bbv KI Webinar - Review/Outlook 2025/2026

Review 2025 and Outlook 2026: AI Between Convergence, Platforms and Sovereignty

The review of 2025 serves not chronology but orientation: which forces are actually shaping the market, and which developments are plausible for 2026? Rather than individual product announcements, the focus is on recurring patterns: how is the performance dynamic of models changing? Where does differentiation take place? What does this mean for costs, integration and dependencies in organisations?

2025 as the Year of Model Convergence

At first glance 2025 looks less spectacular because classic benchmarks barely show major jumps any more. This is less a sign of stagnation and more an indication that many established measuring sticks have been "maxed out". Progress is shifting: not breaking the next percentage points dominates, but efficiency, smaller models, lower resource requirements, stronger performance per compute unit and increasingly smartphone-capable variants.

"The challenge is no longer to crack the benchmark, but to reach it with ever smaller, more efficient models."

At the same time a subjective effect emerges: for everyday tasks, progress often feels like stagnation because many standard tasks are already "good enough" solved. Measurably, capabilities continue to rise, but increasingly in niches that only become noticeable on domain-specific tasks.

Business Perspective: Model Choice Becomes Secondary, Platform Choice Central

When models converge, the question "which model is the most intelligent?" loses significance. More relevant criteria become context length, cost structure, availability, compliance, data flow and above all integration capability. Differentiation thereby shifts clearly away from the model core toward the platform: interface, search and retrieval components, memory, vector databases, governance, transparency and agentic workflows.

"The great differentiator is no longer the model; it is the platform around it."

Many functions that in daily use feel like "model capabilities" (web search, source display, document retrieval, memory) are in practice platform functions. This means: perceived progress increasingly arises from productisation and ecosystem rather than from raw model intelligence.

USA vs. China: Infrastructure Bet Against Open-Weight Strategy

A second leitmotif of 2025 is the rivalry between the USA and China, visible through two contrasting strategies. In the USA enormous infrastructure investments dominate: data centres, training and inference, with the bet that more compute leads to better market position. China relies more on open weights: models and research are made broadly available, accelerating innovation at scale and generating price and efficiency pressure.

"Intelligence is getting cheaper, not necessarily because everything becomes cheaper, but because performance per dollar is rising strongly."

This dynamic lowers costs for all market participants, accelerates open-source ecosystems and simultaneously shifts scientific influence. For organisations this means: options are becoming more diverse, and architecture decisions are gaining ground over provider loyalty.

2025: Promises Are Technically Redeemed - and Generate "AI Slop"

In several modalities 2025 feels like a year of maturity: text intelligence, large contexts, image generation and increasingly video deliver results that in many cases no longer stumble against clear technical limits. At the same time, the volume of generated content is rising faster than its quality. This produces "AI slop": content produced without care and published without verification, with the risk of feedback loops in which AI references other AI content as seemingly legitimate sources.

"The thinking belongs to people; the AI can do the work."

The same mechanism shows up in code: the bottleneck shifts from writing to reviewing, securing and classifying. When a great deal of code emerges very quickly, the risk of unverified adoption and security gaps grows, even when the underlying technology is impressive.

Outlook 2026: AI Value Arises Through Process Change, Not Through the Next Model

For 2026 what is taking shape is less "new model magic" and more an implementation question: the capabilities are there. What will be decisive is whether organisations are willing to adapt their working methods and processes. Experience from software development shows a typical pattern: in the early adoption phase a productivity trough first arises (tool selection, new workflows, new roles), before significant acceleration becomes possible with consistent adaptation.

What is essential here is a shift in role: less "writing code" or "generating output", more "specifying requirements", "providing context", "reviewing results" and "structuring systems so that agents can work reliably".

2026 as the Year of Integration

In the private sphere AI often feels frictionless because users are already working in integrated ecosystems. In organisations that is rarely the case: CRM, ERP, databases, specialist applications, legacy systems and individual process landscapes cannot simply be transferred into a single provider ecosystem. Integration therefore becomes the core condition for AI to genuinely create value in business.

Without integration, AI remains "a chat window next to reality". With integration it becomes part of workflows: data access, tool access, permissions, auditability, approvals and traceable intermediate steps.

MCP as a Standard: Connecting AI Platforms and Business Tools

The Model Context Protocol (MCP) is establishing itself as the mechanism for connecting AI clients (platforms, IDEs, interfaces) with MCP servers (tools, data sources, business systems). The difference from classic APIs lies not in the existence of an interface but in the fact that the interface is conceived agentically: capabilities are describable and usable by models, including interaction patterns such as clarifying questions or human-in-the-loop.

"Not every organisation has to build the interface. An organisation can also deliver value purely as an MCP server."

This also changes business models: value can be created by offering capabilities in a way that allows users to consume them from their preferred AI platform, rather than necessarily pulling users into a proprietary UI.

2026 as the Year of the Login: Lock-In Arises Where Investment Is Made

Because models are becoming more interchangeable, lock-in is shifting to the platform and implementation level: chat history, memory, audit logs and above all the investments in data projects, curated knowledge bases, agent workflows, governance and integrations. The more that is "built" in a platform, the higher the switching costs become.

The greatest lock-in arises not through the model but through your own work that has been poured into platform and process structures.

Sovereignty on Three Levels: Data Centre, Platform, Business Integration

Sovereignty cannot be reduced to hosting. Relevant decisions concern at least three levels:

1. Data centre / operations: where do models run, where does data reside physically and legally?
2. AI platform: who controls memory, audit logs, governance, agentics and extensibility?
3. Business integration: which systems are connected, which data flows arise, how controllable are they?

Open-source options and local providers can play a role at every level. What is decisive is that requirements (data protection, sector regulations, operating model, transparency, extensibility) are made explicit and implemented consistently across all levels.

Concluding Line for 2026

2026 will be interesting less because of the "next model bang" and more through implementation: integration, clean data work, agentic workflows with governance and the willingness to adapt processes and role definitions. The technology provides the building blocks. Value arises where those building blocks are embedded in the system landscape and in everyday operations.

"Don't be dazzled: the thinking belongs to people; the AI can do the work."