CIO CIO

“This is for people in the organization who have data and want to drive insights for the business and for their clients,” Beswick says. “I want to provide an easy and secure outlet that’s genuinely production-ready and scalable. The biggest challenge is data. It’s very fragmented, ownership is often unclear, quality is a variable, but we have teams really working on that and generating data faster than we can possibly catalog and clean up.” Marsh McLennan has been using ML algorithms for several years for forecasting, anomaly detection, and image recognition in claims processing. With Databricks, the firm has also begun its journey into generative AI. The company started piloting a gen AI Assistant roughly 18 months ago that is now available to 90,000 employees globally, Beswick says, noting that the assistant now runs about 2 million requests per month. Beswick is also preparing for extensive generative AI activity within the company based on Microsoft’s implementation of OpenAI, which offers security to his liking. The CIO is quick to point out that Marsh McLennan’s gen AI platform — like its development and analytics platforms — uses industry-standard products but its interface, tooling, core services, and enhanced capabilities, which go “beyond what the model can do on its own,” were built by MMTech at the company’s innovation center in Dublin, Ireland. source

To address compliance fatigue, Camelot began work on its AI wizard in 2023. It utilized Generative AI technologies including large language models like GPT-4, which uses natural language processing to understand and generate human language, and Google Gemini, which is designed to handle not just text, but images, audio, and video. Camelot has the flexibility to run on any selected GenAI LLM across cloud providers like AWS, Microsoft Azure, and GCP (Google Cloud Platform), ensuring that the company meets compliance regulations for data security. Throughout 2024, Camelot’s team of in-house developers built the AI wizard that would become “Myrddin,” training it to understand CMMC guidelines and answer questions quickly with a focus on actionable, real-time guidance. The decision to start in a controlled environment and gradually expand AI capabilities allowed Camelot the time to mitigate risks and hone Myrddin before its rollout in September 2024. “Myrddin is now part of our CMMC dashboard tool that assists users in conducting gap assessments and interpreting cybersecurity compliance guidelines,” says Birmingham. “It has streamlined the entire process, helping IT teams handle CMMC assessments more effectively.” source

Business Email Compromise (BEC) scams pose a growing threat to organizations of all sizes, and they are only increasing in sophistication and frequency. The attacks, in which criminals frequently leverage social engineering to impersonate company insiders, C-suite executives or trusted vendors to request urgent payments, can financially devastate organizations. It can be easy to fall victim to a BEC attack, especially for companies with limited resources and leaner teams handling payments. Many rely on a handful of people to manage tasks, who may feel tremendous pressure to respond quickly to seemingly urgent requests – especially if the request is from someone high up in the organization. It’s a practice that can lead to costly mistakes. Preventing BEC scams Implementing the right technology is critical in preventing BEC scams. Solutions like fraud detection tools, vendor portals and payroll management systems can help safeguard against unauthorized payments. Many businesses are now requiring employees to update payment information through secure portals rather than relying on email communications, which reduces the chance of falling victim to an attack. While AI can play a role in detecting fraudulent activities, BEC scammers are increasingly also using AI to craft more convincing emails that make it harder to identify fraud. This further emphasizes the importance of multi-layered defenses, such as dual approval processes for payments and consistent employee education and training on how to spot potential threats. Keys to recovering from a BEC attack For organizations or individuals who may have inadvertently sent money to a fraudster, time is of the essence. If you suspect fraudulent activity, immediately notify your banking partner. Quick action may stop unauthorized transactions before the funds transfer. We tell our clients, don’t be embarrassed. The sooner we know, the faster we can act. In cases where the victim cannot recover funds, it’s essential to have insurance policies in place to mitigate the financial loss. Many businesses overlook the importance of cybersecurity and fraud insurance, but as BEC scams increase, having this protection is key to reducing the damage should a fraud loss occur. Preventing BEC requires a combination of technology, training and internal processes. Here are four simple and immediate best practices to implement: Test and train employees: Regularly test employees with fake phishing emails to ensure they can recognize fraudulent activity. Those who fail should undergo additional training. Provide ongoing education: Consistently provide education to ensure employees are aware of the latest BEC tactics, such as supply chain attacks and multi-factor authentication (MFA) bypass. Also make sure employees understand internal controls around safeguarding potential points of vulnerability in processes related to sensitive data and money movement. Implement dual controls: Requiring dual approvals to verify and approve payments and changes to vendor information ensures no single employee can authorize a payment without verification. Avoid email for financial requests: Use secure portals to update payment information rather than relying on email, which is prone to phishing attacks. The role of a banking partner in preventing BEC A strong relationship with your bank can serve as a critical line of defense in preventing and mitigating BEC attacks. Most banks offer fraud mitigation solutions such as positive pay, which verifies checks and ACH payments before they are processed. Banking partners can also provide education and real-time updates on emerging fraud trends to help businesses stay ahead of potential threats. At the heart of the prevention strategy is collaboration between businesses and their banking partners. Banks can assist with monitoring suspicious activity, verifying requests for changes to vendor or employee payment information, and working with law enforcement in case of fraud. By adopting best practices, a sound risk management strategy, and by working closely with your banking partner, you can protect your organization from falling victim to a BEC scam and help ensure your financial operations remain secure. For more information on how Synovus can help your organization mitigate BEC fraud, complete a short form and a Synovus Consultant will contact you. source

