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If every time someone mentioned artificial intelligence in our surroundings a bell went off, we’d be treated to a constant symphony of ding, ding, ding!
But are those enthusiastic dings just a noise? Far from it.
Most companies today say they use some form of AI, but that doesn’t mean AI has already changed how work actually gets done. According to McKinsey’s State of AI 2025 survey, about 88% of organizations use AI1 in at least one business function, yet only around one-third have scaled AI solutions beyond pilot projects. For many teams, AI is present — but not yet embedded.
Adoption alone does not guarantee results. Business leaders increasingly point out that the harder part is fitting AI tools into real processes and decision-making, not deploying the technology itself2. This gap between experimentation and value creation is now one of the most common challenges companies report.
Investment trends reflect both confidence and selectivity. In 2024 and 2025, AI accounted for a significant share of global venture capital funding3, with investors focusing less on novelty and more on practical, enterprise-ready solutions. Capital continues to flow, but expectations around real-world performance are higher than before.
But amid the escalating global race in AI innovation now, forecasting the path of AI evolution might appear challenging. Yet, how could we resist the plunge?
The conversation has clearly shifted in the second half of the 2020s. AI is no longer a question of “if” or even “how much”, but where it fits, where it falls short, and what patterns are emerging across industries. These patterns, not abstract promises, define the trends that follow.
To peek into what’s coming, we sat down with Alexey Karankevich, AI Innovation Lead at Oxagile, who shared the most thrilling upcoming AI and machine learning trends of 2026.
From the energy consumption of running models to the computational resources required for training and inference, the focus on cost optimization has become one of the major AI and machine learning trends in 2026.
The reason? AI models are incredibly resource intensive. To put it simply, a single prompt on advanced models like ChatGPT can use as much energy as a light bulb left on for about an hour — seems like too many deep questions for ChatGPT, and we might end up needing a few more candles around. Plus, training large models and running them at scale requires significant compute capacity, making costs increasingly visible as AI systems move from experimentation to regular use.
It is encouraging to see that this challenge is receiving more attention. Across the industry, both private and public initiatives are investing in optimizing AI infrastructure, with a growing focus on efficiency, cost control, and long-term sustainability rather than continuous increases in model size.
But what exactly does cost optimization in AI entail?
At its core, cost optimization means refining every stage of machine learning operations, from data acquisition and storage to model deployment, maintenance, and infrastructure optimization.
Firstly, it closely interconnects with MLOps, and in 2026 and beyond engineers will most likely emphasize:
Secondly, since cost and resource efficiency are also central concerns of Data Science, model optimization has become a stronger focus by 2026. Particularly through distillation — a technique that transfers knowledge from larger models to smaller, more efficient ones while preserving most of their performance and reducing computational costs.
Why is this important? Larger AI models require extensive computing power and often rely on cloud-based processing. However, optimized model versions can increasingly run locally on edge devices such as smartphones or IoT devices. This reduces latency and improves efficiency. A common example is Apple’s on-device AI approach, where many models run directly on mobile devices rather than depending entirely on cloud servers.
As AI adoption continues to expand, the evolution of optimization methods is a given. Beyond established techniques such as quantization (which reduces data volume to speed up performance) and pruning (removing non-essential parts of the model), further progress is expected in optimizing transformer-based models used to understand and generate text. This leads to cost optimization reinforcement — one of the most practical generative AI trends shaping real-world deployment.
The AI industry has largely mastered text processing, while capabilities in images, audio, and video have progressed unevenly and lag behind. As a result, multimodal models have become one of the more visible AI trends 2026 witnesses.
Multimodal AI models are designed to process and integrate text, images, audio, video, and other types of data within a single architecture. Unlike traditional models that specialize in only one type of data (such as text-only language models like Transformer or image classifiers like ResNet), multimodal models combine these inputs to support a wider range of tasks.
They are particularly important because data preparation and data processing remain among the most expensive parts of AI development. Cleaning, labeling, and formatting data for different model types is time-consuming and costly. Multimodal models lighten this burden by allowing the use of raw data in any form and working more directly with diverse data formats. Therefore, the use of more data sources becomes possible, supporting broader model capabilities and better generalization.
Major players such as OpenAI, Google, and Meta continue to invest heavily in multimodal systems. Models that combine text, images, audio, and video are now part of mainstream product development rather than experimental research. While generative video quality still varies across solutions, steady progress through 2026 reinforces the importance of multimodal models in real-world AI applications.
Produce AI-driven, narrative-rich video ads adjusted in real-time for maximum efficacy.
By 2026, many organizations have begun to reassess what they actually need from AI, moving past the initial excitement, shedding the unnecessary layers, and focusing on practical use cases. As the AI and machine learning landscape matures, expectations become more grounded, and attention shifts toward solutions that deliver measurable business outcomes — a pattern that reflects broader current trends in AI.
