Forrester

When President-elect Donald Trump declared that he would “shut it down” during his campaign, many dismissed it as mere rhetoric. Yet beneath the sound bite sits a policy approach that’s surprisingly ordinary — at least by international standards. It’s what many democracies call “machinery of government” (MoG) changes: administrative overhauls — common in countries such as Australia, Canada, and the UK — that align government functions with evolving policy priorities. What Are Machinery Of Government Changes? MoG refers to the administrative restructuring of government organizations: creating new departments, merging existing ones, or redistributing their functions. MoG changes enhance government effectiveness and efficiency by tailoring structures to serve the needs of the moment. In parliamentary democracies, prime ministers regularly wield MoG powers to respond to emerging challenges or shift priorities. For instance: Australia: The Department of Climate Change was reestablished to reflect renewed environmental priorities in 2022. Canada: The Indigenous Services portfolio was divided to address reconciliation with First Nations more effectively. United Kingdom: The Department for Business, Energy & Industrial Strategy was split into three distinct entities in 2023. A Comparison Of US And Commonwealth Approaches In the United States, MoG changes are disdained as chaotic political overreach, especially when considered through a partisan lens. The US, constrained by its separation of powers, requires Congress for major restructuring, making the process slower and more fragmented. By contrast, MoG changes are considered pragmatic in parliamentary systems, where more centralized authority enables swift changes. Aspect United States Commonwealth (e.g., Australia, the UK) Decision-Making President, with Congress’ involvement Prime Minister/Cabinet decision Legal Requirements Often requires legislative action Executive prerogative; less legislative input Speed Of Changes Slow; often crisis-driven Fast, proactive, and systemic Drivers Of Change Crises or political mandates Strategic policy shifts Examples  DHS (2003); DNI (2004); CFPB (2010) MBIE (2012); UK Health Security Agency (2021) Why More Systematic And Regular MoG Changes Could Work For The US Adopting a more MoG-like approach to government restructuring offers several benefits. Future administrations — regardless of party — could use MoG changes to modernize government operations and better serve national priorities by: Aligning better with policy priorities. An MoG approach could reorganize departments to align with evolving national goals, such as reshaping energy and environmental agencies to address climate change. Improving civil service efficiency. The US federal government is notorious for duplication of effort. For example, over 40 federal agencies oversee food safety. Streamlining such fragmented oversight could cut costs and improve service delivery. The incoming Trump administration has proposed merging the Food and Drug Administration’s food safety responsibilities with the Food Safety and Inspection Service to create a single “Federal Food Safety Agency” to unify oversight. Increasing responsiveness to crises. The COVID-19 pandemic highlighted the limits of US bureaucratic agility. Establishing temporary crisis agencies or task forces with clear mandates — as is done in the UK and Canada — could help the US respond more effectively to future challenges, whether public health emergencies or natural disasters. Modernizing the bureaucracy. Eliminating redundant layers of bureaucracy and focusing on digital transformation would make federal agencies more responsive and transparent. For example, the incoming Trump administration has said that it will consolidate narrow commissions and bureaus such as the Bureau of Alcohol, Tobacco, Firearms and Explosives. Similarly, Australia folded narrower agencies into a single Border Force. Enhancing customer experiences. By focusing agencies on customer journeys, the US government could make customer experiences more intuitive and efficient. Service Canada aligns agency operations around life events such as retirement, unemployment, or having a child. The Biden administration has already taken steps toward a journey-centric life events model (Forrester client access only). Simplifying politics. MoG changes could help reduce legislative gridlock by shifting responsibilities within the executive branch. While such changes would require careful planning to maintain checks and balances, they could enable administrations to implement necessary reforms without years of partisan negotiation. Could this be the moment that the US federal government embraces MoG as a strategic tool for more effective public services? Let’s discuss. source

At the ICD Media Group’s Healthcare Burnout Symposium, healthcare thought leaders dove into the origins of and continuing reasons for clinician burnout. Experts on designing for empathy, clinician well-being, organizational transformation, patient experience, human suffering, moral injury, and physician resilience shared ideas and evidence-based solutions for helping the healthcare workforce. As your organization confronts clinician burnout and looks to support your employees’ well-being, consider these issues: The need to align patient and clinician experience efforts. The patient and clinician experience are closely related, yet patient and clinician experience teams tend to operate independently, creating goals that are not aligned. Healthcare leaders can’t improve one without strong consideration of the other. For example, responding to publicly reported HCAHPS Survey scores can have limited impact when a health system also needs