Graph and AI Trends 2026 Why an AI Is Up and Running But Not Delivering

Related Vendors

neo4j

Wuxi InfiMotion Technology Co., Ltd.

95% of all AI projects in companies still do not deliver what is expected from them. AI systems require onboarding like new employees and often fail due to a lack of context. A look beyond the hype and an outlook on what really works.

After four years of AI hype, many electronics developers are asking a central question: Where is the ROI in edge AI projects? AI chips are expensive, inference runs are unstable, and classic embedded architectures are reaching their limits. The graph experts from Neo4j highlight which trends in 2026 could finally make AI productive in embedded systems.

AI Reality Check: Scaling in Focus

The feedback from companies speaks for itself: Most AI projects are not yet delivering what was expected of them. According to an MIT study, 95% of pilot projects yield no measurable results. Gartner predicts that 40% of agentic AI projects will fail by 2027—hindered by costs, unclear ROI, and unresolved risks. This highlights the GenAI paradox: General AI tools and assistants can be deployed quickly but bring hard-to-measure ROI effects. In contrast, truly value-creating, vertically integrated AI systems are making their way into companies only with great difficulty.

Talking about AI frustration is still premature. The end-user hype around ChatGPT, Copilot & Co., and not least the enormous investment sums of tech giants, have simply raised expectations that collide with reality in companies. Here, AI systems need to be deeply and securely integrated into existing processes, data structures, and IT landscapes. This integration takes time and adjustments. Even AI can only accelerate this process to a limited extent. Additionally, AI is experimental: Many prototype projects must fail in order to reveal which approaches work in the long term. The actual scaling is yet to begin.

AI Agents: The New Trainees

AI agents particularly highlight this paradox. In practice, there is little evidence of autonomous "agent armies" replacing entire departments. Most systems operate hidden in the background, primarily handling time-consuming research tasks, for example, in law, compliance, or medicine. While the majority of companies are experimenting with AI agents according to McKinsey, only 23% actually deploy them in a productive area, and in no function does the share of scaled agents currently exceed around 10%. This shows that their utility is highly context-dependent, and their productive use is narrowly limited. Companies must first ignore the AI hype and soberly identify where agents can create genuine impact.

This iterative approach is justified because AI remains unreliable. This is usually not due to the "lack of intelligence" in the models. Rather, context and instructions were not conveyed clearly enough to guarantee relevant and reliable results. For functional integration, agents require a kind of onboarding: they need to be trained, informed, monitored, and regularly corrected. Since they work probabilistically, they do not necessarily produce the same results even with identical inputs. Validation requires testing, feedback, and review processes. However, this involves effort and is only scalable to a limited extent.

This raises another question beyond technical implementation: How can AI agents be integrated into existing workflows, teams, and corporate culture? Companies must not only invest in the training and setup of AI agents but also in that of their employees. In the future, they will be the ones validating the results of their AI colleagues and understanding the underlying model limitations. This requires rethinking work models, including clear governance, new roles, flatter structures, and especially well-defined responsibilities.

Context Engineering And the Information Architecture for AI

AI is only as good as the context it receives, even in agentic, iterative architectures. However, it often receives too little, too much, or too imprecise input. When prompting, many think of direct instructions. In real applications, however, the system's actual task is to dynamically shape the context so that the LLM receives exactly the information it needs for the next step.

LLMs function in some ways like human working memory: they retain the beginning and the end, but lose track in the middle, as a Stanford University study shows. Long context leads to errors, inefficiencies, and decreased focus (Context Rot). Models become confused when too many or very similar tools are used (Context Confusion). Or they stumble over contradictory work steps (Context Clash). Although the models could theoretically process vast amounts of context, practice shows that the more loaded into the context window, the less reliable the results.

When it comes to AI context, as is often the case in data processing: quality over quantity. Models have only a limited attention span. Every additional context element consumes part of it and dilutes the essential. Well-curated context, known as context engineering, is thus becoming a prerequisite for reliable AI.

Push Vs. Pull: Data On Demand Instead of In Stock

With the advent of agent systems, the way AI accesses information is changing. While earlier approaches like Retrieval-Augmented Generation (RAG) operated on a push principle, a pull principle is now taking hold. Instead of gathering information in advance and feeding it to the model, the AI now decides for itself what information it lacks and retrieves it purposefully using tools. Instead of an information avalanche, genuine information selection emerges.

This means AI is increasingly taking on an organizational role: it analyzes tasks, identifies work steps and necessary information, and selects tools or data sources to fill these gaps. It becomes the coordinator of information procurement, which aligns with the capabilities of the language model. For companies, this means thinking like an information architect. The key is not the quantity, but the correct dosage: the principle of "Minimum Viable Context" (MVC). The AI should receive exactly the information it needs for the next step.

Graphs Are the Navigation System for AI Agents

Which information the AI needs in the next step depends heavily on the specific use case: sometimes deep, linear context chains; sometimes broad, branching knowledge structures; clusters of relevant information; or just a single precise excerpt. This is precisely where traditional data structures begin to falter. Graph databases offer a structurally different approach. Forrester refers to this as the backbone for LLMs to represent, capture, and provide context.

Especially in conjunction with AI agents, graphs will take center stage in 2026. As AI systems increasingly coordinate decisions, tools, and processes independently, they require robust and transparent context models. Graphs link knowledge, actions, and interactions in real time, making agents navigable, verifiable, and scalable. This creates a semantic information layer (Knowledge Layer) that not only enables more precise answers but, above all, fosters agents that understand where they stand, what they are doing, why they are doing it, and what the implications of the next step are.

The Database of the Future is Adaptive

Databases and data infrastructures are thus becoming a pivotal point for AI success. After four years of AI hype, it is becoming increasingly clear: While hardware and models are advancing into new dimensions, the databases beneath them are still rooted in 1970s thinking. AI systems are expected to deliver peak performance but operate on architectures that were never designed for them. The central question is no longer how databases can be improved, but what a database built for AI looks like.

The next-generation AI database could, for example, function similarly to "live code." Queries are iteratively rewritten and optimized during execution, inspired by modern compiler designs like Just-in-Time (JIT) techniques. The execution plan continuously adapts to data distributions, load patterns, and the available hardware. This creates a permanent feedback loop where the database becomes more efficient with each iteration, even as complexity and data volumes grow. This dynamic architecture forms the foundation for the Knowledge Layer that AI agents will need in the future. (heh)

Smart-Factory

Access solutions for the Smart Factory - Industry 4.0