The workplace artificial intelligence (AI) revolution is undeniably underway. Our own research found that 77% of project-based firms plan to increase AI investments in 2025, providing concrete evidence that the ‘fastest technological change’ in history is picking up steam.
What makes this particular industrial revolution unique, however, is its stealth. While boardrooms debate implementation strategies, half of UK employees have already voted with their keyboards, adopting personal AI tools to boost productivity and overcome workplace challenges.
This ‘Shadow AI’ phenomenon reflects workers’ determination to deliver better results faster and accelerate project delivery in ways their organizations haven’t yet embraced.
Shadow AI creates hidden risks that could undermine the very benefits it provides, creating potential vulnerabilities around security, accuracy, and accountability. Despite the risks to business, Shadow AI is symptomatic of something bigger.
It signals a dangerous innovation bottleneck that threatens to make traditional firms obsolete, given the breakneck pace of AI advancement. Long-term, sustainable success requires proactive strategies from organizations that balance empowerment with oversight.
VP of Product Management at Deltek.
The hidden depths of Shadow AI
The accessibility of free AI tools—such as ChatGPT, Gemini and Claude—makes managing the spread of Shadow AI particularly challenging. Furthermore, once employees have experienced how AI can eliminate friction and bridge skills gaps, 46% say they would continue using these tools even if their employer banned them.
This defiance manifests differently across professional services. Engineers, for instance, may use generative AI to draft submittal transmittals, summarize plan sets, or prepare field reports streamlining communication and saving time on documentation-heavy work. A consultant, meanwhile, may harness AI to create compelling tender documents that better match client expectations.
Workers are adopting these personal AI tools to boost productivity, automate repetitive tasks, and compensate for skills shortages. As digital maturity accelerates across the sector—with over 56% of UK project-based firms now at a mature or advanced stage—employees increasingly expect faster, smarter tools to support their work.
When organizations do not provide adequate tools, the technologically-curious inevitably find their own alternatives. Firms stuck in this bottleneck risk being left behind as AI transforms entire industries at unprecedented speed. Leaders must recognize and respond to what their workforce is demanding.
Understanding the risks of Shadow AI
The temptation may be to view Shadow AI purely as a cybersecurity challenge requiring IT solutions. This perspective fundamentally misses the point. The increased prevalence of Shadow AI suggests leadership isn’t prepared for the AI transformation ahead. That said, organizations must be cognizant of the real risks and business implications brought on by Shadow AI.
One of the immediate dangers posed by Shadow AI is the potential for damaging data breaches. A lack of employee oversight can inadvertently expose sensitive information, leading to data leaks and breaches. It was recently reported that one in five companies have experienced data leakage because of employees using generative AI.
As a result, three quarters of Chief Information Security Officers now believe that insiders pose a greater risk to their organization than external threats; a figure likely heightened by the prevalence of Shadow AI.
Critically, Shadow AI creates significant compliance vulnerabilities that many organisations haven’t yet recognised. AI models that process and store corporate data may violate industry regulations such as GDPR, data protection laws, and sector-specific compliance frameworks, particularly when data handling policies remain unclear or unenforced.
Shadow AI can result in unintentional compliance breaches, as companies struggle to track where sensitive information is being processed, stored, or utilised within AI workflows.
Finally, the reliance on unauthorized AI models can impact decision-making quality. If not adequately scrutinized the outputs generated by large language models can lead to poor strategic choices and harm a company’s reputation. If the output contains bias or hallucinations, the work may fall short of the organization’s ethical standards or undermine consumer trust.
Building an AI ready culture
Shadow AI should be seen as a positive signal: employees are ready to innovate, and employers must provide them with safe, approved tools. Rather than imposing outright bans, companies must define how and when employees can leverage AI for their work.
This is no simple task and requires a comprehensive strategy that includes leadership buy-in, policy development, employee education, and most importantly, continuous oversight.
The key lies in embedding AI directly into business strategy, rather than treating it as a separate technology initiative. Companies often discuss needing an ‘AI strategy,’ but what they actually need is to incorporate AI into their core business strategy. This integration makes AI safer for business by aligning it with broader organizational goals rather than operating as an isolated project.
Crucially, innovation requires the right culture built on three key foundations. First, open and ongoing dialogue between IT departments, security teams and business units. This fosters a better understanding of both AI’s limitations and capabilities, which can help organizations identify which AI tools are beneficial while also helping ensure compliance.
Second, companies must also foster a culture of experimentation and exploration and be comfortable with failing fast and testing out many options. By providing ‘playgrounds’ that are safe for experimentation, tools can be tested quickly and either adopted or eliminated from consideration.
Third, ongoing training forms part of the cultural backbone. Employees need to understand AI’s limitations, biases, and security implications, with literacy programs covering how models store and process data, and the risks of relying on its output without human validation.
UK project-based businesses are already seeing AI as key to profitability, with 82% aiming for growth in 2025 and AI central to this ambition. Shadow AI isn’t a problem to be solved, but a signal to be heeded. Companies that recognize this reality and respond with governance frameworks that empower rather than restrict will capture competitive advantages their slower-moving competitors cannot match.
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