Forrester

Cybersecurity platform provider CrowdStrike announced plans to acquire Adaptive Shield, a SaaS security posture management (SSPM) vendor. Some sources reported the purchase price to be around $300 million. If that purchase price is accurate, based on Forrester’s estimates of Adaptive Shield’s current revenue, that price represents an approximately 12–15x revenue multiplier and 6 times more than Adaptive Shield’s total funding raised. As CrowdStrike moves past its July 2024 global Windows outage and commits to improving its software quality assurance processes, the time was right for the company to expand its product portfolio. Forrester observes the following: Adaptive Shield brings needed SaaS security insights to CrowdStrike and extends its monitored endpoint range. CrowdStrike acquired Adaptive Shield for its SaaS security and posture management technology — Adaptive Shield will allow CrowdStrike to perform configuration drift detection and malware and ransomware scanning on SaaS endpoints (Box, Dropbox, OneDrive, etc.), adding to the heritage S3, Azure blob, and GCP coverage. Additionally, Adaptive Shield has differentiated configuration compliance libraries and configuration drift detection with its scalable offering. The acquisition supports CrowdStrike’s goal of building a comprehensive cloud security platform, including cloud detection and response capabilities, and follows similar steps taken by Palo Alto Networks and Zscaler. Creating a true cloud and identity security platform is hard. CrowdStrike (and its competitors Trend Micro and Wiz) have been on an acquisition spree: CrowdStrike has bought Flow Security, Bionic, Reposify, and Secure Circle to enhance its organically built heritage cloud workload protection portfolio and growing identity threat protection capabilities. Building a true platform with integrated policy management, unified architecture, generative AI copilots, and central reporting is difficult and time-consuming: Palo Alto, Trend Micro, Wiz, and other cybersecurity platform vendors have struggled to integrate these capabilities completely into a single platform, even without acquiring an SSPM vendor themselves. It is likely that it will take CrowdStrike at least 18–24 months to achieve complete integration here. More SSPM acquisitions will follow. As with any maturing technology area, large vendors start acquiring successful smaller vendors when the hockey stick of enterprise adoption begins. We are now at that point in SSPM. Forrester expects that in the next 18–24 months, AppOmni, DoControl, Spin.AI, and other SSPM vendors will be likely acquisition targets for large cloud security vendors such as Palo Alto Networks, Qualys, Tenable, Trend Micro, and Wiz for platform building. Forrester sees this as similar to the recent ITDR acquisition spree across IAM and non-IAM vendors. CrowdStrike is raising the profile of identity security. Building on its existing identity and threat detection product, with this acquisition, CrowdStrike continues to emphasize the importance of identity-centric security to drive better alignment between IAM and cybersecurity teams. The strategy initially started with CrowdStrike’s 2020 acquisition of Preempt Security for its conditional access technology. Given its cybersecurity market position, CrowdStrike’s emphasis on identity security is a boon to the overall identity security market, but it foreshadows a coming reshaping of the IAM vendor landscape and competitive dynamics over the next 24–36 months. source

The global commercial AI software governance market is poised for exceptional growth. Forrester forecasts that by 2030, spending on off-the-shelf AI governance software will more than quadruple, reaching $15.8 billion and capturing 7% of overall AI software spending. This trend reflects the urgency for organizations to manage AI’s integrity amid rapid AI adoption and increasing regulatory demands. Here are the top three drivers behind this investment surge in the emerging market of AI governance software: Rapid adoption of generative AI (genAI). Businesses across industries are rapidly evaluating and adopting genAI. As genAI continues to integrate into core business operations, companies are investing in governance solutions to maintain model accuracy, reduce bias, and ensure responsible use. Intensifying global regulations. As regulations like the EU’s Artificial Intelligence Act and US regulatory bodies such as the FTC enforce new and existing AI laws, enterprises are looking to address stringent standards for AI transparency, fairness, and security. These regulations compel organizations to adopt governance tools that help meet compliance requirements and provide the ability to respond to investigative requests. Demand for responsible and trusted AI. Organizations are under pressure from stakeholders to demonstrate AI’s reliability and accountability. AI governance solutions provide the needed observability and transparency, addressing concerns over data quality, model performance, and ethical risks — critical for maintaining trust, building AI value, and avoiding potential business risk. With these drivers in play, existing and emerging governance and risk solutions for models, AI, data, privacy, and cybersecurity will increasingly consolidate into a unified cockpit that balances visibility, management, trust, and compliance. Enterprises will invest in these solutions to navigate the complexity of known, changing, and new risks from AI. For more details on the trends and evolution of the AI governance software market, read Forrester’s report, Global Commercial AI Software Governance Market Forecast, 2024 To 2030. source

