Forrester

We’re seeing some wildly exciting things happening with digital experience platforms (DXPs) that present both opportunities and significant challenges for modern enterprises. They are becoming the backbone of how we connect with customers. To harness these exciting possibilities, technology and business leaders must move beyond just knowing about the trends and instead translate and mold them into concrete business realities. Real-world wins require a strategic shift, focusing not just on the technological capabilities of DXPs but on their direct impact on customer engagement, operational efficiency, and, ultimately, growth. Understanding how to bridge this gap between emerging DXP trends and tangible business outcomes is the new direction for driving competitive advantage and ensuring sustainable success in today’s AI economy and digital-first focus. Many leaders are experiencing the sense of urgency around these core principles: Stop chasing shiny objects; start pursuing business outcomes. It’s easy to focus on the “what” before the “why.” Years ago, focusing on the cool new tech was a valid strategy. Early adoption was a differentiator in a “first-to-have” world. The game has changed, and now the advantage lies in how you use the tech to deliver superior customer experiences and achieve specific business goals. A DXP isn’t just a tech investment — it’s a business transformation engine. Tech execs must lead with a clear vision that aligns digital experience initiatives with measurable business goals such as customer acquisition, retention, and revenue growth. Your success hinges on cross-functional alignment, governance, and continuous value delivery. Think composable LEGO bricks for growth. Imagine your entire digital experience as a giant LEGO set. Composable DXPs let you add what you need when you need it — from a vendor with a vision. You get to pick and choose the best “bricks” that deliver against your strategic objectives — a top-tier content management system here, a killer personalization engine there, and a robust e-commerce module from a different vendor. This isn’t random mixing and matching; it’s about scalable innovation. Whether modernizing legacy systems or launching new experiences, modular architectures and cloud-native tools enable faster innovation, better integration, and lower risk. Start small, scale smart, and evolve with your customers. Let AI be your force of gravity. AI is the core and not the add-on. Prioritize DXP platforms that treat AI as a core capability. AI guides practitioners through the vast maze of features that they need to meticulously configure for personalization that doesn’t alienate customers. In recent years, vendors of full-featured DXPs have released their cloud-native composable platforms. Vendors possess a unique ability to use agents to harmonize their own DXPs with a level of integration and coherence that is currently unattainable for enterprises managing complex, multivendor technology stacks.   The AI-Infused Future Of DXPs The DXP market is mirroring the evolution of other business applications — rapidly integrating AI to enhance personalization, automation, and decision-making. Vendors are embedding AI at the core of their end-to-end platforms, not just as add-ons. This shift enables smarter orchestration of customer journeys and empowers practitioners with tools that guide configuration and optimize outcomes. Forrester clients have access to my new research: Executive Guide: Digital Experience Platforms. If you need help rationalizing your DXP strategy, give me a call. Allow me to help make sense of the decision to go with a packaged DXP or architect your own, as well as the trade-offs of each option. Together, we can create your plan for how to drive the selection of your digital experience providers that aligns with your strategic commitment to continuously improving business results through technology. Schedule an inquiry with me to talk about how modern digital experience technology can help you deliver your digital strategy. source

The rapid advancement of generative AI (genAI) and agentic AI has enabled more sophisticated forms of fraud, such as deepfake impersonation scams. In response, financial institutions and merchants are also turning to these technologies to detect and actively combat AI-driven threats. Yet enterprises face significant integration hurdles as fragmented systems and disparate solutions complicate data ingestion, model deployment, and orchestration within enterprise fraud management (EFM) frameworks. Fraud management professionals face a plethora of vendor options and should pay attention to the following market dynamics. AI-Generated Fraudulent Activities Are Proliferating As adoption of the latest 3DS protocol curbs traditional credit card fraud, fraudsters are shifting to genAI-powered fraud, such as synthetic identities and deepfake impersonation. These tactics leverage genAI’s ability to create hyperrealistic fake content, making scams more convincing and harder to detect. Social engineering attacks are becoming more personalized and scalable, eroding trust in digital interactions. Banks and merchants face growing challenges in verifying identities and detecting AI-generated deception. EFM Integration Remains A Challenge Enterprises grapple with fragmented systems, siloed data, and inconsistent protocols across vendors and channels. Disparate solutions complicate data ingestion, model deployment, and orchestration. The migration