Forrester

B2B marketers overwhelmingly agree that their buyers and customers expect experiences relevant to their needs and preferences. Yet they struggle to define and deploy an audience-centric approach to B2B personalization that extends throughout the customer lifecycle. Compounding this challenge is that the “right” content — what is most relevant or meaningful to the buyer — is not absolute but changes with each interaction based on new information obtained and new experiences with the provider and within their buying network. B2B organizations overall have much work to do to catch up to audience expectations and evolving buying behaviors. Even with the proliferation of AI-powered personalization capabilities, no single technology has emerged as a personalization panacea: Most B2B organizations use between four and 10 discrete technologies to execute personalization (source: Forrester’s State Of Digital In B2B Marketing, 2023). Across tech categories, specialized AI agents are now poised to disrupt and democratize the design, deployment, measurement, and optimization of B2B personalization. They’ll soon be able to autonomously execute against your program and performance goals fueled by infinite content variations, a rich signal universe fed by unstructured data, and individualized experiences that enable each unique customer journey. The aspiration for precision and scale, however, will fall flat if B2B organizations fail to first address gaps in strategy and alignment — and documentation. Doing so will help ensure that new AI models and agents enabling personalization orchestration are learning the ideal state, not “the way we’ve always done things.” Focus On Five Elements To Elevate B2B Personalization A clear and actionable strategy for B2B personalization, driven by a shared vision of its role in delivering valuable interactions, helps organizations achieve goals for real-time buyer and customer enablement, revenue process transformation, increased sales efficiency, and improved customer satisfaction. To get there, B2B organizations should assess their personalization maturity across five core competencies: Strategy. B2B personalization requires a well-defined approach to deliver relevance throughout the purchasing journey and postsale experience using customer history, preferences, context, and intent. Data. B2B personalization requires a comprehensive marketing, sales, and product data strategy and the ability to collect and unify various sources of prospect and customer data into a single, holistic dataset. Design. Organizations must understand their target audiences’ information needs, desired outcomes, and interaction preferences throughout the customer lifecycle and use those insights to inform content requirements and signal-driven adaptation. Delivery. No one wants three “Dear [First Name]” emails in a day. B2B personalization must be omnichannel, signal-driven, group-aware, and connected across interaction types and information sources to effectively coordinate personalization across inbound and outbound delivery mechanisms. Measurement. Click-based activity metrics are not enough. A comprehensive view of B2B personalization requires additional mid- and long-term metrics aligned to established SMART (specific, measurable, attainable, relevant, and time-bound) goals and a culture of experimentation to discover new ways to reach, engage, and enable audiences. Evaluate Your Approach To B2B Personalization — Then Reevaluate How does your organization’s approach to B2B personalization stack up? Join us in Phoenix at Forrester’s B2B Summit for the interactive workshop “Dear [Attendee Name]: Could Your Personalization Strategy Be Better?” You’ll leave with a completed B2B personalization maturity assessment and practical next steps for increasing personalization capabilities and impact. source

It’s safe to run your AI agents (they’re mostly chatbots, case summaries, or simple text generators today) on the Agentforce chassis — as long as you run them inside your Salesforce application domain. We spent two days in San Francisco at Salesforce’s TDX developer conference. Together with 5,000 Salesforce developers and administrators (Trailblazers, rebranding as Agentblazers), we touched software, attended classes, and spoke with executives, including President and CMO, Ariel Kelman, EVP of AI engineering, Jayesh Govindarajan, and senior vice president, strategic partnerships & business development, Nick Johnston. We come away impressed with the CRM giant’s commitment to agent-powered workflows and cautiously optimistic that the no-software company can host many, if not all, the AI agents running in the Salesforce ecosystem. We liked the empowerment angle. We don’t love the ham-handed labeling of Agentforce as “the digital labor platform” because what we saw were agents doing mundane work that empower people, not automate away jobs. In subsequent sessions with chief AI officers in