Forrester

Ranging from cultural nuances and generational shifts to the rise of AI, business buying in Asia Pacific (APAC) is evolving rapidly. APAC is one of the most dynamic and diverse regions in the world, and that complexity is reflected in its B2B buying landscape. If you’re a marketer or seller operating in this region, understanding these changes is critical to staying competitive. What can you do about these key trends that are shaping the B2B landscape in APAC? Here are five key APAC buying trends and guidance on what sellers and marketers can do to address them. APAC Buyers Are Younger, Digital-First, And More Demanding These younger buyers are digitally native, AI-savvy, and expect more than just a good product — they want values, expertise, and personalized engagements. Many of these younger buyers: Use generative AI to research vendors. Prioritize diversity, equity, and inclusion (DEI), environmental, social, and governance (ESG), and employee experience more than their global peers. Express dissatisfaction with the winning provider. What to do: Tailor your messaging to reflect values and purpose. Highlight your DEI and ESG initiatives and ensure that your customer experience is seamless from the first touch to post-sale. Buying Groups Are Bigger And More Complex APAC has the largest buying groups globally. In addition, external stakeholders, such as consultants and partners, play a bigger role here than in other regions. What to do: Map out the full buying group, including external influencers. Use intent data and persona research to understand who’s involved and what matters to them. Price Isn’t Everything, Until It Is Price is less of a determining factor in Asia Pacific compared to North America and Europe when considering a provider shortlist, but cost still emerges as a major area of concern when deals stall. What to do: Lead with value, not price, but be ready to justify cost clearly and confidently when it’s time to close. Brand Loyalty Is Weak, Even For Incumbents Only 35% of APAC buyers start their purchase journey with a single vendor in mind, compared to 45% in North America, meaning that even incumbents must fight to stay relevant. What to do: Don’t assume that your brand reputation will carry you. Stay visible, relevant, and responsive throughout the buyer’s journey. Cultural Nuances Matter A Lot APAC is not a monolith. Business etiquette and buyer expectations vary widely across the different countries: Japan values harmony and indirect communication. Australia favors directness and informality. China emphasizes relationships (“guanxi”). India respects hierarchy and seniority. What to do: Customize your go-to-market strategy by country. Localize messaging, adapt your sales approach, and train teams on cultural intelligence. How Providers Can Succeed In APAC To succeed in this complex environment, providers must do three key things: Understand buyers deeply. Use persona research and journey mapping to tailor engagement. Leverage intent data to deliver the right message at the right time. Engage across the lifecycle. Don’t stop at acquisition. Use lifecycle revenue marketing to support buyers post-sale and improve retention — this is crucial, given high dissatisfaction rates. Add value at every touchpoint. Every interaction is a chance to build trust. Make sure that your content, channels, and experiences are aligned with what buyers need at each stage. APAC’s B2B buying landscape is young, diverse, and digitally driven. Embracing this complexity and responding with data-driven and culturally aware strategies will enable your organization and position you to win. Forrester clients can learn more by reading my report, The State Of Business Buying In Asia Pacific, 2024, and scheduling an inquiry with me. source

Volatility Puts Culture Energy At Risk Your organization is navigating turbulent, volatile waters, and my colleagues at Forrester are helping you navigate this, whether you’re in technology; B2B marketing, product, and sales; or B2C marketing, CX, and digital. But what is happening in your team? As a valiant team leader, you’re doing your best to manage the uncertainty your team feels in the face of external volatility and the internal adjustments — changes in budget, project priorities, leadership, etc. — that naturally follow. But this is taxing and gradually erodes your team’s culture energy. Across the four dimensions of culture energy: Cultures that can’t adapt through volatility will decline in culture energy. Managers’ inability to provide motivation will accelerate this decline. Once shortcomings impact team culture energy, purposefulness takes a hit. Commitment becomes a culture’s last hope. Buoy Your Team’s Culture Energy With Data-Proven Manager Actions People managers who continuously foster team trust improve employee loyalty, satisfaction, and retention. My new trend report uses Forrester survey data to illustrate the relationship between trust, team culture, and specific manager behaviors. It’s a short but sweet report that makes it clear how leaders can preserve and elevate their team experience and culture energy despite the challenges all around them. Team leaders: Use this data to improve your own management practices. Senior leaders: Use this data to create standards for your managers that will improve their team cultures to raise overall organizational culture energy. I facilitate educational guidance sessions and workshops to put this data into action with your team. Forrester clients can reach out to their account team to schedule a format that will address specific challenges and goals. source