from legacy systems to unified EFM platforms is often hindered by complex architectures and limited interoperability between fraud, compliance, and customer systems. Fraud management orchestration capabilities can help address this by enabling the seamless integration and coordination of various tools and data sources. GenAI And Agentic AI Are Game-Changers For EFM Fraudsters now exploit genAI aggressively, challenging traditional EFM systems. In response, EFM solutions are evolving to harness genAI defensively: automating risk scoring rules and model generation, enhancing explainability, and generating contextual narratives for fraud cases. Agentic AI-based AI copilots are transforming investigation workflows by summarizing alerts, surfacing red flags, and aligning cases with typologies, boosting efficiency and consistency. This shift from detection to intelligent orchestration marks a pivotal evolution in fraud management strategy. Facing the rapidly changing market environments and increased fraud threats, EFM solutions are more in demand than ever. In our recent report, The Enterprise Fraud Management Solutions In Asia Pacific Landscape, Q2 2025, we identified 28 EFM vendors in APAC with relevant market presence. Some are global vendors and some are homegrown in APAC. Security, risk, and fraud management pros in financial institutions and merchants should use this report to understand the value they can expect from an EFM vendor in APAC, learn how vendors differ, and select one based on size and market focus. To learn more details about EFM vendors in APAC, Forrester clients can read the full report or schedule a guidance or inquiry session. source

My professional career has spanned six major tech changes: minicomputer, PC, internet, social, mobile, and cloud. Each of these revolutions brought a wave of new providers and destroyed swaths of legacy companies. Now comes the Seventh Wave of major tech change, driven by AI in its modern forms — generative and agentic. In the ’80s and ’90s, when faced with a new wave, the legacy tech companies would freeze in the headlights or double down on their suddenly obsolete business models. This allowed new players to gain traction and redefine industries. So the mainframe players largely crumbled in the face of minicomputers, and the minicomputer industry was decimated by the PC. Executives back then were not learning-mindset thinkers, and they defaulted to stonewalling and then responding, but too late. Yes, Wang Laboratories and Digital Equipment did build PCs, but the new market had already been formed. Schumpeterian creative destruction held court. Then came Clay Christensen and “The Innovator’s Dilemma” — a book that succinctly stated how legacy companies get stuck in their old expensive business model and are bypassed by cheap newcomers. The incumbents protect the fort until the fort is worthless (cliche cf. Kodak). The new tech guard read Christensen’s book. Starting around the turn of the millennium, they began to deploy four legacy defensive strategies when faced with a paradigm change: 1) Buy the interlopers (cf. Instagram, WhatsApp); 2) Block the new wave with regulation, pricing, packaging, consortia, and partnerships (cf. Partnership on AI); 3) Pretend that you are part of the new era (cf. Agentforce); and 4) Link existing dominant products with new offerings to keep challengers at bay (cf. embedding Copilot in Office). These strategies are not always successful, but they are far more effective than the old “deny and die” stance of the previous generation of executives. Will these strategies work for the legacy tech companies as the AI revolution intensifies? Here’s my take on what lies ahead. The Enterprise Software Business Before AI ever showed up, this tech sector had three problems: Post-COVID, there have been drastic price increases, irritating buyers and stretching user budgets. The VMware/Broadcom mess is the poster boy of this dynamic. The beauty of the software business for investors (once buyers are in, they can’t get out) is ugly for users. They feel trapped and exploited — it’s not a square deal. For CEOs who seek agility and adaptability in their businesses, enterprise software is often seen as an inhibitor of change and an ossifier of outmoded business process. Inflexible systems of record have made it difficult for companies to build systems of engagement for increasingly more demanding customers. Against this sour backdrop comes AI, which presents three threats to the software industry: Cheap code. TuringBots, using generative AI to create software, threatens the low-code/no-code players. Cheap replacement. Software systems, be they CRM or ERP, are structured databases — repositories for client records or financial records. Generative AI, coupled with agentic AI, holds out the promise of a new way to manage this data, opening the door to an enterprising generation of tech companies that will offer AI CRM, AI financials, AI database, AI logistics, etc. These systems offer the promise of being much more adaptable and learning-focused, as well as being easy to deploy and with a lower cost to deploy. Better functionality. AI-native systems will continually learn and flex and adapt without millions of dollars of consulting and customization. They hold the promise of being up to date and always ready to take on new business problems and challenges without rebuilds. When the business and process changes, the tech will learn and change. Will buyers listen to these