analysis acts as a dedicated research assistant, combing through data and sharing key takeaways for further exploration. The tool reduces the workload for researchers and designers, allowing them to focus on crucial tasks like team collaboration and effective utilization of research.

Transparency, privacy, and security are paramount with Dscout’s AI Analysis. The platform adheres to industry-leading security standards, including ISO 27001, SOC2, HIPAA, and HITRUST, ensuring data safety. Compliance with GDPR, CCPA, and other regulations guarantees the privacy and security of user data. All AI-generated content is clearly labeled and links to source material when available, maintaining transparency throughout the research process. Use of Dscout’s AI features is optional.

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Generative AI is set to be adopted by 85% of the software workforce over the next two years

Three in five organizations see innovative work as the biggest benefit of generative AI use in software engineering; software professionals say generative AI will boost their comms with business teams

Generative AI (Gen AI) is expected to play a key role in augmenting the software workforce, assisting in more than 25% of software design, development, and testing work in the next two years. According to the Capgemini Research Institute’s latest report “Turbocharging software with generative AI: How organizations can realize the full potential of generative AI for software engineering”, a large majority (80%) of software professionals believe that, by automating simpler repetitive tasks, Gen AI tools and solutions will significantly transform their function, freeing up time for them to focus on higher-value-adding tasks. More than three quarters of software professionals are confident that generative AI has the potential to boost collaboration with non-technical business teams.

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While the generative AI adoption for software engineering is still in its early stages, with 9 in 10 organizations yet to scale, the report found that organizations with active Gen AI initiatives are already reaping multiple benefits from its adoption – fostering innovation coming first place (61% of organizations surveyed) followed by improving software quality (49%). They saw also an improvement of between 7 to 18% (on average) in the productivity¹ of their software engineering functions. For certain specialized tasks, time saving was as high as 35%.

Organizations surveyed highlighted that they plan to leverage the additional time freed up by generative AI for innovative work such as developing new software features (50%) and upskilling (47%); while reducing headcount being the least-adopted route (just 4% of responding organizations). New roles, such as generative AI developer, prompt writers or generative AI architect are also emerging.

Improved collaboration between tech and business teams

From better communication to explaining what the code is doing in natural language, Gen AI makes the connection between software engineers and other business teams more effective. 78% of software professionals are optimistic about Gen AI’s potential to enhance collaboration.

Augmented software workforce and employee satisfaction
According to the survey, generative AI tools are used today by 46% of software engineers for assisting them on tasks. Almost three quarters agree that generative AI’s potential extends beyond writing code. While coding assistance is the leading use case, generative AI also has applications in other software development lifecycle activities, such as code modernization or user experience (UX) design.

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Both senior and junior software professionals also report higher levels of satisfaction from using Gen AI (respectively 69% and 55%). They see generative AI as a strong enabler and motivator.

However, according to the report 63% of software professionals declare using unauthorized Gen AI tools to assist them in tasks. This rapid take-up, without proper governance and oversight in place, exposes organizations to functional, security, and legal risks like hallucinated code, code leakage, and IP issues.

Pierre-Yves Glever, Head of Global Cloud & Custom Applications at Capgemini, said: “Generative AI has emerged as a powerful technology to assist software engineers, rapidly gaining adoption. Its impact on coding efficiency and quality is measurable and proven, yet it holds promise for other software activities. However, we must remember that the true value will emerge from a holistic software engineering approach, beyond deploying a single ‘new’ tool. This involves addressing business needs with robust and relevant design, establishing comprehensive developer workspaces and assistants, implementing quality and security gates, and setting up effective software teams. The focus should be on what genuinely generates value. Exciting times lie ahead!”

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Pcloudy has introduced a new and improved Self-Healing AI Engine 2.0 that cuts down the testing team’s maintenance efforts by almost 3 times.