to improve the clinician’s experience. To break this habit and maximize initiatives, leaders of both teams must join forces to align goals and assess the impact of initiatives on patients and clinicians. Technology again makes few headlines. As an analyst, I see many tech firms with products that augment clinician productivity, some of which are underrated, but relationships are at the center of clinical care. Acknowledging social connection among clinicians is at an all-time low. There needs to be more discussion around technology’s role in alleviating burnout and transitioning to more relational encounters. To do this, clinicians need to be aware of digital health tools and vendors need to gather feedback on the efficacy of these tools. A call for sludge removal on the front lines. Before adding anything else to clinicians’ plates (i.e., new initiatives to mitigate burnout or new technology to help with productivity), teams need to remove wasteful activity. Start with a de-implementation list to remove the excessive amounts of sludge that have built up in healthcare. This can reduce cognitive load, remove distractions, and clear minds to focus on what matters most — providing effective patient care. Organizations will succeed in enhancing clinician well-being by fostering social connectedness and emphasizing clinician-patient relationships. If you’re interested in learning more about clinician burnout or workforce technology, check out related Forrester research below or schedule a guidance session. For all other questions regarding research or how to become a Forrester client, please email [email protected]. source

Traditional managed services have long been caught in a fundamental dilemma: achieving high-quality service delivery while maintaining cost-effectiveness. After offshore and cloud, managed services are being reshaped by AI at a fundamental level, introducing a new services paradigm that blends performance-based models, automation, and human-centric refinement. This shift is happening across industries, from HR and supply chain to help desk and manufacturing operations. AI-led services represent the next wave, potentially replacing or significantly augmenting human capital. Contact Centers As The Tip Of The Spear Consider the contact center, a historically manual, low-margin cost center. Enter the AI-powered model: An AI platform handles the lion’s share of interactions and continuously learns from every engagement. AI-first providers such as Crescendo are delivering managed services as software that flip the economics and the value proposition of traditional business process outsourcing. Crescendo’s platform promises to leverage advanced large language models and proprietary IP to handle 50–70% of interactions seamlessly. The rest — complex, high-touch cases — go to top-tier human experts. Knowledge engineers use customer interactions to constantly refine and improve the AI models, ensuring that the system gets smarter and more effective over time. Complexity doesn’t vanish overnight, but the reliance on large manual operations decreases as the AI becomes better at understanding context, maintaining accuracy, and adhering to brand voice. The Economics Of AI-Powered Services Perhaps the most revolutionary aspect of AI-powered managed services is their economic model. Rather than charging for labor hours or headcount, these services are increasingly moving toward outcome-based pricing. This approach aligns provider incentives with customer success metrics, fundamentally changing the dynamics of service delivery. Key advantages of this model include: Predictable costs tied directly to successful outcomes. Reduction of traditional staffing and training overheads. Reduced operational complexity. Scalability without proportional cost increases. The Learning Organization What sets advanced AI-powered managed services apart is their ability to learn and improve continuously. Unlike traditional services where knowledge often remains siloed within individual agents, AI systems can systematically capture and apply insights from every interaction. Knowledge engineers play a crucial role in this ecosystem. This knowledge loop creates a virtuous cycle: Each interaction provides data for model improvement. Enhanced models deliver better customer experiences. Improved experiences generate more positive interaction data. The system becomes increasingly effective over time. The Road Ahead: Fully Managed, Always Improving While the market is in its early stages, venture capital and investment firms are betting heavily on these AI-powered services. They anticipate adoption rates that could surpass the SaaS revolution, driven by clear ROI and immediate operational benefits. Contact centers are proving to be the perfect testing ground, but this model will expand across IT services, HR, supply chain, and other domains of operation where service quality and cost efficiency matter. This is what the future looks like: managed services that aren’t merely offshored or outsourced but are continuously optimized, AI-infused, and laser-focused on business results. Organizations can leverage these AI-powered managed services in two complementary ways: Transform delivery of mission-critical but nondifferentiating capabilities. Customer service, IT support, and back-office operations can be optimized through AI-powered managed services, freeing resources for strategic initiatives. Use these partnerships as learning laboratories. Understanding how AI models operate in managed services will provide valuable insights for future applications in core, differentiating business capabilities. Read our recent report to learn more about how generative AI is disrupting professional services. source