retail, banking, and hospitality, we learned that they too believe generative AI (genAI) is a power tool, not a digital worker. Here’s what CIOs and other technology executives need to know: Salesforce is massively interested in hosting your AI agents. If you’re a Salesforce shop, we think you should try it out and see how the platform works for you. Salesforce is using a monthly release cycle to rapidly improve the product. Features like choose-your-own language model, PII data masking, prompt templates, zero-copy data, vector embeddings, agent benchmarking tools to build a business case, and an agent lifecycle toolkit are available today in the Agentforce platform. Most agents are repaved task paths, not automated workflows. Boston believes its crazy roads are paved over cow paths. It turns out that paved over cow paths are better than paths knee-deep in mud. It turns out agents today repave existing manual processes. That’s OK. There are dozens of scenarios in the Salesforce ecosystem where an AI agent can empower an employee, do the grunt work, and maybe give some advice in a text response. Of course, if that advice is through a customer chatbot, then fewer calls may flow into the contact center, thus reducing the number of reps needed. But does that make the agent digital labor? Nah. It’s just an application to help customers serve themselves. Don’t let the $2/call pricing model confuse you — that’s just a value-oriented negotiating stance on the cost of the “equipment.” If you build on Agentforce, you’re committing to Data Cloud. This is the biggest strategic play we see Salesforce making — and your biggest risk of agent and knowledge asset lock-in. Salesforce, along with ServiceNow, Microsoft, Oracle, SAP, Workday, Deloitte (shockingly), and others, want your proprietary knowledge assets as well as your AI agents. Salesforce could already have your front-office data or could get it through zero-copy retrieval from an Amazon, Databricks, Google, Microsoft, or Snowflake database. But genAI and knowledge graphs have a symbiotic relationship. That means Agentforce also needs your proprietary sales manuals, product literature, marketing materials, process methods, and more to generate. That makes Salesforce Data Cloud a vital component of AI agents, hence a strategic and expensive commitment for you to make. What Technology Executives Should Do If you use Salesforce, then ask a small team to investigate the boundaries of common sense for building and operating AI agents on Agentforce. For ideas, check out Salesforce’s AI library, or try out one of the agent templates for sales coaching or case summaries, for example. Test these out: Build a prompt template for common retrieval patterns. For example, if your team is constantly asking for the same data, give them a chat interface but prepopulated with context and prompt suggestions. That’s a prompt template. Build a simple agent to do something not yet in the product and make it available with a button. More and better summary tools; personalized emails; or simulated coaching for the next sales call are good candidates. Load documents into Data Cloud so they’re available to an agent through vector embeddings. If you load all your sales training material, for example, this could power your sales coach agent. One executive at a healthcare provider network we spoke with is using an agent like this so that a clinician facing a tough patient conversation can get some coaching in the context of the diagnosis. If you want to dig deeper into the CIO’s role in AI agent success, please reach out to me by scheduling a guidance session or an inquiry via email: [email protected]. source

“Liberation” Or Turbulence? April 2 will be, according to the US administration, Liberation Day! While that does have all the makings of a Hollywood potboiler, in reality, it’s a little more sedate — well, only just a little more. On April 2, the US is supposed to introduce a slew of new tariffs, the details of which are shrouded in a fog of uncertainty and confusion that is now par for the course. In a brief preview of what liberation might look like, the US government has already declared a 25% tariff on imported cars. Markets are in turmoil; the Fed has cut growth rate forecasts and declared this a time of remarkably high economic uncertainty, while blue-chip companies are downgrading their financial outlook. It’s not quite Hollywood, but there’s drama nevertheless! Tariffs Are Not A Crowd-Pleaser To understand how consumers are reacting to the constant chaos of tariffs, we polled Forrester’s CommunityVoices Market Research Online Community comprised of online adults from the US and Canada. As you might expect, opinions are polarized along party and national lines, but fault lines are beginning to emerge: Ninety-one percent of Democrats oppose the tariffs while 43% of Republicans support them; 4% of Democrats support the tariffs, and 26% of Republicans oppose them. This is a significant finding — Democrats are significantly more vocal in their opposition than Republicans are in their approval, suggesting that, for many, their pocketbook concerns (of which there are plenty) are beginning to outweigh their partisan convictions. Those without a political affiliation generally view these tariffs as a bad idea — a majority of Independents (55%) oppose tariffs, while only 24% support them. Canada has borne much of the US government’s ire and is, not surprisingly, vehemently opposed to the US tariffs — 91% of Canadian consumers oppose them and 9% are neutral.   