When clients inquire about enterprise business intelligence (BI) platform pricing, I always advise them to simply divide the annual contract price by the number of expected users. If the resulting number is around $10 per user per month, I tell the client they are in a good spot. Why? For platforms with pricey develop/admin licenses, these expensive licenses are a tiny fraction of the overall user base and have little impact on total cost. The predominance of inexpensive licenses keeps the average cost at around $10 per user per month. Generative AI — specifically, conversational interactions with data — is changing the way that BI vendors are pricing. There is a trend away from licensing with more expensive “author” licenses and inexpensive or free “consumer” licenses as genAI erases some of the distinctions. Again, this is shifting the average cost toward the $10 per user per month benchmark. Most importantly, a Microsoft Power BI Pro license has been $10 per user per month for a long time. Some estimates indicate that there are more than 300 million Microsoft 365 users (all with a Power BI Pro license). Therefore, other BI vendors have no choice but to compete with that number. Well, things are changing. Inflation isn’t just impacting the cost of groceries; it is now coming for enterprise BI buyers. For the last year or so, Forrester has been hearing complaints from customers that their BI vendors are taking advantage of contract expirations to jack up the prices. Now it’s Microsoft’s turn. Today, Microsoft announced that it is raising Power BI Pro licenses to $14 (from $10) per user per month and Power BI Premium to $24 (from $20). Looks like we have a new benchmark for enterprise BI licenses. source

“Nobody wants to admit that their own death is coming soon.” Low-code has been swinging the pendulum away from off-the-shelf applications and toward custom development for years. There are good reasons for this. When practical, fit-to-purpose software is best. And the lower cost, risk, and lead time of low-code development — coupled with an expanded developer pool, easier integration, management of apps on a common platform, leveraged licensing, etc. — makes it much harder to justify off-the-shelf software licenses and vendor sprawl. AI-powered enterprises will “build” software instead of “buy” it — and many applications in enterprise portfolios will consolidate onto low-code AppGen platforms. Until recently, this shift was typically unplanned and organic. Even as firms scaled low-code and told us that they were buying fewer apps and building more instead, they were often surprised to realize that their practices and view of “build vs. buy” had changed. It had just sort of happened along the way. But now, this “build first” mindset is becoming a deliberate enterprise strategy. Here is a sample of comments we’ve received from enterprises over the last few months: “We’re freezing all new app purchases. We start by developing [on low-code platforms].” (CIO, North American energy enterprise) “For new applications, our recommendation is [low-code platform] first.” (IT director, global engineering firm) “AI tilts the floor. We see a move away from the big enterprise apps. You’ve got a lot of single-purpose SaaS tools that are expensive when you put them all together. Some of those will collapse into one low-code platform.” (Partner, global consultancy) This last quote hits to the heart of the trend. Advancements in low-code and development practices already made the “build first” and “platform consolidation” strategy unavoidably practical. But it’s generative AI — and its killer use cases in TuringBots and low-code AppGen platforms — that has served as the accelerant for more firms to recognize these conditions and embrace them. AI-powered AppGen platforms will drain the competitive “moat” of domain knowledge encoded in off-the-shelf business apps. There are two benefits of genAI in software development that tip the scales: 1) even more speed and ease throughout the SDLC (self-evident) and 2) the infusion of business and industry “domain knowledge” through AI models into the development act. This second point is monumental. The typical remaining “moat” for many business application vendors is the “domain knowledge” and “industry best practices” encoded in their off-the-shelf software. AppGen will drain this moat. Even a vanilla large language model knows what a CRM is and how it’s put together, or what a truckload shipment process looks like, or what the airspeed velocity of an unladen swallow is. And AppGen platforms make this domain knowledge instantly available in the development act. This means you can ask the platform for the app you need and get it — like the gentleman we interviewed who generated an app for managed sea containers and their documentation. He marveled that the platform knew “his” industry! Where’s all this going? Over the