2026 marks a turning point for technology, media, and telecommunications. The AI revolution has moved beyond hype into habit, transforming how companies create, deliver, and capture value. The lines between software, data, and intelligence are blurring fast, forcing organizations to rethink not only what they sell, but how they operate, price, and grow.
From AI becoming invisible to data becoming the defining asset; from outcome-based pricing to the rise of integrated platforms and augmented workforces, these are the trends shaping the next era of intelligent growth.
The normalization of AI: From excitement to expectation
AI has become embedded in everything. It’s no longer just a feature or a layer; it’s the connective tissue between software, data, and intelligence. And here’s the twist: the most exciting thing about AI right now is that it’s becoming... unexciting.
That might sound strange, but it’s actually a sign of progress. We’re moving past the novelty stage and the “wow” moment. Instead, we’re starting to see AI as something that just is. It’s part of our workflows, our tools, our conversations, blending into the background of our digital lives.
What once seemed like advanced intelligence is now expected. And that’s a good thing. Because while no algorithm will ever replicate the depth or intuition of the human mind, AI’s growing normalcy signals a shift: it’s no longer a futuristic concept to be challenged, but an everyday capability to be harnessed. The winners will be those who create value so natural, we stop noticing it’s powered by AI at all.
Data: From volume to value
As AI becomes embedded in everything, the real question isn’t who has AI anymore, it’s who has something worth teaching it. In the next wave of competition, two dimensions will define success:
What data do you have? Not how much, but how specific and unique it is.
How do you use it? How effectively can you ask questions of your AI, interact with it, and apply it to real-world decisions?
A growing number of companies are waking up to the fact that their greatest untapped advantage isn’t their model, it’s their data. Some are building proprietary datasets through daily interactions with customers, platforms, and supply chains. Others are learning, through trial and error, how to capture and refine the signals buried within their operations.But here’s the catch: even companies that have always sold data are struggling to unlock its full value. They’ve treated it as a product, rather than as the foundation for continuous learning. The real opportunity lies in compounding that data: filtering, contextualizing, and refining it until it becomes a living, evolving corpus that fuels an organization’s intelligence.
Most businesses don’t think of themselves as data sellers, and they don’t need to. What’s changing is that data will increasingly be sold indirectly, as part of the intelligence that powers their offerings. Whether through personalized recommendations, predictive insights, or autonomous decision-making, data will become a core source of competitive differentiation.
Pricing in the age of AI: Technology outpaces human behavior
Even as AI reshapes how value is created, the way companies price and sell that value is evolving more slowly. Many customers still expect, and prefer, traditional user-based models. Investors, meanwhile, are pushing companies to move faster toward usage- or outcome-based pricing. The tension between these forces will define much of the pricing innovation we see in 2026 and beyond.
The future of pricing may be outcome-based, but the present still belongs to the user. Customers have buying habits shaped over years, sometimes decades, and not every market is ready to abandon them. For example, in industries like education technology, where user counts are tied to students rather than employees, the user-based model still fits naturally.
The challenge for vendors is to manage this transition at the pace their customers can accept. Technology evolves at a technology pace; human behavior changes at a human pace. Companies that leap too far ahead, introducing radical outcome-based models without market readiness, risk alienating their customers. A stair-step approach is often more effective: moving from user-based, to usage-based, and eventually to outcomes once trust, data, and adoption catch up.
Predictability or performance? From inputs to outputs to outcomes
Within customer organizations, perspectives on pricing differ. CFOs prize predictability – a pricing model that balances visible ROI with budgeting certainty. Sales and product leaders, on the other hand, are looking for models that reflect the true value created by AI-driven efficiency. This creates a natural tension: as AI increases productivity, the “license count” that once underpinned software pricing is likely to shrink.
Some companies are experimenting with new usage metrics, measuring things like time active, compute consumption, or even AI tokens processed. But as AI becomes more efficient, these measures will lose their power. The better your model gets, the less your customers will pay under traditional usage structures. Efficiency, ironically, erodes the pricing base.
Many companies are now climbing the ladder from input-based pricing (e.g., compute cycles or data processed) to output-based models (e.g., customer interactions completed, documents summarized). True outcome-based pricing, charging based on results like customer satisfaction or sales conversion, remains aspirational for most. Once part of your revenue depends on metrics outside your control, you introduce a new kind of volatility.
That’s why the intent behind outcome-based pricing should be partnership, creating shared incentives to deliver measurable success. This only works when both sides see the relationship as mutually accretive, not adversarial. If customers see outcomes as a way to pay less for underperformance, the model breaks.
Platformization: The shift from point solutions to integrated ecosystems
As technology budgets tighten and buyer expectations rise, organizations are increasingly rejecting fragmented “best-of-breed” stacks in favor of integrated platform ecosystems. The age of point solutions is giving way to platforms that deliver end-to-end value, combining multiple capabilities, seamless data flow, and unified customer experiences under one roof.
This shift toward platformization is being driven by both economic and strategic logic. On one hand, buyers want more value from fewer vendors. On the other, vendors recognize that platform adoption allows for deeper integration, broader data visibility, and stronger retention. A unified platform consolidates costs and creates feedback loops that generate continuous improvement.
When data flows across functions (sales, marketing, customer success, product) companies gain richer insights into how customers engage, where value is created, and where to intervene. That intelligence fuels smarter pricing, targeted cross-sell, and higher customer lifetime value.
For vendors, success will hinge on packaging and pricing strategies that encourage customers to expand across the ecosystem. The more a customer uses, the more valuable and stickier the relationship becomes.
Key takeaway: 2026 is the year intelligence compounds
Across all these trends, a unifying theme emerges: AI is no longer the differentiator. Data, integration, and execution are.
The future belongs to companies that:
Treat data as a living, compounding asset.
Price intelligently, in step with customer readiness.
Build connected ecosystems that create value across every interaction.
At Simon-Kucher’s technology, tedia & telecommunications (TMT) practice, we help companies translate these trends into action. Let’s shape the next wave of intelligent growth together.
