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The race to harness AI agents is on. According to recent research from Cloudera, a near-unanimous 96% of IT and data executives intend to at least broaden their use of AI agents this year. Nearly half plan for widespread integration across their organizations.   This enthusiasm is hardly surprising. AI agents represent a major opportunity for enterprises to quickly boost productivity and efficiency with autonomous task management. These agents can formulate and execute a strategy in service of a specific goal, analyze information, and adjust their approach based on new data with little to no human oversight.   It’s a technology with the potential to redefine everything, and as such, the mandate is clear — enterprise leaders must act now or risk getting left behind. So, what does an organization need to successfully embrace agentic AI?   Let’s dig in.   The value of AI agents   What makes AI agents so powerful is their ability to plan out a task, use reasoning to adjust to new information, and execute in the company’s best interest while staying within security guardrails. Leveraging smart automation, predictive trend analysis, and anomaly detection, AI agents can tackle tasks across a wide range of business functions: from supply chain and operations to customer service or cybersecurity.  Dynamic decision-making and real-time responsiveness work with both simple tasks and complex scenarios. AI agents intelligently analyze situations and reduce the need for human intervention, allowing employees to focus on more creative or strategic endeavors.   However, the path to realizing that potential is not always straightforward. Early adopters have encountered a number of challenges while integrating AI agents.   Barriers to agentic AI adoption   Cloudera’s survey respondents cited several barriers to adoption. Fifty-three percent cited data privacy concerns, 40% noted integration issues, and 39% raised concern over high implementation costs. Many of these challenges boil down to compatibility. Organizational leaders are concerned that an emerging technology like AI agents will not mesh with their existing IT environments, particularly at scale. This point is driven home by the 37% of enterprise leaders who said integrating AI agents into their current systems and workflows has been very or extremely challenging.   As that number might suggest, AI agents are not a plug-and-play magic fix to enterprise challenges. Successfully standing up this technology requires an in-depth evaluation of an organization’s existing infrastructure to understand where it either does or doesn’t currently meet data management, security, and compliance needs.   Agentic AI also has problems of its own, especially considering ethics. Its reliance on historical data can unintentionally introduce or reinforce biases that alter outcomes, as we’ve already seen to be true with broader AI platforms. These biases can spread quickly, finding their way into nearly every stage of the AI lifecycle if not considered early in implementation.   Now that we understand some of the complications that come with agentic AI, let’s examine how to set a strong foundation for agentic AI.   Laying the groundwork for agentic AI  Considering where most enterprise leaders raise concerns with AI agents — data privacy, integration, and implementation — it’s never been more important for organizations to ensure they have the right data management tools in place. Regardless of its intended use, for an AI initiative to be effective, it needs data that is secure and trustworthy. That’s where working with a partner like Cloudera, with its deep expertise in helping businesses achieve trusted data, can prove critical.   Cloudera gives enterprise leaders the support they need to ensure their architecture is secure and scalable, while also providing strong governance to protect highly sensitive historical data. With strong data integrity and compliance, AI agents can function with minimal data bias, building trust at scale.   Then there are the teams who will be responsible for the technology. Technical teams must be able to build and integrate AI agents, but more importantly, understand their reasoning, limitations, and evolving capabilities. That’s where upskilling plays a central role: putting an emphasis on continuous learning to refine strategies and measure impact before committing to full-scale implementation.  Shaping the future with agentic AI  As businesses in every sector push to get more from what they have: higher productivity, increased efficiency, a deeper competitive advantage, and more insights, companies need to prepare their data architecture for the solution — agentic AI. Future business competitiveness and ultimately, success hinges on well-planned and executed agentic AI deployments.   Is your organization ready to capitalize on AI agents? Learn more about how Cloudera can help drive AI success.  source

