The past few years have been defined by astonishment. Each new AI release seemed to arrive faster than society could absorb its implications. Systems grew more capable, outputs more convincing, and public reactions more polarised. Between excitement and anxiety, one thing became evident. Society was reacting to AI faster than it was reasoning about it. As we look ahead to 2026, things start to change. Not because AI will slow down, but because the most pressing questions surrounding it are no longer primarily technical. They are human, institutional, and ethical. The challenge is less about what AI systems can do and more about how society chooses to integrate them into everyday life. In this sense, 2026 may come to represent a period of recalibration. A moment when the conversation around AI shifts from surprise to stewardship, from fascination to responsibility. The following projections do not assume a frictionless future. Instead, they point to signs of a maturing relationship between people and a technology that is already deeply embedded in social, economic, and political systems. One of the most positive developments by 2026 may be that AI becomes less visible. As AI moves past flashy demos and news stories, it starts to work more like basic infrastructure. Rather than standing out, it quietly helps with everyday tasks that people don’t often talk about. Things like translation, document review, scheduling, and office work become areas where AI help is normal, not surprising. This change also helps people feel less stressed. When technology doesn’t always grab our attention, it’s easier to think about it calmly. The urge to compete with machines or keep up with big changes starts to fade. AI becomes just another tool that fits into our usual routines. History suggests that technologies often become most valuable once they lose their sense of spectacle. By 2026, AI may follow the same pattern, not by diminishing its capabilities, but by normalising its presence. Another encouraging development is the gradual clarification of how humans and AI systems are expected to work together. Early deployments of AI were often marked by ambiguity. Who is responsible when an automated system produces an error? How much trust should be placed in machine-generated output? When should human judgment intervene? By 2026, these questions are less likely to be treated as afterthoughts. Organisations begin to define clearer norms around human oversight and accountability. AI systems are positioned as assistants, not decision-makers in sensitive contexts. Their outputs are understood as inputs into human judgment, not substitutes for it. This shift does not eliminate mistakes, but it reduces the risk of misplaced trust and abdicated responsibility. Importantly, these norms also protect human agency. Rather than measuring workers by how closely they emulate machines, institutions recognise the distinct value of human judgment, context awareness, and ethical reasoning. AI supports these capacities instead of replacing them. By 2026, progress in AI will no longer be measured solely by scale. While large, general-purpose models continue to exist, there is growing recognition that not every problem requires the most expansive system available. Purpose-built models designed for specific domains begin to play a more prominent role. In healthcare, education, public administration, and local governance, smaller systems tailored to particular needs prove more practical and easier to govern. These models are often easier to audit, adapt, and align with local legal and cultural contexts. This trend also supports linguistic and cultural diversity. AI systems trained to serve specific communities are better equipped to handle local languages, norms, and expectations. Instead of imposing uniform solutions across diverse contexts, technology becomes more responsive to local needs. The result is not fragmentation, but pluralism. AI changes in ways that reflect the variety of human environments in which it operates. The erosion of trust caused by synthetic media and automated content has been one of the most unsettling effects of recent AI advances. By 2026, this challenge will still be far from resolved. Yet there are signs of adaptation. Technical measures to indicate content origin are becoming more widespread, even if imperfect. Platforms improve their ability to flag synthetic material, while journalists and public institutions refine their verification practices. More importantly, public awareness grows. Instead of assuming authenticity by default, citizens become more attentive to context. This does not mean constant suspicion, but a more informed form of scepticism. Just as society adapted to earlier waves of misinformation, it begins to develop what might be called social antibodies against synthetic deception. Trust does not disappear, but becomes more deliberate. Citizens remain engaged, even as they apply greater scrutiny to what they encounter. This vigilance is crucial, as the intersection of AI and cybersecurity demands constant attention to new forms of digital vulnerability. One of the most hopeful projections for 2026 lies in education. AI literacy increasingly resembles digital literacy in earlier decades. It is not about teaching everyone to build models or write code, but about understanding how AI systems function, where their limits lie, and how incentives shape their behaviour. Public servants, journalists, educators, and citizens alike gain a more grounded understanding of what AI can and cannot do.AI becomes less spectacular, more structural
Human and machine roles become clearer
Purpose-built and local AI gains recognition
Society develops early trust mechanisms
AI literacy becomes a civic competence
Taken together, these developments point toward a broader shift. As AI becomes more integrated into daily life, its success depends less on technical sophistication and more on the values that guide its use.
This is where humanistic thinking becomes essential. AI systems do not exist in isolation. They reflect the priorities, assumptions, and power structures of the societies that design and deploy them. Choices about transparency, accountability, inclusion, and dignity are not secondary concerns. They shape outcomes as much as technical performance does.
Initiatives that emphasise human-centred governance remind us that AI should serve human flourishing, not redefine it. This approach does not reject innovation. It insists that innovation remains anchored in social responsibility. By 2026, the most credible AI systems may not be those that impress through complexity, but those that demonstrate respect for human agency, cultural diversity, and institutional integrity.
Looking ahead, AI does not offer a final verdict on the future of work, trust, or governance. Instead, it functions as a mirror.
It reflects how societies manage power, distribute responsibility, and uphold shared values under pressure. The technology itself neither guarantees progress nor ensures harm. Outcomes depend on choices made by institutions, communities, and individuals.
If 2026 brings a period in which AI is discussed with less alarm and more deliberation, that would itself be a form of progress. Not because the underlying challenges have been resolved, but because society has learned to approach them with greater clarity, restraint, and responsibility.
In this context, optimism does not mean assuming favourable outcomes. It means taking responsibility for how powerful systems are designed, governed, and used.
Author: Slobodan Kovrlija