next several years, these factors will lead to market consolidation as enterprises retire many of the apps in their portfolios (both off-the-shelf and custom) and replace them with bespoke, dynamic applications delivered using AI on low-code AppGen platforms. True story: a frank conversation with an enterprise software vendor. There are caveats to this prediction. Some specific app functionality is too high-risk and legally bound to be done custom by the typical firm (e.g., general ledger), some app vendors will become AppGen platforms themselves, some apps have legitimately differentiated technology that’s not easily replicated, and so on. But the many applications of the business world, which are basically collections of the same generic, fungible software components rearranged into different industry and use case patterns, is clearly under threat. And the vendors know it. To illustrate: Several months ago, we interviewed a leader at a significant software vendor. This vendor’s flagship product is an application in one of the major “three-letter acronym” enterprise software categories (such as ERP, CRM, HCM, etc.), which from here on we will refer to as “app.” In our discussion, he said: “Fast-forward five years. Building an [app] is going to be very easy. Half a dozen prompts, and something will work for you, and it’s going to be very specialized to your use case. So what is the value of our own [app] product? Or anyone’s [app] product for that matter? In 10–15 years, people won’t be buying our software. We might not even be slinging [app] anymore … that product could go to zero; we’re not going to be pulling money that way. People will be accessing that functionality through different mechanisms. There has to be the next-level step of where the value is going to be provided.” We agreed. Many trends in AI and software development point to it, and we had years of research backing it up. But outside our research, we’d never heard the point so boldly and clearly stated. So we asked, “Why are none of the software vendors talking about this?” His response: “Nobody wants to admit that their own death is coming soon.” source

Are you bullish on AI’s potential to transform the way frontline B2B marketers work? My research found that frontline marketing leaders in B2B organizations are very bullish. And according to Forrester’s B2B Frontline Marketing Survey, 2024, frontline marketing teams with advanced lifecycle revenue marketing strategies are the most likely to be AI-ready and have the highest levels of adoption. Here are some further details. Advanced Frontline Marketers Are Blazing The AI Trail My research found a high correlation between advanced lifecycle revenue marketing strategies and high AI readiness: The frontline marketing teams with the highest strategy maturity scores also have the highest levels of AI readiness and adoption. Forrester’s B2B Frontline Marketing Survey, 2024, also found that: Mature frontline marketers see the impacts of AI more clearly. Frontline marketers with high maturity scores are more than twice as likely to say that AI will make frontline marketing more productive, change the way work gets done, usher in a need to hire new roles, and lead to frontline marketing job losses. Mature frontline marketers are trained and ready to go with AI. Frontline marketers with high maturity scores are much more likely to be trained on how to use AI in their daily work, ready to use AI to create a marketing program or campaign, and ready to run a customer-facing program or campaign using AI. Frontline marketers with high AI readiness are more aligned with sales and within marketing. Ninety-one percent of frontline marketing leaders with high AI readiness say their teams are aligned with sales, compared to 72% with lower AI readiness. Frontline marketing teams in Europe are leading in the adoption of “AI for marketing” use cases. In Europe, 33% of B2B companies say they have AI use cases in production, compared to 21% in North American and Asia Pacific regions. Learn More About What Is Driving AI Readiness And Adoption In Frontline Marketing All the details about the global state of AI readiness and adoption in frontline marketing are now available in my new report, AI Is Already Changing The Game For Advanced Frontline B2B Marketers. Forrester clients can read the report and schedule a guidance session with me to discuss AI readiness, frontline marketing maturity, and lifecycle revenue marketing strategy. source

Tech leaders are being asked to do more with fewer resources available, which makes cultivating the right skills in the workforce essential. The implementation of generative AI (genAI) potentially brings productivity gains but is also a point of concern, with employees experiencing what one chief product officer at a tech workforce development company calls an “AI skill threat.” To make matters worse, the longevity of skills is decreasing as the rate of technological change accelerates. How can you ensure that your teams are equipped to support your current business needs while accounting for future shifts both internally and throughout the broader industry? A skills-based talent approach helps tech leaders pin down these moving skill targets and is dramatically more effective than traditional