AI has the potential to fundamentally remake many aspects of how we plan and execute events. Yet, despite its wide-ranging potential, data from Forrester’s 2025 State Of B2B Events Survey shows that current uptake is low. While some leaders we spoke to are cautiously optimistic, there are wide-ranging concerns around safety, privacy, and vendor capabilities. One event leader we spoke to encapsulates the current sentiment: “Right now, AI is more of a promise than a reality.” Analysis of the survey data showed that: Content Creation Leads the Way Among organizations that are experimenting with AI, event content creation is the most common use-case. Thirty-nine per cent of survey respondents report that they use AI for tasks such as building event landing pages and writing emails; while 21% currently leverage AI to repurpose content post-event. Even within this simple use case, uptakes varies significantly. Forty-seven percent of mid-sized organizations are currently using AI for content creation, as compared to 29% of the largest enterprises; while just 19% of organizations in the financial services sector make use of AI here, compared to 40% of high-tech and telecommunications companies. There is low uptake (but high interest) in using AI to improve the attendee experience The current uptake of AI to improve the attendee experience through better targeting, increased personalization, and improved assistance is very low with between 7-15% of organizations making use of AI for these three use cases. Leaders we spoke to were more focused on exploring AI to drive productivity and efficiency improvements and expressed nervousness with AI which directly touched attendees. That said, leaders see massive opportunity here, with more than 40% of respondents tell us that: “we would like to do this but have no immediate plans” – the highest percentages of any use case. High Spenders Lead the AI Charge Organizations with larger event technology budgets are more likely to have embraced AI or plan to do so in the next 12 months, and are exploring more advanced use cases. Among those spending over $250,000 annually on event technology, 68% use (or plan to use) AI for data analysis as compared to 39% of low event tech spenders. They are also more likely to be using or planning to use AI which directly impacts on attendees; while low tech spenders are more focused on using AI for simple content creation, with 55% prioritizing AI for content creation. Conversations with enterprise event tech leaders revealed dissatisfaction with overstated vendor AI claims, leading the most advanced organizations to develop their own AI capabilities. Younger Marketers Show Growing Interest Younger marketers from Gen Z and Millennial cohorts are showing greater enthusiasm for AI’s potential, particularly in areas like attendee assistance via AI-powered chatbots, predictive intelligence for targeting, and attendee experience personalization. However, their interest has yet to translate into action, as older generations control adoption plans within the next year. The Road Ahead Despite its slow uptake, AI’s promise in event management is undeniable. Some trailblazers we spoke to are experimenting with cutting-edge applications such as facial recognition to understand attendee sentiment. As organizations grow more comfortable with AI and its capabilities evolve, the technology will play a transformative role in B2B events, helping marketers deliver smarter, more personalized, and impactful experiences. For now, AI remains a promise—but event teams need to act quickly or risk getting left behind. If you are a Forrester client, you can reach out to schedule a guidance session to dive deeper into this topic and explore how best to utilize AI to maximize the value of your event program. source

I’m excited to announce the publication of The Forrester Wave™: Employee Experience Management Platforms, Q2 2025. What is an employee experience (EX) management platform, anyway? It’s not as simple a question as you might imagine. There are many vendors trying to define their market space as EX management because their capabilities are integral to employees’ daily work and communications. For example, intranet platforms are increasingly marketing themselves this way. But in our view, an EX management platform is one that enables deep research into how employees are experiencing working for your firm — through multiple channels, methods, and contexts — and provides tools for making that experience better. This is disciplined, detailed work that requires a sophisticated toolset to do well. Engagement surveys are integral to this work, but they’re only one method and have serious limits in terms of what they can reveal and how often employees will tolerate answering the questions. In a recent interview, the EX leader for a large European bank explained that, while he uses engagement surveys to understand where either engagement or burnout is happening, they can’t tell him how or why people are feeling the way they’re feeling. So an EX management platform that offers other research methods, such as running virtual focus groups or harvesting insights from employees’ daily interactions with each other, can reveal much more. From our exhaustive (and exhausting!) Wave research, we see three key findings worth highlighting: The tools available today can enable broad participation and leadership support. For many firms, EX is a function that sits within HR with limited visibility into, or influence over, other parts of the organization. If that’s your starting point, take heart, because there is so much more you can do with today’s