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As the initial frenzy around Generative AI shifts to new agentic and automation use cases, enterprises must embrace a holistic and orchestrated approach to achieve meaningful productivity gains.  Enterprises are adapting and deploying GenAI and AI agents across the enterprise. Gartner estimates that by 2028, 33% of enterprise software will include agentic AI, up from less than 1% in 2024. This means 15% of day-to-day work decisions could be made autonomously, according to the research.   The benefits are clear: AI agents facilitate autonomous decision-making and the ability to perform tasks independently. At the same time, they also augment human workers to empower decision-making and drive greater efficiency. Despite the many upsides, however, many companies are struggling to turn the performance gains of individual workflows into systematic enterprise-scale productivity advantages.  As agentic AI proliferates, the lack of effective governance, integration, and orchestration strategies can lead to increased fragmentation and sprawl, making it harder to achieve impactful efficiencies and measurable business value. With heterogeneous business applications and systems in the mix, it’s difficult to seamlessly integrate AI agents and automation across diverse workflows and data silos.  IBM’s differentiated approach to AI agents and productivity  IBM’s focus on AI assistant and agent orchestration, and prebuilt integrations weaves agentic AI into the enterprise fabric. This elevates individual use cases and tasks to solutions that work collectively to deliver business value. Critical to IBM’s agentic AI vision is an orchestration layer that facilitates integration, communication, and information sharing. The orchestration layer allows for  the right data to be sent to the right agents at the right time, encapsulated with the proper context and security models. This is crucial to unleashing multiple agents to successfully solve problems and complete complex tasks.  Unlike competitors with similar agentic AI offerings, IBM emphasizes an open, hybrid approach. This enables organizations to address the complexities of a heterogeneous IT landscape by managing the development and deployment of custom and third party-built AI agents through a single, unified experience. IBM’s deep roots in enterprise integration drives AI agents and assistants to work seamlessly across a broad landscape that encompasses business workflows, business applications, and diverse data sources.   “IBM is focused not just on building an ecosystem of things that fly in formation, but on integration with critical enterprise platforms such as SAP SuccessFactors, Salesforce, and ServiceNow,” says Matt Sanchez, Vice President, Product, watsonx Orchestrate at IBM.  As “client zero,” IBM has put its highly orchestrated, AI approach to work within its own organization. Examples include:  AskHR, a conversational AI solution which automates more than 80 common HR processes and answers 94% of common employee HR inquiries in minutes without human intervention.  Using AI digital assistants for customer care that now resolve 70% of customer support inquiries, accelerating time to resolution by as much as 26% and boosting customer satisfaction.  AskIT,  a self-service system created using AI and natural language processing (NLP) capabilities to surface resolutions to critical support topics, can address 80% of IBM’s top IT issues.   The bottom line   Agentic AI is poised to raise productivity across every aspect of business. Yet without a plan for integration and orchestration, enterprises may benefit from workflow efficiencies while missing the broader opportunity for strategic business transformation. IBM has the deep expertise and hands-on knowledge to turn agentic AI into successful business transformation.   To learn more about how IBM can help you orchestrate AI across your business, visit IBM watsonx Orchestrate.  source

Quantum computing has the power to change that dynamic. The technology’s speed in certain applications sounds like science fiction. Google’s Willow quantum chip, for example, performed in less than five minutes a computation that would take today’s supercomputers 10 septillion years. If bad actors enabled their deepfake engines with quantum computing suited for the task, the results could have 1,000 times better fidelity. It’s a scary thought. The face would be slightly asymmetric, the wrinkles would go in the correct direction, a few hairs would stick up and the background would perfectly match that of a department of motor vehicles. The only way to detect that advanced type of synthetic ID would be to use quantum computing to develop machine learning that can spot the tiny signals in the noise of a fake document. In other words, we’d have to use a quantum computing defensive capability to match the quantum computing attack.  source

