AI adoption in the workplace is rising fast, driven by leaders seeking productivity gains and employees hoping to benefit. But enthusiasm alone isn’t enough to ensure AI aligns with how people actually work.
Many organisations are now rolling out AI tools with a sharper focus on driving real business value. When done strategically, these deployments streamline workflows, enhance decision-making, and unlock new efficiencies.
However, the broader landscape still shows a contrasting trend: AI overload. In many cases, tools are introduced without a clear integration strategy, leading to siloed implementations that complicate work rather than simplify it.
Unsurprisingly, 74% of workers are bypassing company-provided AI solutions altogether, opting instead for their own tools that better fit their needs. This signals a clear disconnect between what businesses offer and what helps people work more effectively.
The opportunity now is to embed AI where it naturally fits.
When more AI means less impact
The problem isn’t a lack of AI – it’s too much of it in the wrong places. New tools are being introduced without a plan for how they interact. One for content generation, another for workflow triage, and another for forecasting. Each one might be useful on its own, but when they don’t speak to each other – or to the systems teams already use – they create noise.
That disconnect shows up quickly. Teams are asked to adopt systems outside their workflow, interrupting the rhythm of the day. And what was meant to reduce manual work becomes another layer to manage. That’s not a technology problem – it’s a product one.
It’s not that employees aren’t open to using AI. Our latest research found that 75% of UK employees are keen to use AI tools to support their roles. However, 88% also say that better collaboration strategies and digital tools are essential for achieving their best work. It doesn’t matter how powerful the model is. Adoption is the real measure of impact.
Embedding intelligence, not just adding tools
This is where embedded AI – designed into the flow of work – makes a difference. Not just intelligent, but well-placed. Not a dashboard to check, but a nudge when something’s off.
We’ve seen this in use cases like document intake, where AI extracts key data points, flags inconsistencies, and triggers the next step. Teams that once spent hours triaging requests now act in real time. No separate interface. Just better output, built into what’s already there.
The temptation, still, is to keep adding new AI layers. Another tool for insights, another assistant, another plugin that promises to simplify things. But impact doesn’t come from scale alone – it comes from coherence.
Are we building systems that learn across departments, preserve context, and reduce people’s need to repeat themselves? That’s the difference between an AI strategy that grows and one that eventually collapses under its own weight.
This is also where productised AI comes in. Productised AI refers to AI capabilities built directly into software in a way that’s ready to use – intuitive, accessible, and designed for real-world tasks. Not one-size-fits-all solutions, but flexible, modular tools that can be tailored to specific roles, teams, and workflows.
It’s about giving teams intelligent building blocks they can shape to fit their needs without requiring a data scientist to make it work.
From experimentation to AI adoption in the workplace
The challenge many businesses now face is deciding where to focus, what to monetise, and how to do it in a way that delivers value. Experimentation plays a big role here. When teams are given space to trial AI in a low-risk way – without needing to commit budget, overhaul systems, or retain staff – they’re far more likely to find use cases that stick.
Of course, as experimentation deepens, so does the need for trust. That means AI must respect the same rules as every other part of the system – only accessing data users are already allowed to see and working within existing permission structures. Teams need clear guardrails, the ability to override or escalate, and transparency about when AI acts on their behalf.
At the same time, teams need to understand what AI is doing, why it’s being introduced, and how it supports their work. Employees involved through pilots, feedback loops, or open workshops are more likely to embrace AI as something helpful, not imposed.
For professionals beginning their journey with AI – or facing growing pains in adoption – start small, stay curious, and prioritise education across teams. The goal isn’t to automate everything. It’s to make work more connected and less manual – so people can focus on the parts that require judgment, creativity, and collaboration.
When AI is designed to work with people, it creates the space for better work to happen and unlocks the full potential of AI adoption in the workplace.