tools and with the right processes and strong leadership support. If the goal of the organization is to improve EX holistically and durably, then most of the solutions we reviewed have methods for helping you do so. But choose wisely, because there are significant yet nuanced differences between them. For example, data model and visualization limitations might make it impossible to segment the data well enough to share it with other stakeholders, effectively limiting participation. Some solutions have more effective and proven methods for inspiring behavior changes across the organization that will improve EX over time. For all of them, though, leadership support is crucial for success. Surveys are an important tool but must be coupled with action. Many firms I speak with are stuck in the cycle of annual surveys, followed by inaction on the results. Accordingly, they make few gains and participation wanes over time. But some of the EX management platforms here have exceptional capabilities for increasing participation with managers with expert guidance and nudges, while others offer strong rewards and recognition capabilities. This latter capability is especially powerful because research shows that people are far more likely to be engaged when they feel uniquely seen and valued for who they are and what they contribute. Conversely, they’re more likely to burn out when they don’t. Nudges and recognition capabilities are effective ways to shift culture to foster better EX. Deep listening is crucial to the future of EX management. Imagine a scenario where you could ask your EX management platform what employees are worried or upset about — or you could look at a group of employees who are showing signs of burnout in survey results and then be able to look back in time at when their emotional states shifted, how and when the topics of their conversations with others changed from optimism to pessimism, and what the topics of those conversations were. On one hand, this sounds creepy, but on the other, it’s also incredible insight when employees’ identity is fiercely protected and their anonymity is assured. Ready or not, this kind of deep listening is what’s coming in the months ahead as vendors ramp up their AI and neurolinguistic processing capabilities, coupled with generative and agentic AI capabilities that make it far easier to produce powerful insights on demand. The leading vendors in this evaluation are all taking this opportunity seriously and developing capabilities for it. What’s Next? Forrester clients can access the full Forrester Wave report to learn much more about our evaluation scoring and see how each vendor performs — and not a moment too soon! As we emerge from an EX winter, and as new capabilities become available from vendors, 2025 should prove to be an excellent year for taking stock of where you are on your EX maturity journey and investing in new capabilities to get to the next level. Reach out to me for help. source

In today’s fast-paced and ever-evolving business landscape, organizations often face the challenge of ensuring smooth communication, efficient collaboration, and seamless information flow across departments and teams. Despite well-defined hierarchies and formal reporting structures, many companies struggle with silos, bottlenecks, and suboptimal performance. Sound familiar? The good news is that there are powerful tools and methodologies available to help detect and rectify these issues. In this blog post, we demonstrate the use of three key approaches: organizational network analysis, social network analysis, and social infrastructure analysis.   These methods offer unique insights into the intricate web of relationships and interactions within your organization, enabling you to identify areas for improvement and drive meaningful change. Imagine being able to visualize the actual flow of information within your organization, beyond the formal org chart. Organizational network analysis provides a bird’s-eye view of how communication and collaboration occur across departments and teams. By mapping out these networks, you can pinpoint communication gaps, identify key influencers, and uncover opportunities for enhancing cross-functional collaboration. Now let’s zoom in on specific teams within your organization. Social network analysis allows you to map out the social relationships and interactions within these teams. By understanding the central players, those who have the most influence, and how information flows among team members, you can gain valuable insights into team dynamics and decision-making processes. This knowledge can help you optimize team structures, identify potential leaders, and foster a more collaborative and productive work environment. But it’s not just about the people — the overall organizational environment and culture play a crucial role in shaping how individuals interact and work together. Social infrastructure analysis assesses the invisible structures, norms, and practices that influence collaboration and communication. By examining factors such as office layout, company policies, and meeting dynamics, you can identify barriers to effective collaboration and implement changes that promote a more open and innovative culture. The potential benefits of these analysis methods are immense. By leveraging organizational network analysis, social network analysis, and social infrastructure analysis, you can: Identify and break down silos, improving cross-functional collaboration. Optimize communication channels and information flow. Uncover hidden influencers and change agents within your organization. Foster