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Key takeaways:
Software demand continues to grow. At the same time, enterprise buyers are shifting, increasingly considering solutions and platforms that can deliver measurable outcomes and built-in AI over standalone, seat-based SaaS software.
Platformization is a credible business strategy and a path to rebuild the moats SaaS once provided. A PwC survey shows that some leading companies are already investing in platformization—first to scale, then to monetize and create new customer value, with efficiency as a byproduct.
The execution gap for platformization is multifaceted, not just tech-related. Organizational hurdles like stakeholder alignment and process change pose challenges. Legacy integrations and capability gaps across ecosystems and engineering are often key barriers to scaling.
Platformization advances in three waves: Generating momentum through new monetization models, evolving standalone products into platforms, and reinventing the operating model. Organizations that understand where they are today can better align investments and capabilities to position the business for its next stage of growth.
Platformization emerges as key strategy
The SaaS moat is no longer sustainable. Many public SaaS companies face declining valuations while more vertically focused AI- and cybersecurity companies continue to command premiums. Meanwhile, overall demand for software keeps rising. What’s changing is how the market evolves and how buyers reallocate capital in line with new user expectations. Seat-based pricing is eroding as AI agents alter deployments across human users and buyers increasingly express the desire to pay for measurable business outcomes.
Platformization is emerging as a compelling business strategy and organizational response as LLMs quickly evolve into a commoditized marketspace. The value of next-generation software in the enterprise is increasingly about more than having the “best AI.” It’s also based on how embedded you are in the customer’s business, how integral your solution is for the customer’s core business—either on the product side or in operations, or both. To thrive, companies should aim to become key to their customers’ mission-critical workflows, backed by intelligence that your platform can uniquely generate and deliver.
Why platformize now?
In a recent PwC survey of about 200 software and technology executives, respondents mainly defined platformization as the shift from standalone products to a single connected experience built on a modernized, flexible platform—so the company, its customers, and its partners work from the same shared infrastructure.
Many companies are making moves now. More than half (55%) of respondents report making significant investments in platform capabilities. The draw is mainly operational: 62% rank scalability, efficiency, and cost leverage among their top three reasons to invest, making it the single most common driver.
But the ranking tells only part of the story. While buyer experience places third overall, 23% of executives name it their No. 1 reason to invest—more than any other single priority. Companies often begin with scale and operational leverage, but the larger, more compelling opportunity is new customer value and monetization.
Companies appear to be responding to three structural shifts that are reshaping how software gets bought, built, and sold—which is making platform models increasingly hard to avoid.
The market is growing, and capital is being reallocated. Enterprise spending is reorienting toward AI, cloud and security infrastructure, with server spend alone projected to grow 36.9%. Buyers are also rejecting rigid per-user (PUPM) lock-ins in favor of usage- and consumption-based pricing. Mature application categories now face pressure to prove business outcomes.
Expectations for enterprise technology are increasing. Buyers now expect more from software that enables their work. They are increasingly seeking flexibility and solutions that move from solely delivering an analysis to delivering insight inside the workflow itself, something that can be difficult for standalone tools to provide.
The barriers to entry are falling. Agentic tooling has slashed the cost of building orchestration, distribution, and automation layers. One result can be the “micro startup,” new entrants that reach platform-scale economics earlier in their life cycle, raising the competitive stakes across the market.
Together, these shifts help explain why platformization has become increasingly relevant, as companies seek a path to business model reinvention. But also, agreeing that platformization should happen is the easy part. The harder question is often how to build or integrate a platform-based value proposition—and how to turn it into a revenue-generating engine at scale. That comes down to which choices to make, how to build in AI and how far to push toward reinvention in the go-to-market (GTM) and operating model.
Where companies are placing their platform bets
Building a proprietary platform or migrating existing products to a platform model is the most common approach (55%) reported by respondents to the PwC study. For good reason. It can offer greater control and differentiation.
But there’s an execution gap. Building your own platform can be a highly product- and engineering-intensive route. And yet product and engineering (48%) and partner and ecosystem management (49%) top the list of areas least aligned to support platformization at scale. That mismatch may be why platform programs often stall between stages.
For the roughly 45% who say they’re not building, platformization still pays off—as a buying strategy. Consolidating on fewer, connected platforms beats stitching together disconnected tools, each with its own costs, data access, and security exposure. One platform means one place to manage risk, limit data exposure, and keep AI spending predictable.
Whether they build or buy, companies face a similar test: defining and executing on a coherent platform strategy, and that increasingly depends on how AI is infused in the platform itself.
