Forrester

Agentic AI is rising. The evolution toward pervasive AI agents paves the way for an “internet of agents” where AI entities seamlessly interact and coordinate tasks across diverse ecosystems. Just as standardized protocols fueled the internet’s exponential growth, a shared framework is crucial to actualizing a globally interconnected agentic workforce. Today’s AI agents, however, remain trapped in walled gardens. Agents built on a common platform share similar architectures and common orchestration, data and memory structures, tool calling, and execution mechanisms. Outside platform boundaries, interoperability standards and frameworks aimed at enabling agents developed on different platforms or by different vendors to work together simply do not exist yet. Without these, AI agents risk becoming isolated silos of automation, hindering the emergence of a truly connected AI ecology. Nine Dimensions Of Agentic Interoperability Interoperability among AI agents is a complex, multilayered challenge. For seamless cross-platform functionality, agents need standardized frameworks addressing not just technical compatibility but also security, governance, adaptability, and intent. Here are a few key dimensions: Tool use and integration. Ensuring interoperability at this level allows agents to seamlessly access databases, automation platforms, and enterprise applications without vendor lock-in or brittle, hard-coded logic. Anthropic’s Model Context Protocol and IBM’s Agent Communication Protocol are examples of such a standard. Interagent communication and coordination. AI agents, to collaborate effectively, need a structured way to exchange messages, delegate tasks, and resolve dependencies. Establishing a shared communication standard would allow agents from different frameworks to work together without friction, enabling cross-platform orchestration for everything from business process automation to multiagent scientific discovery. There are several initial efforts toward this end: AGNTCY, an industry-standard agent interoperability language backed by Cisco, LangChain, LlamaIndex, and others; the Open Voice Interoperability Initiative; and more diffuse collaborations, such as that announced by Qualtrics with LangChain. Identity and trust. Agents must verify the authenticity of entities that they interact with, determine permissions, and enforce security constraints. A standardized approach to authentication, authorization, and trust scoring would enable agents to evaluate new interactions dynamically. This would create an AI-native equivalent of Zero Trust security, allowing agents to autonomously assess whether another entity is legitimate before engaging in transactions or sharing sensitive information. While there are a few early attempts at evolving standards for data provenance for AI or for decentralized trust on the internet at large, nothing currently exists that’s specific to AI agents. Many emerging agentic platforms are either extending their retrieval-augmented generation governance systems, attempting to repurpose existing governance tools for agents, or have this as a roadmap item to tackle within the next year. Memory. Agentic networks must have a shared way to remember past interactions, retain relevant knowledge, and apply context across multiple exchanges. A standardized model for memory persistence and retrieval would allow agents to maintain a continuous understanding of their tasks and relationships. Currently, each major agentic AI development platform implements its own memory components. Knowledge sharing and reasoning. A common protocol around how agents share, verify, and refine knowledge would enable collaborative intelligence where multiple agents contribute specialized expertise to solve complex problems. This would also contribute to improving reliability and trust between both different system components as well as system components and humans. Marketplaces and transactions. As AI agents become more autonomous, they will need mechanisms for negotiating payments, purchasing services, and compensating other agents for computational and economic work. A shared financial protocol would enable seamless transactions while ensuring that they are secure, verifiable, and fraud-resistant. Stripe’s “agent toolkit” is directionally indicative but still far from a standard. Governance. AI agents might operate across different regulatory frameworks and ethical guidelines, but there is no standardized way for them to interpret and enforce these constraints consistently. Interoperability in governance will enable agents to dynamically recognize, apply, and comply with shared policy standards, ensuring that cross-platform interactions remain legally and ethically aligned. Discovery. In an open ecosystem, agents must be able to find and identify each other dynamically, rather than relying on hard-coded connections. Current agent systems lack a universal way to advertise their capabilities, verify their credentials, and negotiate interactions in real time. This makes it difficult to create scalable agent networks that function like the internet, where new services can be discovered and integrated without manual intervention. A standardized registry and discovery protocol would allow agents to locate compatible peers, assess their trustworthiness, and initiate collaborations autonomously. Error handling and conflict resolution. AI agents operate on significantly