Superficial or novelty-driven AI applications are increasingly being phased out, meaning AI-powered coffee machine advisors suggesting flavors will likely be gone with the grind. Instead, the focus is on automation systems that improve efficiency, reduce costs, and support core business processes. Organizations are prioritizing use cases where AI can operate reliably over time, rather than one-off or consumer-facing experiments.
More mature organizations are leveraging automation to optimize internal operations, often building on existing data and technology stacks. In some cases, successful automation setups become reusable templates that others can adopt or adapt. For example, when automation significantly improves areas like supply chain or operations management, similar approaches can be applied across different organizations, even with varying levels of scale or expertise.
Agent-based AI systems play a central role in this shift. These systems consist of autonomous agents that can perform tasks, make decisions within defined boundaries, and coordinate actions across workflows. Rather than operating in isolation, AI agents interact with their environment, adjust to changing conditions, and support more flexible forms of automation across both small and large organizations.
Such systems are commonly built using existing frameworks like LangChain, DSPy, and cloud services and orchestration layers, forming the foundation of more advanced automation setups. However, deploying agent-based AI at scale requires more than basic task automation. It depends on stable system design, clear coordination between agents, and reliable orchestration mechanisms that connect models, tools, and data sources into a coherent whole. It will demand strong technical foundations and system-wide AI orchestration. Such systems might take various forms, from low-code API-with-contract editors like ChatbotBuilder to more advanced frameworks like Model Context Protocol (MCP).
This is a distributed approach to training in which multiple users, each working with their own data chunks, contribute to building a robust model. Why is this particularly noteworthy?
By 2026, one of the main constraints in AI development has become increasingly clear: access to high-quality data. We’ve almost exhausted readily available data, so where do we find new? Traditionally, we rely on public datasets from centralized sources like Google, Facebook, Instagram, etc. However, a substantial amount of data is generated locally by individuals or stored on servers and databases that are not publicly accessible.
In this context, federated learning stands out as one of the AI technology trends 2026. It enables organizations — including those in highly regulated industries such as finance or healthcare — to benefit from shared model training without exposing sensitive or personal data. Institutions can contribute knowledge derived from their data while keeping the data itself private.
This approach supports collective learning and feedback across participants. It’s a win-win scenario that fosters collective growth and knowledge sharing. While working with distributed and often unstructured data introduces technical complexity, the potential benefits remain significant, especially in environments where data sharing is restricted.
And the best part is that with federated learning is that the training process is moved closer to the data source. Instead of transferring raw data to a central server, models are trained locally and only updates are shared. This preserves data privacy, improves security, and allows locally generated data to be used more effectively across a wide range of applications.
At the moment, most stakeholders have moved away from claims that AI will fully replace human roles. Expectations have become more restrained, shaped by practical experience rather than early enthusiasm.
AI is increasingly treated as a system that supports decision-making, not one that replaces it. This shift reflects a broader reassessment of how AI is used in real-world settings and where its limits are, a pattern visible across many gen AI trends.
Explainable Artificial Intelligence (XAI) has become a central part of this reassessment. Explainability focuses on making AI outputs understandable to those affected by them, including users, organizations, and regulators. As AI systems are deployed in more operational contexts, explanations are needed not as a theoretical feature but as a functional requirement.
This is particularly relevant in regulated environments. In areas such as healthcare, finance, insurance, and manufacturing, errors can have direct financial, legal, or safety consequences. Different stakeholders require different types of explanations: what is meaningful for a patient is not the same as what is needed by a clinician, an auditor, or a compliance system.
Regulation reinforces this need. AI systems are increasingly required to demonstrate how decisions are made, how risks are managed, and how bias is addressed. After deployment, systems are monitored through post-market processes to detect errors or unexpected behavior and to ensure continued compliance as conditions change.
Challenges remain. There is currently no unified framework for providing these explanations, despite the existence of various approaches to explain models and data in general. The more complex and extensive internals are, the harder it becomes to explain. Many existing explanation methods rely on mathematical abstractions that are not easily accessible to non-technical audiences.
As AI use continues to expand, attention is shifting toward clearer boundaries: deciding which decisions AI can support, which it should not make, and which must remain entirely human. Just as important is understanding how these decisions interact within larger systems.
Deepfakes — images, videos, or audio altered or generated using AI tools — remain a growing challenge for organizations and societies worldwide. These can depict either real or entirely fictional people. Over the years, deepfakes have contributed to a surge of misinformation online and there have been significant real-life incidents involving deepfakes causing serious issues.
According to a Deloitte analysis, advances in generative technologies have made deepfake content easier to create at scale, prompting heightened concerns about misuse in fraud, misinformation, and identity deception. The report highlights that deepfake-related fraud losses in the United States alone are expected to rise to around $40 billion by 20274, reflecting both the volume and sophistication of attacks.
Deepfake proliferation has spurred a strong focus on detection and verification methods. Deloitte and other industry bodies advise digital platforms and organizations to adopt multi-faceted strategies, including content analysis tools and risk assessments, to address synthetic media threats as part of broader cybersecurity efforts5. Independent forecasts also emphasize that improving deepfake detection and digital provenance systems will be a priority as part of trust and safety frameworks in regulatory and commercial environments.