Consumers Brace For Impact In the face of uncertainty and imminent price increases after four years of sustained inflation, people are bracing for what is yet to come. As a result, consumers are (in their own words): Reducing spending “Cut back on spending on unnecessary items” “Buying less often and more affordable products” Saving more “Saving, saving, saving; following a strict budget, only purchasing the necessities, and living below my means” “I am trying to save more money, as the stock market keeps losing money due to the whiplash and uncertainty of the current administration” Stocking up “I have purchased items in bulk to stock up” “Stocking up now on things that I need before the prices increase” Avoiding major purchases “I am currently avoiding major purchases, such as a car, due to already high prices” “Holding off on large purchases” Growing their food “Growing my own food — getting chickens” “Purchasing from farmers’ markets; filling my freezer now and preparing to plant a large garden” Monitoring the situation closely “At this point, I am just paying close attention to the news and what is going on” “I am waiting to see what happens so I can react appropriately” Canadian Consumers Push Back An overwhelming majority of Canadians are miffed about the tariffs (among other things, such as the threat of annexation) and view these developments negatively (“The US will alienate its closest ally and trading partner”). There remains a small minority with hope of an amicable resolution: “We are neighbors with the US and always will be; it makes more sense to work together” and “There needs to be a compromise.” In the meantime, they, like their southern neighbors, are changing their buying behavior by: Avoiding US products “Boycott products made in USA” “I will no longer buy anything from American companies” “I’m dumping the services I use that are American; that includes giants like Amazon” Buying Canadian products “Buy all-Canadian products” “I will purchase more locally made products and use services from my country” Being financially cautious “I plan on cutting back on spending everywhere with everything” “I will be buying fewer items that are not a necessity” “Buying things in bulk and on sale” “When something storable is on special, I will buy more” “I will be trying to grow vegetables in my house and buying only food items” To better manage your brand and business through this period of uncertainty and shifting consumer behaviors, please read our report: Consumer Marketing, CX, And Digital Leaders: How To Thrive Through Volatility (US). (Tyler Castro contributed to the analyses and research for this post.) Follow my work: Go to my Forrester bio and click “Follow.” Chat with me: If you are a Forrester client interested in discussing these topics, please schedule time with me for an inquiry or guidance session. Plan a session: If you are a Forrester client looking to host a strategy session on a related topic, please contact your account team or email me at [email protected]. source

AI is transforming the way we live, work, and play. It’s altering how we make decisions and interact with technology. But for all its power, it still needs humans (for now) — not just any humans but those who understand how AI works, the dependencies between good data and useful AI outputs, and where human judgement is irreplaceable. Amidst a world rushing towards automation, data and AI literacy isn’t just a skill — it is how you become THE human in the loop. What Does It Mean To Be “The Human In The Loop”? The phrase “human in the loop” (HITL) comes from AI and machine learning, referring to the humans who step in to guide, correct, or make sense of AI-driven processes. Sometimes, it means reviewing AI-generated decisions to catch mistakes (think fraud detection or medical diagnoses). Other times, it’s about injecting human expertise where AI lacks context, nuance, or ethical reasoning. If you’ve attended a conference in the past year, the HITL is what vendors point to when assuring people with AI concerns that humans still will be a part of key governance structures and decision-making. What is often overlooked is how many humans will be in the loop, what the loops might look like, or how many AI/software loops one human can be responsible for. Here is our reality: Not all humans in the loop will be equal. Some will be