AI rightly has taken center stage in the public cloud platform market, but it’s not the only major transformation underway. First, cloud users are changing the way that they provision and consume cloud services, with serverless-first approaches gaining momentum. Second, sovereignty concerns are carving the market into regional and national components amid trade tensions. Each of these key trends are featured in the public cloud platform evaluative reports, the result of our collaboration over the past year to identity evaluation criteria in a rapidly changing technology environment and assess competitive differentiators. The most recent of our reports, The Forrester Wave™: Public Cloud Platforms, Q4 2024, highlights how hyperscalers are remaking core infrastructure services for the generative AI (genAI) moment with a focus on data, even as they push up the tech stack to reach business users with AI-infused versions of services that operate largely beyond the boundaries of traditional enterprise IT. The Forrester Wave™: Public Cloud Platforms In China, Q3 2024, by Charlie Dai, shows Chinese cloud providers driving platform innovation AI services and foundation model support across several domains. The Forrester Wave™: Public Cloud Platforms In Europe, Q3 2024, by Dario Maisto, puts a spotlight on how European users’ (and their governments’) priorities on sovereignty and sustainability have created an opportunity for Europe-focused cloud providers to offer competitive options up and down the tech stack. Forrester clients have access to the full reports. Here are some key points: The serverless-first (and often only) public cloud is here. Cloud providers and customers have been wrestling with both legacy technologies from data centers and the open-source complexity of cloud-native technologies such as Kubernetes and function as a service (FaaS). Today, Kubernetes and FaaS have become implementation details — checklist items for cloud customers who want services that provide a pipeline into nonproprietary open-source-based services but who don’t want or need to put resources into complex platform build-outs and integrations. The Chinese cloud providers are particularly innovative on this front, for example, with serverless machine learning/deep learning and support for mini-app mobile development. Digital sovereignty and cloud sustainability influence cloud procurement in Europe. European regulators are setting the pace on digital sovereignty and cloud sustainability. Here, digital sovereignty moves beyond the cloud infrastructure domain to involve hardware, software, network, and data, each with broader implications. The most obvious example is in-region (or, sometimes, in-country) data centers and supply chain controls. But a cloud provider that offers European operations physically, legally, and logically separated from the rest of the world isn’t necessarily a sovereign cloud vendor, which requires adherence to a mix of regulatory and certification regimes. Cloud sustainability is gaining traction, too, as providers face spiking energy usage from power-hungry genAI services. In both arenas, Europe is set to influence globally. Cloud AI moves from scattershot to orchestrated services. The requirements of genAI and the global scale of public cloud made it inevitable that hyperscalers would dominate the AI boom, albeit with disruptive upstarts and resurgent older tech providers getting into the mix, as well. Our evaluation of cloud provider AI offerings focuses heavily on the infrastructure firepower. We also looked at both AI-assisted TuringBot developer tools for general application development as well as others specifically for developing AI applications and agents. We paid particular attention to genAI-enabling technologies such as retrieval-augmented generation, which is often critical to move foundational models into production. While these services are not all yet generally available, the essentials are there for users who are prepared to accept complexity to achieve genAI results in the near term. For more details, Forrester clients please read the global, Chinese, and European reports and book an inquiry or guidance session to discuss. If you’re not a client, be sure to attend the upcoming Predictions 2025 webinar on January 30. source

Source: GIPHY For companies, it’s a foregone conclusion that consumers want personalized interactions, but this is not entirely true. In Forrester’s October 2024 Consumer Pulse Survey, 33% of US consumers said that they never want to receive personalized interactions from companies. Consumers are also increasingly privacy-aware and feel that companies need to provide something in return for giving up their personal data. Even then, our consumer data dating back to 2020 shows that at least 30% of consumers consistently indicate that nothing will motivate them to share more personal data with companies. Consumers simply want relevancy and value. Relevancy doesn’t mean that it needs to be a 1:1 interaction with a consumer or customer. A personalized interaction could deliver relevancy via broad segmentation to a large group of consumers or at an individual customer level. What matters is that it’s a contextually appropriate interaction. Consumers need to realize value from that personalized interaction across Forrester’s four dimensions of customer value: Economic value. Consumers realize economic value when they save money, get free things, or feel that they’re paying a fair or predictable price. In today’s environment, 62% of US consumers want economic value from personalized interactions, according to Forrester’s October 2024 Consumer Pulse Survey. Functional value. When consumers find usefulness during product or service decisions, the buying process, or support and help, they feel functional value. From the same survey, 36% of US consumers want personalized interactions to provide functional value. Experiential value. Interactions and sensations such as design and sensory allure or courtesy and reassurance support experiential value. Twenty-eight percent of consumers care about personalized interactions having positive experiential value. Symbolic value. When consumers feel meaning such as self-affirmation, social standing, support and caring, or belonging and connection, they realize symbolic value from companies. Fifteen percent of US consumers want personalized interactions to provide symbolic value.   