pitches? CEOs and business leaders certainly will — they are desperate for more agility. But development staffs and CIOs who have staked their careers and skill sets on legacy systems will resist. Business is ready to move on; technology teams will drag their feet. So the enterprise software business won’t change quickly, especially as the incumbents deploy their typical arsenal of weapons to defend their positions — Buy, Block, Pretend, and Link. But the promise of AI computing is going to make this old vs. new battle very hard-fought. The Impact On Other Tech Sectors While software will be most changed by AI, there will be impact across the breadth of the industry. AI needs cycles, so the hardware segment will get a very big boost from this wave. Yes, there will be a transition away from CPUs to GPUs, and the NVIDIA stranglehold will take another 12 to 15 months to break, but systems from cloud to laptops will be vastly stimulated by this change. Expect this business to grow in the 8–10% range per year over the next five years. Technology services, which have been under massive pressure since 2023 due to overexpansion in the 2021–2022 timeframe, will experience whiplash from AI. On one hand, the legacy software systems that PwC, Deloitte, and others have implemented for decades and that comprise much of their expertise will be challenged in the short term and shrink in the long term. Simultaneously, there will be massive demand for expertise in AI. Cognizant, Capgemini, and others will be called on to help companies implement AI computing systems and migrate away from legacy vendors. Forrester believes that the tech services sector will grow by 3.6% in 2025 — I believe that rate could increase to 5–6% per year from 2026 to 2030 — driven by AI. Forrester has telecommunication and communications equipment growing 1.5% and 0.8% globally in 2025. These growth rates could be doubled in the upcoming years by the movement of prompts and answers between users and AI data centers. Yes, there will be good distilled systems sitting on laptops and in edge computing, but at least 70% of AI computing will run off private and public clouds. The Seventh Wave will require and will stimulate communications and network investments and infrastructure. The Cool Kids How will AI impact the

We’ve just wrapped up another successful CX Summit North America. As I leave Nashville, I feel energized, inspired, and more optimistic than ever about the future of experiences. Attending the sessions, hearing from our guest speakers and award winners, and most of all, talking one on one with so many of you and seeing your passion and dedication gives me great confidence and hope. Thank you! This year’s Summit was also memorable in that we unveiled Forrester’s new Total Experience Score, based on our (also) new Brand Experience Index (BX Index™) and long-standing Customer Experience Index (CX Index™). The Total Experience Score is the first to enable companies to quantify the combined effects of brand and customer experience, which, as Dipanjan Chatterjee eloquently explained in his Tuesday keynote, is vastly more powerful than the impact of one of those alone. Because the Total Experience Score measures perceptions throughout the customer lifecycle, it can help companies pinpoint areas for improvement. This is good for companies, and it’s good for customers. The timing for a total-experience focus couldn’t be better, as experience is in urgent need of an overhaul. Our latest CX Index scores found that CX in the US fell for the fourth consecutive year and now sits at a new all-time low. Though the technology and tools to deliver stellar experiences exist, misalignment between CX, marketing, and digital strategies and teams too often prevents that from happening. The sessions and workshops at CX Summit gave attendees strategies and actionable advice to bridge these gaps. Now Is The Time To Act This year’s CX Summit offered many takeaways for teams to start delivering a compelling total experience. To recap just a few: Building the total experience starts with an experience mindset. As AI and other new technologies progress, customers expect increasingly seamless and intuitive digital experiences. Brands must evolve their experiences to be ever more assistive, anticipatory, and empowering, particularly as consumers are more distracted than ever before. In their keynote sessions, Kelsey Chickering and AJ Joplin shared what it takes to build an experience mindset and design great total experiences now and in the future. Equipping your people is an ongoing endeavor. Effective use of AI and other cutting-edge tools takes perseverance and prioritization. Summit attendees learned the importance of cultivating employees’ artificial intelligence quotient (AIQ) to drive better results and how to prioritize use cases for new technology and tools. We were also reminded that sound fundamentals, such as building journey maps with clearly defined goals, are still critically important. Leadership must show the way forward. To build an effective CX function, CX leaders need to model customer obsession and instill it in their teams. This can be especially challenging at a time when leaders are also navigating intense change and economic volatility. Our track dedicated to leadership and culture provided advice to help leaders stay focused on their mission. It also explored how leaders can foster an effective employee experience that connects to CX strategy. Measurement needs to be