higher cognitive levels than deterministic tools do. A standardized way for them to detect, report, and respond to failures would be a key aspect of failure management, enabling agents to communicate, escalate, negotiate, and resolve conflicts and errors across diverse agent ecosystems. This is particularly important in larger-scale heterogeneous systems where individual AI agents may be used as part of larger workflows and each of those larger workflows may have different levels of accuracy required or different regulatory requirements associated with each. Achieving seamless interoperability across these foundational dimensions is essential to unlocking the full transformative potential of agentic AI. We stand at the nascent frontier of this evolution, with a handful of early interoperability standards emerging in patches and with varying degrees of maturity. There is still a long way to go. source

E-commerce isn’t just changing; it’s undergoing a metamorphosis. For those at the helm of B2B organizations, digital routes to market represent a beast that demands not just feeding but understanding and anticipation. The architecture of your e-commerce ecosystem decides your trajectory in this dynamic realm — will it be a rocket ship to stellar growth or an anchor in shifting sands? More Than A Blueprint: Reinventing The E-Commerce Ecosystem Think about having a strategy that goes beyond the usual e-commerce playbook. This is about more than just following trends; it’s about creating them by putting your customers first and integrating technology and strategy in a way that really makes sense. Imagine setting up your e-commerce ecosystem to not just do its job but to excel, providing real value at every turn. It’s about leading with a plan that’s as thoughtful as it is innovative, where every piece works together seamlessly to not just meet but anticipate the needs and desires of your market. Customer-First: A Mantra To Live By Forget transactions. Today’s B2B landscapes thrive on relationships that offer more than a product or service exchange. Buyers yearn for a seamless journey reminiscent of their personal buying experiences — intuitive, effortless, engaging. How can your B2B e-commerce platform not just meet but anticipate these demands? The secret lies in weaving technology and strategy around the core of customer needs. Deciphering The Tech Labyrinth In a sea of CRM, ERP, AI, and machine learning solutions, the challenge isn’t in acquiring technology but in discerning which tools align with your vision. The magic happens when these technologies seamlessly integrate to streamline operations, foster innovation, and catalyze growth. It’s about smart choices that resonate with your strategic ambitions. Innovation: The Only Constant In the e-commerce odyssey, standing still is retreating. Innovation is your compass guiding through uncharted territories, with data as your map and experimentation as your strategy. But innovation isn’t merely about embracing the new for the sake of novelty; it’s about nurturing a culture that’s resilient, adaptive, and always a step ahead. An Invitation To Explore What we’ve touched upon here is merely the surface. My latest exploration, Architecting The B2B E-Commerce Ecosystem, delves deeper, offering insights and strategies for those poised to redefine the digital commerce landscape. But the dialogue shouldn’t end with the last page of a report. If these insights have piqued your interest, consider this an invitation to a deeper conversation. Let’s discuss how to transform your e-commerce ecosystem into a beacon of innovation, efficiency, and growth. Please contact [email protected] for further conversations. source

NASSCOM recently hosted its flagship event, the Technology Leadership Forum (NTLF) 2025, with noticeably stronger attendance than in recent years. Returning to its familiar venue — the Grand Hyatt Mumbai — proved valuable. The compact setting was ideal for high-impact interactions, enabling us to engage in over 50 client meetings across a wide range of topics shaping the IT and IT-enabled services industry. Unsurprisingly, the largest share of conversations was with service providers — ranging from niche specialists to full-spectrum players, both global and India-based. Several clear themes emerged from these discussions, offering insight into the evolving priorities of the sector. AI’s Impact On Service Delivery Is Just Beginning AI — specifically generative AI (genAI) — couldn’t be kept out of the conversation. It has massive implications for knowledge industries, including IT and business services of all kinds. The discussions at NTLF 2025 revealed a broad spectrum of interpretations on how agentic AI will reshape the industry. IT services firms are evaluating how automation and self-directed AI agents could accelerate software delivery, potentially reducing headcount dependencies and altering traditional managed service models. Business process outsourcers (BPOs) are reimagining the concept of a “digital workforce,” where AI-driven agents could take over routine, structured workflows, shifting the focus from scale-driven efficiency to expertise-driven value. Product vendors, too, are rethinking their roadmaps, as agentic AI threatens to disrupt traditional software categories. Despite the buzz, hard truths dominated many conversations. Job displacement, regulatory gray zones, and the ethics of AI-led decision-making were impossible