{"id":16962,"date":"2026-01-02T13:35:19","date_gmt":"2026-01-02T12:35:19","guid":{"rendered":"https:\/\/haimagazine.com\/uncategorized\/ai-2026-a-maturity-test\/"},"modified":"2026-01-05T13:37:49","modified_gmt":"2026-01-05T12:37:49","slug":"ai-2026-a-maturity-test","status":"publish","type":"post","link":"https:\/\/haimagazine.com\/en\/it-and-technology\/ai-2026-a-maturity-test\/","title":{"rendered":"\ud83d\udd12 AI 2026: a maturity test?"},"content":{"rendered":"

After a period of intense experimentation, organizations are cutting back their AI projects to the ones that actually make money or save time. McKinsey data<\/a><\/mark> shows that the biggest hurdle isn\u2019t access to models anymore, but weaving AI into how companies’ real operations: its processes, decisions and day-to-day life.<\/p>

So that means the era of scattered “because everyone\u2019s doing AI” initiatives is over. Only use cases you can run day to day and that make financial sense will stick. That shakeout will drive AI\u2019s growth in 2026.<\/p>

From chatbots to teams of agents<\/h4>

The consequences are very real. In a Gartner study <\/a><\/mark>(321 customer service leaders, fall 2025), a full 91% of respondents say their leadership is putting direct pressure on them to implement AI. And fast.<\/p>

This lines up with the fact that AI isn\u2019t going it alone anymore. Now it\u2019s starting to work in teams of agents, which hugely expands how organizations can use it.<\/p>

That, in turn, means more than 80% of surveyed companies expect to reduce staff over the next 18 months through natural attrition, hiring freezes or layoffs. At the same time, 84% claim the people who stay need training. Because without new skills, you can’t work with AI.<\/p>

Professor Dariusz Jemielniak from Kozminski University, a member of the CampusAI Program Council, lays out his vision for this transformation:<\/p>

“We’ll move from single chatbots to ‘orchestrated workforces,’ where large models coordinate the work of smaller, specialized systems. Finance, HR, customer service\u2014everywhere you’ll see autonomous assistants handling tasks end-to-end, escalating to humans only for exceptions. This could be a game changer, because we’ll enter a phase where many agents work in synergy and replace teams rather than individuals\u2014exactly what we’ve been teaching at Campus AI from day one.”<\/p>

“I hope we\u2019ll get better at telling genAI from AI as a whole,\u201d adds Natalia \u0106wik, Product Team Lead at CampusAI. \u201cContent generation is just one slice. Other areas of artificial intelligence are equally or even more important: data analysis, prediction, optimization, decision-support systems, security, and the whole realm of responsibility and impact on people.\u201d<\/p>

Data is key.<\/h4>

The shift to complex agent-based systems makes an existing problem even more pressing: AI is already producing more content than people. And that content gets fed back into training the next models. The result? By the end of this year there were as many as 8 million <\/a><\/mark> deepfakes, a 1500% jump compared to 2023. And we correctly identify only 1 in 4.<\/p>

This leads to a double crisis. First: people stop trusting anything online. Second: models trained on that chaos of content keep getting worse. Synthetic data messes up more synthetic data. It\u2019s a vicious cycle.<\/p>

According to Professor Aleksandra Przegali\u0144ska, vice rector at Kozminski University and a member of the CampusAI Program Council, this needs to stop.<\/p>

“In 2026, I expect a push for quality over scale. We\u2019ll see filters for AI slop (technical and institutional), and ironically, content created more slowly, at higher cost, and with a clear author will matter more. AI won\u2019t disappear from content creation, but it will stop being invisible\u2014labeling, provenance, and reputation will become key. In other words: after the mass-generation phase, we\u2019re moving into selection and accountability”, she adds.<\/p>

As always, people are the weakest link<\/h4>

The best data and systems won’t help if people don’t know how to use them. MIT research <\/a><\/mark>shows a paradox: companies rolling out AI often experience a productivity dip at the start. Why? Because the tools don’t fit the processes. There’s not enough training. No one has rethought what the work should actually look like.<\/p>

That edge shows up over time, but only if the company invests in training and truly changes how it works, which is still rare. Today, 95% of organizations aren\u2019t seeing results from the initiatives they\u2019ve rolled out. And employees are talking about “workslop”: junk work churned out by weak AI content.<\/p>

Miko\u0142aj Sznajder, Head of AI Business Advisory at CampusAI, explains why:<\/p>

“Turning to AI for help like an amateur, without understanding how the models work, how they differ, whether they check facts online or just string together likely words without much thought, still leaves people frustrated all too often when working with AI.”<\/p>

But change is coming. Models are getting better and better at understanding what we really want.<\/p>

“Models should keep getting better at interpreting prompts, and who knows, they might even get bolder when asking users follow-up questions about their intentions, something they almost never did before, instead of just proposing a solution regardless of how well or poorly the prompt was written,” Sznajder adds.<\/p>