meaningful. Many CX and marketing professionals struggle with knowing what to measure and how best to measure it. We dove into those challenges head on with sessions focused on choosing the right metrics, using CX measurement to enhance organizational performance, and sourcing data for effective marketing measurement. Customer obsession is (still) hard — but it’s critical. This year’s CX Index results underscored that a gap still exists between executives’ perception of the CX they deliver and how customers perceive their experiences. Customer obsession is hard, but as Shar VanBoskirk reminded us in her keynote yesterday, difficulty is irrelevant. Customer obsession wins and retains business — and this year’s Customer Obsession Awards winners reminded us that it’s achievable. CX Summit gave us much to reflect on and reminded us that there is much work left to do. Attendees now have strategies and tools to help them build a game plan to start delivering on the total experience. (Attendees also have one year of digital access to CX Summit content. And if you’re a Forrester Decisions client, you can schedule guidance sessions with the very same analysts you heard from this week.) I’m already looking forward to seeing you again next year and hearing about your progress! source

The One Big Beautiful Bill Act will reshape healthcare for years to come. While it presents challenges, especially for vulnerable populations, it also opens the door to efficiency and new market opportunities. Healthcare organizations (HCOs) must act now to develop strategies that protect their business and their customers. Sweeping Changes For The Healthcare Industry As a result of the legislation, an additional 11.8 million individuals are expected to become uninsured by 2034. The law significantly changes healthcare access and funding, via: Affordable Care Act (ACA) subsidy rollbacks. The expiration of enhanced premium tax credits, along with changes to plan criteria for cost-sharing reductions (CSR), will result in fewer covered individuals. The initial Congressional Budget Office (CBO) projection estimates that 300,000 people will lose coverage. Medicaid restrictions. Medicaid’s new work requirements and exclusions for certain adults, along with shortening the redetermination period to six months, will likely increase churn and reduce enrollment. CBO estimates expect that at least 10 million fewer individuals will be covered by Medicaid by 2034. Provider tax limitations that leave gaps on Medicaid funding. States that rely heavily on these provider taxes will face budget gaps that could lead to reduced provider reimbursement rates, narrowed eligibility, fewer covered services, lower provider participation, and limited access for enrollees. For example, New York expected to generate $1.5 billion annually from the tax. Under the new law, however, this tax will be eliminated by January 1, 2026. The addition of a rural health fund. The Senate added a rural hospital relief fund (RHRF) to soften the impact of restrictions on provider taxes for states that did not expand Medicaid. Nearly 800 rural US hospitals are at risk of closure due to financial problems, with about 40% of those hospitals at immediate risk of closure. The fund will supply some mitigation but not enough to stem the spread of medical deserts for rural America. Dual eligibles that will continue to face complex enrollment processes. Medicare Savings Programs will face delayed implementation of the final rule, which would streamline Medicaid and Medicare determinations and enrollment and under which Medicaid can cover the cost of Medicare premiums/costs for low-income seniors and individuals with disabilities. This delay may reduce member enrollment for health insurers offering Dual Eligible Special Needs Plans and lead to members avoiding or delaying care and medication due to lack of affordability. Expansion of HSAs and related provisions. The legislation expands access to health savings accounts (HSAs) by classifying any ACA-marketplace bronze or catastrophic plan as a high-deductible health plan (HDHP). The law allows HDHPs to cover telehealth services on a pre-deductible basis, reclassifying them as preventive care. Additionally, HDHP enrollees may now participate in direct primary care service arrangements. These changes aim to improve access to affordable preventive care and align with the broader “Make America Healthy Again” policy agenda. ICHRA becoming CHOICE. The individual-coverage health reimbursement arrangement (ICHRA) was based on regulatory guidance. Formally establishing the custom health option and individual care expense (CHOICE) arrangement in federal law provides long-term stability for employers and employees using defined contribution health models. What To Watch For As The Industry Adapts The legislation is reshaping the healthcare industry, introducing significant financial and operational changes for providers, insurers, pharmacy benefit managers (PBMs), pharmacies, and employers, such as: Providers’ uncompensated care costs will increase. Financial pressures may accelerate industry consolidation and exacerbate medical deserts. While the law allows rural hospitals to convert to rural emergency hospitals, urban areas face significant spread of medical deserts already, and all geographies should prepare for shortages. Health insurers will feel pain in multiple lines of business. The rollback of enhanced ACA premium subsidies and changes to CSR eligibility could reduce enrollment in