Another factor that increases gen AI risk and costs is the “massive ‘shadow IT’ in most organizations, as employees use personal accounts to use tools like ChatGPT with company data,” Baier says. One way organizations can get a handle on AI use is by implementing an AI governance platform, according to Gartner, which identified the technology as its No. 2 strategic trend for 2025, predicting that by 2028 organizations that implement AI governance platforms will experience 40% fewer AI-related ethical incidents compared to those without such systems. The benefits of AI governance platforms, Gartner claims, include creating, managing, and enforcing “policies that ensure responsible use of AI, explain how AI systems work, model lifecycle management, and provide transparency to build trust and accountability.” source

The impact of DXA on customer experience For product leaders, Digital Experience Analytics (DXA) has provided a transformative shift. It has freed them from the relentless cycle of crisis management, allowing them to focus on implementing meaningful enhancements to the customer experience. By providing visibility into where and why customers encounter roadblocks, DXA linked these insights directly to business impact, such as revenue opportunities. This clarity not only eliminates internal conflicts and guesswork but provides the business case for prioritizing improvements with confidence. DXA allows users to anticipate and address issues before they become problems, shifting focus from constant fire-fighting to strategic innovation. Here are a few factors to consider when evaluating a DXA platform. One: Comprehensive data capture The power of a DXA platform is in its data capture. It should automatically capture every interaction—swipe, click, scroll, API response, page view—across all touchpoints: web, mobile, and kiosks. Quantum Metric does this out-of-the-box, logging over 60 behavioral and 300 data points without manual tagging. It tracks user interactions, technical performance, and business outcomes. With remote precision eventing and a visual event editor, you can create events and analyze experiences effortlessly—no manual tagging, no code changes. Choose a platform that delivers comprehensive data from day one and evolves with your digital needs. Two: Seamless integration A DXA platform should be designed from the ground up, rather than cobbled together through acquisition and loose integration of disparate third-party platforms and point solutions. It needs to seamlessly integrate with your existing tech stack to ensure cohesive and efficient data management. It should easily connect with tools like VOC and survey systems. Integration is crucial for real-time, actionable insights from customer feedback, both online and offline. This connection lets you visualize behavior, contextualize feedback like CSAT and NPS, and trigger surveys based on actions. Opt for platforms with robust APIs and pre-built connectors to ensure a smooth, unified view of customer interactions. Three: Advanced AI capabilities and future-forward vision AI is crucial in DXA. It uncovers hidden patterns, predicts behavior accurately, and automates decisions. Look for platforms with a practical use case for AI, like Quantum Metric’s Felix AI. Felix AI delivers instant summaries of user sessions in under 3 seconds, cuts down on lengthy session replays, and enables one-click quantification of friction points. It integrates smoothly with your systems, supporting rapid, data-driven decisions. Quantum Metric’s position in the DXA space Building on this innovation, we’re thrilled to be recognized as a leading Digital Experience Analytics Platform by Research in Action’s 2024 Vendor Selection Matrix™. Our commitment to exceptional customer experiences and cutting-edge solutions underscores our dedication to transforming the DXA landscape. Why Quantum Metric stands out in this evaluation: Most recommended by users: Quantum Metric is highly recommended for its performance and user satisfaction. Ranked #1 in customer satisfaction and value: We lead the industry in customer satisfaction and offer exceptional value. Ranked #1 in price vs. value: We provide the best price-to-value ratio in the market. Cross-team activation: Our platform supports a wide range of enterprise teams and enables smooth, seamless collaboration across different departments. Full stack, organic growth: Quantum Metric was built and expanded from the ground up as a cohesive solution, not by assembling disparate third-party tools. Vision and go-to-market: We address both current digital marketing and e-commerce needs, while also preparing for future demands in product operations and engineering. AI leadership: Felix AI distinguishes us by quickly prioritizing and contextualizing issues, driving more efficient decision-making. Financial strength: With $100 million in ARR and backed by experienced leadership, Quantum Metric is well-positioned for continued success. Maximizing potential with the optimal DXA platform For enterprise leaders, Digital Experience Analytics (DXA) has been about reclaiming freedom—freeing up time to focus on what truly matters: enhancing customer experiences. By automating data capture and eliminating manual tasks, DXA has enabled them to transition from routine data management to strategic initiatives that drive customer satisfaction and personal fulfillment. To learn more about top DXA platforms and their benefits, download the full 2024 RIA VSM DXA report, and register for our webinar with James McCormick, Research Director at Research in Action on ‘Key DXA trends shaping the market’. source