To guide your consumer personalization research journey, check out our new The State Of Consumer Personalization, 2024 report, then follow up with the strategy, data, technology, and measurement modules. There’s a lot to unpack on the topic of consumer personalization, so let’s continue the conversation. Schedule a guidance session or inquiry with us. source

Just adding a chatbot on your website or mobile app will not reduce customer service calls. Sometimes as an analyst, it is my job to break someone’s heart, such as the times when I must tell a customer service team that it is not surprising that their well-designed chatbot is not reducing customer service calls. In fact, it’s doubtful that they will ever see the return on the investment they promised their managers. Here’s the wacky part: In fact, that bot may be more valuable to the brand than the cost savings that increased customer service automation might have provided. But the remaining problem is that, in their business case, they committed to the wrong success metric. What do you mean, “the wrong success metric”? Aren’t bots supposed to reduce my costs? While there is some overlap, people who want to chat are often not the same people who call customer service. Think about a prospect on your website. If they can’t find an answer to their question, they might be willing to try a chatbot, but they are not likely to dig around to find your 800-number. That prospect may simply go to your competitor’s website, and you just lost a sale or a deal that you didn’t even know was in play. But if that chatbot deftly engages a prospect who ends up buying, that chatbot shows immense value to the brand. That chatbot will not absorb customer service calls, however. Make the best of a bad situation: Use chat for call deflection. If your goal is to move more customer service interactions to digital, use chat to deflect calls. This can be as simple as offering a chat to anyone on hold with customer service and sending a link to your chatbot if they take you up on the offer. If you do this, make sure that your digital support is as high-quality as what you offer by phone. If you send them to digital and your analytics tell you that they call you back within minutes, you’ll know that you have just executed an epic customer-experience fail. Make sure that your chatbot is comprehensive and well designed, and have live agents in place as backups to ensure that any customer issue that isn’t successfully handled in self-service can be solved with a human agent. What if I’m still creating the business case for our chatbot? If you are reading this before you promised your leadership that your chatbot will reduce customer service calls, huzzah! To build the right business case, you need to do some analysis on your web or mobile application users via tools such as journey mapping, voice of the customer, confidence-building measures, and customer analytics: Where (and why) do they get hung up? What are their “pain points”? What can’t they find on your website or in your mobile app? Where is the information that the customer needs to move forward? What questions aren’t you answering throughout the customer journey in your digital touchpoints? With these insights, you can identify what your potential chatbot will do — e.g., generate more sales, assist with payment, or whatever it may be. It’s possible that your customer support site has problems and that people are calling customer service after failing there, but you need to validate that assumption, not build a business case on it. … And analyze whether a voicebot may be a better option. If your domain is customer service and you want to reduce contact center costs, explore an advanced voicebot that automates more calls. A modern voicebot will also reduce agent call durations by ensuring that all calls will be escalated to the best available agent with all the pertinent customer information required to solve the customer’s problem quickly. Many brands look to deploy chatbots first, thinking that they are simpler to deploy and a better experience for customers, but if your goal is to reduce customer service calls, put your bot on the phone. Every call that the bot handles is a call that did not need an agent. If you do go down the voicebot path, think about omnichannel for the long run, meaning you should find a vendor that can provide digital as well as voice services. This way, when you deploy your chatbot, you will provide consistent customer experiences and leverage your workflow and integration work across all interactions, saving development time. Want to discuss your chatbot business case, success metrics, or modern voicebots? Forrester clients can schedule a guidance session or inquiry with me.  source