Pcloudy, an AI Augmented Unified App Testing Suite has introduced a new and improved Self-Healing version 2.0 that cuts down the testing team’s maintenance efforts by almost 3 times. After constantly improving their Self-Healing AI Engine since its launch in March 2023. Today, Pcloudy’s Self-Healing AI Engine 2.0 stands as a testament to its continuous improvement process. The new Self-Healing AI Engine version 2.0 is faster, more accurate, and easy-to-use.

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We are confident of Pcloudy’s Self-Healing AI Engine capabilities to revolutionize automation in not just the testing space but also in various tech industries and global capability centers.”

— Avinash Tiwari, Co-founder of pCloudy

What is it?

Pcloudy’s Self-Healing AI Engine 2.0 is a self-healing capability/feature that enables testers and testing teams to cut down their automation maintenance efforts by identifying and updated elements and object locators in automation scripts for a smooth test execution. This improves test reliability, reduces test flakiness and automation failures leading to shorter release cycles and increased business revenues.

Key Highlights of Self-Healing AI Engine 2.0

Continuous Monitoring – The Self-Healing AI Engine 2.0 meticulously monitors the automation scripts, identifying and rectifying errors as they occur. The system learns from each interaction, continually improves and refines itself.

Simplified Maintenance – It simplifies the maintenance process by automatically detecting changes in the application and adjusting the scripts accordingly. Development teams no longer need to spend countless hours updating scripts for minor changes in the UI or functionality.

Accelerated Automation – Speed is of the essence and the Self-Healing 2.0 AI Engine dramatically accelerates the automation process. It automatically updates and corrects the scripts in real-time, eliminating the bottlenecks associated with manual maintenance.

Avinash Tiwari, co-founder of Pcloudy said “We’ve come a long way with the Self-Healing AI Engine at Pcloudy. We are confident of its capabilities to revolutionize automation in not just the testing space but also in various tech industries and global capability centers.”

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A recent social media post by Pcloudy showed how a simple change in automation scripts caused a test failure and took over 9 minutes to fix and run again successfully. Pcloudy compared this with their new and improved Self-Healing AI Engine 2.0 which fixed it 3 times faster. This simple comparison highlights the impact of this feature as it goes a long way in saving millions of dollars for organizations that spend their time and effort in maintaining test scripts to ensure Test Reliability.

Pcloudy’s new Self-Healing AI Engine 2.0 is setting the bar high in the automation maintenance market. This Self-Healing AI Engine is all set to accelerate the digital transformation journeys of many businesses in the world over and ignite a scale of unprecedented growth.

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• Investment aims to bolster Canada’s technology sector on the global stage

Sanctuary AI, a company on a mission to create the world’s first human-like intelligence in general purpose robots, has announced a strategic investment from BDC Capital’s Thrive Venture Fund and InBC Investment Corp. (InBC). This brings the total investment in Sanctuary to over $140 million to date. BDC is Canada’s most active venture capital investor, and InBC is a strategic investment fund created by the Province of British Columbia (BC) to benefit BC. The additional funds will be used to further accelerate Sanctuary’s progress towards bringing AI into the physical world.

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Sanctuary’s existing investors include Accenture, Bell, Export Development Canada, Evok Innovations, Magna, SE Health, Verizon Ventures, and Workday Ventures. It also received a $30 million Strategic Innovation Fund (SIF) contribution from the Government of Canada in November 2022.

“Following the incredible impact of pre-trained transformers on the digital world over the last seven years, with acceleration in the last few, we see AI in the physical world as being the next major frontier for impacting the way we work and live.” Said Olivia Norton, Co-founder, Chief Technology and Product Officer of Sanctuary AI. “With aging populations, plummeting birth rates, and a changing view on work, intelligent embodied systems, or general purpose robots will play an important role in provincial and national productivity. We believe that Canada has an opportunity to be a world leader in this space. It is great to work with organizations like BDC and InBC who understand and share this vision.”

“At BDC capital, we’re proud to invest in Canada’s most innovative women-led businesses, like Sanctuary AI, that are disrupting today’s market. We’re thrilled to support Sanctuary AI’s team as they realize their next groundbreaking achievements.” Said Michelle Scarborough, Managing Partner at BDC Capital’s Thrive Venture Fund.