If you are a CIO, we know it will come as no surprise to find that you’re under more pressure this year than ever before. And we fully understand how daunting that pressure of expectation can be. You will be expected to go beyond delivering technology solutions. To drive business value and innovation and demonstrate measurable outcomes from investments in emerging technologies. At the same time, you’ll need to champion agility while also ensuring cybersecurity, compliance, and cost efficiency.  If you are based in Australia or New Zealand, your success will hinge on how effectively you align IT with business strategy, realise value from AI, optimise your digital investments, and foster a culture of integration and architectural awareness. And when all’s considered, that’s no small (or easy) ask. At Fusion5, we understand these challenges. That’s why we partner with CIOs to help them surpass expectations – whether from the board, the executive team, or the wider organisation.  Here’s how we do it. Fostering stronger business-IT alignment For years, IT has been viewed as a back-office function – a necessary cost centre rather than a driver of strategic growth. But in 2025, that narrative has shifted.  Going forward, the most successful CIOs will be those who break down the siloes between IT and business teams. By fostering strong cross-functional collaboration, technology can become what it was always intended to be; a true enabler of innovation rather than a stack of applications protectively ringfenced by the IT team.  As a result of disbanding these siloes, technology investment becomes more purposeful and impactful, empowering your business teams instead of constraining them. And your new-look CIO role will be epitomised as one of transformation rather than technology management. Fusion5 can help you improve alignment between business and IT teams by: Facilitating communication: Providing tools and platforms that enhance communication between your IT and business teams, promoting knowledge sharing, collaboration, and understanding. Aligning your technology strategy with business objectives: Delivering independent workshops, strategy sessions, and stakeholder-level guidance to help develop a shared strategic vision and roadmap. Training and development: Offering training programs that focus on understanding both IT and business perspectives to bridge knowledge gaps, including workshops on business processes for IT staff and technical training for business teams. Data integration and accessibility: Implementing integrated systems that allow both IT and business teams to access and share (the same) data easily – reducing misunderstandings and enhancing collaboration and informed decision-making. By helping you to create a more integrated and collaborative environment between IT and business teams, we can help deliver more successful outcomes. Our approach ensures IT isn’t just keeping the lights on but working with you in the business to drive home a competitive advantage. Realise value from AI  While AI may be a hot topic in your business, and you are doubtless equally excited about its potential impact for good, it comes with sky-high expectations of fast and demonstratable value. However, for many CIOs, the ROI of investing in AI is hard (if not impossible) to showcase.  To forge new pathways to AI success, you’ll be required to manage often volatile and unpredictable expenses and executive and board expectations of a quick win – and maybe even invest in a new tech stack.  Fusion5 can help turn AI into an investment ROI by: Providing industry-leading AI leadership: From needs assessments to fail-fast prototypes, leverage an innovative best-practice approach to ensure business impact and value. Helping you build viable use cases: Conducting feasibility studies to evaluate the technical and economic viability of the proposed AI use case, including assessing the potential return on investment and the resources required. Optimising your data: Improving ROI and outcomes by ensuring the accuracy and relevance of your data for better model training and more robust AI models that enhance performance and productivity while complying with your governance policy. Justifying your investment in AI and managing stakeholder expectations (whether realistic or not) means that AI potentially poses as many risks as it does benefits. Fusion5 can help you minimise the challenges and deliver on the promises of a transformative technology.  Optimising digital investment decisions Your technology investments need to do more than ‘tick the boxes’. They should be catalysts for measurable business improvements – otherwise, why bother?  The right digital strategy enhances your customer experience, ensures compliance, and streamlines internal operations. However, we know that it can be a struggle to deliver on the promise of your strategy when you have inherited fragmented legacy digital investments that fail to deliver long-term value. Fusion5 can help you to make smarter digital investment decisions (and drive the success of your strategy) by focusing on: Ease of use and adoption: With dedicated change and project management offices, we can ensure you maximise the value of your technology investment through high rates of user acceptance and adoption. The lowest possible Total Cost of Ownership (TCO): Minimising hidden costs and ensuring sustainability beyond the initial purchase, as well as constantly reassessing and measuring value. Robust product roadmaps. Helping you choose and build a business case for investing in platforms with long-term viability, not short-lived trends. With our deep expertise in digital transformation, we guide CIOs in prioritising technology investments that align with business goals while maximising return on investment. Delivering digital solutions with built-in safety and compliance As a CIO, you walk a fine line. On the one hand, you need to deliver innovative digital solutions your business units can readily adopt, and on the other, you must ensure security, compliance, and architectural integrity. Fusion5 can support you in achieving that perfect balance of innovation with security by: Providing platforms that protect your future: Safeguarding your users, customers, and business with built-in security and compliance frameworks that ensure governance is a priority. Prioritising data, integration, and development roadmaps: Ensuring your solutions fit seamlessly into the organisation’s broader technology ecosystem. By embedding these principles into our solutions, we help you move from reactive problem-solving to proactive business enablement. Instilling enterprise-wide architectural awareness Enterprise architecture is no longer just an IT concern – it’s a business-wide imperative. 