to ignore. Vendors agreed: AI may unlock efficiency and cost gains, but it also forces a reckoning — with legacy business models, outdated workforce assumptions, and traditional value levers. NTLF 2025 was a mirror to the industry’s mindset: bullish on the promise of AI and agentic systems, yet clear-eyed about the complexity of real-world adoption. BPOs Gear Up For AI, But Real Impact Is Still On The Horizon There’s broad consensus — across clients and service providers alike — that a large-scale disruption of services and work types is inevitable. Yet, for most BPOs, the impact remains more potential than reality. Nearly every provider has invested heavily on two fronts: 1) building an AI-first BPO service portfolio; and 2) preparing their workforce to operate effectively alongside AI. However, the current state of play is still nascent. Most AI deployments are limited to foundational genAI and agentic use cases, primarily in customer service and back-office transaction processing. The transformational promise is clear — but for now, it’s still early innings. The BPOs taking a truly strategic path to becoming AI-powered are going beyond generic solutions. They’re investing in domain- and vertical-specific AI models that are both scalable and fungible, while also designing agentic workflows that span entire service lifecycles. A consistent thread across these efforts is the emphasis on human-in-the-loop systems — which is prompting a reexamination of roles, skills, and job definitions across the BPO workforce. Fully autonomous, end-to-end workflows remain more vision than reality for now. Achieving that future will demand not just new tools, but a fundamental shift in mindset. GCCs Take Center Stage We have seen rising interest in global corporates to set up their global capability centers (GCCs) in India. NASSCOM’s projection of GCCs growing from over 1,700 to more than 2,100 by 2030 reflects a significant trend. We expect both the number of GCCs and the staff they deploy will, on average, increase during this period. Service providers were keen to discuss the implications of this growth on their business. Many providers are consolidating their GCC offerings — previously scattered across business units — into a single, integrated service line. To deliver end-to-end value, they’re forging new partnerships in areas traditionally outside their scope, such as real estate, tax, legal, audit, and compliance. The service models span the full spectrum: from build-operate-transfer for greenfield GCCs to traditional outsourcing arrangements for mature centers. The Four Forces Reshaping Tech Services In 2025 And Beyond Service providers entered 2025 with cautious optimism. Yet, under the surface, concerns around geopolitics, currency volatility, and the dual impact of AI — both accretive and potentially dilutive — were front of mind. In our view, four major forces will shape the trajectory of the services industry this year. GenAI will lead the charge, but it won’t stand alone. Providers will need to help clients consolidate and scale their core tech stack, forge the right ecosystem partnerships and alliances to enable that transformation, and navigate a new, nonlinear relationship between talent and revenue — a shift that challenges long-held operating assumptions. source

Last week, SUSE hosted SUSECON 25 in Orlando, Florida. Although SUSE has been in the open-source game for decades, it’s been getting increased attention with some seasoned leadership coming from competitor Red Hat, its acquisition of much-loved Rancher, and changing expectations/norms in the open-source ecosystem. SUSE’s big message started with a commitment to Linux, open-source innovation, and choice. CEO Dirk-Peter van Leeuwen presented awards to 12 different customers emphasizing broad enterprise reach. The company also revealed a rebranded portfolio focused on four key platforms: Linux, cloud-native, edge, and AI. Notable rebrands include SUSE Rancher (Kubernetes management), SUSE Storage (formerly Longhorn), SUSE Virtualization (formerly Harvester), and SUSE Multi-Linux Support (formerly Liberty Linux). These changes aim to simplify navigation and address market need. The big changes announced include: Linux support. Firms operating multiple distributions of Linux in production must address Linux support issues, especially when maintenance or support has lapsed. SUSE’s rebranded Multi-Linux Support (formerly Liberty Linux) offers lifecycle management for legacy systems, providing flexibility without forced licensing changes. This package enables lifecycle management of legacy systems, offering flexible migration options according to business timelines rather than a vendor-imposed deadline. SUSE also touted deep expertise in integrating Linux across various platforms with support for customized use cases like telco and manufacturing. Cloud-native workload support. SUSE is poised to support composable multicloud platforms across cloud environments, data centers, and the edge (given the SUSE-backed K3s Kubernetes base). The rebranded SUSE Observability (formerly StackState) enhances operations by tracking application states and addressing anomalies via SUSE Rancher. Notably, SUSE Rancher now manages AWS EKS workloads directly. Coupled with the embedding of Neuvector as a container-focused security solution, Rancher has emerged as a converged platform for holistic container application operations. Edge