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“We have invested in Sanctuary whose mission is helping to build a new growth industry in British Columbia.” Finished Leah Nguyen, Chief Investment Officer (CIO) at InBC. “Sanctuary is a great example of the creative and innovative spirit in BC, and by investing in leading innovators like Sanctuary we will continue to grow our technology sector, creating new jobs and anchoring IP in the province for a stronger, more sustainable economy that works for everyone.”

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Aerospike, Inc. (“Aerospike”), a leading database solutions provider, today announced the appointment of Venkatesh Guntur as the new Country Manager for Southeast Asia (ASEAN). In this strategic role, Venkatesh will spearhead Aerospike’s business operations in the region, focusing on elevating the company’s presence, driving growth, and delivering exceptional customer service.

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“I am honored to join Aerospike and look forward to further building on the company’s reputation as the most scalable, efficient, and cost-effective real-time database solution in the region”

Speaking about his appointment, Aveekshith Bushan, Vice President and GM, Asia Pacific and Japan, Aerospike, commented, “With his extensive experience and proven leadership, Venkatesh will be an invaluable addition to our team as we continue to expand our footprint in the enterprise and fast-growing AI-centric business applications segments in the ASEAN region. We are delighted to welcome him aboard.”

Venkatesh brings over 25 years of experience in enterprise IT sales leadership, having driven strategic sales initiatives and fostered innovation in the technology sector for several successful organizations. Before joining Aerospike, he held key leadership positions at companies such as Couchbase, ADP, Blue Prism, and Ramco Systems. Venkatesh is passionate about business outcome-based value selling through technologies like real-time databases, artificial intelligence, and machine learning.

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“I am honored to join Aerospike and look forward to further building on the company’s reputation as the most scalable, efficient, and cost-effective real-time database solution in the region,” said Venkatesh.

Aerospike is a massively scalable, millisecond latency, real-time multi-model database that cost-effectively processes transactions, documents, graphs and vectors for real-time operations and decision making. It enables organizations to feed AI/ML systems high volumes of real-time data faster, with up to 80% less infrastructure than other database providers.

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The platform acts as an intermediate layer that reconciles data silos and automatically adds context and meaning to data, enabling reliable and transparent AI initiatives

illumex, the Generative Semantic Fabric platform for structured enterprise data, announced that it has raised $13M in seed funding. The round was led by Cardumen Capital, Amdocs Ventures, and Samsung Ventures, with participation from ICI Fund, Jibe Ventures, Iron Nation Fund, Ginossar Ventures, ICON Fund, Today Ventures, and renowned angel investors. illumex empowers organizations to overcome data challenges that hinder generative AI (GenAI) initiatives by automating the creation of a semantic layer that unifies data silos and adds business context, as well as generating a consistent vocabulary of domain-specific terminology. The company already has large enterprises, like Teva and Carson, leveraging illumex for their data AI readiness and has forged partnerships with Microsoft, Google Cloud, and AWS.

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Adoption of generative AI has surged over the past year, with two-thirds of organizations now using the technology, according to a recent McKinsey survey. Importantly, GenAI is now driving significant business value, with some organizations reporting a contribution of upwards of 10% to bottom lines. However, concerns about the inaccuracy and the risks of GenAI have only grown, and nearly a quarter of organizations say they’ve already experienced negative consequences. Existing solutions like retrieval-augmented generation (RAG) enhance accuracy by providing enterprise data for AI models. However, RAG has drawbacks, especially with structured data: it demands continuous manual maintenance and doesn’t ensure a single source of truth or accurate LLM responses. Unstructured data, such as text documents, is semantically meaningful, but structured data lacks inherent context, needing manual labeling that often results in ambiguity and inconsistencies across enterprise data sources. This disorganized data, with issues like duplicate records and conflicting terminology, erodes trust in GenAI applications.

illumex solves this by using generative AI to automatically discover, map, and add semantic meaning to structured enterprise data. The platform analyzes metadata — without accessing the underlying sensitive information — to create a unified semantic knowledge graph. This graph acts as a single source of truth that aligns all data with consistent business terminology and context. By automatically constructing a domain-specific ontology, which formally defines the entities, properties, and relationships that represent an organization’s knowledge structure, illumex enables consistent, contextually relevant data interactions across the enterprise. This serves as a foundation that enables LLMs to reliably map user questions to the relevant data points that should be retrieved in order to deliver accurate results while ensuring proper governance.