a culture of innovation and creativity. Align your operating model with your strategic objectives. We are embarking on a new research initiative to delve deeper into these analysis methods and their practical applications in transforming operating models. We believe that by understanding the intricacies of organizational networks and social dynamics, we can unlock the true potential of organizations and drive meaningful change. If you have experience using organizational network analysis, social network analysis, social infrastructure analysis, or similar approaches to address organizational challenges, we would love to hear from you. Your insights and success stories can contribute to collective knowledge and help shape the future of organizational design and transformation. If you’re interested in learning more about our research or sharing your experiences, please reach out to us. We look forward to connecting with you and exploring the exciting possibilities that lie ahead. source

As generative AI moves from experimentation to enterprise-scale deployment, the conversation is shifting from “Can we use AI?” to “Are we using it wisely?” For AI leaders, managing cost is no longer a technical afterthought—it’s a strategic imperative. The economics of AI are uniquely volatile, shaped by dynamic usage patterns, evolving model architectures, and opaque pricing structures. Without a clear cost management strategy, organizations risk undermining the very ROI they seek to achieve. However, AI enthusiasts may forge ahead with AI without cost accounting and favor speed and innovation. They argue that AI cost and even ROI remains hard to pin down. The reality is, to unlock sustainable value from GenAI investments, leaders must treat cost as a first-class metric—on par with performance, accuracy, and innovation. So, I took the case to David Tepper, CEO and Founder of Pay-I, a leader in AI and FinOps to get his take on AI cost management and what enterprise AI leaders need to know. Michele Goetz: AI cost is a hot topic as enterprises deploy and scale new AI applications. Can you help them understand the way AI cost is calculated? David Tepper: I see you’re starting things off with a loaded question! The short answer – it’s complex. Counting input and output tokens works fine when AI utilization consists of making single request/response calls to a single model with fixed pricing. However, it quickly grows in complexity when you’re using multiple models, vendors, agents, models distributed in different geographies, different modalities, using pre-purchased capacity, and accounting for enterprise discounts. GenAI use: GenAI applications often use a variety of tools, services, and supporting frameworks. They leverage multiple models from multiple providers, all whose prices are changing frequently. As soon as you start using GenAI distributed globally, costs change independently by region and locale. Modalities other than text are usually priced completely separately. And the SDKs of major model providers typically don’t return enough information to calculate those prices correctly without engineering effort. Pre-purchased capacity: A cloud hyperscaler (in Azure, a “Provisioned Throughput Unit”, or in AWS, a “Model Unit of Provisioned Throughput”) or a model provider (in OpenAI, “Reserved Capacity” or “Scale Units”) introduces fixed costs for a certain number of tokens per minute and/or requests per minute. This can be the most cost-effective means of using GenAI at scale. However, multiple applications may be leveraging the pre-purchased capacity simultaneously for a single objective, all sending varied requests. Calculating the cost for one request requires enterprises to separate traffic to correctly calculate the amortized costs. Pre-purchased compute: You are typically purchasing compute capacity independent of the models you’re using. In other words, you’re paying for X amount of compute time per minute, and you can host different models on top of it. Each of those models will use different amounts of that compute, even if the token counts are identical. Michele Goetz: Pricing and packaging of AI models is transparent on foundation model vendor websites. Many even come with calculators. And AI platforms are even coming with cost, model cost comparison, and forecasting to show the AI spend by model. Is this enough for enterprises to plan out their AI spend? David Tepper: Let’s imagine the following. You are part of an enterprise, and you went to one of these static pricing calculators on a model host’s website. Every API request in your organization was using exactly one model from exactly one provider, only using text, and only in a single locale. Ahead of time, you went to every engineer who would use GenAI in the company and calculated every request using the mean number of input and output tokens, and the standard deviation from that mean. You’d probably get a pretty accurate cost estimation and forecast. But we don’t live in that world. Someone wants to use a new model from a different provider. Later, an engineer in some department makes a tweak to the prompts to improve the quality of the responses. A different engineer in a different department wants to call the model several more times as part of a larger workflow. Another adds error handling and retry logic. The model provider updates the model snapshot, and now the typical number of consumed tokens changes. And so on… GenAI and LLM spend is different from their cloud predecessors not only due to variability at runtime, but more impactfully, the models are extremely sensitive to change. Change a small part of an English language sentence, and that update to the prompt can drastically change the unit economics of an entire product or feature offering. Michele Goetz: New models coming into market, such as DeepSeek R1, promise cost reduction by using less resources and even running on CPU rather than GPU.  