The new year is a time for reflection, and true to the theme, the technology trends I’ve been reading are doing an excellent job of reflecting each other. Allow me to take a step beyond the obvious and share my thoughts on what we can expect to see this year:
The boundaries between real and simulated experiences have become irrevocably blurred. This lack of distinction is causing Generation Alpha to grow-up in a world of confused reality without the guardrails of certainty.
We can expect to see trust rise further up the agenda in 2026, with provenance being critical to our understanding of trust (the central thesis behind blockchain). This isn’t something that can be pushed aside any longer. When it isn’t possible to distinguish which interactions, images, news-stories, job-applications, or comments are real and which are artificial, we are presented with a fundamental conflict to the human psyche.
As the geopolitical and macroeconomic climate continues to drive uncertainty this year, enterprise technology investment will push further to the right. The impact will be slower AI adoption, growing technical debt, and fuelling resentment between the people who demand the change and the leaders tasked with delivering it.
As CEO and Board expectations rise, enterprise technology leaders will seek investment and roadmap clarity that CFOs are not able to underwrite, leading to increased churn and attrition among CTOs, CIOs, and their top teams, stalling velocity and strategic progress.
In a scramble to label AI advancement, we’re going to see increased levels of short-term rhetoric attached to incremental change. ‘Machine learning’ and ‘low-no-code’ were consigned to the archives in 2025 as ‘generative’ and ‘agentic’ gathered momentum and ‘vibe-coding’ swept up anything in-between. Each of which is set for a short shelf-life.
Even the building blocks of AI, ‘data foundations’ and ‘data governance’, have been nudged off stage by the glossier ‘AI enablement’.
Just as an army can only advance as far as its logistical supply chain allows, the battle for mass enterprise AI adoption at scale in 2026 will depend on increased private and public investment in critical technology infrastructure.
Government spending, already under macroeconomic pressure, will struggle to keep up with spiking demand for AI infrastructure, data-centres, advanced semiconductors, and associated research and development.
Your AI strategy is in direct conflict with your carbon reduction commitments. Advancements in green computers and the longer-term potential for DNA data-storage are not going to keep up with the growing computing demand and data centre energy consumption driven by increased AI adoption.
Until the improved energy efficiency promised by quantum computing can achieve scale, enterprise businesses will have to make a choice this year between achieving their AI ambitions and honouring their carbon targets.
High profile ransomware attacks in 2025 showed us that the foundations of enterprise cyber defence are, at best, brittle. Regrettably, these events will come at a higher frequency and greater cost in 2026 as the speed and agility of bad-actor innovation asks damning questions of public and private sector cybersecurity standards.
AI will play a role in both offense and defence, protecting (paired with machine learning and predictive intelligence to form a preemptive security shield) and penetrating, depending on the motivations of the user.
State-backed attacks born of geopolitical volatility with the intent to disrupt and destabilise economies and political systems will remain out of the spotlight, unreported, unattributed, and everpresent.
There are only two strategies: bundle and unbundle, and in 2026, we’re going to see the fully unbundled matrix of interconnected, narrow specialist agents deployed en-masse in multiagent systems (MAS).
The theory is ‘best of breed’, and the enemy is the monolithic AI platforms, but sure as night follows day, an unbundled strategy will be superseded with a bundled strategy, and the fight for dominance will likely swing in favour of the hyperscalers.
Decades of globalisation have left ambitious technology roadmaps dependent on global hyperscaler clouds. In parallel, extended geopolitical tensions and technology supply chain disruption have stoked the desire, especially in highly regulated sectors, for greater domestic control over critical workloads through sovereign cloud capacity and an increased return to on-premise risk mitigation.
Increased adoption of generative AI has the potential to manage existing debt in enterprise workflows. But these gains are likely to be outweighed by a deeper cost in 2026.
Technical debt is set to increase as hastily deployed AI-generated code masks poor documentation and misalignment with architectural and security standards. While providing the illusion of short-term progress, unmanaged complexity and customisation will lead to new MLOps processes, exaggerating existing DevOps burdens, and longer term technical debt.
I’d be interested in hearing your thoughts on these trends and whether they resonate with you.
One thing I’m sure we can agree on: 2026 promises to be another bumpy year, one where we have to be mindful of where to slow down, accelerate, or change direction.
Follow me on LinkedIn