methods such as recruiting for specific job titles or academic credentials. Instead, identify the skills that are most important for achieving your technology organization’s current and future goals. Assess current employees’ skills to understand their strengths and any gaps that need filling. It’s also key to not just focus on skills but make sure that you align organizational needs with career paths that support employees’ desired goals. Internal skilling and mobility efforts may turn out to be a viable and cost-effective alternative to hiring and training from scratch. Implementing skills-based talent practices encourages resiliency and adaptivity in a tech org, as you will know what is needed to pivot if the current skills of the day fall out of favor with new technologies. With skills-based practices, you can adapt at greater speed and more easily size up whether existing resources are sufficient for unexpected priority shifts. My report, Skills-Based Talent Practices, provides recommendations for tech leaders looking to implement this approach in their organization. If you are a Forrester client and want to discuss further, set up a conversation with me here. source

Edge intelligence marks a pivotal shift in AI, bringing processing and decision-making closer to where it matters most: the point of value creation. By moving AI and analytics to the edge, businesses enhance responsiveness, reduce latency, and enable applications to function independently — even when cloud connectivity is limited or nonexistent. As businesses adopt edge intelligence, they push AI and analytics capabilities to devices, sensors, and localized systems. Equipped with computing power, these endpoints can deliver intelligence in real time, which is crucial for applications such as autonomous vehicles or hospital monitoring where immediate responses are critical. Running AI locally bypasses network delays, improving reliability in environments that demand split-second decisions and scaling AI for distributed applications across sectors like manufacturing, logistics, and retail. For IT leaders, adopting edge intelligence requires careful architectural decisions that balance latency, data distribution, autonomy needs, security needs, and costs. Here’s how the right architecture can make the difference, along with five essential trade-offs to consider: Proximity for instant decisions and lower latencyMoving AI processing to edge devices enables rapid insights that traditional cloud-based setups can’t match. For sectors like healthcare and manufacturing, architects should prioritize proximity to offset latency. Low-latency, highly distributed architectures allow endpoints (e.g., internet-of-things sensors or local data centers) to make critical decisions autonomously. The trade-off? Increased complexity in managing decentralized networks and ensuring that each node can independently handle AI workloads. Decision-making spectrum: from simple actions to complex insightsEdge intelligence architectures cater to a range of decision-making needs, from simple, binary actions to complex, insight-driven choices involving multiple machine-learning models. This requires different architectural patterns: highly distributed ecosystems for high-stakes, autonomous decisions versus concentrated models for secure, controlled environments. For instance, autonomous vehicles need distributed networks for real-time decisions, while retail may only require local processing to personalize shopper interactions. These architectural choices come with trade-offs in cost and capacity, as complexity drives both. Distribution and resilience: independent yet interconnected systemsEdge architectures must support applications in dispersed or disconnected environments. Building robust edge endpoints allows operations to continue despite connectivity issues, ideal for industries such as mining or logistics where network stability is uncertain. But distributing intelligence means ensuring synchronization across endpoints, often requiring advanced orchestration systems that escalate deployment costs and demand specialized infrastructure. Security and privacy at the edgeWith intelligence processing close to users, data security and privacy become top concerns. Zero Trust edge architectures enforce access controls, encryption, and privacy policies directly on edge devices, protecting data across endpoints. While this layer of security is essential, it demands governance structures and management, adding a necessary but sophisticated layer to edge intelligence architectures. Balancing cost vs. performance in AI models and infrastructureEdge architectures must weigh performance against infrastructure costs. Complex machine-learning architectures often require increased compute, storage, and processing at the endpoint, raising costs. For lighter use cases, less intensive edge systems may be sufficient, reducing costs while delivering necessary insights. Choosing the right architecture is crucial; overinvesting may lead to overspending, while underinvesting risks diminishing AI’s impact. In summary, edge intelligence isn’t a “one size fits all” solution — it’s an adaptable approach aligned to business needs and operational conditions. By making strategic architectural choices, IT leaders can balance latency, complexity, and resilience, positioning their organizations to fully leverage the real-time, distributed power of edge intelligence. source