Does that mean enterprises will see AI cost decrease? David Tepper: There are a few things to tease out here. Pay-i has been tracking prices based on the parameter size of the models (not intelligence benchmarks) since 2022. The overall compute cost for inferencing LLMs of a fixed parameter size has been reducing at roughly 6.67% compounded monthly. However, organizational spend on these models is rising at a far higher rate. Adoption is picking up and solutions are being deployed at scale. And the appetite for what these models can do, and the desire to leverage them for increasingly ambitious tasks, is also a key factor. When ChatGPT was first released, GPT-3.5 had a maximum context of 4096 tokens. The latest models are pushing context windows between 1 and 10 million tokens. So, even if the price per token has gone down 2 orders of magnitude, many of today’s most compelling use cases are pushing larger and larger context, and thus the cost-per-request can even end up higher than it was a few years ago. Michele Goetz: How should companies think about measuring the value they receive for their GenAI investments? How do you think about measuring things like ROI, or time saved by using an AI tool? David Tepper:

The current economic climate presents a significant challenge for CIOs. Tariffs and macroeconomic uncertainty are likely to temper IT spending growth in 2025 and 2026, and inflation continues to erode purchasing power, complicating CIOs’ efforts to secure meaningful budget increases. Yet amid these fiscal headwinds, artificial intelligence remains a paramount priority. In fact, Forrester’s Budget Planning Survey, 2025, indicates a strong commitment, with 78% and 74% of technology leaders planning to increase their budgets in generative AI and predictive AI, respectively. To strategically fund these initiatives, your organization must embrace a disciplined approach to managing your IT spend. Forrester’s new IT spend management framework is built on three indispensable capabilities: visibility, control, and optimization. Mastering these elements will empower you and your team to align expenditures with strategic imperatives, unlock significant cost savings, and ultimately fuel the innovation necessary to thrive. The Three Core Capabilities Of IT Spend Management A robust IT spend management practice is built on these three capabilities: Visibility serves as the bedrock of effective IT spend management. It begins with a clear, comprehensive understanding of the entire IT financial landscape. Visibility goes beyond basic tracking. It involves delivering actionable insights into where resources are allocated, why, and how that allocation aligns with strategic business objectives. Achieving this requires a combination of analytical tools, automated data aggregation, and robust cross-functional collaboration. Without this clarity, decision-making remains reactive and crucial opportunities for optimization might be missed. Control institutes guardrails for financial discipline. Control is about establishing the necessary guardrails to ensure that IT spending is intentional, compliant, and strategically aligned. This involves implementing formalized governance structures, automating approval workflows, and embedding financial policies directly into development pipelines. Far from stifling innovation, effective control provides a stable framework for disciplined resource allocation. This empowers teams to act decisively within strategic boundaries and ensure operational efficiency. Optimization transforms cost savings into strategic value. Optimization involves systematically identifying and eliminating waste, automating workload management for peak efficiency, and leveraging cost insights to inform strategic prioritization. But high-performing IT organizations don’t just bank these savings; they strategically reinvest them into high-priority initiatives such as AI. They turn cost efficiency into a powerful engine for growth and competitive differentiation. Navigate The Path To High-Performance IT Spend Management Achieving mastery in IT spend management is an iterative journey. Progress across visibility, control, and optimization is often spurred by specific triggers: unexpected budget pressures, new compliance mandates, or the strategic imperative to fund initiatives such as AI. To achieve substantial progress in IT spend management maturity, CIOs should guide their teams to tackle one capability at a time rather than all at once. Identify your most critical gap first; for instance, if runaway cloud costs are plaguing the budget, initial efforts should center on visibility and transparency. Create a centralized dashboard that encompasses all cloud spend in a single chart, then gather cross-functional stakeholders to communicate cost management initiatives transparently. With a reliable view of spending established, you can then confidently implement effective control measures to strengthen your overall financial governance. Remember, these three pillars are interdependent: Heightened visibility facilitates precise control, and robust controls in turn lay the groundwork for effective, targeted optimization. Over time, these synergies cultivate a virtuous cycle of continuous improvement, enabling organizations to adapt even amid a challenging economic climate and the need to manage technical debt. source