individual market plans, particularly among low- and moderate-income consumers. Stricter Medicaid eligibility verification and redetermination rules may increase churn, affecting managed care organizations. At the same time, CHOICE will likely encourage more employers to transition their employees to individual market coverage, leading to more complex enrollment patterns and evolving plan requirements. PBMs get a (temporary) reprieve. For now, PBMs walk away mostly unscathed but shouldn’t wait until they are forced to transform their business. A ban on spread pricing will require PBMs to disclose actual drug costs, limiting profits from opaque pricing but reducing price volatility. This may lead to PBMs pivoting to value-based, cost-plus, or pass-through pricing models. Pharmacies gain indirect support for underserved areas. The new RHRF may indirectly benefit rural pharmacies by stabilizing healthcare infrastructure in underserved areas. This creates the opportunity for rural and independent pharmacies to explore partnerships with hospitals and clinics that stand to receive funding through the RHRF. Employers gain flexibility in the face of rising medical costs. Reduced ACA subsidies could make coverage less affordable for low-income workers. Under CHOICE, employers can offer defined contribution models, and small businesses may now provide both CHOICE and traditional group health plans to the same class of employees — a flexibility not permitted under ICHRA. More employers are expected to experiment with CHOICE and other new models to combat rising medical costs. Get Ahead Of The Changes Healthcare consumers are looking for stability and clarity and, so far, have felt little impact from policy change. While Forrester’s April 2025 Consumer Pulse Survey found that 34% of US online adults reported feeling little to no impact from changes in health policy, data from June’s survey shows that data point increasing to 42%. Roughly one in five US online adults also say that they don’t know if recent changes to health policy make it easier or harder to access healthcare services. Consumers impacted by the new law’s changes run the risk of being blindsided.   HCOs can respond to federal budget changes by prioritizing empathy, clarity, education, resources, and technology. They should validate concerns, simplify complex policies, and proactively educate communities to reduce confusion and build trust. Leveraging clear resources and adopting resilient, intuitive technologies will enhance care access and improve patient experience. Let’s dig deeper into the changes and volatility unfolding

I just published a case study on Bank of America’s AI strategy. Bank of America has been quiet about AI since it launched Erica back in 2018, but the bank opened up to us recently in a series of interviews with its executives, including Hari Gopalkrishnan, head of consumer, business, and wealth management technology, and Nikki Katz, head of digital. Here are some highlights:  AI Is About The Basement, Not Just The Branch  When Bank of America’s leaders talked about their approach to AI, they didn’t open with generative razzle‑dazzle; they talked about it as a foundation. “AI isn’t a project at the bank. It’s the infrastructure layer that powers everything.” That mindset — patient, data‑first, and deeply operational — explains how the bank lifted assets under management 90% while holding its workforce flat over seven years.  But this kind of AI strategy takes preparation. Years before Erica debuted for clients, the bank had already aggregated the lion’s share of its customer interaction and transactional data, giving digital analytics and AI experts a clean runway for insight discovery and model training. Hari Gopalkrishnan’s pointed advice to peers? “Get started — don’t wait for data perfection.”  Erica Is The Cornerstone  Erica is unquestionably the cornerstone of their AI foundation. Launched in 2018, it has shouldered 2.7 billion client interactions at a 98% containment rate. Freed from routine calls, the bank could pivot many employees to higher‑value work, while the same natural‑language engine powers “Ask Merrill” and “Ask Private Bank.” The principle is simple: invest once, reuse everywhere — discipline that keeps budgets in check while building on the past to deliver the future.  That mindset also shows up in how the bank continuously invests. Nikki Katz told us: “AI is never finished. You launch, and that’s when the real work begins.” Product teams have pushed continuous waves of algorithmic updates since launch, turning Erica into an enterprise platform, not a frozen product. Those evolutions protect the assistant from the one‑and‑done fate that sinks many experiments.  How AI Turns Data Into Dollars  Bank of America’s AI engine hums along a Data → Triggers → Insights → Treatments → AI loop. That flywheel enrolled 2 million new Preferred Rewards members and lifted digital sales 16%. Each interaction feeds new data back into the loop, sharpening the next recommendation and compounding value.  Reuse Accelerates The Future  The bank’s mantra for AI investment is reuse: The first project in a portfolio “pays the freight charge” — the foundational heavy lifting, such as enterprise‑grade data pipelines, a governed feature store, and reusable APIs. Every follow‑on effort moves faster at lower costs, like Ask Merrill and Ask Private Bank.  The lesson? Treat foundational work as a shared asset, not a cost center. Reuse turns what looks like overhead into a compounding advantage, but it takes time and discipline.  