Long practiced in emerging companies, product-led growth (PLG) has been touted as one of the fastest ways for B2B firms to grow. Perhaps even more compelling than rapid growth is the lower cost of sales in PLG motions. Because the methodology is based on simplified products targeting individual users for self-serve purchase, initially, there is no need for business development reps or sales outreach. Traditional sales-driven firms, don’t despair! You can still get in on the action by adopting PLG strategies that complement your sales efforts at each stage of the customer growth trajectory — and drive faster and more profitable growth. Here are five steps to drive customer growth with PLG: Step 1: Drive user growth to seed the market. PLG can generate rapid user growth by relying on users to do the selling. Offer free trials and make sure that the product has a network effect where users gain more value the more others are using it — by collaborating within their team (e.g., Jira), across their company (e.g., Slack) or even across companies (e.g., Calendly). Create user referral programs where users are incentivized to share the product. These network and viral effects can drive “exponential growth” across markets and accounts. Step 2: Turn heavy user companies into product-qualified accounts. With users seeded across multiple companies, segments, and even regions, it’s easy for PLG organizations to identify the accounts where more users have adopted their product. Accounts with enough active users become new opportunities in the pipeline for a sales rep to close. This process is typically less costly than traditional top-of-funnel marketing efforts, and these product-qualified accounts are considered to be “better than the best” of traditional pipeline opportunities. Step 3: Leverage product telemetry to optimize the experience and build loyalty. A product that delivers fast time to value is foundational to PLG success and will help drive growth and retention for all selling motions. Build in product analytics so you can pinpoint user friction and optimize the time and effort it takes users to achieve their desired outcomes. This type of product telemetry can be used across small and simple or large and complex software modules and is instrumental in improving the user experience and building ongoing loyalty. Step 4: Use in-product, personalized messaging to upsell customers to higher tiers. In PLG motions, the product is the primary marketing and selling method. Create contextual, personalized messages that both provide tips for specific activities and showcase additional offerings that could extend the value that users receive. In the context of existing workflows, alert users to new features, product extensions, or higher-tiered offerings. Offer trials for premium capabilities to make it easy for users to experience the value before expanding their purchase. Step 5: Combine product- and sales-led efforts to expand into new buying centers. Now that you’ve set up a PLG motion, use it to extend to new buying centers with the support of traditional sellers. Account teams should scout out new buyers and identify new use cases for offerings within accounts. Gain cross-sell business through PLG motions using trials and referral programs to incentivize users to share across buying centers. PLG strategies, while practiced successfully at smaller firms, have become additional arrows in the toolkit of go-to-market practices for many larger B2B firms. Pursuing a bottom-up PLG strategy in conjunction with traditional sales efforts has been shown to have the best results for rapid and scalable revenue growth. Just look at the success of Atlassian, Airtable, Dropbox, Calendly, HubSpot, and others to see how well the PLG and sales combination works. Interested in finding out more about PLG? Read this blog on adopting PLG strategies. Clients can access the reports B2B Companies Must Implement Product-Led Growth Practices To Remain Competitive and Leverage Product-Led Growth Strategies For Customer Acquisition, Retention, And Expansion on the Forrester portal or set up a conversation with me. You can also follow or connect with me on LinkedIn. source

As I reflect on my 2024 blog posts, one theme stands out: excellence in revenue enablement. The primary mission should be to serve your internal customers. Every micro effort — whether in learning, adoption, management, or culture — must be laser-focused on seller success. Nothing else matters. Here is a summary: “32 To 36 Courses” Is Not Revenue Enablement — Too often, sales learning is conducted as a reaction to negative lagging outcomes, such as “We lost a bunch of similar deals, so we need to retrain everyone.” Or more positively, a new product launch is pending, necessitating seller education … but no one thought months ago to request time in the revenue enablement learning calendar. This blog delves into the key filters for generating ever-boarding experiences: Enablement teams must ascertain if it’s necessary, who should learn, when it’s optimally delivered, and how to provide it most effectively. Why Can’t My Sellers Adapt More Quickly? — There isn’t a B2B seller whose remit today is exactly the same as two years ago, and most quota-bearing professionals recognize that their compensation, territory, offerings, buyers, and competitors are constantly in flux. This blog guides enablers through the essential needs to communicate through change management best practices and to establish and maintain role-specific sales competency maps to transparently broadcast how selling duties are adapting, as well as lays out scenarios where it might just be foolish to expect overwhelming evolution among certain individual contributors. What’s Lurking In Your Sales Culture? — While our research team has already been at work preparing for B2B Summit North America 2025 since October, the lessons of our 2024 event continue to resonate with revenue enablement leaders charged with high-altitude thinking around their sales culture. I thoroughly