No ROI, No AI Readiness

Over the past seven months, we’ve been gathering data through our ‘AI Readiness Scorecard’ at industry events across cleaning, waste and recycling, and food production hygiene. One finding stands out: 71% of respondents aren’t tracking whether AI is saving them anything.

That’s striking, especially given what we’re seeing on the ground. Businesses are investing in generative AI, employees are using the tools, and experimentation is well underway. But when we ask what AI is delivering – time saved, costs avoided, or capacity created –  very few have the numbers.

Using AI isn’t the same as being AI-ready

AI readiness isn’t simply about using AI. It’s about understanding where it creates value and being able to demonstrate it. That means measurement needs to be part of the conversation from the start. Without this, it’s difficult to turn experimentation into a sustainable business case or know where to invest next.

Our sample is small and self-selecting, but this isn’t just an issue for our industries. Microsoft and LinkedIn’s 2024 Work Trend Index found that 59% of business leaders struggle to quantify the productivity gains from AI, while 60% worry their organisation lacks a plan for implementing it. Only 39% of AI users had received training from their employer.

Are guardrails in place?

Some 78% of our respondents are already using generative AI for content creation, yet half have had no training and 45% work in companies with no AI policy. Around a third are using AI for company work with neither training nor organisational guardrails in place.

Think of AI as a bright graduate joining your business. They’re fast and capable, but on day one they know little about your clients, processes, or expectations. You wouldn’t hand them a major tender and say, “Off you go.” You’d provide context, guidance and review their work.

Generative AI is much the same. Good results depend on good briefing and oversight. It’s not about slowing AI adoption down, but about doing it properly.

A different proposition

One of the biggest lessons from our scorecard isn’t technology; it’s about leadership. Cleaning, FM, and environmental services already use AI, from robotics to predictive maintenance. But generative AI is different. It requires informed leadership, clear policies, training, data awareness, and human oversight.

Five building blocks of AI readiness

For leaders, a useful starting point is to assess five areas:

1. Leadership literacy: Do leaders understand generative AI well enough to identify opportunities and make informed decisions?
2. Governance: Do employees know which tools are approved, what data is safe to use and when human review is required?
3. Capability: Have people been taught how to brief AI, assess its outputs and apply it effectively to their roles?
4. Workflow integration: Is AI part of real business processes, or are individuals simply experimenting with chatbots?
5. Measurement: Can you demonstrate what AI is saving or improving in terms of time, cost, quality, capacity, or commercial performance?

AI readiness doesn’t mean having all five areas perfected from day one. Understanding where the gaps are gives you a practical place to start – and helps turn investment in AI into real business value.

Want to understand where your organisation is on its AI journey? Get in touch to explore your AI readiness and discover ways to move forward. If you’d like to take our ‘AI Readiness Scorecard’ to see what stage you’re at and practical next steps, follow this link or scan the QR code below.