Did the 2025 AI Predictions Hit the Mark? The Reality of Enterprise AI and '2026: The Year of Evals'
'Did the 2025 AI predictions hit the mark or miss?'—this article aims to 'grade' that question from the perspective of enterprise AI. The participants look back on the predictions they made six months ago and, in the end, make new predictions about 'what will happen in the first half of 2026.' In short,technology has moved forward, but the barriers to 'using it with full confidence' in an enterprise setting remain high. And, as the key to overcoming these barriers, 'evals' and 'handling real-world data' are moving closer to the center.
1. Summary of 2025 Predictions: Progress has been made, but it's not a 'total victory'
Looking back at the predictions from six months ago, the sentiment among participants was consistent.
There is a deep valley between 'it has become possible' and 'it has been widely adopted', is the consensus. The final scores were also symbolic, with self-assessments settling around 'C to D.' This reflects the reality that enterprise adoption is not just about 'technology.'
2. Agents have been deployed to production, but they are 'limited'
Prediction: 'Agents will be deployed in production at enterprises'
Evaluation: Partially correct (though limited in scale and scope)
The feeling on the ground was this:
'Agents are in production. But the way they are used is still 'cautious'.'
A particularly poignant point was thatthe term 'agent' is too broad. Everything from light tools that only gather information to those that execute complex procedures across multiple tools is called by the same name.
And,the decisive brake is reliability.
'Without 99% to 99.9% reliability, there is no real value.'
Currently, while 'collect and organize' use cases like deep research are growing, actions that rewrite databases or finalize business operations are scary. For a company, an 'accident' is not an interesting failure, but a loss.
3. Work-life balance won't improve; in fact, it might even get worse
Prediction: 'Back to 9-to-5 thanks to agents'
Evaluation: Mostly incorrect (at least in the short term)
A cynical remark symbolizes this:
'This is about the enterprise, not about me.'
In other words, few people on the ground can say they feel 'relieved.' There are two reasons.
First, AI often creates 'increased expectations' rather than 'reduced workload.'
It is the phenomenon of 'Now that you can do it, you can produce more, right?'
Second, the cost of mastery.
'In the end, the time spent understanding, debugging, and checking AI behavior can increase.'
Companies also need high-quality evaluation in the early stages of adoption, which actually makes stakeholders busier. The conclusion was that a redistribution of productivity will occur before any short-term 'time savings.'
4. Autonomous, long-running agents are 'still in the imagination'
Prediction: 'Moving toward autonomous agents that don't require a human to be glued to them'
Evaluation: Incorrect at this point (though there are 'signs')
Everyone was cautious here. Drawing a parallel to self-driving cars,
'We can be optimistic. But the time until they are 'commonly on the road' is longer than imagined'
is the analogy that came up.
On the other hand, the exception is 'coding'.
'Coding agents will do the work while you are away.'
However, the reality remains that humans check it afterward.
In other words,autonomy advances faster in low-risk areas. Areas directly linked to money or customers in a company will not advance at the same speed—this is the crux of this chapter.
5. Prediction for the first half of 2026 (1): Multimodal goes from 'zero to non-zero'
Prediction: 'Multimodal will increase in enterprises'
The phrasing was clever; it was a modest expression of 'becoming non-zero' rather than 'surging.' Companies hold 'dirty data' from the real world—images, audio, drawings, videos, PDFs, etc.—rather than work that is completed only with text.
'Enterprise data is messy. That is precisely why multimodal is the key'
. The reading is that as model performance improves, it will become easier for companies to invest in 'preparing it into a usable form.'
6. Prediction for the first half of 2026 (2): It will be the 'Year of Evals'
Another major prediction is an area where companies are finally coming to terms.
'To deliver value, you need evaluation data tailored to the use case and experts'
It used to be thought that 'because the model is smart, we can manage it in-house.' But the reality is the opposite; designing evaluations that satisfy representativeness, diversity, and continuous improvement is difficult. The point that 'humility' was born here is important.
Furthermore, the focus of evaluation is shifting from just the 'quality of answers' to agent behavior (tool use, traces, and decision-making). However, the sentiment was that this hasn't completely flipped yet, but is rather in the middle of a transition.
7. Prediction for the First Half of 2026 (3): Companies Will Start Collecting 'Gaps'
Finally, an interesting point is the view that 'tuning/customization will take center stage.' Right now, companies are aiming for 80% satisfaction with general-purpose models, with humans finishing the final 20%. 'Due diligence (research reports)' was cited as an example.
From here on, 'collecting data on where things are lacking, rather than the final deliverable'
—in other words, visualizing the points where professionals make corrections, which are domain-specific requirements, and moving toward a design that fills those gaps. This is the final step for enterprise AI to move from 'just a demo' to a 'business weapon.'
8. Bonus Prediction: The Next Area to Grow is Education
When asked, 'Which domain will grow in the next six months?', education was mentioned. The reason is that there are many users (students) and a lot of data, allowing for a feedback loop. Furthermore, in education, the value lies not in the 'answer' but in 'creating a path for learning (curriculum, gamification, and retention of understanding).' It was also noted that risks are relatively easier to control.
Conclusion
What this discussion showed is that while enterprise AI in 2025 focused on 'proving possibilities,' the trend for 2026 is entering the 'science of operations.' Agents will increase. However, there are barriers of reliability and responsibility. That is precisely why evaluation (Evals), data preparation, and 'customization to fill the gaps' will come to the forefront.
Rather than flashy breakthroughs, steady improvements will determine the winners and losers—that is the 'reality of enterprise AI' found here.