enjoyed working with Katy Tynan in combining her team’s culture energy research with the ups and downs of B2B sales-specific cultural growth. This blog explores the entire sales talent lifecycle, from hiring and onboarding through ever-boarding and career development. The TL;DR? Even in the crustiest, most traditional sales organizations, paying attention to the culture within your revenue team impacts the lagging indicators that matter to the C-suite. How Quickly Should A Sales Rep Be Onboarded? — This common question is one of the few we’re comfortable not answering with the typical analyst response of “It depends.” Because the answer is: Onboard B2B sellers well, not quickly. Look, I’ve been a sales leader, too, in urgent need of territory coverage, who rushed reps into the field; it never works out well for reps, the company, or especially the customer. This blog highlights the fact that adult learning science contradicts how too many B2B organizations ramp sellers: by quickly teaching them too much at their start, praying that it sticks, and rapidly releasing them into the field. Instead, high-performing revenue enablement teams learn how to feather in selling and feather out learning over a more extended period of time. The year-one and long-term results are almost always better than hurried sales onboarding. The Chief Sales Officer And Cultural Leader: Not A Contradiction In Terms — Returning to Katy and her “future of work” colleague Angelina Gennis and their wonderful research on organizational leadership and culture, this blog applies their lessons to sales-specific subcultures. The main takeaway remains true: “Today’s CSOs have unprecedented access to leadership best practices, along with the tools to amplify how they effectively motivate, inspire, and coach their team.” First-Line Sales Managers: Promote Or Hire? — In June, we published irrefutable but controversial research findings: All else being equal, chief sales officers are better off hiring first-line sales managers than promoting from within. No, Forrester isn’t broadly advising CSOs to eliminate sales career development, but we do find that most firms are poor at effectively growing sales leaders. This blog showcases a spectrum of FLSM staffing approaches from four external organizations that serve as superb, thoughtful examples of the many options available to revenue leaders in need of new management talent. Revenue Enablement Is Not In The Tool Business — … Because buying technology solves no problems and enablement teams have little value if they’re not agents of change and orchestrators of efficiency and effectiveness within their revenue engine. Your job as a revenue enabler is first and foremost applying insight-driven expertise to friction points within the sales organization; once your processes and people are optimized, then of course the opportunity to scale and automate enablement excellence should follow. Under no circumstance should your sellers, however, think of your team as the purveyor of shiny-object technologies. It’s been a busy, exciting, challenging year. What will 2025 bring? source

Today’s enterprise architecture (EA) practices are critical to enable a firm’s customer-obsessed digital transformations. As more traditional EA practices become commoditized, enterprise architecture management suite (EAMS) vendors that show strong use cases to support real transformations will emerge as market leaders. These transformation-enabling use cases include AI-powered features, support sustainability goals, offer modeling and assessment capacities, and provide architecture-empowering functions. We just published The Forrester Wave™: Enterprise Architecture Management Suites, Q4 2024, in which we evaluate the 12 most significant vendors — Ardoq, Avolution, Bee360, Bizzdesign, BOC Group, MEGA International, North Highland, Orbus Software, SAP LeanIX, Software AG, Sparx Systems, and ValueBlue — on their current offering, strategy, and customer feedback. Of these 12 vendors, four Leaders emerged: Orbus Software, MEGA International, Bizzdesign, and Software AG. Our assessment unveils that the leading vendors stand out because: They possess competitive AI use case capabilities. AI is in its nascent stage, and forward-thinking Wave Leaders have swiftly capitalized on this emerging technology. Key features offered by the Leaders include text recommendation engines, chatbots, smart agents, and AI-assisted roadmap capabilities. These functionalities leverage common large language models and incorporate the retrieval-augmented generation technique to enhance performance and accuracy. They offer ways to support sustainability goals. Sustainability features are a key differentiator for each Wave Leader. These include a hub capacity that enables strategic planning and measurement of the IT estate, from materiality assessment to carbon-footprint calculation, as well as integration with sustainability software. They provide the best modeling and assessment capacities. Diagramming and visual comparisons of objects are foundational features of EA tools, rooted in the core competencies of architects. Leading providers offer advanced capabilities such as process modeling, process mining, business capability mapping, and comprehensive assessments, all essential for effective analysis and communication. They empower architects, helping them be more proficient. Leaders consistently focus on empowering architects through various means, including digital twins, EA democratization, process mining, low-code/no-code solutions, demand management, strategic portfolio management, and architecture decision records. They also promote the use of APIs and microservices to encourage loose coupling. Forrester clients should use the report to create a shortlist of relevant EAMS vendors. Forrester clients can also book a guidance session or inquiry with me to discuss how to apply the Wave to their specific requirements. I would like to thank my colleague Paul McKay for his continuous support and editorial guidance and of course Min Say, who made this complex project a relatively easy task. source