Efforts to develop and deploy effective deepfake detection technologies are now a key area of investment for businesses and governments alike. The United Nations’ International Telecommunication Union6 has urged stronger measures and advanced verification tools to counteract misinformation and fraud driven by AI-generated media, highlighting the need for global standards and collaboration in addressing this evolving risk.
So there will be a concerted effort to develop methods for accurately determining whether content is AI-generated or not. This will involve sophisticated techniques and tools to analyze the authenticity of content, ensuring that both the message and the medium are verified with precision. As these methods advance, we can expect a significant emphasis on fact-checking to identify and manage AI-generated content, making it a crucial aspect of Generative AI trends.
Real-time interaction and speech integration involve systems that can understand and respond to human speech and actions with minimal latency. This technology is widely used in virtual assistants, customer service bots and support systems, as well as interactive applications. Such systems rely on natural language processing and speech recognition to support voice-based interaction.
Multimodal interfaces that combine speech with visual signals and contextual cues are increasingly being deployed, enabling more natural forms of interaction in specific use cases rather than as a universal interface.
As deepfake technologies continue to advance, real-time speech analysis is being explored as one of several supporting tools in fraud prevention. In this context, conversational systems may contribute voice authentication by identifying unusual speech patterns or inconsistencies, while primary detection and verification remain handled by dedicated security and biometric systems.
AI has become a core component of modern marketing rather than an experimental add-on. Its role has expanded from campaign optimization to broader areas such as customer behavior analysis, performance measurement, brand reputation monitoring, and brand safety. Instead of isolated tools, AI is increasingly embedded into marketing workflows and decision-making processes.
Several AI-driven directions now define how marketing teams operate.
AI-powered personalization has moved beyond basic segmentation. Highly individualized experiences are already delivered at scale across both digital and physical touchpoints. Marketing messages, offers, and creatives are adjusted based on recent behavior, location, timing, and contextual signals. Online activity increasingly informs offline and real-world advertising, while mobile and in-app notifications are aligned with daily routines rather than generic schedules.
Predictive analytics has become a standard capability in marketing. AI systems process large volumes of structured and unstructured data to forecast customer behavior, identify emerging opportunities, and detect shifts in consumer sentiment. These models incorporate external factors such as seasonality, local events, and historical purchasing patterns, enabling more adaptive and responsive campaign strategies.
At this time, data readiness is recognized as a prerequisite for effective AI use in marketing. Efforts focus on reducing data silos, improving data quality, and aligning data governance with marketing objectives. This allows performance analysis and trend detection to be handled more consistently, making data an integral part of planning rather than a post-campaign reporting tool.
Brands are placing greater emphasis on where and how their ads appear. AI-driven contextual targeting has evolved beyond keyword matching, using language and sentiment analysis to ensure relevance and brand safety. Dynamic Ad Insertion continues to expand across connected TV, live streaming, and gaming environments, enabling ads to be adjusted in near real time based on content context and audience signals rather than fixed audience segments.
Overall, AI in marketing is less about experimentation and more about coordination — aligning data, content, channels, and timing to support consistent, measurable outcomes across increasingly fragmented customer journeys.
Upgrading ad campaigns for marketing agencies tired of outdated methods and uncertain audience responses.
The question is no longer whether companies use AI. Most do. What matters is whether it actually fits the way they work. The trends described above show a shift toward fewer experiments and more practical choices, where cost, data limits, and reliability matter as much as model capabilities.
AI is becoming part of everyday systems rather than a separate initiative. Teams that treat it this way tend to get more stable results than those still testing isolated ideas. This difference is likely to shape how AI is used after 2026.
Which tool is the best one? How do you integrate it seamlessly into your existing processes? But what comes next after implementation?
These are the questions we love answering. Our goal is to provide clear, actionable insights on AI tools, implementation, and integration to guide you in making the best choice.
1. The State of AI 2025 — McKinsey
2. Business leaders agree AI is the future — they just wish it worked right now (2025) — Reuters
3. Venture Capital Bets on AI — Barron’s
4. The rise of deepfakes: What digital platforms and technology organizations should know — Deloitte
5. Deepfake-enabled fraud: A growing financial risk — Deloitte Center for Financial Services
6. UN report urges stronger measures to detect AI-driven deepfakes — Reuters
The focus has shifted from experimentation to practical use. Current priorities include cost control, better use of data, automation of routine processes, and clearer rules around where AI should and should not be applied.
By 2026, AI is less about trying new tools and more about making existing systems work reliably at scale. Compared to current trends in AI, there is more emphasis on efficiency, integration with core operations, and long-term sustainability rather than quick wins.
Gen AI trends are most visible in content creation, automation, and multimodal systems, while marketing-related ones focus on personalization, predictive analytics, and ad placement control. These areas show where AI is becoming part of everyday business rather than a separate initiative.
