In this article, we cover:
- What is employee-driven innovation?
- Are employees already using AI to innovate?
- How is AI changing who can innovate?
- Can employees use AI to solve everyday business problems?
- How can businesses encourage employees to innovate with AI?
- What does the future of innovation look like with AI?
AI is transforming how work gets done, but not everyone is experiencing this change the same way. Research shows 85% of workers are being encouraged to rely on AI for tasks, but many don’t know why.
Tools such as Microsoft Copilot are making it easier for employees to experiment with new ways of working, automate repetitive processes and create early solutions to everyday business problems.
Those who work with the same processes, systems, and challenges every day are often best placed to spot where something could be done differently.
What is employee-driven innovation?
Employee-driven innovation is the idea that innovation doesn’t have to come exclusively from specialist teams. People across an organisation can identify opportunities for improvement within their own work and experiment with innovative solutions. They often have a detailed understanding of the processes they use every day. They know where information gets duplicated, which tasks take longer than they should and where small changes could improve their ways of working.
More complex ideas may still require support from technology and AI specialists, but the initial innovation can come from anywhere in the organisation.
Explore how AI is changing the skills employees need for human-AI collaboration.
Are employees already using AI to innovate?
Research found that 63% of the UK workforce has used GenAI for work. However, adoption is not always being governed. One in three GenAI users say they use the technology without their employer’s knowledge, while half have received no training.
Organisations need to create the conditions for employees to use AI confidently, responsibly and purposefully.
At FDM, we are seeing this shift in practice.
How is AI changing who can innovate?
AI has democratised innovation – through agentic engineering and spec-driven development – both of which we are doing at FDM. Tools like GitHub Copilot and Codex have democratised tech, allowing anyone to now “write code” and build agents tailored to their work. No-code platforms and natural language programming have all contributed to opening up the market to more people.
FDM Senior Account Manager, Nat Rowland, identified a recurring challenge in his work.
“A common challenge within our recruitment and account management activities is converting candidate profiles from a variety of formats into FDM’s standard CV template. This can be a time-consuming process and often requires manually restructuring information to ensure consistency and quality.”
Nat adds, “Using Copilot, I built an agent that takes a candidate profile and automatically converts it into the FDM CV format. The agent extracts the relevant information, restructures it into the standard FDM sections, and creates a strong first draft that can then be reviewed and refined before being shared with clients.”
This shift is not only changing who can build with AI, but also how organisations think about individual contribution and success.
FDM Product Centre of Excellence Consultant Sabapathy Kirusaanth believes, “AI will change our idea of what individual contribution and success look like within a business. As technology takes on more routine and technical work, the value organisations place on human attributes will shift too. Curiosity, adaptability, creativity and the willingness to challenge established ways of working will become increasingly important.”
He saw this first-hand while leading the delivery of innovation proofs of concept for a large energy company. One initiative explored developing a generative-AI assistant.
Sabapathy shares, “Some highly experienced developers understandably struggled to see its immediate value: the technology was still immature and could not compete with the expertise or efficiency of a seasoned engineer. I agreed that the product, at that point, was premature for significant investment – but I saw its longer-term value differently.”
Can employees use AI to solve everyday business problems?
Yes. Nat’s example follows a simple but increasingly accessible innovation cycle and shows how easy it is to solve a business problem and add value:
Employee spots a problem → uses AI to explore a solution → creates and refines it → measures the improvement → considers where it could be used elsewhere.
He shares, “Firstly, it significantly reduces the time spent on CV formatting and administration – this was a task that would previously take an hour per candidate; it is now two minutes. Secondly, it helps improve consistency, ensuring consultants are presented in the same format.”
Nat adds, “It’s been a great example of applying AI to solve a practical, repeatable business problem while freeing up more time for value-add activities.”
Find out how FDM Consultants can integrate seamlessly into your teams and accelerate your timeline from pilot to production.
Another example is FDM Product Centre of Excellence Consultant, Ziyi Huang, who’s been building AI agents and running workshops with FDM’s Finance team to explore real use cases in their day-to-day work.
They share, “I was drawn to AI because of its potential to make innovation more accessible and help people turn ideas into practical solutions more quickly.”
Through workshops and solution development sessions, Ziyi has been exploring how AI can reduce manual work and make information easier to access. They’ve also been supporting Finance Copilot Champions, helping them find practical ways to use AI and drive adoption within their teams.
Ziyi adds, “The biggest lesson I have learned has been to start with the business problem rather than the technology. Working closely with Finance SMEs, testing ideas, gathering feedback, and iterating on solutions helped us find the most valuable opportunities. I have also found that using real examples and continuous experimentation made a much bigger impact than theoretical use cases.”
The focus when making these agents was less on producing a ready solution and more on teaching others how to build, configure, and maintain them, so they can apply these skills to build their own agents for their own use cases.
How can businesses encourage employees to innovate with AI?
Employees should have the support of their organisation to identify problems, suggest ideas and experiment with potential solutions. They should facilitate a culture of continuous learning.
FDM is actively facilitating this culture of learning and experimenting with AI.
In Hong Kong, FDM collaborated with HSBC Corporate and Institutional Banking to deliver an AI hackathon focused on spec-driven development, supported by our AI specialist partner MISSION+.
Within just five hours, FDM consultants and HSBC engineers tackled real financial services use cases using agentic AI to move quickly from concept to working solutions.
One key takeaway was how quickly progress can happen when domain expertise, clear business requirements and AI capabilities are brought together.
Across our London and Leeds centres, we brought together consultants, clients and colleagues to experiment with emerging AI tools and develop practical prompt engineering skills.
Participants worked in squads to build working prototypes of products ranging from AI meeting assistants to voice-enabled agents.
Businesses can also encourage employees to innovate with AI by:
- Having access to appropriate AI tools
- Establishing sensible guardrails around data and security
- Sharing successful use cases
- Connecting employees with AI and technology specialists
Discover more about AI skills for human-AI collaboration in the workplace.
What does the future of innovation look like with AI?
As AI tools become more accessible, employees do not necessarily have to wait for a technology team to identify an opportunity, develop a solution and deliver it back to the business. The people closest to a problem can increasingly play a role in exploring what a solution might look like.
Ziyi believes, “Looking ahead, I think the real skill will be knowing how to use AI effectively to achieve what you’re trying to do. The pace of change is incredible, so staying adaptable is important, but above all, I think curiosity will help people keep learning, experimenting and innovating.”
FDM Product Centre of Excellence Consultant, Sahra Yusuf, shares her thoughts: “Agentic AI and LLM tools have recently been introduced into so many workflows. Not just for developers, but for BAs and other non-technical roles too. Because of how quickly this is happening, I think access to training and education on how LLMs work is really important. When people gain an understanding of where AI excels and where it falls short, they can make better decisions about where to use them and where not to. AI isn’t the right solution for every problem, and knowing the best places to implement it leads to better outcomes and innovations.”
That means giving people access to the right tools, developing AI literacy and critical thinking, establishing sensible governance and creating routes for successful experiments to be shared and scaled.
FDM’s own Workforce 2.0: AI Adoption and the Future of Jobs research found that 58% of organisations have limited or early-stage AI proficiency, while only 6% report high proficiency.
How FDM can support
At FDM, we help organisations combine people, technology and skills to adopt emerging technologies and turn them into practical business outcomes. Our AI-ready talent bring the skills needed to help organisations move from experimentation to implementation.
Discover how FDM can support your AI and technology transformation.