passive overseers, clicking “approve” or “reject” on AI recommendations (the hospital scene from the 2006 film “Idiocracy” comes to mind here). Others will be active decision-makers driven within a culture of inquiry who shape how AI is used, train models with better data, and ask questions before being prompted by an algorithm. The key difference between passive human drones and those actively involved in guiding AI decisions is data and AI literacy within a culture of inquiry. Why AI And Data Makes You Indispensable Two short anecdotes illustrate the point: Over the past year, I’ve been showing a friend who works at a bank how the simple use of AI tools outside of her company can help her improve engagement and impact at work. She was just highlighted at work for being “forward-thinking and proactive” for getting creative without sacrificing security. KPMG recently gave me a demo of its “Curiosity Workbench,” an AI tool that helps its employees locate and leverage decades of knowledge, data, and expertise to help with clients and get them moving quickly. Both of these examples depend on humans interpreting information and learning more by being curious and inquisitive. After all, AI is only as good as the data it learns from — and data is only as useful as the humans interpreting it. If you want to be the human in the loop, you need: Data literacy: the foundation | AI depends on clean, consistent, relevant, and representative data. Without data literacy, you’re just a spectator to the AI revolution. With it, you’re the one shaping impact. Ask yourself: Can you spot bad data before it leads to bad outcomes? Do you understand how bias can slide into datasets like a creepy social media stalker can slide into your DMs? Can you interpret AI-driven insights to make business decisions, rather than just accepting whatever a model spits out? AI literacy: the next level | AI literacy isn’t about coding your own model from scratch. It’s about understanding how AI influences decisions, where it’s useful, and where it needs a human course interaction. In 2025, I ask our clients to imagine that AI is like the world’s best intern: It can do 80% of most common jobs very well, but that remaining 20% is still pretty suspect and needs the guidance of a wiser mentor who can work with it to get you 100% there. Ask yourself: Do you know how AI models make predictions and where they can go wrong? Can you question AI outputs instead of blindly trusting them? Are you aware of ethical risks, compliance issues, and real-world AI failures? Enterprise culture of (data) inquiry | AI is just software, but without a body of users who are enabled to find it, ask questions of it, grow using it, communicate with it, and trust it, it is as worthless as the grains of sand that its chips are built from. A culture of inquiry is one where all are empowered in a psychologically safe environment to ask questions and share commentary. A culture of data inquiry ensures that, within that safe environment, users can locate, leverage, trust, and communicate those insights found within data without fear. Ask yourself: Do I work within an environment where all can locate data? Do I work in an environment where all can leverage data? Do I work in an environment where all can trust data? Do I work in an environment where all can communicate data? Be The One Behind The AI Automation is here for many routine tasks. But organizations will need humans who: Understand when AI is making good vs. bad recommendations. Know how to validate AI insights before acting on them. Can explain AI-driven decisions in clear, human terms — to coworkers, executives, regulators, and customers. Can translate business challenges to more technical and data-focused AI engineers while also listening and learning from them in turn. Being the human in the loop isn’t about resisting AI. It’s about being the person who knows how to use it responsibly, effectively, and strategically. Now What? Reach out for an inquiry ([email protected]) with me today to uncover your natural strengths and purpose, via your own roles, goals, and values VIP evaluation, to improve your own data communications and data storytelling skills, and then to discover how to build your enterprise culture of data inquiry via curiosity velocity and data and AI literacy programming. I look forward to working with you! If you are a vendor looking to share insights on your AI literacy offerings or have a use case of how you’ve helped others with the above,

