Seven Strategic Shifts That Separate Teams Delivering Real AI Value from Those Still Chasing Benchmarks
AI Overview Summary: AI in 2026 will be judged by whether it works, not by how impressive the demo looks. The hype bubble burst in 2025 when ChatGPT-5 disappointed consumers expecting magic. Teams that succeed in 2026 will prioritize protocols over prompting, treat LLMs as constrained software rather than content generators, and design agentic workflows that reduce entropy rather than amplify chaos. The market will reward professionals with dual fluency—deep AI understanding combined with domain expertise.
The Hype Bubble Burst Conversation
I am optimistic today about AI because we are exiting the era when AI gets judged by how clever the release is, how fancy the benchmark looks, and how exciting the demo feels. We are entering an era in which AI is judged by whether it works.
That shift matters enormously. It means we can finally focus on delivering results with AI. That work is hard, but it is meaningful.
The bubble of hype truly burst in 2025. I felt it when ChatGPT-5 disappointed so many consumers. The most instructive conversations I have had over the second half of the year did not focus on model roadmaps or benchmark charts. They focused on the critical edge cases that arise when you try to ship real systems—real multi-agent systems, real tool-use systems, real systems that enable a human to accomplish far more than they could before.
We Can Finally See in High Definition What AI Makes Possible
Think back over the year. Claude Code is less than a year old—it was in private beta in February 2025. Reasoning models were brand new at the start of 2025. Codex did not exist until partway through the year. These tools, now essential for 2026 systems, came into being over the course of 12 months.
We are starting to see in high definition what is possible with these models in a way we had to guess at before. For much of 2025, we colored in the gaps with hope because we could not imagine the specifics. Now we can.
That is why my optimism for this year centers on the ecosystem around AI, not just AI itself.
Protocols and Process Will Matter More Than Prompting
One bet I feel strongly about: protocols and processes will matter even more than prompting in 2026.
We have been tempted to treat prompting as the primary interface. That was true in the chat era. Now, prompting becomes one layer in a more standardized toolchain for agentic workflows.
The teams that win will not be the ones with the cleverest instructions. They will be the ones whose systems can reliably call tools, pass structured outputs, hand off work between components, and recover gracefully when something goes wrong.
What I am hopeful for in 2026 is that we will reinvent the wheel less. There will be less bespoke glue holding everything together and more composable AI systems that snap together predictably.
In my experience helping European SMEs design AI-enabled workflows and building dozens myself, the organizations that struggle most are those still treating every AI integration as a custom science project. The organizations that thrive have standardized their protocols—consistent error handling, predictable handoffs, structured outputs that downstream systems can parse without guessing. This approach to workflow automation design and AI automation consulting separates leaders from laggards.
Taking Constraints Seriously Transforms LLMs Into Software
This sounds like a strange thing to be optimistic about, but I think it matters: 2026 will be the year teams take constraints in AI seriously.
Constraints are the difference between content and software.
If you are saying "write me 200 words" or "help me with this prompt," you are unconstrained and asking for a chat response. But as we move into agentic workflows, we give our LLMs very tight constraints to enable practical, repeatable work at scale.
We are moving from LLMs as content generators to LLMs as software.
Teams that take constraints seriously will get the layouts right. They will get validation rules, graceful degradation, repair steps, and fallbacks baked in. Before they know it, their workflows will be production-ready software—not chat experiments hoping for good outputs.
This enables a new class of AI-native experiences that go far beyond chat. We have all the building blocks. The only thing standing in the way is the discipline to slot LLMs into workflows properly.
Understanding Where AI Belongs in the Workflow
I think we spent much of 2025 believing LLMs could do everything in a workflow. Where we have arrived by year's end: LLMs are most useful in narrowly scoped, high-value roles within agentic workflows that feature specific deterministic transforms and checks.
The insight is to decide where the model excels at generating smart tokens and abstract away everything else so it does not have to do that work.
Let the code do what code is good at. Let it count. Let it route. Let it validate. Let it retry. Let it diff. Do not ask the LLM to do that in the prompt.
Some people would say this is anti-agent. I say it is pro-reliability. It is understanding what LLMs are good at and building systems that let them thrive.
Entropy Management Separates Chaos From Disciplined Magic
This will sound theoretical, but it has intensely practical implications: teams are finally understanding how entropy works with LLM systems.
In 2025, many teams accidentally built systems that increase entropy and chaos. Too many unconstrained steps. Too many loops. Too many opportunities for the model to get creative in the wrong place.
People sometimes view token generators as uncontrolled, probabilistic, and unmanageable. One approach is to put business rules around them. But a higher-level approach is recognizing that LLMs can be entropy reducers, not just entropy drivers.
If you structure where the LLM lives in line with your business outcomes, what was magical before becomes disciplined magic now.
Examples of Low-Entropy AI Design
We are starting to see this in AI-native interfaces. Figma's approach to AI at the end of 2025 demonstrates the same principle.
These are places where LLMs produce more compelling, coherent, beautifully designed experiences that on the whole decrease entropy.
There is less entropy when I can get the answer I need within the interface I have, without spraying tokens everywhere and searching across the internet. There is less entropy when I can talk to my Figma design, get it correctly laid out, and pass it directly into Claude Code.