computing. SUSE Edge, a cloud-native platform that manages edge devices at scale, interfaces with various network options and ensures management and security. The Edge Image Builder open-source project customizes SL Micro base configuration images to address network complexities including low-bandwidth or fully air-gapped environments. SUSE Edge is tailored for telecom, retail, and industrial firms. AI and edge intelligence. AI-enabled edge intelligence drives localized experiences with streaming analytics, edge ML, and real-time data management. The new SUSE AI platform supports secure deployment of AI models with observability, security guardrails, and agentic AI capabilities in industrial, retail, and healthcare, which addresses only a subset of the broader edge AI market. SUSE Is Keen To Make A Move As we look to 2025, SUSE has new opportunities to transition from its perception in the market as an important but low-profile provider of several solid IT infrastructure offerings into an enterprise IT platform vendor that gets more visibility among C-level enterprise IT leaders. That’s no easy task in an IT world dominated by multitrillion dollar hyperscalers. However, the generative AI shakeup of the tech landscape has prompted a rethink of IT strategy in big companies and government agencies. SUSE therefore has an opportunity to expand its customer, partner, and product reach among strategic enterprise technology and infrastructure decision-makers. How can the company execute on this vision? Forrester believes that to do this effectively, SUSE must make more traction in North America via its AI capabilities, up-level positioning to more senior executives, and go all in on the open-source AI ecosystem opportunity. Why It Matters: You Need Flexible, Holistic Tech Platforms Enterprise technology systems are evolving into layered platforms. That doesn’t come without cost, maintenance, or risk, which must be managed over time to address the changing needs of the business. It’s called platform engineering, and it’s the main enterprise standard. Yet, many platform providers haven’t adjusted to this way of working. SUSE’s latest efforts do. SUSE’s realignment and messaging taps into the platform-oriented model and makes a promise to improve those platforms over time to match customer needs, while avoiding the introduction of prescriptive frameworks that limit choice. That’s a good thing; it’s moving in the right direction as a product company. Most businesses are being blindsided by the rapid iteration of AI, disruptions resulting from geopolitical unrest, and core vendor strategy changes. In the face of uncertainty, SUSE’s message of technology choice and autonomy should resonate. Expect similar language from its competition over the course of the year. If your organization is faced with the difficulty of how to choose your next platform, Forrester is here to help you find the right path. If you’re already a client, you can click here to schedule a guidance session or request research to help you take the next best action. source

With the average cost of a data breach at $2.7 million and 33% of enterprises reporting being breached three or more times over the past 12 months, understanding and learning from past incidents is not just beneficial — it’s essential. Our detailed examination of the top 35 breaches and privacy fines of 2024 has unearthed critical insights into the evolving cyberthreat landscape. Among the key findings: Attacks cause more than just monetary damage; inadequate data protection severely impacts customer trust; and healthcare in particular is at a critical juncture, because it’s not just brand reputation at stake but delivery of critical medical services. 2024 also saw hefty fines levied on organizations. GDPR is once again the most enforced privacy regulation in the world, but it isn’t the only regulation with sharp penalties. In the US, more states are putting privacy laws in place and holding organizations accountable. Not only does Meta hold the record of the highest-ever GDPR fine at €1.2 billion in 2023 from an Irish regulator, but in 2024, Meta took home the largest US state fine ever at $1.4 billion. While some companies can pay off their fines like parking tickets, most organizations do not have the capital or lawyers to copy this behavior. From our analysis of the top breaches and fines, we found the following: Massive breaches and outages drive regulatory proposals and changes. In early 2024, US Executive Order 14117 focused its attention on bulk sensitive personal data, with emphasis on telecommunications and the healthcare market. The US Federal Communications Commission has proposed telecom cybersecurity and supply chain risk management rules. The proposed HIPAA Security Rule that is currently open for comment is the first major update to the rule in over a decade. New York State, acting independently, implemented strict cybersecurity mandates for hospitals. And not to be outdone, the EU has focused on operational resilience, as the Digital Operational Resilence Act (DORA), which has been years in the making and has sweeping demands on security practices, went into effect January 17, 2025. Organizations need to worry about more than regulatory fines. It is important for firms operating within the US to be aware that, although the regulatory penalties they face can be substantial, there is another financial risk on the horizon that can’t be overlooked. Recent