Sarah Winchester, heiress to the Winchester rifle fortune, expanded a small farmhouse to a 160-room mansion in California in the 1880s. It became known as the Winchester Mystery House. She continuously added on to her house for 36 years. She rebuilt a seven-story tower 16 times. The house became a maze. There are walled-off exterior windows and doors that weren’t removed as the house grew in size, hallways that lead to nowhere, and an indoor garden with slanted floors that carries water to an outdoor garden. What does this have to do with customer relationship management (CRM) software? Over the last 20 years, CRM software has become overengineered, and complexity is killing its value. When working on The Forrester Wave™: Customer Relationship Management Software, Q1 2025, I heard over and over again from marquee customers that they struggle to keep up with the tsunami of new features that vendors roll out every year. They struggle with the fear of missing out and spend countless cycles understanding what this new innovation means for them. More and more features does not make the product better. More is often worse. I heard that customers struggle to understand how to best deploy CRM and drive adoption for their front-office employees and that they likewise struggle to make sense of price lists that are sometimes 100 pages long and that contain different license tiers, add-ons, and marketplace extensions. I also heard that they struggle to make sense of the new AI features, which add another layer of complexity and business decisions around their use. The CRM Market Is At A Point Of Reckoning Like the Winchester Mystery House, our old notion of CRM must be torn down and rebuilt with AI at the core of these products. AI will streamline the purchase, deployment, and value realization of CRM software. Also, CRM vendors must have a clear point of view on how they want their customers to use their products. For example, CRM software must be: Easy to buy. It must have well-defined product tiers with AI at every tier, a clear focus on the value of add-ons, and transparent, consumption-based pricing. Easy to deploy. Deployment costs can’t be many more times more expensive than software license costs. It must offer low-code tooling to let business users take greater control, along with curated marketplaces — and more. Easy to use. It must have a simple user experience that masks product complexities, that has a point of view on how a task should be accomplished, and that nudges and guides users to the right outcome. Easy to learn. Like online gaming, users must be able to learn as they use the product. CRM software must have AI at its foundation, not as a bolt-on feature. Vendors must have robust AI privacy, security, and governance practices. And vendors must guide their customers on how to start and further mature their AI adoption journey at scale, as well as how to quantify its value at every adoption step. This will be even more important as we move into an AI agent world. The market is moving in this direction. Read The Forrester Wave™: Customer Relationship Management Software, Q1 2025. Connect with me to talk about what you are seeing in the space. source

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

B2B buyers rarely act alone. They are part of large, complex buying groups that rely on a network of trusted sources throughout the decision-making process. Forrester’s B2B Trust research delves into these preferences, offering insights that are invaluable for shaping marketing and sales strategies and influencer relations programs. We find that B2B buyers have distinct preferences for the sources of information they trust most (and least) which shapes who they turn to for insights and guidance: Core insiders lead as trusted information sources. Familiarity breeds trust, not contempt. The most trusted sources for B2B buyers are coworkers and management within an organization, with 82% of buyers saying they are trusted. Close behind are vendors they currently work with, trusted by 79% of respondents. This reveals a strong incumbent advantage, illustrating a preference for the “devil you know” over the uncertainty of new relationships. This pattern suggests a defensive, risk-avoidant approach to change, emphasizing the importance of existing relationships in the B2B landscape. Independent experts come next. Beyond their immediate inner circle, B2B buyers also value the insights of independent experts: industry peers, analysts, and even vendor executives and customers fall into this category, with trust levels ranging from 66% to 72% of respondents. These figures underscore the importance of authoritative voices that offer privileged insights or firsthand experience, free from the perceived bias of direct sales efforts. Other outsiders are the lowest trusted sources. Our research indicates a more cautious approach to sources perceived as having biased interests. Salespeople from vendors, news media, and government officials garner lower levels of trust, with social media influencers at the bottom with only 44% trust. This skepticism reflects a broader trend of declining trust in traditional institutions. Leverage Trust In Marketing And Sales Strategies Understanding these trust dynamics is crucial for developing effective B2B marketing and sales strategies. Marketers should focus their influencer relations programs on the most trusted