Three Takeaways For Banking (And Everybody)  Our deep dive into Bank of America’s AI blueprint reinforced three key learnings for financial services and, as it turns out, everybody else:  Unify your data before you over‑rotate on AI. Bank of America’s early investment in a clean, connected data layer made every downstream AI move easier.  Boost AI’s value with insight. Raw data fuels models, but an insight layer multiplies return.  Reinvest talent into the next wave. Your people, not the algorithms, remain your greatest asset. Bank of America didn’t trim headcount; it redeployed staff freed from rote tasks into higher‑value, AI‑building roles.  What To Do Next  Clients: Read the full case study for a deeper look at how Bank of America delivers value by unifying data, insights, and AI.  Prospects: Contact Forrester to set up a proof‑of‑value session with me, and I’ll share more insights on aligning your data and AI strategy with value. source

According to Forrester’s Marketing Survey, 2025, only 21% of marketing leaders report that a “Transition from lead-focused efforts to buying groups and opportunity management” was among the most important priorities for their marketing strategy in the next 12 months. However, with buyers becoming more autonomous, buying groups increasingly more complex, and customer expectations higher than ever, B2B CMOs need to step out of their comfort zone, step up as strategic leaders, and work with their peers to transform their revenue processes. From Leads To Lifecycles: A New Mandate For CMOs Gone are the days of linear buyer journeys and single lead conversion models. Modern B2B marketing demands a shift to dynamic, customer-centric engagement across the entire opportunity lifecycle – from presales to pipeline to postsale – and across the entire buying group. This transformation requires a strategic overhaul of revenue processes. Lead change by championing transformation within the marketing, sales, and product growth engine. Engineer alignment by unifying marketing efforts with sales and customer success teams. Harness capabilities by leveraging data, technology, and cross-functional expertise. Presales: From Awareness To Opportunity Activation The presales stage is no longer about generating leads (and if you’re still reporting on leads to your board, you’re putting yourself at risk). It’s about understanding and engaging buying groups early and meaningfully to activate opportunities. Lead change in marketing by refocusing on engaging buying groups that include all relevant personas for an opportunity by account. Today’s B2B purchases involve an average of 22 stakeholders, 13 internal buying group members, and 9 external buying network members. CMOs must orchestrate marketing strategies and partner closely with sales based on this growing complexity. Engineer alignment across demand/frontline marketing, product marketing, campaign teams, and marketing operations to ensure shared understanding of target personas and their preferences. Drive strategies that seek to engage buyers based on their preferences, and then act on buying signals and intent data to ensure timely and relevant campaigns. Harness capabilities by leveraging cross-functional expertise to develop programs that nurture both new and existing opportunities to get them to “sales readiness”. Pipeline: From Handoffs To Hand-In-Hand In the pipeline stage, marketing must work hand-in-hand with sales to accelerate deals and optimize engagement. Marketing has access to buying signals and intent data that can better empower sales teams to engage more relevantly and appropriately with buyers, accelerating conversions. Lead change by proactively enabling customer-facing roles with buying signals and intent data to drive opportunity progression. Gone are the days of sending leads to sales and wishing them the best. Now a hand-in-hand approach – utilizing insights to craft the next best interaction experience with the buyer – should take center stage. Engineer alignment between sales and marketing to identify buying group members and deliver targeted, persona-based engagement across pipeline stages. Harness capabilities by using intent data and internal expertise to build high-impact programs and campaigns that drive conversion. Postsale: From Avoidance To Engagement Despite 73% of B2B revenue coming from existing customers, on average, most marketing teams still focus primarily on acquisition with a limited focus on expansion. Avoiding postsale engagement by assuming that’s sales’ and customer success’ job alone is a fallacy. Marketing should engage postsale to facilitate customer engagement and support retention (the foundation for existing customer growth). Lead change by expanding marketing’s postsale role beyond developing customer references to build and execute programs that drive retention, upsell and cross-sell, loyalty, and customer lifetime value. This requires recalibrating KPIs to reflect customer-centric metrics as well. Engineer alignment with customer success and account management teams to share goals like customer delight and engagement as well as coordinate on customer communications. Harness capabilities by integrating data across systems to build more complete customer profiles and deliver personalized, high-value experiences. The Payoff: Increased Value At All Stages By expanding marketing’s role across all stages of the opportunity lifecycle, CMOs will maximize value for the buyer and the customer through personalized, data-driven engagement. They’ll improve collaboration with sales and account teams through alignment across the customer lifecycle. They’ll focus functional teams on shared goals and customer-centric strategies that drive growth and greater efficiency for their organizations. Forrester clients can read more in my latest research report and schedule a guidance session to discuss how to champion and drive change in your organization. source