Google recently released some blogs about Willow, its next-generation quantum processor. These blogs are currently making headlines and causing substantial confusion. Let’s break them down so you have a clear picture of what they mean: In this blog post, Google introduces Willow with a bold claim about its performance and details the team’s breakthrough in error correction. This more technical post describes what Google did in error correction more deeply. Google’s Big Breakthrough: Scalable Quantum Error CorrectionGoogle’s most significant achievement is a major advancement in quantum error correction — a critical challenge in quantum computing due to the fragile nature of qubits. Qubits exist in a “superposition” state, making them highly susceptible to errors from environmental interference. Without effective error correction, qubits lose stability too quickly to perform useful computations. To address this, Google improved on a method to group “physical qubits” into more stable “logical qubits” using a well-established technique called surface codes. Traditionally, increasing the number of connected qubits in a “surface code lattice” has led to higher error rates — the opposite of what is needed to create logical qubits from physical ones. Google scaled from a 3×3 to a 7×7 physical qubit lattice while reducing the error rate by a factor of 2.14, effectively doubling the lifespan of logical qubits compared to its earlier Sycamore chip. This achievement demonstrates that we can add more physical qubits while exponentially improving the stability of logical qubits. And we will need much, much larger lattices to correct logical qubits to the point of usefulness. It looks like we might get there now. While this is a significant milestone for quantum computing, it is not a sudden leap to “quantum advantage” — the point that quantum computers outperform classical ones for practical tasks. Instead, it marks a critical step forward in the development of large-scale quantum systems. Given the number of companies developing quantum chips, it remains uncertain whether Google’s approach can be replicated by other chipmakers or applied to different chip architectures. Quantum Supremacy, Not Quantum AdvantageGoogle said in its blog that “Willow performed a standard benchmark computation in under five minutes that would take one of today’s fastest supercomputers 10 septillion (that is, 1025) years.” This sounds like the giant leap we’ve all been waiting for, but the reality is more measured. Google’s announcement firmly establishes quantum supremacy — it first claimed this in 2019, but it was refuted by IBM. To understand this, recognize that quantum computing hype revolves around two key terms: quantum supremacy and quantum advantage. While they sound similar, they have crucial differences. Quantum supremacy occurs when a quantum computer performs a task that no classical computer can match, regardless of its usefulness — this is what Google announced with Willow (though the term was not directly used). We believe that Google has achieved it this time around, but it did not achieve quantum advantage. Quantum advantage refers to a quantum system that can solve a practical, real-world problem faster and cheaper than a classical one. We care far more about quantum advantage. Unfortunately, that is still perhaps a decade away. Consider that the Willow chip only has 105 physical qubits. Achieving quantum advantage will require a thousand or more logical qubits, as I pointed out above. You can do the math on a napkin and see how far away we are from that. Plus, other industry players such as Microsoft are exploring approaches like qubit virtualization and topological qubits, which could reduce the number of physical qubits required to produce a useful logical one. But these efforts are far from mature, so on we go. What CIOs, CTOs, And Security Leaders Should Do NextWhile Google’s Willow chip doesn’t alter our 10–15-year timeline for large-scale, fault-tolerant quantum computing, it sends a clear signal: Start preparing now, because it could be sooner. For business and technology leaders, this means taking practical steps: The first priority is to prepare with quantum security. Companies must prepare by adopting post-quantum cryptography and crypto agility, which both are part of quantum security, a top 10 emerging technology for 2024. The speed at which quantum computers will make breakthroughs is still anybody’s guess. The second priority is to experiment with hybrid quantum-classical systems. This will help organizations build skills for more advanced quantum capabilities in the future. It will also help leaders grab smaller advantages that may come in smaller-scale computers in the next five years or so. Finally, manage expectations. Willow is an achievement that may accelerate our long-term outlook for quantum computing, but only time will tell. Early quantum advantage for specific use cases, like quantum simulation and hybrid workflows, may emerge in the next two to five years for intermediate-scale and somewhat noisy qubits. Want To Learn More?Forrester clients can read the report, The State Of Quantum Computing, 2024, for the latest insights on error correction, quantum advantage, and what it means for your business. Get practical guidance on how to prepare for the future of quantum today. source