When meeting with business leaders, there is excitement around the potential of what agentic AI can do for an organization. There is also a clear need to answer the question of how business leaders can effectively and efficiently deploy agentic AI. New research from the IBM Institute for Business Value shows the buy-in and excitement from business leaders: 86% of those surveyed expect process automation and workflow reinvention to be more effective with AI agents by 2027. Traditional AI or automation tools are offering useful, yet still marginal, productivity gains but aren’t transforming the underlying process. With agentic AI, we can really start driving bigger and more strategic business outcomes that can create greater productivity and efficiency in an organization. It’s not just about AI telling us what to do—it’s about AI starting to do it. We need to move beyond AI assistants and expand what’s possible with AI agents that can execute and adapt processes under human supervision. This shift requires real reengineering of how work gets done, unlocking the kind of value business leaders genuinely want to achieve. Already 76% of executives surveyed say they are operating and delivering proof-of-concepts that enable autonomous automation of intelligent workflows through AI agents, according to the IBM Institute for Business Value. Every client I’ve worked with wants us to have a deep understanding of agentic AI, a credible point of view, and experience scaling agentic AI. And for good reasons. Agentic AI comes with a lot of promise and immense potential to transform your business, but with it also comes technical demands and the need for a cultural shift within an organization. From my own experience, I’ve learned the ‘how to’ has become a prominent focus for clients and organizations. They are keen on seeing incredible results in cost saving, efficiency, and productivity. Below are my insights on how to integrate this technology and scale it to great outcomes. AI agents built for business Specific areas that we’ve seen agentic AI work include customer service, procurement, finance, and the whole IT process, but what we’re seeing in customer service specifically is a significant opportunity. In fact, we have transformed contact centers that used traditional chatbots and automation tools, by pivoting to an agentic approach. Our agentic conversational experience approach introduces a coordinated team of AI agents capable of handling a much broader and more complex range of customer queries, instead of a single scripted assistant, like a chatbot, to realize significant efficiencies. All while operating with a foundation of defined guardrails to drive compliance and consistency. What makes agentic AI more effective than traditional chatbots is its ability to operate holistically—not just following scripts, but dynamically coordinating actions, adapting to exceptions, and continuously learning. Agents don’t work in a fixed sequence; they collaborate with each other and with humans to determine the most efficient way to resolve complex tasks in real time. 4 steps to prepare for agentic AI integration There are preemptive measures and preparations that must be taken to implement agentic AI effectively and efficiently before an organization can scale solutions and see improved outcomes. Step 1: Find the opportunity The first thing is identifying an opportunity within your business. For example, let’s say I want my procurement function to be more efficient, and I want to get an agentic solution implemented. IBM has a methodology developed for clients to formally assess whether an agentic solution will provide added value and enhance the process or workflow.   Our agentic AI readiness assessment approach is: A structured assessment executed using a blend of Process Mining and LLM powered process analysis Designed to identify business processes best suited for agentic AI and autonomous transformation Includes five pillars to evaluate how well a process can be re-engineered using AI Step 2: Understand your architecture The second part of the ‘how’ considers underlying enterprise architecture capabilities and identifies how architectures may need to evolve. This might mean going beyond traditional integration layers and establishing a modern architecture designed for autonomous, AI-driven workflows. Some of the necessary capabilities include: Multiagent orchestration and event-driven integration Centralized agent catalog and lifecycle management Agent memory and long-term context stores Modular, AI-ready data products Governance, observability, and security layers tailored to AI agents Step 3: Address your data strategy for AI Data remains core to the successful deployment of AI and is a critical part of the conversation at the start. Our point of view at IBM is that this agentic AI application can only deliver value if you combine experience, process, and data. Managing structured and unstructured data, ensuring data quality, and protecting data privacy are ongoing challenges. Yet, with the right strategies in place, businesses can harness the power of AI to drive transformation and future growth. There are three core challenges to consider when preparing a business for AI transformation. Access to data. It is estimated that in 2022, 90% of data generated by enterprises was unstructured. Organizations need to access that data wherever it resides and unify it for their use case. Quality and intelligent data for real-time analytics and AI. Your AI is only as good as the data youinput. Can you trust that data for your AI models? Is it of sufficient quality, and how do you objectively evaluate the quality of your data? Answer these questions before deploying AI. Data security. Whether on-prem or multi-cloud, data security needs to extend to the entire landscape. Consider all data no matter where it is in motion and whether it’s structured data or unstructured data. Step 4: Manage the necessary cultural shift Another key factor clients must consider is strong change management. Specifically, clients must take into consideration the people who will need to adopt AI as part of their daily work. A tangible example is from the HR transformation perspective, a use case where we really need to rethink the roles of people and where AI might be most valuable. Many of our clients in the HR function think about upskilling and