For seniors, Medicare Advantage (MA) remains a valuable alternative to traditional Medicare, offering comprehensive, cost-effective healthcare, but policy shifts and market changes have made it harder for insurers to achieve performance and profit goals. Amid declining financial performance, insurers such as Cigna have reduced their presence or exited MA altogether. Humana, after a significant quality-rating drop, still expects MA to negatively impact its results despite a stronger Q3. Yet some smaller insurers such as Devoted Health are making advances in a challenging market, with a 4.28 average CMS Star Rating for 2025 and expansion plans in 20 states. As insurers’ strategies diverge, consumers will have fewer high-quality plan options in 2025 and will have to navigate shrinking provider networks. Building Enduring Value Through Dynamic Experiences And Relationships Higher medical utilization by members, the growing difficulty of achieving CMS Star Ratings, and reimbursement pressures are pushing many health insurers (HIs) to the brink, but HIs can persevere by delivering great experiences that achieve lasting value for members and for their organization. To build value with members, HIs will: Create hybrid experiences to build strong relationships. While digital engagement becomes more prevalent in healthcare, consumers, especially seniors, still place high value on human interaction. Health insurers can boost customer satisfaction by quickly achieving first-contact resolution in customers’ preferred channels. By leveraging these interactions, insurers can guide seniors in using digital tools while maintaining a personal touch. Health insurers that invest in hybrid experiences achieve higher customer experience scores. In turn, improved member experience can connect to better Star Ratings. Make benefits easy to find and use. In recent years, to maintain a competitive edge, health insurers have continually added benefits to MA products. Unfortunately, adding has only diluted value and led to a lot of “me too” in the market. Seniors often feel overwhelmed by or unaware of their benefits. HIs put too little effort into educating plan members and integrating their benefits. Many insurers adopt a “set it and forget it” approach, providing information only during initial sign-up and hoping members remember what they have (or don’t). To optimize the impact for both members and the plan, insurers should tailor benefits more closely to individual needs, streamline access, educate continuously in terms they understand, and improve integration of benefits. Get Tactical About Your Next Move On Medicare Advantage Forrester clients can read the report — Medicare Advantage: Golden Goose Or Albatross? — to discover the seven steps health insurers can take to enhance future plan performance. Along with these recommendations, Forrester clients will gain insights from real-world examples across health insurers, as well as from technology and service providers that support Medicare Advantage. Forrester clients can also schedule a guidance session for deeper understanding of their next move in MA. Thank you to Accenture, Florida Blue, HealthScape Advisors, Optum, Papa, Priority Health, and Soda Health for their time and contributions to this research. Tiffany Do contributed to this blog post. source

Annual marketing planning is tricky on a number of fronts, and in this blog post series, I’ve addressed a few of the main challenges that B2B marketing leaders face. In the first post, I shared an outline of a marketing planning process. In the second, I described the critical strategy, audience, product, and revenue information needed for an effective marketing plan. Here, I’ll share how that information is incorporated by the marketing planning process and transformed into the elements of a marketing plan. Setting Marketing Plan Objectives One of the most important steps in marketing planning is to make sure you’re on the same page with the sales and product functions. That’s why our marketing planning process starts with an alignment step. Use your strategy insights to confirm growth and brand objectives. Use the audience and offering information to confirm what you’re targeting, competing with, and launching. And use the revenue plan information to confirm that your audience segments are aligned with where sales intends to drive their efforts. Orienting Marketing Planning Priorities Marketing needs to select the initiatives it will execute, and it needs to balance the urgent projects that will affect the business right away with the important efforts that will yield value in the long run. Marketing can’t do everything, and every marketing plan is a trade-off between near-term initiatives such as new product launches, competitive response actions, and demand efforts and longer-term priorities such as developing new partner programs, brand efforts, and shifting focus to customer retention. Determining Marketing’s Definitions For Success For a marketing organization to show that it is delivering value to the business, it must set objectives and meet them. From strategy, marketing