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“The key to enterprise generative AI success lies in solving the fundamental data challenges that have caused so many projects to fail,” said Inna Tokarev Sela, Founder and CEO of illumex. “Our Generative Semantic Fabric aligns organizational data with business meaning and domain-specific context, allowing organizations to finally trust the results of their AI initiatives. By enabling business users to interact reliably with data using natural language, without teaching them the precise technical definitions, we’re democratizing AI and empowering enterprises to make better decisions.”

“For enterprises in complex and regulated industries, adopting generative AI isn’t just a nice-to-have, it’s an existential imperative,” said Gonzalo Martínez de Azagra, Founder and General Partner at Cardumen Capital. “However, they face the unique challenge of needing to balance AI-powered innovation with strict data governance and security requirements. illumex perfectly bridges this gap, enabling enterprises to maximize the GenAI opportunity while ensuring the integrity, lineage, and trustworthiness of the data fueling these applications. illumex plays a crucial role in both reassuring organizational data AI-readiness and enabling contextual and governed GenAI interactions.”

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Automatically and continuously learns business logic to block attacks as they occur

Radware, a leading provider of cyber security and application delivery solutions, announced it has enhanced its API Protection solution with a new AI-driven, auto-learning protection engine designed to immediately detect and mitigate business logic attacks. Working in real-time, the engine exposes bad actors’ identities and automatically detects and blocks malicious API calls by continuously learning the application’s business logic. The solution offers organizations comprehensive coverage for the OWASP API 2023.

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API attacks that leverage business logic vulnerabilities are becoming more sophisticated and harder to detect, often mimicking legitimate API usage. According to Radware’s latest Global Threat Intelligence Analysis Report, malicious web application and API transactions increased by 171% in 2023 compared to 2022. Retail (37%) and transportation (19%) were the most attacked industries, followed by software as a service (8%) and carriers (8%).

“Radware is helping organizations take the guesswork out of API protection,” said Gabi Malka, Radware’s chief operation officer. “Unlike competitive solutions that rely on past attack log analysis for detection and remediation recommendations rather than immediately blocking the attacks, Radware’s AI-powered protection works automatically, continuously, and in real-time. It not only learns the business logic, but also accurately reveals bad actors’ identities and blocks their attacks as they occur, resulting in frictionless, optimized protection and reduced risk.

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Radware’s enhanced API Protection solution takes a multi-layer approach to real-time detection and mitigation of business logic attacks. It leverages:

• Continuous auto-learning of the application business logic to deliver real-time insights into the legitimate or malicious intent of API calls.
• Real-time AI-driven context analysis of security policies to enhance the reliability of API attack detection and mitigation.
• Precise identification of bad actors that surpasses simple IP blocking to accurately block malicious users and clients.

Radware’s API Protection is part of the company’s comprehensive Cloud Application Security Protection Service. The service also includes the company’s industry-leading web application firewall (WAF), bot detection and management, and client-side and application-level (Layer 7) web DDoS protection. Combining end-to-end automation, behavioral-based detection, and 24/7 managed services, the solution is designed to offer organizations the highest level of application protection with the lowest level of false positives.

Radware has received numerous awards for its application and network security solutions. Industry analysts such as Aite-Novarica Group, Forrester Research, Gartner, GigaOm, KuppingerCole, and Quadrant Knowledge Solutions continue to recognize Radware as a market leader in cyber security.