sources, namely core insiders like coworkers and current vendors, followed by independent experts. Outsiders, while less trusted, still play a role in the broader strategy, serving as channels to amplify the messages from more trusted sources. Sales should heed similar advice and focus on leveraging and promoting information from more trusted sources whenever possible. Bring A Strategic Focus On Trusted Sources For B2B marketers, aligning with trusted insiders and independent experts offers a pathway to gaining the confidence of potential buyers. By focusing on building relationships with these key influencers (not the social media ones), marketers can ensure that their messages are more likely to be received positively. B2B marketing leaders should ensure that trusted preferences are built into buyer persona development and that influencer relations programs are an active and vital part of campaigns. In the same way, content marketing teams should work directly and indirectly with trusted sources and include their perspectives. Make it your goal to create a chorus of voices that builds a positive consensus among buyer groups. You can learn more about who buyers trust and how to influence these influencers at Forrester’s B2B Summit North America. See you there! source

Forrester is delighted to announce the opening call for our annual global Technology Awards in three categories this year: the Technology Strategy Impact Award, the Enterprise Architecture Award, and the all-new Data & AI Impact Award. These awards aim to recognize high-performing organizations that have enhanced business outcomes with their IT, AI, and data capabilities. (Links to submit your nomination based on your region are available at the end of this blog.) The Forrester Technology Strategy Impact Award The Forrester Technology Strategy Impact Award is the only award dedicated to recognizing excellence in IT strategy that continuously improves business results through the use of technology. We aim to highlight and reward organizations for enhancing business outcomes with their technology and IT capabilities, emphasizing alignment, trust, and adaptivity. This is the essence of high-performance IT. We invite nominations from companies that exemplify one or more of the following high-performance IT styles: Enabling capabilities: demonstrating excellence in stabilizing, operating, and protecting the business. Amplifying capabilities: utilizing automation, data and analytics, AI, and other technologies to optimize outcomes and drive business efficiency at scale. Cocreating capabilities: showcasing the rapid development, delivery, and operation of new products, features, or services. Transforming capabilities: scaling and rapidly deploying emerging technologies that create or disrupt new business models. Showcase how your IT strategy aligns with business goals, adapts to change, fosters trust, and improves business outcomes. Winning the 2025 Forrester Technology Strategy Impact Award offers prestige, significant exposure, and recognition for your organization’s achievements. The Forrester Enterprise Architecture Award The Forrester Enterprise Architecture Award is the only global awards program dedicated to recognizing excellence in enterprise architecture. We continue our partnership with The Open Group — author of the TOGAF® Standard, developed by The Open Group Architecture Forum — to co-judge the EA Award category this year. The recipients of this award can demonstrate their EA practice’s material contribution to their firms in managing risk, driving cost-efficiency, improving customer experience (including the employee experience), and increasing revenue (or supporting mission outcomes for nonprofit, governmental, and military organizations). They may also demonstrate: An EA roadmap that contributes to the high performance of the technology organization and the entire business. EA practices that drive change, growth, and differentiation through timely yet strategic decisions. An EA organization that embodies the six foundational priorities: valuable, influential, agile, accountable, innovative, and collaborative. EA metrics that focus on customer outcomes and experiences. This year, we continue our special award considerations with two categories: generative AI and platform engineering. We encourage all companies that have achieved success in outcome-driven EA practices to participate in the Forrester Enterprise Architecture Award. The Forrester Data & AI Impact Award The Forrester Data & AI Impact Award is designed to celebrate the impact of data, AI, and analytics initiatives that directly contribute to business results, especially in the backdrop of AI’s rapid pace of development. Newly empowered data, AI, and analytics leaders are meeting this challenge with innovative technology and intentional leadership approaches that solve today’s needs and set their enterprises up for the future. We want to recognize and honor their