The recent announcement that PG Forsta acquired competitor InMoment has the potential to shake up the staid customer feedback management (CFM) market, but the new organization will need to quickly offer something distinct from market leaders Medallia and Qualtrics to succeed. While it’s too early to predict the long-term impact of this acquisition, one thing is clear: CFM vendors are under pressure to figure out their identity in an evolving landscape. Both vendors and CX leaders are grappling with an uncertain future, one where AI plays a growing role in customer experiences in ways we can’t yet fully imagine. At the same time, many organizations still rely heavily on traditional surveys and manual analysis to understand customers and make customer-centric decisions. When I speak with CX leaders about their technology needs and vendor selection, I’m often reminded of the famous line from the band Queen: “I want it all, and I want it now.” It’s easy to sympathize with their high expectations. The sheer volume of options available under the umbrella of “CX tech” can be overwhelming. A quick Google search reveals a long list of vendors branding themselves as “CX technology solutions.” While it’s encouraging to see so many companies embracing the importance of customer experience, the term “CX tech” has become more of a buzzword than a meaningful category. So how can CX leaders cut through the noise and find the right solutions for their organizations? My latest reports propose Forrester’s Function-First Tech Buying Framework as the solution for vendor selection. Use a function-first approach to focus on your organization’s goals first, then identify the technical functions required to achieve them. Too often, technology evaluation begins with a predetermined desire to acquire a specific category of tech, whether it’s a new CFM solution, analytics tool, or CRM software. Starting with technology instead of business needs frequently leads to overbuying, whether that means purchasing more features than your organization can realistically use or acquiring tools that duplicate capabilities you already have. Our methodology also encourages CX leaders to look beyond the confines of their current tech stack and explore how other parts of the organization’s ecosystem might offer complementary or overlapping functionality. Collaboration across teams can uncover new ways to address needs and avoid siloed thinking. Of course, function-first thinking isn’t a crystal ball — it won’t help you predict the future of CX tech and how generative AI for experience will evolve. But it does provide a structured approach to navigate the complexities of today’s market. Where CX technology is headed is an exciting topic, one I’ll be diving into more deeply in my research later this year. Interested in learning more about function-first thinking or discussing the latest trends in CX technology? Join me in Nashville at CX Summit North America in June — I’d love to connect and explore these ideas with you! Forrester clients can check out the new Function-First Tech Buying Framework reports now: source

IBM Think 2025 wasn’t just a showcase — it was a signal. The company is aligning its portfolio for the AI-first era. For CIOs, IBM’s message is clear: The future of enterprise IT is hybrid, intelligent, and orchestrated — and IBM wants to lead the way. OK, so what did we really think? AI and hybrid cloud take center stage. IBM reinforced its commitment to hybrid cloud and AI, highlighting the synergy between the two. IBM positioned watsonx Orchestrate as the centerpiece for deploying AI agents and automations. IBM also emphasized small language models such as Granite and AI at the edge — areas where it can differentiate from foundation model builders and hyperscalers. Any company wishing to differentiate itself through AI must put its proprietary knowledge to work, including through small language models. Composability and API-driven integration shift the conversation to platforms. IBM is shifting from siloed tools to composable, API-first platforms. This enables enterprises to orchestrate AI agents and workflows more efficiently. The keynote emphasized integration across IBM’s portfolio, reducing reliance on bespoke consulting and enabling more scalable, repeatable solutions. In the quest for flexibility and technology leverage, composable applications scale when firms invest in service mesh technology. Mainframes are reinvented for the AI era. IBM’s zSystems remains vital for high-volume transaction processing. The z17 platform introduces AI inferencing capabilities, but IBM must better communicate its value to new customers. Forrester suggests exploring a more consumable, composable mainframe experience to attract modern workloads. Watsonx.data sets the course for the future of enterprise data. IBM’s watsonx.data platform is evolving into a hybrid, open data lakehouse with data fabric capabilities. This supports unified access, governance, and AI readiness across hybrid and multicloud environments — critical for scaling AI initiatives. In particular, this approach lays the groundwork for the knowledge infrastructure that firms need to reach AI success. Security and consulting lie on the road ahead. While IBM made strides in AI and hybrid cloud, AI security was underemphasized. Consulting practices remain uneven, with strong performance in SAP and a significant payoff from AI-powered delivery through IBM Consulting Advantage but weaker alignment between consulting and products elsewhere. IBM’s challenge is to unify its services and better communicate