data indicates that the proportion of companies confronted with class-action lawsuits has reached its highest point in 13 years, and it is projected this year that the expenses associated with defending against these class-action lawsuits could exceed the costs of regulatory fines. Not all breaches are for financial gain. This past year, US ISPs and telecoms found their systems infiltrated by Chinese state-affiliated actors. After the investigation of these breaches, it appears that the focus was on a small number of individuals of political interest. In a separate incident, state-sponsored Chinese attackers breached the US Department of the Treasury through third-party vendor BeyondTrust’s support software. The objective was to gain sensitive information and conduct reconnaissance. To see the rest of our analysis and, more importantly, get the recommended actions you can take to protect your organization, read our report, Lessons Learned From The World’s Biggest Data Breaches And Privacy Abuses, 2024, or schedule a guidance session with us to talk more. (written with Danielle Chittem, research associate) source

I’ve had the privilege of taking over digital asset management (DAM) system research from my Forrester colleague Chuck Gahun this year, which means I’ve spent the last 60 days immersed, receiving briefings from several dozen vendors, attending events including the Henry Stewart DAM conference in Los Angeles, and getting the scoop on content supply chain at Adobe Summit. As I finish writing my first DAM report, the upcoming “Strategic Technology Selection Guide For Digital Asset Management Systems,” my take is that the opportunities are vast for both DAM vendors and users as DAMs evolve from their traditional role as “systems of record” for rich media and content assets to AI-powered “systems of action.” Below are six capabilities that are increasing their fidelity in DAMs and will likely have a big impact on the future of digital experiences: AI integration and automation. AI continues to shake things up in the DAM space, saving time and creative resources. More vendors are offering AI to generate asset renditions, take over repetitive tasks, and boost metadata management through automatic tagging. They’re ramping up productivity while keeping content relevant and accessible across different markets, such as through video transcription and localization. Personalization for all. It’s a given that audiences and customers want personalized digital experiences, but so do the marketers who deliver those experiences. In addition to expanding tailored content delivery through rich media transformation, many DAM solutions offer customizable, brand-compliant portals and personalized user interfaces, improving the marketer experience. Enhanced content discovery and management. Vendors are investing in AI-powered natural language search and content discovery tools that make digital assets easily accessible and manageable, boosting efficiency and improving the overall user experience. Support for emerging content types. New content formats like 3D models and AR/VR experiences are on the rise, presenting DAM vendors the opportunity to expand their offerings, including tighter integration with e-commerce platforms that enhance customer engagement. Focus on compliance and security. With more regulatory requirements and an increasing volume of AI-generated content, DAM vendors are prioritizing compliance and security, expanding rights management with features such as forensic watermarking, and automating brand and compliance checks. Sustainability initiatives. As businesses become more environmentally conscious, DAM vendors that support sustainability initiatives will have a competitive edge. Many are committing to reducing digital carbon footprints and optimizing data storage efficiency by continuously optimizing and offloading assets to cold storage so that they can align with their clients’ environmental goals. I have captured this and more in the upcoming “Strategic Technology Selection Guide For Digital Asset Management Systems,” which will help technology and digital business leaders find the right solution to engage better with their target customers. source

  The enterprise world is on the brink of a fundamental transformation, and I believe we’re underestimating just how deep it will go. Over the past month, I’ve had a series of epiphanies about generative AI, knowledge graphs, and AI agents. What I’m now seeing is a reinvention of one of management’s most foundational practices: continuous learning and improvement. The classic continuous improvement models such as Deming and Juran were meant to drive cycles of progress through measurement and feedback and, in the broad history of management, were enormous contributions to progress. But let’s be honest: In too many organizations, they devolved into bureaucratic rituals. Continuous improvement became a department, a toolbox, not a way of working and being. Feedback loops broke. The data was stale. The insights, pro forma and shallow. The excitement, gone. That changes now. Unlocking Your Knowledge Is Finally Possible We are seeing the reemergence of continuous learning and improvement at enterprise scale but, this time, fueled by AI, operationalized through agents, structured in graphs, and enriched with live telemetry. Imagine the modern enterprise as an organism constantly producing digital exhaust: transactions, reports, artifacts, documents, source code, logs, alerts, collaboration threads, service tickets. Far from being waste, this exhaust holds untold potential to fuel innovation, growth, and continuous learning. For decades, though, we lacked the means to turn this torrent of information into coherent, trustworthy, operationalized knowledge. Knowledge management tried to harness it but struggled under the weight of manual curation, siloed formats, and weak engagement. With the convergence of genAI, retrieval-augmented generation (RAG), knowledge graphs, and autonomous agents, we stand at the edge of a new era. Why It Works Now The new feedback loop is radically different. It looks like this: Enterprise operations continue as they always have: massive estates of digital and physical complexity. They generate data and information: records of activity, structured or unstructured. Harvester AI agents (empowered by large language models [LLMs]) observe and interpret the information in real time and structure and curate it into a semantic graph. Connected, unstructured information is translated into embeddings in a vector store.* They monitor for quality, semantic drift, alignment gaps, emergent patterns, and unknown unknowns, feeding raw data into LLMs for synthesis and analysis. Operational agents continuously analyze the graph and vectors, comparing new information with previous, making the knowledge alive and actionable — and feeding it back to step one. And unlike human analysts, agents don’t get tired. You can have dozens running, constantly surfacing insights, looking for contradictions, comparing past patterns with current anomalies, and suggesting actions. They aren’t replacing judgment — they’re finally making relevant, continuous feedback real. The control and improvement of these agents will be a significant task; humans will work with TuringBots (LLM-based coding engines) to evolve them continuously. This Isn’t Just About IT To be clear, this applies well beyond IT. The IT industry — where Forrester has documented the rise of a new control plane architecture — and its telemetry is already rich and digitized, therefore well suited to reap these new benefits. Vendors such as ServiceNow, Atlassian, and Wiz are already implementing large-scale graphs. But every function in the enterprise can start moving toward graph-driven, agent-enabled learning: sales, marketing, R&D, HR, finance, risk, supply chain, customer service, etc. Any domain that produces traceable work can benefit. Why Graphs Are Essential It’s tempting to think that LLMs alone can solve this. But we’re finding that, without structure, genAI alone drifts. The graph is essential. It is the skeleton to the LLM’s flesh. Graphs allow agents to: Track dependencies across domains. Represent evolving relationships (versioned capabilities, changing ownership, dynamic markets). Identify semantic similarity and drift. Enable reasoning over time. We can stop treating knowledge management as a static repository and instead see it as a living, navigable, self-healing system. Systems thinkers know that there is nothing more powerful than a true reinforcing (“positive”) feedback loop. I believe this is now forming, and major new feedback loops in the economy don’t appear all that often — the “network effect” observed in the early internet days is the most obvious comparison. Yes, there will be balancing dynamics: security, privacy — but most of what I am talking about here can take place within the boundaries of an enterprise, assuming that it can afford to run its own large-scale LLMs. There will be many experiments. Are the agents acting as advisors? Regulators? Traffic cops? Auditors? Judge and jury … ? People are going to try all of these. (We’ll need responsibility assignment tools for agents … agentic decision rights, to coin a new term, will be a new business analysis challenge.) Implications For The Enterprise Enterprise architects and CIOs must rethink knowledge systems not as forms-based or document-centric but as graph-centric, with unstructured information a first-class operational citizen, all with continuous agent interaction. Vendors must offer agent-integrated platforms that allow customers to define, extend, and control their semantic models. Venture capitalists, acquirers, and enterprise customers should look for graph-native architectures and agent ecosystems in their due diligence, as well as LLMs and RAG. Knowledge governance must evolve to accommodate autonomous curation, semantic versioning, and feedback validation. Every operating model needs a feedback loop that includes telemetry capture, AI interpretation, graph enrichment, and agent-led action. The Historical Parallel You have to go back to the early 20th century — to the creation of the modern corporate operating model at General Motors and DuPont — to find a change this significant. Back then, accounting and management science reshaped industrial capitalism. I believe that genAI, agents, and graphs will have comparable impact on digital capitalism. It’s time to move beyond the brittle forms of continuous improvement we inherited from midcentury management. Let’s reclaim its spirit and deliver on its promise, with tools that can finally make it real. The new feedback loop is not a theory. It’s starting to happen. Will your organization lead or follow?   *And the graph itself can have vector embeddings, but I’ll save that rabbit hole for the footnote.