unique contributions at this critical juncture of technological change. The Data & AI Impact Award honors those remarkable enterprise data, AI, or analytics teams that catalyze effective decision-making to drive business outcomes. The winner will demonstrate the ability to: Deliver solutions at scale. Deliver data, AI, and analytics as a product for multiple enterprise functions, with demonstrable business results. Effectively enable stakeholders. Invest deliberately in driving literacy and competencies to amplify data, AI, and analytics across organizational objectives. Continuously build and maintain trust. Establish and maintain trust in data, AI, and analytics with effective frameworks and technologies. Nominations for the inaugural edition of this award will begin on Wednesday, April 23, 2025, and are only open for organizations based in North America. Who Should Apply? Nominations for the three award categories are open to organizations with 1,000 or more employees in sectors other than software or professional services that are executing a high-performance IT strategy and/or pursuing outcome-driven architecture as defined by Forrester. Technology leaders, including chief information officers, chief technology officers, chief digital officers, chief data officers, and enterprise architects, across three regions — North America; Europe, the Middle East, and Africa (EMEA); and Asia Pacific (APAC) — are encouraged to apply. (Note again that the Data & AI Impact Award is for North American organizations only.) Last year’s winners for the Technology Strategy Impact Award were Best Buy and First Student in North America, Smart DCC and BNP Paribas Bank Polska in EMEA, and Macquarie’s Banking and Financial Services group (Macquarie BFS) in APAC. For the Enterprise Architecture Award, the winners were Scotiabank in North America, DRÄXLMAIER Group in EMEA, and Contact Energy in APAC. You can find more information about the award categories, plus detailed submission packages and instructions, on the websites for our three Technology & Innovation Summits below. Please choose the appropriate link for your region: North America EMEA APAC Submissions close on May 27 for APAC and July 16 for North America and EMEA. We will notify the winners and finalists by the end of July 2025 for APAC and by the end of September 2025 for North America and EMEA. At that time, we will ask for additional information. We will then announce finalists and award recipients ahead of Forrester’s Technology & Innovation Summits in North America, EMEA, and APAC. Spread the word, and if you’re entering, good luck! source

Let’s start with some good news. According to Forrester’s Priorities Survey, 2024, business and technology leaders tell us that the number-one priority for their firm is to “improve the experience of our ultimate customers.” The top three priorities for this year are: 1. Improve the experience of our ultimate customers. 2. Meet measurable commercial growth targets. 3. Reduce costs. Improving customer experience (CX) is up from the third slot in 2023. That’s great, but it begs the question: How? Cross-Functional Journey Teams Are Here ( … Ish) We asked the respondents who said improving CX was a priority how they planned to do it, and almost a quarter said they are going to “create cross-functional teams aligned around customer journeys.” Cutting the sample by broad industry groupings backs up what we’ve heard from clients — firms in sectors like telecom, hospitality, utilities, and banking, which sell ongoing service-based “products,” are more likely to embrace cross-functional journey teams than industries like retail, where transactional channels can still dominate. We’ve published deep case studies on journey-centric organizations like E.ON, Lloyds, and Nissan. In a pair of recent reports we looked at how the banks that lead our Customer Experience Index (CX Index™) organize to improve the quality of their customer experience through two lenses, frontstage and backstage. The common theme is how these firms see helping customers achieve their goals as the focus of their organization — their marketing, their org structure, their budgets, their technology, their metrics, and more. Customers achieve their goals through journeys: We call this approach journey centricity. Understand The Three Stages Of Journey Evolution Journey centricity doesn’t happen overnight. The common threads in the case studies, as well as the wider research we’ve done, shows most firms progress through three distinct stages of evolution: Journey mapping to identify pain points and build foundational business cases for tactical action. Temporary project teams focus on pain points. This is where many CX teams stall. To break through, consider adopting the language of journey management, embrace increasingly available tooling, and infuse journey maps with data to elevate them from descriptive assets to living operational tools. Journey