can call out business and corporate initiatives and establish metrics that will show how they have been met. From the audience and offering information, define market penetration and retention objectives and set contribution and engagement goals aligned to the revenue plan. Capturing Dependencies And Risks In The Marketing Plan The marketing organization is an important cog in the corporate gearbox, but to yield overall value, the other gears must work, too. In many cases, marketing plans are built on the assumption that other parts of the business are meeting their commitments. If achieving success with planned initiatives depends on planned investments, infrastructure, product availability, access to segments, and sales priorities, then they should be called out. Using The Plan On A Page To Guide Marketing Initiatives A good marketing plan should be a combination of a communications tool and a step-by-step outline of what marketing will do over the coming year. At Forrester, we help our clients take the output of their planning activities and produce a streamlined marketing plan in a template we call the B2B Marketing Plan On A Page. It’s a direct output of the marketing planning process that we use for: Communication. It’s a powerful tool for sharing a marketing plan with the CEO, laterally with the peer C-suite, and inside the marketing organization with the marketing leadership team and down. Cascading. The Marketing Plan On A Page can be extended to cascade into campaign, geographic, distribution channel, and business-unit plans. We don’t recommend cascading it into marketing team-level plans, though, because that risks losing the synergy of alignment to specific business objectives. Operating model. The Marketing Plan On A Page is an excellent starting point for ensuring that the overall marketing operating model is aligned. It helps with validating the structure and focus of shared services and centers of excellence. In these three blog posts, I have outlined a marketing planning process, identified key information that you need to develop a marketing plan and where to find it, and described how this information can be distilled into an easily readable form using the B2B-Marketing-Plan-On-A-Page Template (client-only; non-clients can access a version here). source

What’s keeping you from hitting your revenue goals? We’ve heard from many B2B clients that they’ve been qualifying opportunities for sales but that these deals aren’t progressing through to closed-won. This isn’t a sales problem — it’s a business problem, one that all revenue-responsible teams must help tackle. B2B buyers interact with all manner of channels when they are in an active buying cycle. But demand teams are often in the back seat once sales has accepted an opportunity — sometimes going completely hands-off unless a seller or account manager asks for support. Instead, as in any opportunity stage, marketing and sales need to work together to help buyers buy. But it’s not as simple as just continuing to treat buyers the same as before. Demand programs for pipeline acceleration don’t exist in isolation and can’t be expected to succeed without active involvement from sales. To support pipeline deals, B2B marketers need to change their approach in fundamental ways: Don’t launch programs unless sales data is in order. Adaptive programs depend on signals, and insights from sales are of utmost importance in active opportunities. Even before the point of sharing and acting upon the insights that come from sales conversations, cross-functional coordination is impossible without sellers following an entrenched data management process to update stages, buying group members, deal values, and expected close dates. Make sure that pipeline management is solid before scaling or automating active deal support. Let the buyer be your guide. Revenue-responsible teams must seek to understand pipeline progression through the buyer’s eyes. Without this common understanding of what buyers are going through and what causes them to progress or stall in the pipeline, frontline marketers won’t be able to effectively support and influence the purchase decision — they’ll either be flying blind or following orders from sales (who often don’t realize everything that frontline marketers can offer). Instead, examine pipeline trends and historical data to prioritize areas of focus, and work with sellers and account managers to understand the buyer’s process, concerns, and preferences. Recognize that progression is impacted before opportunities enter the pipeline. Purchase decisions don’t begin once sales is involved — buyers have been researching and considering the potential value well before requesting a proof of concept or demo. Activation and validation programs are just as crucial as pipeline acceleration programs to build and support pipeline strength over the long haul. Set expectations, goals, and timelines appropriately so that you invest focus and resources where it will pay off the most in customer lifetime value. Forrester clients can access our latest guidelines and recommendations for adaptive programs to support pipeline progression in our new report and work with us to get personal recommendations and hands-on support by booking a guidance session. source