Five years after the start of the COVID-19 pandemic, the world in many ways feels even more tumultuous and unpredictable. And unlike five years ago, when the source of the disruption was a single, unknown pathogen, today’s volatility comes from myriad forces: global outages, AI, cyberthreats, new tariffs, trade wars, and, of course, economic concerns. For business leaders, the impulse may be to hit pause on planned initiatives and spending and wait to see how things play out. But here’s the thing: There’s no end in sight to this volatility. Yet there is opportunity in disruption — even in times as tumultuous as what we’re experiencing. Companies that thrive through volatility and come out ahead will master three critical domains: spending and resource optimization, change leadership, and risk management. Our just-released report provides details on how to do this. (Forrester clients can also access our reports tailored to technology, security, B2B, and consumer leaders; non-clients should look out for blog posts with key takeaways from those reports in the coming weeks.) The reports draw from our collective expertise, thousands of conversations with clients, and decades of extensive research into what works and what doesn’t. While we suggest reading the full report to build an adaptable action plan, the high-level takeaways include the following. Ruthlessly Optimize Spend And Focus Resources This is not the same as ruthless cost cutting, since reactive, brute-force budget cuts typically hurt more than they help. Instead, it’s about finding opportunities to streamline — by clearing out duplicative software, for instance, and renegotiating contracts when doing so would enable greater efficiency and savings. It’s also about reprioritizing (not pausing) modernization plans to help you be more nimble, secure, and prepared to make best use of AI and other game-changing technologies. Take the same laser focus to understanding and serving your customers. Reevaluate your customer segments to determine who to prioritize, then double down on customer insights (leaning on zero-party data) to deliver stellar customer service. Master Change Leadership Adapting to perpetual change and volatility is exhausting. To be effective, leaders must act as stabilizing forces, providing confidence and clarity despite navigating terrain that most have never experienced. Recognize that leading in this climate takes balance — between keeping a steady hand on the future while meeting change at a moment’s notice and between people and processes. Employees may also be struggling with the effects of change, so develop bidirectional listening strategies and communicate transparently to maintain cohesion and engagement. Cultivate a culture of continuous learning and upskilling to foster adaptability and agility. Embrace Risk Management While you can’t control volatility from happening, you can manage it by taking a continuous, holistic approach to risk. Risks fall into three categories: 1) enterprise risks, or those connected to your strategy, business model, and other factors fully within your control; 2) ecosystem risks, or those arising from third-party relationships; and 3) external risks, which today encompass everything from tariffs and technology bans to pandemics and wars. During times of volatility, all business leaders must fully understand their specific risks, create scenario plans for addressing them, and have the best courses of action ready to ignite for whatever comes their way. Mastering these three areas will equip you to not only thrive through volatility but also, potentially, to tap innovation and profit. If you’re a Forrester client, read our report for detailed, actionable strategies for each of these three areas so that you can navigate volatility with confidence. Not yet a client? Let us know how we can help. source

In 2025, European technology spending will witness a significant uptick, growing 5% to reach a monumental €1.4 trillion. Software and IT services capture three-quarters of tech spend, a notable rise from its 68% share in 2016. Cloud computing, cybersecurity, generative AI, and the burgeoning digital economy will drive much of the growth. Highlights of European tech spending growth include: Software spending. Software will see 10.4% growth and will capture more than a third of Europe’s tech spend by 2029. Enterprise adoption of cloud services are accelerating, with 45% of European enterprises using cloud computing in 2023, up from a 41% share in 2021. SAP’s EMEA software and cloud revenues indicate robust demand, with 