Artificial Intelligence (AI), a term once relegated to science fiction, is now driving an unprecedented revolution in business technology. From nimble start-ups to global powerhouses, businesses are hailing AI as the next frontier of digital transformation. Nutanix commissioned U.K. research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. The Nutanix State of Enterprise AI Report highlights AI adoption, challenges, and the future of this transformative technology. 1. AI adoption is ubiquitous but nascent Enthusiasm for AI is strong, with 90% of organizations prioritizing it. However, many face challenges finding the right IT environment and AI applications for their business due to a lack of established frameworks. Currently, enterprises primarily use AI for generative video, text, and image applications, as well as enhancing virtual assistance and customer support. Other key uses include fraud detection, cybersecurity, and image/speech recognition. Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. AI applications are evenly distributed across virtual machines and containers, showcasing their adaptability. 2. AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. The majority (91%) of respondents agree that long-term IT infrastructure modernization is essential to support AI workloads, with 85% planning to increase investment in this area within the next 1-3 years. Data mobility across data centers, cloud, and edge is essential, but businesses face challenges in adopting edge strategies. However, 93% of respondents recognize the importance of an edge strategy for AI, and 83% plan to increase investments in edge technology over the next one to three years. While early adopters lead, most enterprises understand the need for infrastructure modernization to support AI. Key challenges include designing and deploying AI infrastructure, with priorities such as data security (53%), resilience and uptime (52%), management at scale (51%), and automation (50%). 3. AI skills remain a concern: investment is coming As AI evolves, organizations are recognizing the need for new skills and competencies. Over the next one to three years, 84% of businesses plan to increase investments in their data science and engineering teams, with a focus on generative AI, prompt engineering (45%), and data science/data analytics (44%), identified as the top areas requiring more AI expertise. Additionally, 90% of respondents intend to purchase or leverage existing AI models, including open-source options, when building AI applications, while only 10% plan to develop their own. This allows organizations to maximize resources and accelerate time to market. 4. Sustainability and ESG are not off the AI table ESG is now a critical business imperative. Survey respondents ranked ESG reporting as a top area needing AI skills development, even above R&D and product development. Companies are seeking ways to enhance reporting, meet regulatory requirements, and optimize IT operations. Many believe that responsible AI use will help achieve these goals, though they also recognize that the systems powering AI algorithms are resource-intensive themselves. 5. Data security, data quality, and data governance still raise warning bells Data security remains a top concern. Respondents rank data security as the top concern for AI workloads, followed closely by data quality. Cost, by comparison, ranks a distant 10th. AI applications rely heavily on secure data, models, and infrastructure. Data governance is also critical, with AI pushing it from an afterthought to a primary focus. Consistent data access, quality, and scalability are essential for AI, emphasizing the need to protect and secure data in any AI initiative. 6. Cost Roadblocks will start to emerge Early AI adoption often comes with a “honeymoon phase” where costs are overlooked in favor of staying ahead of the curve. However, 90% of respondents already recognize that AI applications will drive up daily IT and cloud expenses. As budgets tighten, AI will soon face the same financial scrutiny as other IT investments. This highlights the need to justify costs, identify infrastructure options that offer optimal total cost of ownership (TCO), and strategically plan AI investments for sustained value. Implementing enterprise AI is a long-haul journey The journey to AI maturity is complex, with no single path or definitive approach to infrastructure decisions. Success will come to enterprises that adopt AI and embed it into their operations, making thoughtful infrastructure choices, investing in talent, and building long-term strategies. As businesses embrace AI, they stand poised for unprecedented innovation and transformation. Read the full Nutanix State of Enterprise AI Report for valuable insight into AI adoption, challenges, and the future of this transformative technology. source