Author

Simon Hayward

Published

7 August 2026

Estimated Read time

4Mins

Simon Hayward, GM & VP Sales International at Freshworks explains why AI needs to work effectively for mid-sized organisations to truly succeed.

Determining the impact AI will have on the future of enterprises lies in the hands of the global mid-market sector. Sitting between SMEs and large corporations with revenues between £25m and £500m – mid-market businesses employ 40% of the global workforce and are vital growth engines to the global economy and determining the extent of AI’s global impact.

The path to successful AI adoption

Investors and board-level decision makers will spend much of 2026 identifying key target markets and the mid-market should be top of every list.

Identifying and assuring investors’ delivery of AI ROI is critical to not only the future of the technology but also the success of so many global businesses. Mid-market companies represent roughly a third of private-sector GDP and around 40% of global employment.

They sit at the heart of the global economy, yet they are often overlooked in AI discussions that focus primarily on hyper-scalers or the largest enterprises. These companies do not have the luxury of multi-year transformation programs or unlimited budgets. For them, AI must deliver tangible value quickly or it simply will not stick.

Speed, simplicity and real outcomes

Mid-sized organisations are under constant pressure. They compete with far larger players while working with fewer resources. Cost efficiency matters, meaning time to value is critical.

Software that takes months to deploy or requires swathes of consultants is a non-starter. AI that creates more complexity than it removes will fail, regardless of how sophisticated the underlying models may be.

For AI to succeed in the mid-market, it must be designed around how people actually work. It must integrate easily into existing systems, automate real tasks and deliver measurable outcomes in weeks, not years. Anything less becomes shelfware.

This is where the conversation around AI needs to mature. The future of AI adoption is not about who has the most advanced models. It is about who can turn intelligence into impact, at speed, for teams that are already stretched thin.

The companies powering the economy

The future impact of AI will not be determined by a handful of global giants. It will be shaped by the millions of companies powering the economy every day.

Businesses with revenues of around a billion dollars, not ten or one hundred billion, are using AI to help their teams work more effectively, serve customers better and grow more efficiently. When AI works for them, it scales across the economy.

What is especially encouraging is how traditional businesses are embracing this shift. Companies with decades of history are using AI to modernise core operations, automate routine work and improve both employee and customer experiences. These are not abstract experiments or innovation theatre. They are practical applications that free up investment, improve productivity and support sustainable growth.

This is where AI becomes truly transformative for the mid-market: not as a futuristic concept, but as a tool that helps people focus on higher-value work and helps businesses compete more effectively.

Reducing complexity, not adding to it

If AI is going to deliver meaningful economic impact, we need to meet companies where they are.

That means reducing complexity, not adding to it. It means building technology that is intuitive by design, not powerful but inaccessible. And it means focusing relentlessly on outcomes, not features.

The most successful AI deployments I see share a common trait. They are invisible when they work well. They remove friction instead of introducing it. They make teams faster, smarter and more effective without requiring a fundamental rewrite of how a business operates.

This approach is especially critical for the mid-market, where every investment must justify itself quickly and clearly.

The real test for AI

The mid-market does not need more hype. It needs AI that works. If AI can help these companies scale, compete and grow more efficiently, the economic impact will be great. Productivity gains will compound. Innovation will accelerate. Opportunity will spread more evenly across industries and regions.

The real test for AI is not whether it can impress in a demo. It is whether it can deliver value where it matters most. And that future will be defined by how well we serve the companies at the heart of the global economy.

Written by Simon Hayward, GM & VP Sales International at Freshworks

  • AI in Procurement
  • AI in Supply Chain
  • Data & AI

Author

Nell Walker

Published

6 August 2026

Estimated Read time

3Mins

92% of operators say AI demand is speeding up build timelines, but supply chain challenges are creating costly remediation risks

New research from data centre infrastructure specialist Onnec has found that surging demand for AI is heaping pressure on to data centre operators to accelerate builds – increasing the risk of delays, higher costs and remediation after go-live.

The survey of 300 senior decision-makers at data centre operators in the UK, Ireland and Nordics found that 92% of operators say they are being forced to compress build timelines to keep pace with AI demand. But 75% say speed-to-market pressure is forcing design decisions before infrastructure requirements are fully understood.

As a result:

  • Safety and cost pressures are rising: 74% say the pressure to accelerate data centre builds is increasing health and safety risks on site, while 67% say their AI-ready build or retrofit costs have increased in the past 12 months. Among those reporting a rise, costs have increased by an average of 42%.
  • Quality control is being squeezed: 45% say compressed timelines have reduced time for testing, commissioning or quality assurance, while 43% say they have increased risk of quality issues or rework on completed builds. A further 43% say infrastructure has required upgrades or remediation after go-live.
  • Supply chain disruption is delaying delivery: 61% have had a project delayed by supply chain issues, while 79% agree that geopolitical instability will affect the cost or availability of data centre components.

“Operators are under enormous pressure to deliver AI capacity quickly, but speed and readiness are not the same thing,” comments Matt Salter, Global Head of Data Centres at Onnec. “A data centre can go live on time and still need optimisation for the AI workload it was built to support. Reworks, retrofits and remediation are increasingly common. And network infrastructure is often where problems show up first. No amount of compute or GPU power can compensate for infrastructure or cabling that wasn’t designed to keep pace with today’s AI demands.”

Supply chain bottlenecks are holding back delivery

Almost half (45%) of operators are leaning towards new builds to deliver AI infrastructure, but one of the biggest pressure points impacting new builds is the supply chain. While 87% of operators are confident they have the right supply chain ecosystem to support fast, reliable delivery, delays are still occurring across the whole data centre environment.

Among those who have delayed a project due to supply chain issues, operators reported issues sourcing GPUs and compute (53%), cooling systems (45%), specialist staff (45%), power distribution equipment (43%) and cabling (39%).

Meanwhile, 29% of operators are leaning towards retrofitting to expand AI infrastructure and maintain pace without sacrificing design quality. But retrofitting is rarely a simple fix. Key challenges include cooling limitations for high-density racks (35%), retrofitting live environments without disruption (33%) and insufficient power capacity (30%).

“The operators who come out ahead in the AI race won’t be the ones who moved fastest,” continues Salter. “They’ll be the ones who took a holistic approach to design from the outset, treating cabling, power, cooling and compute as one connected system rather than separate workstreams managed under deadline pressure. Without that, operators risk locking in performance constraints that are harder and more expensive to address after go-live.”

  • AI in Supply Chain