Artificial intelligence (AI) is no longer a separate “innovation project” and is becoming an operational layer of the business: it affects costs, the pace of product delivery, security and the quality of decisions. From a CEO/CTO perspective, the key question is no longer “should we implement AI”, but “where should AI deliver a measurable result and how do we avoid walking into the risks”. A good framing of this comes from the IBM report for 2026, which collects expert predictions for the coming year. The conclusions are practical: in 2026 the winners will be companies that build systems based on AI (not individual models), take care of data quality and permissions, and shift the emphasis from “scaling everything” to efficiency.
Spis treści
Compute efficiency will replace simple scaling
The market has matured into an uncomfortable truth: compute is not infinite, and the cost of “an even bigger model” increasingly fails to make economic sense. Forecasts for 2026 strongly emphasize a turn toward hardware and architectural efficiency: alongside GPUs, specialized accelerators, inference optimizations (for example quantization) and running smaller models wherever they are good enough will matter. For the business this has a simple logic: if you can get 80-90% of the value at 20-30% of the cost, your advantage comes not from “the biggest AI” but from the best designed system and process.
In Web Systems project practice this means: from the outset we plan variants for running AI depending on the requirements (latency, privacy, unit costs), instead of pushing one model into every case. In e-commerce, the key scenarios (search, recommendations, handling inquiries, automating descriptions) can be delivered so that your margin does not depend on infrastructure costs. If you want to see how we approach building production solutions, take a look at our projects.
The advantage comes from systems and orchestration, not from “picking one model”
One of the most important shifts in thinking for 2026 is moving the emphasis from models to systems. Models will increasingly be “a commodity”, and the real difference will show up in how you combine them with tools, workflows and data inside the company. Orchestration covers routing between models (a smaller one for simple tasks, a larger one for hard ones), integration with search and knowledge bases, cost control and mechanisms for verifying results. In short: you stop buying “AI” and start building a production line for decisions and automation.
This is exactly the point where companies most often lose time and budget: they test tools but do not design workflows. At Web Systems we step into the role of integrator and architect: we care not about whether the model “answers nicely”, but about whether it can complete a task within a process, with the right permissions, logging and the ability to audit. For a decision maker, the end result is what counts: shorter handling times, fewer errors, better conversion, faster rollout of product changes.
AI agents move from “assistants” to teams that do the work
Forecasts for 2026 strongly amplify the topic of agency: instead of single assistants, we will see teams of agents that plan, split up tasks and carry them out in different environments (the application, the admin panel, the repository, analytics tools). From a business perspective this is not about the “wow” effect, but about a new model of work: a human defines the goal and the acceptance criteria, and the agent system handles the intermediate steps, asks for approval at checkpoints and reports the result.
This has enormous consequences for e-commerce and B2B platforms: an agent can, for example, prepare campaign variants, generate content consistent with brand policy, test changes against historical data and finally launch activities in marketing tools. Such a scenario is realistic only if you have well-organized integrations and access rules. Otherwise the agent becomes a risk rather than an advantage. That is why in Web Systems projects we treat agents as system components: we design their permissions, scope of action, activity logging and “human-in-the-loop” mechanisms, instead of counting on AI to “somehow manage”.
Processing documents and company knowledge will become modular and cheaper
In 2026 the move away from the monolithic “drop in a PDF and wait” toward modular processing pipelines is expected to accelerate: one model for tables, another for images, another for text and metadata. This modularity lowers cost and raises quality, but above all it gives the company control over sources and the “lineage” of information. For the board this matters in two places: in the speed of decision making (because knowledge is available and organized) and in risk (because it is easier to prove where a given answer came from).
This is the practical foundation for tools such as “AI for sales/support/purchasing” that really work, because they are based on organized data with proper permissions. At Web Systems we start such projects by mapping the sources (CRM, CMS, e-commerce, documents, files), and then we build processes for indexing, versioning and access. Without this, AI starts to “hallucinate” not because the model is bad, but because the organization has not prepared the data layer.
Security, data sovereignty and IAM will become a precondition for rollouts
IBM puts a strong emphasis on the topic: private and secure deployments with an expectation of real ROI, plus growing risks related to data leaks, prompt injection and losing control over information. This is not “a problem for the security department” – it is a problem for the board, because it touches business continuity, legal liability and reputation. In 2026 it will become standard to require that AI works on data with permission control, in an environment consistent with company policies, and that results are verifiable.
In practice this means the need to put IAM (Identity and Access Management) in order with agent actions and automation in mind. If an agent is to perform tasks in company systems, it has to be treated as a new type of “user” with clearly defined roles, limits and monitoring. This is an area where many companies will painfully collide with reality, because their systems grew over years without a coherent access architecture. Web Systems designs solutions so that security is not “a brake at the end”, but a built-in part of the process: from integrations, through event logging, to control over input and output data.
Open source and interoperability will accelerate, but governance will be mandatory
Forecasts for 2026 indicate that open source in AI will keep growing and diversifying globally, and that the advantage will come from interoperability standards (including agent-to-agent communication and shared “cards” describing tools and resources). At the same time, the pressure for tougher governance is expected to grow: security audits, transparency of data pipelines, more mature release practices. For business this is good news, because it reduces the risk of vendor lock-in and gives more cost options, provided you are able to manage it.
From the Web Systems perspective this is a natural direction: we prefer solutions that give the client control and room to grow without burning the budget on licenses, while keeping production-grade quality. Choosing open source does not release you from responsibility for architecture, testing and maintenance – quite the opposite, it demands discipline. That is why in conversations with decision makers we focus on the operating model: who maintains it, how we measure quality, what the update process looks like and what we do when requirements change.
How to approach AI in 2026 without burning the budget
The most sensible strategy for 2026 is less “spectacular” and more operational: pick a few processes that have a clear cost and a clear effect, then implement AI in a controlled way, with metrics and limited risk. In practice a staged approach works: first integrations and data, then orchestration, and agency at the end. If you try to “build a super agent” right away, you usually end up with a demo that never makes it into production.
Web Systems has been working in this model for years: we design digital solutions so that they can be developed, measured and maintained. If you want to approach AI pragmatically, start with a conversation about what is supposed to improve (time, cost, quality, conversion), and only then choose the technology. Explore our offer and see how we combine e-commerce, applications and integrations into coherent systems that can be safely “equipped” with AI.
What this means for e-commerce and digital products being built today
In e-commerce, AI in 2026 will be standard in the areas that directly affect results: personalization, search, content automation, customer service, behavior analysis and support for operational decisions. The difference is that the advantage will go not to those with “the flashiest AI”, but to those with the most coherent system: data, permissions, processes, monitoring of quality and costs. The same applies to web and mobile applications: AI will become part of the product, but only when it is built into the architecture and the UX rather than glued on at the end.
If you are planning product development in 2026, a sensible step is an audit: where AI makes sense, what data is available, what the risks are, how to measure the effect. At Web Systems (a company from Łódź) we run such work in a way that is geared toward management decisions and pragmatic rollouts, without a “laboratory” approach. If you want to move from trends to an action plan, write to us and we will come back with a proposed scope, priorities and realistic success criteria.