management to drive improvements and make the business case for transformation. As in some of the examples above, dedicated journey managers and journey teams begin to emerge, working cross-functionally to bridge silos in service of driving better customer outcomes. Navy Federal Credit Union maps and optimizes enterprisewide member journeys across product lines and business units. Journey centricity where journeys become the business operating model. Customer outcomes become the organizing principle behind teams, technology, budgets and operations. Rabobank organizes in value streams, such as mortgage, business lending, or daily banking. Journeys Connect Brand And Experience To Drive Growth At our CX Summit EMEA on June 2–4, 2025, we’ll be showcasing how brand and CX combine to create a total experience. Essentially, if your brand is the promise you make to your customers, your CX is your ability to deliver on that promise. Customers don’t interact with your brand in isolation, and (unless you’re Disney or Spotify, and even then … ) they don’t interact with you for fun. They come to you to achieve a goal. Journey maps help you understand those goals. Journey management helps you operationalize helping customers achieve those goals. Journey centricity aligns your organization around creating a total experience. source

Change isn’t just hard — it’s a continuous battle, and one that automation will make more intense and frequent. And if you think you’re prepared for automation’s pace of change that is coming, you’re already behind. We’ve barely grown comfortable with the past few years’ changes — remote work, video calls, hybrid chaos — and that’s just the warm-up. Take note from Forrester’s Automation Survey, 2024: 82% of companies are about to invest in generative AI (genAI), which will drown out the old ways of working. And here’s the kicker: The very teams deploying AI agents are admitting they don’t know how to manage the change they’re creating. Automation teams struggle with CM (change management) and admit it’s one of the biggest challenges of adopting automation. The Common Pain Points For Automation Initiatives Our new report, Change Management: Taming The Automation Beast, breaks down the challenges that automation decision-makers face and highlights how effective change management is more crucial than ever. Our analysis revealed the following CM concerns brought on by automation projects: Proving ROI. Change management initiatives related to automation projects require investment. Securing budget for these efforts can be challenging, as business leaders need to see ROI for change management itself. Employee retention, mental state improvements, and the value of ongoing skills development are hard to quantify. Job loss. Introducing new ways of working, especially automation, brings uncertainty and fear of job loss. In Forrester’s Future Of Work Survey, 2024, 39% of global workers said they fear losing their jobs to automation in the next 10 years. According to the customer service operations manager at a large manufacturing company, “Automation is perceived as a big threat … employees are not welcoming of change, and some view automation as an opportunity to reduce headcount.” Growing skill gaps. Ensuring that employees have the necessary skills to use automation and AI technology requires ongoing training. Many organizations lack proper training programs for nontechnical employees. Only 19% of global individual employees say they have been through formal training on how to use AI for work. As many employees are being given access to AI tools/AI agents, this poses a real challenge. Confidence And Trust In Change Management Is Missing Change management has a basic problem. Top-level executives report a high degree of confidence in their CM practices, yet employees believe the opposite: CM initiatives are inadequate. Employees lack confidence in CM practices and lack trust in their organization’s leaders. What this means is simple: Folks in charge of implementing CM are not trusted to follow through on promises. Employees would like CM to be more integrated into leadership responsibilities. However, without addressing this chasm of confidence and trust, it is unlikely to work. It’s Time To Bring Your Change Management Up To Speed The accelerating pace of automation demands that traditional CM methods adapt to its speed and impact. You first need to address your organization’s AIQ (the AI quotient), which measures the readiness of individuals, teams, and organizations to adapt to, collaborate with, trust, and generate business results from genAI and other forms of AI. You then need to focus on Change Management: Taming The Automation Beast. This report highlights the following key areas for automation teams to prioritize: clear communication around pending automation, giving them more control over automation that impacts their work, and adopting an iterative and continuous CM approach. source