“It’s such a crazy time, and it’s only accelerating.”
This issue of Redeployed is brought to you by Tecla: As AI shifts from experiments to architecture, the challenge isn’t speed, it’s structure. Tecla helps you hire senior nearshore engineers who know how to co-build with agentic systems, verify automation, and reinforce trust where orchestration breaks down. These aren’t plugin coders. They’re system thinkers who operate inside the loop. Because models don’t govern themselves. Yet.
IBM opened 2026 with a report that didn’t just list trends. It issued a warning: AI isn’t plateauing, it’s embedding itself everywhere. Systems are getting smarter. Workflows are getting automated. And machine partners are starting to shape the structure of teams, not just their output.
Most headlines missed the real signal. This isn’t about bigger models. It’s about how AI is moving deeper into the architecture.
Here are three shifts that engineering leaders can’t afford to ignore.
1. Systems Over Models
The next wave of AI leadership won’t come from size. It will come from orchestration. IBM argues that the smartest orgs in 2026 won’t just select the best model. They will build the best systems around those models. Think pipelines, agentic loops, and toolchains that can reason across steps and manage complexity in motion.
Benchmarks might tell you which LLM is fast. They won’t tell you which one plays well with your workflows. Integration is now the hard part.
2. Compute Isn’t Dead. It’s Diversifying.
The age of GPU-only thinking is fading. IBM points to growing investment in ASICs, analog inference, chiplets, and even quantum-assisted workloads. These aren’t research prototypes anymore. They are entering production, especially where cost and latency are at stake.
Engineering teams that ignore compute strategy will find themselves locked into architectures that can’t keep up. The real optimization in 2026 won’t just be in the model. It will be in how and where it runs.
3. Agentic Logic Is Becoming the Default
By the end of this year, AI agents will stop being novelties. They will start acting as embedded logic layers across enterprise systems. Not helpers. Not copilots. Core actors.
IBM predicts these agents will generate, prioritize, and even execute workflows unless humans step in and define the loop. That means engineering teams need to rethink ownership. Who verifies the action? Who overrides the agent? Who patches the process when something goes wrong?
If you don’t answer that now, your org will be answering it after something breaks.
Trust as a System Requirement
The report brings it back to one idea that still doesn’t get enough airtime: trust. Not just in the model, but in the full stack around it. Can your system be audited? Can it be governed? Can it fail gracefully?
This isn’t about buzzwords. It’s about survivability. In a landscape full of automated decisions, trust becomes a design constraint. Without it, speed turns into liability.
Where Architecture Still Needs People
IBM’s forecast makes one thing clear: as AI systems become more agentic and more deeply integrated, the hard part shifts from models to orchestration. Designing the loops, owning the handoffs, and deciding where automation stops still requires experienced engineers who understand both the system and the stakes. That’s why some teams are pairing AI-heavy architectures with senior engineers who can move fast, collaborate across tools, and co-build with AI, without losing accountability when the system makes a call.
What This Means for Engineering Leaders
If you are managing software teams in 2026, your job is no longer just to ship fast. It’s to ship with foresight. The question isn’t whether your team can deliver. It’s whether your systems can adapt, recover, and improve without spinning out of control.
Accountability is no longer tied to what humans create. It’s tied to what the system decides when they aren’t looking.
Why This Isn’t About the Future
IBM’s trend report isn’t speculative. It’s a reflection of where enterprise teams are already heading. The shift from AI as a feature to AI as foundation is well underway. The real divide will be between those who design for that shift, and those who bolt automation onto processes that were never built to handle it.
The risk isn’t just bad architecture. It’s lost visibility, misaligned decisions, and governance debt that compounds faster than tech debt ever did.
The smartest teams won’t just adopt AI. They will structure around it; carefully.
More to come…
Recommended Reads
✔️ AI Trends to Watch in 2026 — CompTIA
✔️ Strategic Predictions for 2026 — Gartner
✔️ AI in 2026: Outlook on Agents, Governance & Real‑World Impact — OpenDataScience
– Gino Ferrand, Founder @ Tecla