As AI continues to redefine the way organizations think and work, retrieval-augmented generation (RAG) is a pivotal tool for enterprise adoption of generative and agentic AI: It enhances AI models by providing authoritative knowledge at inference time. While leading vendors have introduced the first generation of commercial RAG offerings, the inherent complexity of RAG architecture continues to present significant challenges. Building effective RAG systems requires alignment on terminologies and extensive engineering efforts, particularly as the demand for scalable and reliable AI solutions grows. There’s no magic bullet. RAG empowers AI systems to improve content quality, deliver domain expertise, and support agentic AI capabilities; however, organizations face mounting challenges related to technical complexity, infrastructural scalability, and conceptual clarity. The integration of agentic AI adds additional weight on this pressure, requiring RAG architecture to evolve beyond basic retrieval and generation into adaptive, problem-solving systems. Building Scalable And Adaptive RAG Systems Scaling RAG-based systems demand cohesive engineering practices that go beyond straightforward product adoption. Establishing a strong foundation for RAG and agentic AI will require organizations to optimize indexing, retrieval, and generation processes to ensure accurate knowledge grounding and seamless integration of components. Best practices include preventing information fragmentation, enabling dynamic knowledge updates, and implementing self-correcting loops. Continuous evaluation is essential to maintain system performance and reliability. For agentic AI to deliver an experience like no other, these RAG optimizations transform static retrieval mechanisms into autonomous systems capable of reasoning, adapting to new information, and solving complex problems effectively. Moving Forward: Collaboration And Innovation So where can we go from here? Cross-team collaboration and clear alignment is imperative in your RAG journey. Through innovative RAG engineering, we see industry pioneers overcoming these challenges. By learning and adopting these best practices, enterprises can build robust RAG architectures that support scalable and adaptive AI systems, ensuring the delivery of authoritative knowledge and reliable performance in high-demand environments. Forrester clients can read our two reports on “Getting Retrieval-Augmented Generation Right”: Part One and Part Two. To learn more about how organizations can stay ahead, Forrester clients can schedule an inquiry with me. source

Enterprise architects (EAs) are under pressure. AI is reshaping business models, accelerating transformation, and demanding new levels of agility. Yet many EA teams are struggling to keep up, not because of a lack of frameworks or tools, but because of a growing skills gap. Please keep in mind that EA teams aren’t made up solely of enterprise architects: They include all types of architects, such as business, data, solution, infrastructure, or security architects. Training serves as the glue that builds a strong mindset across a team that leverages the same foundational knowledge.  In our latest report, Training Architects Has Never Been So Important, we explore why EA training is a strategic imperative. Based on dozens of interviews with EA leaders and practitioners, we outline a comprehensive training strategy that helps architecture teams stay relevant, resilient, and ready to lead.  EA Talent Is Scarce And Getting Scarcer  The numbers are stark: According to Forrester data, 28% of CIOs report a shortage of EA skills. Hiring is slow, retention is tough, and the competition for experienced EAs is fierce. The result? Many EA teams are stuck in reactive mode, unable to scale their impact or evolve their practices.  Training is the key. But not just any training.  A Three-Part Strategy For EA Training That Works  Our research shows that high-performing EA teams invest in three distinct but interconnected dimensions of training:  Technical training. From TOGAF to Agile, or cloud to generative AI, architects need to master both foundational frameworks and emerging technologies. But they also need to understand how to apply these tools in the context of their organization’s strategy and operating model.  Behavioral training. Architects don’t work in isolation. They lead, influence, and mediate across business and IT. That requires strong communication, facilitation, and collaboration skills — all of which can be taught, practiced, and refined.  Career development. Training must align with career paths. Whether it’s mentoring, certifications, or speaking at industry events, architects need opportunities to grow and organizations need to show they’re invested in that growth.  Training isn’t just about individual development — it’s a lever for scaling enterprise architecture’s impact. In federated architecture models — where architects embed across domains — consistent training ensures shared language, common practices, and aligned goals.  