Tapping into the vast power of emerging technologies, like AI, requires flexibility and scalability to support rapidly evolving data needs. With that, most organizations have come to recognize the importance of adopting a hybrid approach, opting to leverage multiple environments spanning everything from cloud to on-premises. A hybrid cloud approach to data management is essential for improving data quality and availability, both of which pose immense challenges to enterprises today.   But an impactful hybrid approach must go beyond simply moving data between environments. It demands reliable and trusted data. Without trusted data, organizations risk inaccurate insights, flawed decision-making, and biased AI/ML outputs.   Enter: the importance of “true” hybrid cloud.  What elevates a hybrid cloud strategy to the level of “true” hybrid? Significantly, “true” hybrid is the ability to operate as a single platform across both data center and cloud, as well as at the edge, accounting for the entirety of the data lifecycle, whether that’s the point of ingestion, warehousing, or machine learning. This approach helps unlock data insight from across the enterprise, maximizing the potential value of data analytics, AI, and LLMs.  To achieve true hybrid, enterprises can follow a clear roadmap to take their IT infrastructure to the next level. Here’s where to start.  Enable seamless integration across environments  True hybrid is a mixed infrastructure environment that can act as a single cloud environment. It seamlessly integrates on-premises, cloud, and edge environments into a unified infrastructure, allowing data, workloads, and applications to move freely. From a user perspective, there is no noticeable distinction between where data resides. This flexibility enhances adaptability, supports all data types, and ensures consistent functionality across platforms. This is a critical factor for organizations aiming to scale AI and analytics efficiently.  So, how can enterprises determine if their approach meets the criteria of true hybrid? It’s key to remember that while supporting multiple clouds offers major benefits, organizations with a considerable data center investment or who keep datasets on-premises require a more complete solution.   A true hybrid model enables organizations to keep data and workloads moving multi-directionally and friction-free while having identical and portable functionality available across infrastructures. Importantly, to be “true” hybrid, it also must be able to move this data, whether structured, unstructured, or semi-structured, with consistent governance and security. This allows organizations to orchestrate data and workloads across the entire IT landscape.   Establish a foundation of trusted data  Organizations that accelerate their AI initiatives with trusted data are better positioned to deliver reliable insights, maintain regulatory compliance, and scale innovation across the enterprise. But establishing trusted data at a foundational level means overcoming multiple obstacles. Issues around transparency, control, access, and a number of other factors all require robust privacy, security, and governance frameworks to adequately address them.  For an enterprise looking to establish that baseline of trustworthiness, there are a few critical points to examine: what data is available, who may (or may not) access it, and what environment it will be used in. Gaining a granular understanding of an organization’s data flow — and managing access to sensitive information — can protect against regulatory vulnerabilities, government penalties, and reputational damage.   Solving for those three points of availability, accessibility, and environment requires the right solutions. For example, working with a trusted partner like Cloudera and leveraging the Cloudera platform enables organizations to break through data silos, increase visibility, and ensure data quality by embedding consistent security, governance, lineage, and metadata management across environments.   Implementing the ideal data infrastructure  As enterprises navigate addressing the data requirements that enable true hybrid, three key approaches deliver key functionality in terms of flexibility and scalability of data management, and ensure that the right, trusted data can be used to feed AI and analytics effectively. These include:  Unified data fabric: Data fabric helps make disparate data sources available in a safe, compliant, and self-service manner across the organization. This helps enterprises bring together disparate data into one single source — a critical part of what differentiates hybrid cloud and true hybrid cloud.   Scalable data mesh: A data mesh is designed to be decentralized and self-service, giving the ability to implement data as a product, providing simple authorized access, and giving each domain autonomy, agility, and flexibility to craft their data as a product of their organization.  An open data lakehouse: A data lakehouse offers a single location for storing and accessing data across all analytics engines. It offers significant benefits, including reducing the need for data duplication and synchronization, which lowers costs.   With these in place, organizations have the foundation to begin capitalizing on emerging technologies, knowing their data will deliver the best possible outcomes.  Tapping intro true hybrid  By now, most organizations understand the value that comes with adopting a hybrid cloud approach. But the enterprises that are able to stand out and gain a competitive edge are the ones that go a step further. Those organizations embrace true hybrid. Enterprises that lay a foundation of trusted data, stronger data management standards for security and compliance, and a next-gen tech stack will not only win the moment — they will future-proof their organizations at scale.   Learn more about how Cloudera can help you achieve true hybrid.   source

