AI is no longer a novelty or passing trend. If you want to stay ahead as an organisation, it’s a necessity.

Despite the power of AI, the way businesses use it remains a varied picture. While AI usage is growing, uncontrolled approaches and limited expertise are impacting success.

For long-term adoption and success, businesses must understand the potential of AI and implement it effectively. But that requires work to be done first.

In this blog, we explore the state of business AI readiness and what you can do to better utilise AI in your organisation.

AI readiness: a definition_

Let’s start by examining exactly what AI readiness means. As you may have guessed, it refers to how prepared a business is for AI. But achieving AI readiness isn’t as easy as it may seem.

It’ s a multifaceted concept that focuses on not only your ability to adopt AI, but to do so in a way that adds value and minimises disruption. This includes preparing your:

• Technological infrastructure (building the necessary hardware, software and network capabilities to support AI workloads, including data storage, processing power and cloud computing resources)
• Data infrastructure (having robust systems for collecting, storing, cleaning and managing data to drive data quality, accessibility and security)
• Culture and strategy (fostering a culture that embraces innovation, encourages experimentation and supports change management)
• Skills (employees with the necessary skills to develop, implement and manage AI systems)
• Governance (establishing ethical guidelines and governance frameworks to ensure responsible and transparent use of AI)

In essence, AI readiness is about creating an environment where AI can be successfully integrated into an organisation’s operations to drive innovation, efficiency and growth, while minimising any risk.

Are businesses using AI?

The data suggests AI usage is on the rise across businesses.

According to research from Microsoft, generative AI usage jumped from 55% in 2023 to 75% in 2024. Companies are partially driving this rise, with 35% reporting using AI in some form and another 42% exploring it in the near future. 24% report deploying AI organisation-wide already.

In terms of the business functions using AI, IT, marketing, sales and service operations take the lead. According to a survey from McKinsey, usage in IT has increased the most in the last six months.

The McKinsey survey also found that more businesses are reporting using AI across multiple functions, rather that just one area.

Alongside a rise in businesses leveraging AI, many are considering are part of their long-term vision, with 91.5% of leading businesses investing in AI on an ongoing basis.

Leaders are acknowledging the importance of AI in the future, too. 58% of tech leaders believe that generative AI will play an important role in employee productivity and 80% see AI usage as a core competitive differentiator.

However, the data also shows there is a gap between what leaders believes will make their organisation stand out and what they’re prepared for, with only half of organisations reporting being ready to utilise AI use cases like data analysis. This suggests overall readiness needs to improve for businesses to truly leverage AI and bring their vision to fruition.

Finally, data has found one common trend among businesses: workers want AI. 70% of employees say they’d delegate as much work as possible to AI to reduce their workload. 88% say they would rather have access to Copilot (Microsoft’s AI assistant) than free lunch.

With employees keen to use AI, it’s likely they will – whether their organisation is prepared or not. But if the organisation isn’t ready, they’re more likely to use AI in ineffective ways, through unapproved tools, that could put your organisation at risk.

How is AI being used?

It’s apparent that AI is being used more often, backed by interest from the workforce and beliefs from leaders that effective usage is key to long-term business success. But is this reflected in how businesses are using AI?

Well, it seems most businesses who are leveraging generative AI understand the productivity potential. Microsoft studies found that 92% of AI users are leveraging AI for productivity, with 43% reporting the greatest ROI from these types of uses.

Businesses are finding even more success when it comes to innovating their offerings through AI, with 96% seeing measurable value.

So far, the emphasis has been on generative AI and the results have also been grounded in this. However, agentic AI is changing the trend. It’s relatively early days for this technology, but it seems businesses are already embracing it.

81% say they expect agents to be moderately or extensively integrated into their AI strategy in the next 12–18 months, in a bid to boost internal capacity.

If businesses continue to embrace agentic AI in the way they have generative AI, it’s likely bigger productivity benefits will be reported, improving cost-efficiency and capacity.

Attitudes to risk_

Whatever type of AI you’re using, there is risk. And research suggests governance is lacking in many organisations.

According to McKinsey data, while organisations have tuned into the importance of human oversight over AI tools, the extent can be lacking. Only 27% of businesses say all AI-generated content is reviewed by humans, with 30% saying less than a fifth of it is checked.

This suggests that a large proportion of organisations are still using AI somewhat blindly, without appropriate intervention to ensure quality, accuracy and fairness. Ultimately, this results in the risk of AI delivering the wrong messages and damaging reputation.

On top of this, 78% of AI users are bringing their own AI tools to work. And only a small percentage of organisations have AI policies in place to govern what is being used and how. This leads to many negative implications, including data leaks, stolen IP and poor quality output impacting customer experiences.

Only just over 50% of businesses believe AI is an important long-term strategic goal. This suggests that many organisations aren’t aware of the work required to get the most value from AI, which could lead to many making themselves vulnerable or missing out on potential rewards.

What are the risks and barriers around AI readiness?

There are a few commonly cited reasons for businesses not being ready to embrace AI. This includes:

The reason for this unpreparedness can stem from a few areas, including infrastructure, skills, security, costs and return on investment.

71% of business leaders don’t feel their IT is completely ready for AI implementation, and 3 in 5 report feeling that they struggle to keep up with technological advancements.