As we wrote in our report, Scale And Federate: The EA Journey To Business Value, training is a key enabler of distributed EA success. It builds trust and accelerates time to value.  Don’t Wait For The Skills To Arrive — Build Them  The AI era is raising the bar for architects. Architects should guide ethical AI adoption, evolve data infrastructure, and shape digital strategy while managing technical debt and navigating organizational change. That’s a tall order; however, with the right training strategy, it’s achievable.  Read the full report, Training Architects Has Never Been So Important, to learn how leading firms are designing EA training programs that drive retention, relevance, and results.  If you have more questions or would like to book another discussion about training for architects, please submit a request or reach out to your account team.  source

Dell Tech World 2025, where Dell Technologies unveiled a bold vision for the future of enterprise AI. Is this truly AI’s “Model T moment”? Let’s dive into the key announcements, insights, and what they mean for businesses. In his keynote, Michael Dell compared today’s AI revolution to the early 20th-century shift from horse-drawn carriages to motor vehicles. Ford’s Model T lowered the cost of cars, making them accessible to the masses. Similarly, Dell Technologies is driving down the cost of AI training and inferencing, aiming to make AI usable everywhere — from hardware stores like Lowe’s to server farms at JPMorgan Chase. Dell’s founder believes this is AI’s “Model T moment” and that Dell is positioned to be the Ford of the AI era. But is he right? Let’s explore the announcements behind this bold claim. The company announced a ton of products, both shipping and available in the near term. Collectively, its portfolio all fits under the banner of the Dell AI Factory — an integrated portfolio of infrastructure, software, and services designed to simplify and accelerate AI adoption across enterprises of all sizes. Key Announcements Dell AI Factory expansion. Dell introduced a major expansion of its AI Factory, emphasizing modularity, performance, and ecosystem integration. Highlights include: Dell Pro Max Plus. This is the world’s first mobile workstation with an enterprise-grade discrete NPU for secure, on-device AI inferencing at the edge. PowerEdge XE9785 and XE9785L Servers. These servers support AMD MI350 GPUs, offering up to 35 times greater AI inferencing performance. Dell PowerCool. A new enclosed rear door heat exchanger that reduces cooling energy costs by up to 60%. Dell AI Data Platform and Project Lightning. These enhancements streamline access to structured and unstructured data and deliver up to 2x throughput over competing file systems. Strategic partnerships. Dell deepened its AI ecosystem with integrations across: NVIDIA. Dell announced new PowerEdge servers with eight-way NVIDIA HGX B300 and support for NVIDIA Vera Rubin Superchip. Intel. Dell now integrates the Dell AI Platform with Intel Gaudi 3 accelerators for scalable AI infrastructure. Cohere, Glean, Google, Meta, and Mistral AI. Dell announced tailored AI solutions for enterprise search, generative AI, and agentic applications. Professional services and security. Dell launched AI Security and Resilience Services to provide full-stack protection across infrastructure, data, and models — addressing growing concerns around AI governance. Dell PCaaS with AI PCs. A Forrester TEI study highlighted how Dell’s PC-as-a-service (PCaaS) offering, now bundled with AI-enabled PCs and 5G, reduces total cost of ownership (TCO) and boosts IT productivity. Insights: What This Means For Enterprises 1. Enterprise technology buying: from experimentation to execution Dell’s announcements signal a shift from AI experimentation to operationalization. With integrated stacks, validated partner ecosystems, and modular infrastructure, Dell is positioning itself as a one-stop shop for CIOs seeking to scale AI without overhauling their entire IT architecture. This will likely accelerate procurement cycles and favor vendors offering vertically integrated solutions. 2. AI adoption: lowering the barriers By embedding AI capabilities into endpoints (e.g., AI PCs), edge devices, and data center infrastructure, Dell is democratizing AI access. The emphasis on hybrid and on-prem solutions also addresses regulatory and data sovereignty concerns — key blockers for AI adoption in sectors like healthcare, finance, and government. 3. Cost of using AI: efficiency gains and TCO reduction Dell’s innovations in cooling (PowerCool), data management (Project Lightning), and workload optimization (AI Data Platform) directly target the operational costs of AI. These enhancements, combined with PCaaS and AI-as-a-service models, offer enterprises more predictable cost structures and faster ROI. 4. Your one-stop shop for AI Dell’s ecosystem of partners makes Dell a single point of contact for your AI needs. Whether infrastructure, software, cloud, models, and/or implementation is needed, Dell and its partners have you covered. Final Thoughts Dell Tech World 2025 showcased a company not just building AI infrastructure, but rethinking how enterprises consume and scale AI. For enterprise buyers, the message is clear: AI is no longer a moonshot; it’s a managed service, a platform, and — increasingly — a utility. Forrester clients can book a guidance session or inquiry with us to discuss further. source