12.5% growth in 2024. The AI growth opportunity is significant, as only 11% of EU enterprises were using AI in 2024. AI software spend in EU-5 is growing at twice the rate of the software market. Hardware. Demand for AI-capable servers and the transition to Windows 11 following the end of support for Windows 10 help drive 10% hardware growth in 2025. Companies such as Dell and Lenovo reported strong revenue growth in 2024, and that trend is expected to continue in 2025. IT services. IT services will see modest 2.9% growth in 2025, as companies like Capgemini, which generates 63% of its revenues from Europe, and Accenture saw slower growth in 2024. Infrastructure as a service remains a bright spot, helping outsourcing capture a larger share of the IT services market. Markets in transition. The European telecom market, characterized by its fragmentation, will consolidate as companies optimize costs and achieve economies of scale. Europe faces the dual challenge of enhancing productivity to compete globally while balancing the environmental impact of increased tech adoption. Investments in AI, smart buildings, and the digitalization of sectors such as healthcare and automotive are key to addressing these challenges. Although the EU is currently falling short of its digital transformation plans, the more that countries invest in intangible goods like software, the more they transform their economies. Ireland, the Netherlands, Belgium, Sweden, the UK, France, and Switzerland focus more on high-value-add activities. With a focus on software, cloud services, and AI, Europe is poised for significant tech-driven growth, provided it navigates the complexities of adoption, consolidation, and sustainability. To learn more about how to balance these competing factors and to see the numbers behind Europe’s forecasted €1.4 trillion tech spend in 2025 (which accounts for 31% of global tech spend), Forrester clients can read our new report, European Tech Market Forecast, 2024 To 2029. Other connected forecasts include our US and global tech spend forecasts for 2024 to 2029. source

AI agents are hitting the mainstream in 2025 as major AI vendors launch offerings aimed at enterprise clients (e.g., Microsoft Copilot, Salesforce Agentforce). These AI agents enable enterprises to build end-to-end autonomous workflows that can both analyze data and provide answers, as well as complete actions without human intervention.   Domo unveiled its AI agent capability — Agent Catalyst — last week at Domopalooza 2025. To celebrate the release of Agent Catalyst, Domo is offering to build customers’ first agent for free upon signing up at agentcatalyst.ai. Users can build agents through a four-step process: selecting a large language model (LLM), giving instructions, providing knowledge sources, and assigning tools. Key enhancements include: DomoGPT, a secure, platform-hosted language model supporting multiple LLMs. FileSets, which manages unstructured data such as PDFs and images using retrieval-augmented generation for better AI context. A semantic layer to help AI understand business relationships and logic. An upcoming AI assistant and AI agent builders to simplify the creation of AI-driven workflows. Agents Still Need Human Cooperation While agents are powerful and rapidly improving, they are not (yet) a replacement for humans in many situations, and their effectiveness hinges on several important factors involving the people behind the screen: Successful AI implementation hinges on the willingness and ability of employees to adopt and integrate these technologies into their daily workflows. Forrester’s research highlights the importance of building psychological safety and fostering a culture of trust and collaboration to ensure smooth adoption of data and AI products. A positive digital employee experience is essential for maximizing the benefits of AI agents. This includes giving employees access to the right tools and resources as well as providing ongoing training and support. Effective data and communications governance is critical for ensuring that AI agents operate within a framework that promotes transparency, accountability, and security. Enhancing data and AI literacy within the organization is key to empowering employees to make informed decisions and leverage AI technologies effectively. For more insights and guidance on AI adoption, digital employee experience, data governance, and data literacy, Forrester clients can refer to our comprehensive research reports and schedule guidance sessions with us here: [email protected] source