How companies are integrating AI into platform strategy
Executives have been viewing AI first as a path to run leaner. In our PwC survey, 73% of respondents point to operational optimization as the main role of AI in platformization, ahead of every other use. AI is used widely to cut costs and enhance existing processes but less so to monetize data (e.g., data as a service) or to help drive growth through offerings with new monetization models.
The sustainable competitive advantage, then, will not likely come from just using AI. Nearly all organizations already have some form of AI active in operations. Buying into AI that generates incremental insights and reduces a few steps in workflows within existing business applications is unlikely to be transformational. Companies often apply AI to the same areas. Menaingful ROI will likely come from the areas they've overlooked: the product portfolio, the core customer value proposition, and the monetization model.
Three waves define platformization
The PwC survey shows a clear progression in how platformization takes hold, defining three waves. Companies are furthest along in efficiency, product innovation, and customer retention, with roughly three-quarters saying these efforts are scaling or fully embedded. Monetization lags. Expanding monetization opportunities and data monetization remain in early or exploratory phases for most but can hold substantial opportunity.
Platformization is often discussed as a technology initiative, but in practice it should unfold as business transformation. The opportunity begins by finding new ways to monetize existing offerings, then evolving products into platforms, and ultimately reinventing the operating model around platform-based value creation. The three waves provide a practical way to assess progress and identify the next set of priorities.
Importantly, the waves are not defined by tech adoption. Instead, they reflect a progression in how software companies create value. The framework helps leaders assess where they are today, what capabilities they require next, and where to focus investment.
Wave 1: Generate momentum through monetization. Organizations generate momentum by repackaging existing offerings to create new monetization opportunities. Rather than waiting for large-scale transformation, companies can unlock value using their current products and services, augmented where appropriate with AI-enabled capabilities. This approach departs from the conventional “cost first, growth later” model, prioritizing growth and monetization from the outset.
One measure of success is creating near-term commercial value from existing offerings. The key challenge is proving that new monetization opportunities exist and building the momentum required to justify deeper product transformation.
Wave 2: Reset the product roadmap. After generating momentum through new monetization opportunities, software companies should turn their attention to deeper product transformation. This wave is about product re-platforming by rethinking product roadmaps, redesigning offerings around platform-based architectures, and building the capabilities required to create value at scale.
Rather than simply layering AI onto existing products, leaders should tackle a more fundamental question: What tasks are customers trying to accomplish and how can the product help them achieve better outcomes? As AI changes how work gets done, the opportunity shifts from delivering standalone features to enabling holistic business outcomes through interconnected, platform-enabled experiences. Organizations that embrace this shift can create a foundation for new offerings, new business models, and sustained growth.
Success is measured from proving value and scaling it. The challenge is often evolving products and roadmaps into platform-based models that can support new capabilities, new business models, and sustained growth.
Wave 3: Reinvent GTM and the operating model. Organizations move beyond product transformation to reinvent how value is created, delivered, and captured. AI is no longer an enhancement to existing offerings but an embedded part of the customer outcomes, the go-to-market model, and the operating model itself. Platforms are managed as products, ecosystems, marketplaces and partners become critical growth engines. This is where platformization begins to reshape the organization holistically.
Success in Wave 3 is measured by an organization’s ability to embed platform thinking across the enterprise, turning platformization into a sustainable competitive advantage. The challenge is readiness: aligning talent, ecosystems, partnerships, and GTM models around platform-based value creation at scale. Key capabilities —product and engineering leadership, ecosystem management, and partner enablement—are often the least mature. Acquisitions can help close gaps, but the risk lies in moving faster than the organization can adapt.
The waves are intended to help leaders assess where they are today and what comes next. Organizations generating new revenue from existing offerings are likely operating in Wave 1. Those undertaking product re-platforming and roadmap transformation are entering Wave 2. Companies redesigning their operating model, ecosystem strategy, and go-to-market approach around platform-based value creation are progressing into Wave 3. While organizations may advance across multiple waves simultaneously, the framework provides a practical guide for prioritizing investments, capabilities, and management attention.
Moving beyond the SaaSpocalypse
Platformization is becoming the next growth play for software companies but it can be difficult to execute.
That gap closes only with discipline—an honest read of which wave a company occupies and the commitment to build the foundation each wave requires before reaching for the next. The software companies most likely to command premium valuations may not necessarily be those with the best AI. They’ll likely be the ones embedded in mission-critical workflows, generating multiple revenue streams, and delivering outcomes customers struggle to replace.
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