Teams are starting to intuitively grasp this, even if they do not have the language for it. They recognize that LLMs need significant harnessing to produce beautiful experiences. But when you do that work, you deliver far more than a chat interface provides.
Post-ChatGPT Software Creates Massive Middleware Opportunity
I am excited about what I would call the post-ChatGPT software future.
Cursor has shown that even if you are "just a wrapper," you can absolutely thrive in the middleware layer. That was a powerful insight from 2025. There is enormous room to run in 2026, especially in non-technical areas, for middleware and AI tool integration.
Much of this comes down to what I have been describing: designing effective agentic systems, decreasing entropy, and making customer experiences more beautiful and valuable.
Not All Requests Are the Same
One critical insight we are learning: you can stop treating all requests as identical.
ChatGPT trained us to treat every request the same way. But new systems recognize that users have dramatically different needs, and you can build different experiences around them.
Generative UI is downstream of the core insight that you can route users to experiences that matter to them outside the chatbot—in ways that are beautiful and useful. If I want to cancel my phone bill, I should see a generative UI to do that. I should not have to click six levels deep.
We are at the beginning of mapping customer intent to what is likely a power-law distribution of user utterances. Ninety percent of utterances are ordinary and usual—handle them with optimized flows. Use multi-agent workflows and generative UI to handle the long tail. Suddenly, you have a powerful experience that drives retention and engagement across your entire user base.
Dual Fluency Becomes the Most Valuable Career Asset
Careers are repricing around dual fluency right now.
The market will reward people who can do two things at once: understand how AI behaves at a high level of detail, and understand the underlying craft of their role and their customer.
Most organizations are still split between an "AI person" and a "domain person" who pair together. I believe this year we will see more roles that bring both capabilities together.
When you pair an AI person—even a very technical one—with a domain person, each head has only half the answers. Companies that find fully rounded people who deeply understand a particular domain and also understand how AI behaves in high fidelity will have seen something extraordinarily valuable.
The AI Skills Gap Is Closing From Both Directions
We are going to see HR systems rewrite job descriptions to attract these people. Organizations are recognizing the value. Professionals now have a year of AI experience under their belts. They are training themselves, building things they could not make before, and demonstrating their talent in ways that matter.
In my daily work on workforce AI readiness and AI training for teams, I see this pattern clearly. The most valuable team members are not the pure AI specialists or the pure domain experts—they are the people who have invested in both dimensions and can move fluidly between understanding what the model can do and understanding what the customer actually needs.
Robotics Will Have a Breakthrough Year in 2026
I am optimistic about robotics in 2026—and I am not talking only about humanoids. I mean robotics broadly.
We have spent a year laying the groundwork in reinforcement learning. Back in January 2025, Nvidia announced its digital warehousing concept: giving robots thousands of digital years of experience in simulated environments so they would be safer in real environments.
We have had twelve months to run on that. Toward year's end, we saw a breakthrough: personal POV cameras watching hands enable robots to infer hand motion and learn from human movements.
The arc of the year has been getting our learning infrastructure in order so that 2026 can rapidly scale out LLM-driven robotic capability.
Over-the-Air Updates Will Define Robotics Winners
It will look like constrained environments at first—cheaper computing for deployment in designated warehouse areas. There will be a significant push on home robotics—whether that means we finally get the laundry-folding robot, we will see.
The winners in this space will be those who can reliably ship and update the brains of robots they sell. Consumers accustomed to LLM updates every two to three months will not accept a household robot shipped in November that still runs January's software in March.
We will see ecosystems develop in which the robot primitives are all present, and users—whether business owners or home consumers—expect over-the-air updates that make the robot's brain smarter over time. The robot learns to use its hands or pinchers more effectively month after month.
All the building blocks are there. I am optimistic we get there this year.
Key Takeaways
The move from hype to results is the most crucial development in AI as 2026 begins. When the standard becomes "does it work" rather than "is it impressive," the organizations that have invested in reliability, constraints, and systematic deployment will pull ahead of those still chasing the latest model announcement.
Seven strategic shifts will separate winners from laggards this year. Protocols matter more than prompting—build composable systems, not bespoke glue. Constraints transform LLMs from content generators into software. Understanding where AI belongs in workflows means letting code handle what code does well. Entropy management turns chaos into disciplined magic. The middleware layer offers a massive opportunity for non-technical applications. Dual fluency becomes the most valuable career asset. And robotics will scale rapidly on the foundation of breakthroughs in reinforcement learning.
For European SMEs, the practical implication is clear: stop waiting for the next model release to solve your problems. The models are good enough. The question is whether your protocols, constraints, workflow design, and talent are ready to extract value from what already exists. An AI readiness assessment for EU SMEs, combined with business process optimization and operational AI implementation, can accelerate your path to real results.
The teams that win in 2026 will be the ones that ship real systems—not the ones with the cleverest demos. That is a future worth being optimistic about.
Written by Dr. Hernani Costa and originally published at First AI Movers. Subscribe to the First AI Movers Newsletter for daily, no‑fluff AI business insights and practical automation playbooks for EU SME leaders. First AI Movers is part of Core Ventures.
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