the value of its acquisitions. Our Advice To Technology Leaders This advice is triggered by what we heard at IBM Think 2025, but it applies to every technology and service provider. CIOs and technology leaders should: Prioritize hybrid cloud strategies that support AI workloads both on-premises and at the edge. Evaluate how small language models can reduce costs and improve efficiency in your AI deployments. Use this moment to reassess enterprise architecture. Align the roadmap with partners that offer integrated, AI-ready platforms and can support your journey from experimentation to scaled deployments. Explore how AI-enabled mainframes can improve performance and reduce costs. For others, assess whether a composable mainframe model could fit emerging needs. Demand clarity from every product provider on AI security and integration strategies. When engaging consulting, ensure that it offers consistent expertise across platforms and can articulate how its assets deliver measurable outcomes. Begin evaluating IT strategy through the lens of AI-first transformation. Look for vendors that offer not just tools but a cohesive ecosystem that supports integration, governance, and scalability. source

Measurement, measurement everywhere and not a drop to drink. Customer experience (CX), digital, and marketing leaders are navigating a sea of measurement but struggling to identify what matters most to their customers and business. Despite spending millions of dollars and hours, CX scores continue to drop across the world, with APAC no exception. Simply doubling down and expecting things to turn around won’t work. Hope, after all, is not a strategy. New thinking, methods, and tools are needed to turn the tide. Forrester fortunately has the research and guidance required and will be sharing it at CX Summit APAC 2025. Our keynotes will cover the four key themes that you need to know. Total Experience: Your Brand, Their Journey The total experience recognizes that brand experience (BX) and CX are two sides of the same coin, and they must work together to deliver seamless and impactful customer journeys. At Summit, you’ll gain actionable insights on how to align your BX and CX strategies to maximize growth. In our first keynote session, Tom Mouhsian will explain how to “Power Growth With Your Total Experience” by introducing the Brand Experience Index and the newly refined Customer Experience Index, two powerful tools that work in harmony to measure and deliver total experience. You’ll learn how these indexes can shape acquisition and retention strategies with real-world examples. Digital Experiences That Matter Digital-first engagement is now the norm, but measuring digital experiences requires more than basic interaction metrics. In her keynote session, “How To Measure Digital Experiences,” Zhi Ying Barry will share how to adopt a holistic approach that connects technical performance, customer behavior, and perception to business outcomes. Practical guidance will be shared on linking digital measurement to revenue growth, customer satisfaction, and operational efficiency with a robust digital measurement framework to optimize digital experiences across your organization. Trust In AI AI is transforming customer engagement in profound ways, but many consumers remain skeptical of companies using AI, fearing unethical practices, biased decision-making, and misuse of personal data. Without trust, even the most promising AI strategies are at risk of failure. Martin Gill will outline how to “Build Customer Trust In AI” by adopting trustworthy AI practices. In his keynote, you’ll learn what drives customer trust in AI, gain insights from successful implementations, and discover how to apply best practices to your own AI projects. The Future Of Customer Experiences Next-generation customer experiences are transforming relationships at an unprecedented pace. To keep up, companies must adapt design strategies to leverage artificial intelligence and emerging technologies. Riccardo Pasto’s keynote, “Design For The Future Of Experiences,” will show that customers now expect brands to deliver a total experience through intuitive, conversational, and humanlike interfaces. The required approaches will deepen insights into preferences, automate tasks, and streamline digital interactions, resulting in more assistive and empowering experiences. Ric will share practical tools to start evolving how your company builds design practices and technical capabilities to deliver next-gen experiences. Real-World Inspiration And Engagement Theory is powerful, but action drives results. That’s why we’ve planned many opportunities for engagement, learning, and development: Case study sessions are designed to showcase real-world stories and solutions. Hear directly from industry leaders as they share their journeys, lessons learned, and strategies for leveraging the best in CX, marketing, and digital technologies. Forrester’s Customer-Obsessed Enterprise Award winners will share the best practices and guidance that distinguish their organizations. Smaller discussion groups, including facilitated engagement, analyst-led roundtables, and Q&A with analysts after their keynotes, will create opportunities for casual, intimate networking exchanges. At the conclusion of the event, we’ll have a reception to network and unwind. Don’t miss this opportunity to join the brightest minds in CX, digital, and marketing from across the APAC region. Register now to secure your spot at CX Summit APAC 2025. Together, let’s redefine CX, BX, and the total experience — and shape the future of customer engagement in APAC. source