As AI transitions from experimental deployment to enterprise-critical infrastructure, CIOs and IT leaders are being asked to guide their organizations through a rapidly evolving technology landscape. One of the most significant trends is the rise of AI agents — systems capable of making decisions and performing complex, multi-step tasks with minimal human oversight. Recent data shows that 72% of IT professionals report their organization is actively using AI agents, with another 21% stating they are planning to implement agentic AI systems within the next 24 months.  But as promising as these systems are, they face an all too familiar roadblock: integration. AI agents, like many emerging technologies before them, often operate in isolation from the core systems where business data and operational logic live. Without standardized integration, these agents are difficult to scale, expensive to maintain and limited in business impact. Gartner even predicts that through 2026, organizations will abandon 60% of AI projects due to a lack of AI-ready data.  Enter the model context protocol (MCP) — an open, vendor-agnostic standard that enables secure, two-way connections between AI agents and enterprise systems. For IT leaders, MCP is more than a technical innovation; it’s a strategic shift in how IT infrastructure can support intelligent, autonomous operations.  source

Again, that is the future, right? But, you know, sign up the human workforce for success in an AI driven world. Like, what kind of things should it decision makers be thinking about this? Well, it’s really one of the critical, tools in the needs of, the company, but but realistically, you know, breaking it down to the individual divisions. source

Fong concurs, emphasizing the importance of annual legal awareness sessions for product teams. “CIOs should make it their responsibility to bridge the gap between legal and product, and make sure new features are developed with compliance in mind from day one,” he says. “Innovation doesn’t slow down when privacy is part of the process. It accelerates because you avoid costly rewrites later.” Nick DeMelas, chief experience officer at software developer Sourcetoad, says his company proactively maintains awareness of regulatory trends, geopolitical developments, and emerging technologies with research, alerts, RSS feeds, and keeps an eye on the industry overall. “Our team actively participates in ongoing internal training sessions, regularly sharing insights about privacy and security developments,” he says. “We also hold internal discussions and talks, such as recent sessions on differences between EU and US privacy standards, helping our team anticipate shifts rather than react to them.” source

Overview In today’s global tech landscape, local capability is becoming a strategic advantage for CIOs. In this CIO Leadership Live interview – in partnership with Brennan – Nick Sone, Chief Customer Officer at Brennan, explores why more Australian CIOs are turning to trusted, local partners who can deliver global capability – without the complexity and red tape of offshore integrators. As Brennan sets its sights on becoming Australia’s first truly global systems integrator, Nick shares how the company is backing that ambition with deep local expertise, sovereign service delivery, and customer intimacy that global players often struggle to match. He unpacks how CIOs across government and enterprise are balancing AI pressure, cybersecurity risk, and digital transformation priorities – and why having immediate access to local decision-makers and context-aware teams can be a game-changer in driving outcomes. Register Now source

If you’re in upstream oil and gas, highlight the operational cost of downtime and the premium on speed. In that context, product teams aren’t a structural change — they’re a competitive advantage. They reduce handoffs, resolve issues faster and get solutions in the field when they matter most.  One SaaS company embraced product thinking to improve internal IT service. A cross-functional product team launched a suite of self-service AI tools that deflected 43% of help desk tickets, saving millions annually. They tracked and published those savings on a dashboard each month, using it to validate the model and expand adoption. The initial investment — training, tooling and role changes — was quickly eclipsed by the returns.  Another client, a global manufacturer, built a product team around pricing. By rolling out real-time pricing that charged premiums for peak delivery windows, they boosted revenue per order by hundreds of basis points. Because the team was cross-functional and empowered to move quickly, they delivered the innovation in half the time it would have taken under a traditional project model.  source

