Investment in AI is at an all-time high. Recent projections indicate that global spend on artificial intelligence currently sits at around $244billion and is expected to grow to more than $800billion by 2030. On top of this, 40% of organisations globally are already using AI, and this number surges to 82% when you include those businesses exploring the technology. However, delays in feature rollouts threaten to stall this phenomenal momentum. So, how is this uncertainty impacting the sector?
How delays affect the broader AI ecosystem
Like any industry, it’s obvious to say that delays stall momentum. For the tech space this uncertainty ripples through to both end user confidence and investment, while it can also stifle innovation amongst vendors.
When major vendors postpone high-impact features, it slows down the pace of innovation across the ecosystem. Businesses wait, plans pause, and investors hold back. For UK SMEs, these delays create uncertainty. Many have bought into the vision of AI-enabled productivity but are left in limbo when promised features don’t arrive on time or lack a clear roadmap. Businesses need to be able to trust that their investment will pay off in the long term, with many hoping to adopt AI-driven operations over time.
Momentum is everything in tech, and when the major players delay, it limits what others can confidently build on top of their own platforms. This smothers the kind of innovation that drives real value. This impacts smaller developers, resellers and the businesses using the tech day-to-day; for momentum to continue, organisations have to feel as though the businesses at the top of the pyramid will continue to progress the capability of the tech.
The role of regulatory pressures in the timing and scope of AI feature rollouts
In the UK, the regulatory stance is still developing, and while it likely isn’t the primary reason for delays, the uncertainty around future requirements certainly contributes to caution from the sector. On the other side of the pond, US-based vendors serving global markets are building products to meet a mixture of US, UK, and EU regulations, and that complexity often means features are slowed down or quietly reprioritised.
As a result, most large vendors are playing it safe, which is creating a slower rollout cycle, especially for features involving data handling, decision automation, or anything generative. If these delays continue, there will be questions asked from the main players themselves about ROI. With hundreds of billions of dollars set to be spent on development in the next five years, there will come a point where the technology needs to begin showing a return which outpaces spend. We are still a way off that currently.
Opportunities for smaller tech companies to fill the gaps
The longer the big players take to roll out functionality, whether due to risk or regulatory restrictions, it always leaves the door open for smaller firms to begin plugging the gaps.
In the UK, we’re seeing emerging players build lightweight tools that solve specific business problems, like automation tools, AI-powered integrations, and productivity boosters that don’t need enterprise-scale backing. These firms are unencumbered by global compliance, legacy infrastructure, and massive user bases. That makes them faster, more agile, and often more relevant for UK SMEs. This dispersion of development and expertise can be key to ensuring technology doesn’t stagnate.
This is something we’ve seen with DeepSeek. When the competitor to OpenAI launched earlier this year they stated that their total spend on computing power to train the model was $6m, less than a tenth of what social media giant, Meta, has spent, or the “over $100m” supposedly spent by OpenAI to train GPT-4. Although industry experts believe this number massively undersells the amount of investment spent by the Chinese company, it still doesn’t mitigate the fact that, with less capability, expertise and funding, this company was able to create a product rivalling industry leaders. This highlights the opportunities for smaller developers to move into the AI space.
Integrating advanced AI capabilities into existing products
Large vendors are having to prioritise features that work across different business models and multiple sectors, from enterprise and SMEs to education and healthcare, for example. That’s a massive and complex undertaking, one that can lead to substantial integration challenges.
The delays often come down to this complexity, adding powerful AI to existing products requires more than technical readiness, demanding infrastructure updates, UX changes, security reviews, and performance testing. These large vendors are managing risk, reputation, and reliability at scale. That means slower, more conservative rollouts.
Long-term consequences for failing to keep pace
Waiting is a risk. The technology is moving fast, and businesses that sit on the sidelines will find themselves behind. Not just technically, but competitively too.
You have to start somewhere. Even if it’s with a small pilot or internal use case, experimenting early gives organisations the experience they’ll need as AI becomes embedded in more areas of work.
The gap will widen, and those that get started now will learn and adapt faster and deliver better outcomes, while those that wait will have to play catch-up under more pressure.
