We’re already using AI, but are we using it well? That’s the question that will define 2026.

ChatGPT’s arrival at the end of 2022 brought AI into the public consciousness. It sparked a debate about the role of artificial intelligence at work and in our personal lives. It also brought hype and hyperbole, predictions about the end of the world and the end of humanity. All of it driven by fear of the unknown. Fast forward two years, and as more people use AI – and become familiar with its advantages and limitations – the conversation has started to mature. 

The question for 2026 is no longer whether AI will replace jobs or rewire how organisations operate. What really matters is how we use it. Those who remove human-in-the-loop involvement risk building fast systems that make poor decisions. And that “computer says no (or yes)” reliance can make it hard for others to successfully challenge or present their point of view. But it’s the people who choose to work alongside AI, to use checks, balances, and, dare we say, common sense, who will define how the technology is used in 2026 and beyond. Those who deploy AI thoughtfully, transparently and in ways that embrace human judgement rather than sideline it will be winners in the long run.  

People-led automation, not machine-led workflows 

To that end, organisations seeing the most significant benefits from AI today are removing repetition rather than people from their processes. They’re using automation to reduce cognitive load so people can focus on interpretation, creativity, and decision-making. 

That’s a sensible approach because poorly implemented automation can accelerate errors or obscure how decisions were made. But when humans guide the logic, review the outputs, and set the boundaries, AI becomes a powerful extension of human capability rather than a substitute. 

Even where AI could theoretically run an entire end-to-end workflow, many teams still prefer the reassurance of human oversight. Offering both modes – zero-touch automation for those who want it, and optional checkpoints for those who don’t – will be a defining characteristic of responsible AI deployment in 2026. 

This approach lets teams build gradual trust in their AI and automation systems, understand how they behave, learn their quirks (and there are many), and still develop confidence in the underlying logic — all before embracing greater levels of automation. It’s like the dual controls on a driving instructor’s car during those early lessons. 

All that said, there’s still the question about whether graduates and those new to their profession need to learn those manual, repetitive tasks at all. Do they need to develop a feel for the processes they’re automating and build the confidence to question AI’s outputs? There’s no easy answer here. But it will be interesting to see how the first generation of AI-natives engage with the technology when they join the workforce.  

The rise of prompt literacy 

Every major technological transition requires a new skillset. That can be frightening or intimidating for some people. It was true during the Industrial Revolution, and it was true twenty years ago, when search engines arrived. Almost overnight, the need to conduct library-based research vanished. For some, this was a revolution. For others, it was a technical minefield for which they were not equipped. They held onto their microfiche and reference books before finally – and reluctantly – giving in and entering the new age. 

The majority of us quickly learned how to search the internet and, as search engines evolved, we subtly refined our queries in line with that evolution. We also learned which sources to trust (most of the time). 

AI requires the same kind of interaction. You need the ability to query the data, refine your request, and think critically about the responses it provides. In 2026, that means developing prompt literacy. Large language models are exceptionally good at giving plausible answers. But as with early search engines, the quality of the output depends almost entirely on the quality of the request. Put rubbish in, get rubbish out – as the old saying goes.  

Teams that learn how to question AI systems clearly, precisely and critically will gain a measurable advantage – whether they’re analysing data, drafting reports or automating routine tasks. This is about cognitive rather than technical fluency: the ability to frame a problem clearly, provide sufficient context, and interrogate the reliability of an output. It’s also knowing when to pause, question, and ask for evidence or alternative interpretations. Prompt literacy means acting as the interface between the AI and the information you’re curating. In practice, it also makes you the point of reference when colleagues want to understand or challenge the data. 

Prompt literacy also helps teams recognise the limits of AI. Models can surface valuable insights, but they can just as easily hallucinate, miss crucial context, or misinterpret what they’ve been asked to do. Teams that accept outputs without verification will be building decisions on foundations they can’t fully see. But, as with all forms of digital literacy, prompt literacy is learnable. So, organisations that invest in prompt-based literacy training alongside wider AI education over the next 12 months will see benefits as their staff gain confidence and competence. 

Transparency and trust 

But what does this mean for software developers in 2026? Hopefully, they’ve been engaging with their user bases and looking at ways to address fears, particularly around job losses and the risk of surrendering too much control. If they have, they’ll have been designing workflows where AI takes the strain of repetitive work – capturing data, routing items, suggesting classifications – but in ways users can interrogate, override, pause or slow down whenever needed. This will create an environment where people-led automation and prompt literacy can thrive, and where users can feel secure as they step up their use of the technology. 

For that to happen, users should have a direct connection to AI within their software solutions. Ideally, this should be at the prompt level, so they can directly question workings and outputs. We also think AI responses should include a confidence level so users can decide when to trust an output or to interrogate it further. We see it as a built-in guardrail that helps people question AI while they’re actually using the system. 

This will help users understand why the system made a particular recommendation, which data sources were used, and where human intervention remains essential. Displaying confidence levels also allows teams to set thresholds. They can decide whether they’re happy for AI to proceed without intervention when it is 60%, 75% or 95% confident in its response – and automatically route anything below that threshold for human review. 

AI and search convergence  

2026 will also be defined by the growing convergence between AI and search. Where prompt literacy equips us to ask better questions, search convergence requires that we refocus and reevaluate the results we’re presented with.   

We’re already seeing the limitations of this in everyday use. Ask a traditional search engine for a specific recipe, and it will retrieve the exact page you need. Ask an AI model, and you may receive a confident, plausible, but entirely invented set of ingredients and instructions. AI isn’t retrieving; it’s predicting – and prediction is not the same as truth. 

This differentiation is relatively harmless in low-stakes situations (although one of us very nearly had a cake-related disaster over the weekend thanks to ChatGPT!). The same behaviour in a commercial environment can be far more damaging. What if an AI system generated a close approximation of a regulatory threshold, incorrectly summarised a policy, or invented a financial definition based on patterns rather than facts? 

Large language models are now woven into mainstream search engines, offering summarised answers instead of traditional lists of sources. The experience is frictionless, but it also blurs the line between information retrieval and information generation. Teams may assume they’re reading a verified fact when, in reality, they’re reading an intelligent guess. For that reason, organisations need to help employees question, validate and cross-reference AI-driven search engine outputs. Verification has to be a core competency and can’t be treated as an afterthought. This training will be vital over the next 12 months. 

Thoughtful adopters vs unchecked users 

All these trends – prompt literacy, people-led automation, transparency, and convergence with search – point to a single conclusion: the major divide in 2026 won’t be between organisations that use AI and those that don’t. It will be between the ones who choose thoughtful adoption and those who let AI operate unchecked. Thoughtful adopters will ensure human oversight remains central and maintain visibility into how automated decisions are made. They’ll also develop AI-literate workforces capable of questioning outputs, and design systems that remain resilient as technology evolves. 

Unchecked adopters may gain early speed, but they’ll also accumulate risk. Errors compound more quickly in automated environments, especially when no one can explain how the system reached its conclusions. And as workflows become increasingly AI-driven, the cost of reversing poor decisions – or simply understanding them – will rise. 

Conclusions 

As the year unfolds, the winners in the AI stakes will be those who combine automation with clarity, human oversight with efficiency, and speed with sound judgement. Not those who use it most aggressively or across the broadest range of tasks. 

AI is already powerful; no one disputes that. But in 2026, the challenge and opportunity will be to ensure it is trustworthy, transparent, and thoughtfully applied. Ethics, common sense, education, and strong guardrails will be key here. Forward-thinking organisations will use AI as a collaborator rather than a shortcut. They’ll also recognise that human intelligence is key to fully benefiting from artificial intelligence.