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New study from SoftServe reveals less Gen AI value than expected despite growing use cases and internal limitations

SoftServe, a premier IT consulting and digital services provider, released its findings of the latest study commissioned to evaluate current Generative AI (Gen AI) use across global businesses conducted by research and advisory firm Forrester Consulting. After more than 18 months since the release of ChatGPT and growing availability of Gen AI products, solutions, and services, the study revealed businesses are still experiencing less value than executives expected with only 22% of organizations reported effectively using the technology across all business functions.

Despite falling short of expectations, Gen AI enthusiasm grows unabated according to the global survey of 777 technology purchasing decision-makers involved with their organization’s use of Gen AI. Their responses indicated organizations continue to invest and pursue many use cases in search of ones that will deliver the biggest impact while simultaneously struggling with internal data readiness, governance, and skill development.

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Key findings include:

• More than half of decision-makers say their company established business goals for using Gen AI, yet 79% or more are concerned with their organization’s ability to execute those goals with current levels of internal or external expertise.
• Despite 75% or more experiencing Gen AI skill readiness challenges, organizations continue to pile up use cases as most have implemented at least three use cases and will expand the use of two more, including plans to pilot at least one more use case in the next 12-18 months.
• Just 51% of leaders are extremely confident their current strategy will allow them to reach maximum Gen AI value from future use cases, while concurrently, 77% are concerned with their company’s potential to realize business value in either the short or long term.
• Only 42% of organizations have the capabilities to train Gen AI models and a staggering 89% face difficulties preparing business data for Gen AI use.
• Less than one-fourth (24%) have governance plans in place, even though 90% agree adopting a governance plan is imperative to ensuring the responsible use and risk mitigation of Gen AI.

“Despite a swift start to the Gen AI race, many initiatives get stuck in the piloting stages as more organizations realize their data infrastructure isn’t ready to adequately deploy Gen AI technologies beyond the proof-of-concept,” said Alex Chubay, SoftServe’s CTO. “Gaps in skills and knowledge of emerging Gen AI technologies, technical feasibility, and data readiness hinder companies from moving beyond tactical wins in pilot mode to full-scale deployments enabling novel business capabilities and experiences. To make that qualitative leap to the next level, a holistic approach is required to orchestrate business priorities, use cases, and data across the technology ecosystem from the initial strategy down to the final execution.”

Gaps in Expectations vs. Reality
While respondents agree data is paramount to effective Gen AI strategies, only 3% said their organizations’ models can leverage a full range of six or more types of business data (operational, customer, employee, source code, public, and partner data), which was double the respondent average of three data types used. Moreover, a gap in technical skills persists as 88% say deeper technical expertise is becoming increasingly important for data integration, model optimization, use case development, and further application development.

Crucial External Expertise Needed
Despite the over-abundance of use cases, 80% of decision-makers claim their employees are currently struggling with use case awareness and general understanding of Gen AI complexity and 90% say their organizations need a partner with more advanced technical capabilities to see transformative value in future use cases.
According to the study, companies are looking for partners with accelerated deployment support (89%) and a better understanding of their industry (88%) to help with execution and implementation.

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Notable Trends in Gen AI Results
The study revealed organizations reaping substantial value from Gen AI prioritized data, governance, and skill development with help from technical partners and experts. Of four countries surveyed, the U.S. took the lead in unlocking Gen AI value, followed by the UK, Singapore, and Germany, respectively.

Among industries, retail was most likely to harness Gen AI value and train their organization’s models on owned data, while FSI (financial services and insurance) reported more likely to encounter challenges before yielding any Gen AI gains. FSI leaders also reported releasing fewer governance plans than retail and other sectors. Companies across healthcare, life sciences, oil and gas, manufacturing, ISVs, and enterprise technology depicted an even divide in achieving Gen AI value. Separately, larger businesses with revenues greater than $5 billion were less likely to show Gen AI successes due to difficulties organizing the required capabilities needed across expansive hardware, software, and infrastructure landscapes.

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