As he points out, there’s another issue: people are afraid to admit they use AI.<\/p>

“A lot of people feel they have to hide that they use AI in their day-to-day work, just so their work isn\u2019t dismissed, so their creative input, their approach to putting the material together and solving the problem doesn\u2019t get buried just because they used AI at some point in the process.”<\/p>

It\u2019s like back in the day with Wikipedia\u2014’a real expert doesn\u2019t quote Wikipedia.’ Or with Photoshop\u2014’a real graphic designer works by hand.’ Over time, those worries fade. What\u2019s new becomes normal. In 2026, that shift will speed up, though we\u2019ll still have to wait for deeper change.<\/p>

Education or chaos <\/h4>

Data from OECD reports<\/a><\/mark> also shows that the real problem for organizations today isn\u2019t access to technology, but a lack of skills to put it to good use.<\/p>

“AI is a huge challenge for educators,” says Natalia \u0106wik, Product Team Lead at CampusAI. “It’s changing not just the tools, but how people find information, create, learn, and… what they believe. But there’s no time to hesitate anymore. If we don’t roll out smart education now (in schools, in companies, in organizations), the gap will fill itself with random practices, clickbait, and ‘tutorials’ that teach shortcuts instead of understanding.”<\/p>

This issue goes far beyond formal education. According to research by the World Economic Forum<\/a><\/mark>, nearly half of young people say they\u2019re anxious about AI and worried about their cognitive abilities, while MIT research points to risks like reduced knowledge retention and shallower thinking when people lean too heavily on generative systems. Meanwhile, WEF shows that demand for distinctly human skills (analytical and critical thinking, resilience and the ability to read context) is rising as AI adoption grows. That supports the idea that 2026 will be the year when education stops being about \u201cusing the tools\u201d and starts being about building judgment, interpretation and responsible use of technology.<\/p>

Where should we look for success?<\/h4>

The data and experience from 2024\u20132025 show that in 2026 there won\u2019t be a technological breakthrough, but a test of maturity. AI is no longer an experiment, but it’s not an automatic source of value either. Three things will decide the outcome:<\/p>

First: rebuild your processes.<\/strong> Don\u2019t just bolt AI onto existing structures\u2014rebuild how you work from the ground up. Most companies still aren\u2019t doing this.<\/p>

Second: quality over quantity.<\/strong> Mechanisms that check inputs and outputs. Verification systems. Labeling AI content. Fighting AI slop. It\u2019s not cheap, but without it models will keep getting worse.<\/p>

Third: skills and culture.<\/strong> Training that combines the technical side with critical thinking. A culture where AI is normal, not something to be embarrassed about or threatened by. That’s the hardest part, because it takes a mindset shift.<\/p>

2026 isn’t the year for new promises. It’s the year of consequences for decisions made earlier. Organizations already investing in these three areas will build an advantage for years to come. Those still treating AI as just an IT project will fall further behind in terms of productivity, talent and real value.<\/p>

The most lasting results are possible when AI supports people instead of trying to replace them.<\/p>","protected":false},"excerpt":{"rendered":"

Practical implementation, tighter cost tracking and better quality. We’re heading into a year where what matters isn’t AI’s technological promises, but whether it makes sense to use it and whether organizations can actually transform.<\/p>\n","protected":false},"author":465,"featured_media":16945,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_lock_modified_date":false,"footnotes":""},"categories":[803],"tags":[],"popular":[],"difficulty-level":[38],"ppma_author":[892],"class_list":["post-16962","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-it-and-technology","difficulty-level-medium"],"acf":[],"authors":[{"term_id":892,"user_id":465,"is_guest":0,"slug":"kmironczuk","display_name":"Krzysztof Miro\u0144czuk","avatar_url":{"url":"https:\/\/haimagazine.com\/wp-content\/uploads\/2025\/10\/awatar-2.png","url2x":"https:\/\/haimagazine.com\/wp-content\/uploads\/2025\/10\/awatar-2.png"},"first_name":"Krzysztof","last_name":"Miro\u0144czuk","user_url":"","job_title":"","description":"Od lat zajmuj\u0119 si\u0119 nowymi technologiami w biznesie, edukacji i codziennym \u017cyciu. W centrum mojej uwagi pozostaje cz\u0142owiek \u2013 i to, by technologia wyr\u00f3wnywa\u0142a szanse, zamiast tworzy\u0107 bariery."}],"_links":{"self":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts\/16962","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/users\/465"}],"replies":[{"embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/comments?post=16962"}],"version-history":[{"count":1,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts\/16962\/revisions"}],"predecessor-version":[{"id":16963,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/posts\/16962\/revisions\/16963"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/media\/16945"}],"wp:attachment":[{"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/media?parent=16962"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/categories?post=16962"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/tags?post=16962"},{"taxonomy":"popular","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/popular?post=16962"},{"taxonomy":"difficulty-level","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/difficulty-level?post=16962"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/haimagazine.com\/en\/wp-json\/wp\/v2\/ppma_author?post=16962"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}