Predicting the future of technology is always risky. The signal-to-noise ratio is high, hype cycles are short, and certainty rarely survives contact with reality. But patterns do emerge, especially when you look beyond tools and focus on how organisations actually behave.
We asked three of Counter’s Tech Leads what they see coming in 2026. Their answers converge on a few themes: accountability, maturity in AI adoption, and a renewed focus on value over velocity.
Here’s what stood out.
The End of Tech’s “Phase in the Sun”
James Heggs, Tech Lead
To understand where tech is heading in 2026, James suggests looking backward before looking forward.
For a long time, the industry enjoyed a period of abundance. Salaries rose year on year. Perks multiplied. Decisions were sometimes more about office astroturf than customer outcomes. In many cases, value creation was assumed rather than demonstrated.
That era is over.
Today, tech leaders are increasingly expected to explain why. Why this technology. Why this hire. Why this investment. And crucially, to back those decisions with commercial reasoning. James sees this as a positive shift. It demands a broader skill set from tech leaders, including curiosity, realism, and a clear awareness of organisational context.
AI sits squarely in this transition. Much of the spending on AI has been positioned internally as a way to free up humans for higher-value work. By 2026, James predicts leaders will be expected to show evidence that this is actually happening, or risk the perception of an AI bubble.
On the engineering side, code generation tools like Claude are becoming table stakes. Teams that ignore them may fall behind. But James is cautious. Typing code has rarely been the bottleneck in software delivery. Optimising the wrong part of the lifecycle will not magically unlock value. The hard problems remain coordination, decision-making, and delivery.
From Writing Code to Shepherding Systems
Tom Legg, Tech Lead
Tom sees AI continuing to dominate the technology landscape, but in a more structured and systemic way.
The ecosystem of AI tools will keep expanding, with increasing momentum around MCP-style approaches and shared tooling frameworks. Rather than reinventing solutions for each platform, engineers will apply common patterns across technologies.
In this world, effectiveness shifts. The best engineers will not be those who write the most code, but those who can guide, validate, and shepherd systems into production. Understanding why and when to use AI will matter as much as knowing how.
Tom also highlights a divergence in adoption. In the public sector, AI uptake will be encouraged, but shaped heavily by safety, governance, and data privacy concerns. This will drive greater use of local and on-premise models, striking a pragmatic balance between innovation and trust.
From a mobile perspective, Tom expects a rise in smaller, highly optimised models running directly on devices. This shift will strengthen offline-first experiences, improve performance, and reduce reliance on constant cloud connectivity. It marks a meaningful evolution in how mobile applications are designed and delivered.
More AI, More “Slop,” and a Rethink of Agile
Rich House, Tech Lead
Rich predicts AI adoption will continue along two parallel tracks.
The first is innovation. Teams will find genuinely new ways to use AI to accelerate delivery and unlock possibilities that did not previously exist. The second is learning what bad AI usage looks like. This includes inefficient prompts, misplaced trust, and low-quality outputs. Expect plenty of slop before norms and best practices settle.
This maturation will not happen in isolation. Rich expects it to coincide with a rethinking of Agile-adjacent processes like Scrum and Kanban. As teams integrate AI tools into their workflows, existing ceremonies and structures may start to feel misaligned.
The opportunity is for leaner, more adaptable approaches to emerge. Ones that prioritise enabling individuals to deliver value rather than enforcing rigid process. The warning is familiar. Be wary of evangelists promising a single system that solves everything. They rarely do.
Looking Ahead
Taken together, these predictions point to a more grounded phase for technology.
Less hype. More accountability. AI that is embedded thoughtfully rather than bolted on. And leadership that is judged not by adoption metrics, but by outcomes.
If you are thinking about how your team is structured, how AI fits into your delivery model, or where to focus investment next year, we can help.
Counter works with organisations to build high-performing technology teams, embed experienced engineers, and support leaders navigating change with clarity and confidence.
If you are looking for support with your team in 2026, get in touch today to see how we can help.