Aligning stakeholders and systems for co-evolution The hardest part of this transformation wasn’t the technology; it was getting humans to work together in new ways. We learned that if the people and processes don’t evolve in sync with the new systems, the transformation will falter. In fact, roughly three-quarters of digital transformations fail to deliver ROI, and of those that fail, 70% are due to a lack of user adoption and behavioral change. Armed with that knowledge, we doubled down on stakeholder alignment and change management. First, my colleague Hugo Michou took the lead in establishing a strong governance structure. A steering committee with executives from every major function (IT, operations, finance, merchandising, etc.) met bi-weekly to review progress and resolve disputes. This was not a perfunctory committee…it had teeth. If marketing and supply chain had a data-sharing issue, it got aired and addressed in these meetings rather than festering. The governance team’s mantra was “no blind spots.” By having all the stakeholders at the same table, we caught misalignments early. For example, when we discovered that a new inventory system feature might slow down front-line staff workflows, the operations lead flagged it and we adjusted the rollout plan on the spot. In the past, that kind of issue might only surface after full deployment (and lead to finger-pointing between IT and business). Governance gave us a forum to navigate complexity collectively. Next, we focused on communication and culture. We knew from prior failed projects that sending a few emails about “new software coming, get ready” wouldn’t cut it. So we tried a more personal approach. We identified influential employees in each department (respected veterans, informal leaders) and recruited them as change champions. We shared with them not just what was changing, but why, and even showed them that messy “spaghetti” map to illustrate how their work fit into the bigger network. This transparency helped win allies. People started to see the transformation not as an IT dictate, but as a necessary evolution for the company’s survival and growth. One warehouse manager told us that seeing the full picture of the supply network made her realize the importance of standardizing processes: “We can’t master the whole value chain the same way as a simple chain…we need to understand our complex ecosystem, where all systems and agents communicate with each other.” Comments like that were a good sign: mindsets were shifting. source

Artificial intelligence (AI) is redefining security — not just by creating new threats but also by becoming a threat surface itself. As intelligent systems are integrated into core workflows, IT teams are now responsible for governing how AI accesses data, interacts with users, and introduces risk into the environment. In the Q1 2025 IT Trends Report from JumpCloud, 67% of the surveyed IT administrators said AI is advancing faster than their ability to secure it. That gap highlights the urgent need for new frameworks that go beyond traditional security thinking. AI-generated threats are reshaping security priorities AI is not just a target for attack; it’s also a tool attackers are using. The report shows that 33% of recent security incidents were linked to AI-generated threats. These attacks often bypass traditional security measures, by using adaptive techniques, synthetic identities, or AI-crafted phishing campaigns. As a result, IT teams are shifting from passive perimeter defenses to proactive detection and response. Tools that can spot unusual behavior, monitor AI access patterns, and detect anomalies in real time are becoming essential. Security strategies now need to account for both the behavior of AI tools themselves and the ways those tools might be exploited. Governing how AI systems access data Unlike users, AI systems don’t follow fixed schedules or patterns. Their access to data can be continuous, complex, and opaque — unless they are managed carefully. That’s why organizations are beginning to define access policies for AI agents, enforce least-privilege models, and log all AI activity with detailed audit trails. Modern identity and access management (IAM) tools must evolve to support these needs. AI agents often require their own identities, application programming interface (API) –level access controls, and model-specific permissions. Legacy tools such as Active Directory are often too rigid to meet these demands. A shift to more flexible IAM platforms, which are designed to handle nonhuman identities and fine-grained authorization, is already under way in many forward-looking organizations. Shining a light on shadow AI Unauthorized AI use is another growing concern. With 88% of IT professionals reporting worries about shadow IT, the risk of unsanctioned AI tools’ slipping into the environment is real — and growing. Discovery tools and endpoint detection and response (EDR) platforms can help identify unapproved AI usage across networks and endpoints. But visibility alone isn’t enough. IT teams must also define acceptable-use policies, educate departments on AI risks, and monitor integrations to ensure they comply with governance rules. Preparing for the next wave of AI threats Real-time readiness is becoming a critical capability. Security teams are increasingly turning to AI-powered detection systems to monitor behavior patterns and catch anomalies before damage is done. But tools alone aren’t enough. Organizations also need: Incident response plans tailored to AI attacks Regular audits of AI access and activity Continuous training on emerging AI security threats Cross-functional communication between IT, security, and business stakeholders The goal isn’t just to secure AI — it’s also to treat it as a first-class component of the security ecosystem. JumpCloud’s Q1 2025 IT Trends Report reveals how IT teams are adapting to AI across support, infrastructure, and security. Download the full report to see how your peers are navigating this transformation — and what it means for the future of IT. source

CIOs with an eye for AI’s promise of improved productivity often find their efforts hindered by data quality, skills gaps, and sabotage, among other factors. source

We build the models, but not the culture. It’s time to start designing for AI friction and scale from there. source