Resilience and agility: two top-of-mind attributes for today’s enterprise organizations. As organizations face everything from talent shortages and regulatory changes to a reset in customer expectations and shifting market trends, embracing resilient and agile  IT environments is no longer negotiable. Just how important are these practices? Research from PwC  finds organizations adopting agile practices can achieve up to a 30% gain in efficiency, customer satisfaction, employee engagement, and overall performance.1 That is a major improvement, one that gives enterprises that master agility a massive advantage in a competitive landscape. But IT infrastructure that is both resilient and agile doesn’t just appear. To get there, IT leaders need to invest in the right tools and solutions. That means empowering developers with advanced capabilities that lets them move faster, work smarter, and deliver more for their organization and customers without adding undue risk or unnecessary complexity. Equipping developers with AI-driven tools Any solution under consideration should serve a clear purpose, empowering developers with more ways to build and optimize modern IT operations. This is especially true as newer generations of IT professionals continue to enter the workforce. We’re already seeing a trend: they bring expertise in emerging technologies but have limited understanding of legacy systems. As more experienced IT leaders retire, AI-driven tools will play a critical role in closing this gap. That’s where capabilities that make knowledge more accessible to developers become crucial. For instance, natural language interfaces, like those found in Rocket Software’s Skills and Efficiency solution, can make it easier for developers to query data or generate reports without needing deep domain expertise. These solution sets also offer AI-assisted workload automation that can streamline processes and reduce manual overhead. These innovations help developers onboard faster, reducing time-to-value and allowing developers to spend more time on creating meaningful business outcomes instead of getting bogged down in menial tasks. With less time spent searching for content and handling manual processes, organizations can maintain institutional knowledge and data integrity while matching system capabilities with a much wider pool of IT professionals. That enables a level of agility and operational resilience they may have previously thought to be impossible. Driving scalable impact through operational efficiency Effective modernization also requires an intense focus on operational resilience. Risk is ever-present for IT teams. Whether it’s a natural disaster or a massive cyberattack, the result remains the same—downtime and exposure that could spell disaster for any organization. And with more governmental bodies putting emphasis on resilience, it’s never been more important to prioritize solutions that have key elements like AI-powered monitoring, anomaly detection, and predictive analytics baked in. These tools enable IT teams to work more strategically—shifting from reactive maintenance to proactive innovation. Automation also plays a crucial role, helping reduce the burden on IT teams by handling repetitive or complex tasks and allowing them to focus on more value-driven initiatives. That shift in responsibility means IT departments become much more scalable, impactful, and more deeply aligned with broader business goals. Building a scalable and resilient future Long-lasting business success and growth is built on systems that embrace agility and resilience. Getting to that point means empowering developers. Organizations must prioritize solutions that empower their developers and ensure operational efficiency at scale. In doing so, they create a workforce that is prepared and able to scale with the future of IT operations. Learn more about how Rocket Software can help your organization modernize, without disruption. 1 “2024 Cloud and AI Business Survey,” PwC.com. source

A 2024 survey by NTT Data found that 80% of organizations felt that inadequate, outdated tech was hindering their innovation capabilities. The general consensus supports this, so strategic tech partners are in particularly high demand right now. Not only will they accelerate innovation internally, but they can provide a crucial leg up over the competition. Here are six ways collaborating with a good partner can help. 1. They can better define the innovation strategy The right tech partners can go a long way in helping you define more clearly your innovation strategy. Their expertise and resources can offer much-needed guidance for how to focus your innovation efforts. Developing an innovation strategy generally starts with clearly defined goals and objectives based around an understanding of the business’ customers and its key competencies. However, translating this information into meaningful innovation with a solid action plan can often prove more difficult. A new tech partner can provide the resources and guidance to create actionable steps to turn goals into reality, so your innovation strategy can be fully realized. 2. They introduce methods to optimize existing processes For many businesses, one of the chief reasons to find new partners is to take advantage of tech that’ll optimize existing processes. Salesforce reports that over 90% of workers say that automation tools increase their productivity, while 85% say they improve team collaboration efforts. Jahan Ali, CEO of mobileLIVE, says technology partners can introduce automation tools that streamline internal operations, reduce costs, and improve accuracy, freeing teams to focus on higher-value tasks. “Beyond tools, the real value lies in a partner’s ability to simplify complex problems with integrated solutions, building systems where multiple technologies are orchestrated in a seamless, optimized way to deliver the intended outcome,” he says. “That outcome is what matters: driving efficiency, innovation, and customer relevance.” source

Poor productivity inevitably leads to overburdened support teams. You may find that your organization isn’t aligning with industry-standard IT user ratios, Sullivan says. “This can be a sign that you’re losing investment in your IT team since, instead of spending time strategically focused on advancing business objectives, they’re routinely fighting fires, trying to keep aging client devices, software, servers, or network infrastructure running,” he warns. “The end result is always a losing proposition, since you’re sinking valuable IT human capital into trying to keep systems afloat, only to have their performance remain less than optimal due to their age.” 7. Rising maintenance costs One of the clearest signs of an outdated system is rising IT maintenance costs, says Stoyan Mitov, CEO of software development firm Dreamix. “If you’re consistently spending money keeping your systems from collapsing, rather than using that budget to automate or scale, this is a clear sign that your IT systems might need an update.” Outdated IT systems create performance bottlenecks that eat into engineering time and delay service delivery, Mitov says. “Time gets lost to work-arounds and burdens teams with an infrastructure that isn’t built to handle the scale and speed current operations demand.” source