Among AI decision-makers, 30% say that a lack of technical skills to use AI is one of their biggest barriers to adopting it. Plus, businesses haven’t actively been able to tackle the skills gap, with just 45% say their organisation understands the AI skills their workforce needs to be successful today.

Another one of the most significant concerns is around security and compliance, concerning 71% of business leaders. 30% businesses state they’re holding back on AI adoption across their organisations due to security worries.

Costs and proving ROI is another obstacle businesses must overcome. Part of this involves being able to quantify the gains of AI effectively, which 59% of businesses aren’t confident they can do.

Concerns around AI also shift depending on business size. Research highlights that large businesses are more likely to worry compliance and security of sensitive customer data, while small businesses worry about costs, ROI and a lack of expertise.

This suggests, for SMBs in particular, there is a strong need for financial support and skill development for their staff.

Whatever the reason, uncertainty is stalling vision. 60% of leaders worry their organisation’s leadership lacks a plan and vision to implement AI. Half of businesses also describe a gap between AI ambition and action within their organisations.

In order to move forward, businesses need to take practical steps to overcome these barriers, with the right guidance and support.

What businesses need to do now to get AI-ready_

If you want to excel your AI readiness, we’ve put together our top areas of focus, with practical tips to implement in your business.

1. Think long-term about AI_

You may feel the pressure to utilise AI as fast as possible but, if you want success, it has to be a long-term play. This will enable you to do the foundational work that ensures greater value from AI and safer adoption.

Start by pinpointing your vision for AI. In an ideal world, what would it look like for your business? How would you expect staff to use it? How may it apply to your products and services?

Once you’ve defined the vision, it’s time to turn that into a strategy. This should cover the steps you need to take to make the vision reality, including upskilling your workforce, preparing your infrastructure or embedding AI into existing processes. This will then form an AI roadmap for your organisation, allowing you to adopt it in a sensible, considered manner.

2. Start small and scale_

As AI should be considered long-term, you don’t need to rush to the finish. Many organisations fall into the trap of trying to get AI to immediately solve the big problems, only to be frustrated when results aren’t achieved.

We always recommend starting with small use cases. This gives you the chance to learn as you go, with relatively low stakes. You’ll then be faster to resolve the issue and understand the value of AI.

This can then be applied to more significant problems, allowing you to scale as you get more confident in your AI usage.

3. Set AI policies_

For all its gains, AI does pose risk, especially when not used correctly. It’s crucial that you set specific policies to govern usage.

A good AI policy should include:

• Preferred/approved tools (or non-permitted tools)
• Expectations for how company data should be used (in line with regulations)
• Protocols to prevent AI-driven decisions based on sensitive attributes
• Processes for reviewing AI output
• Strategies for detecting and mitigating biases in AI algorithms
• Accepted and unaccepted AI use cases
• Best practices and guidelines for using AI

By having a policy in place, you can encourage employees to make the most of AI, but in a safe way that limits risk.

4. Train your staff_

Due to the AI skills gap, there aren’t enough people with AI expertise to go around. This gap will widen if work isn’t done now to upskill workforces.

Your business can play a part in resolving this issue. By investing in AI training and pointing staff towards useful resources, you can help them build the skills they need to utilise AI optimally.

This doesn’t necessarily mean spending vast sums of money. There are now plenty of resources available for free which can teach people AI best practice (such as around prompting) and inspire them with use cases. It’s also important that you give employees time to experiment safely with AI.

By investing in AI, you can address the skills issues, while ensuring your business has access to the experts of tomorrow, fuelling your progress.

5. Apply it to real-life problems_

One of the concerns around AI is being able to measure ROI. The best way to achieve this is by applying it to your genuine business problems and looking at the impact.

As we’ve already touched upon, the problem can be small. A common example is an area that requires a lot of repetitive, manual intervention, which can be automated through AI.

Once you apply AI, you will want to measure things like time required before vs after. This can often by turned into cost savings through resource hours saved. It’s also worth seeking qualitative feedback, such as how it impacts employee satisfaction or customer experiences.

Over time, this will enable you to track the impact of AI in a tangible way, warranting future experiment and investment.

6. Get your data into shape_

AI is most valuable when it’s grounded in your data. Customised AI, which is AI that is tailored to your business, is becoming increasingly popular, thanks to the increased rewards offered. Tools like Copilot are also making it easier for businesses to integrate AI into their data, allowing for more relevant responses without excessive prompting.

However, output will only be as good as your data. So, it’s crucial to first get your data into shape.

This means reviewing data sources across your business and ensuring everything is up to date and complete. You should also seek to standardise data with the same format and approach for consistency. Being able to store data in one location, such as a data lake or warehouse, will also enable easier integration with AI.

You will also want to label and classify your data, which will include marking sensitive data so it isn’t utilised by AI, and setting permissions to control who gets access to what. This will adhere to compliance standards and prevent data breaches.

7. Adapt your IT infrastructure_

As part of your roadmap, you will need to adapt your infrastructure to integrate with AI successfully. Preparing an infrastructure for AI demands a strategic approach, focusing on key elements to support the complex demands of AI workloads.

Alongside the right data structure, via a warehouse or lake, you need significant processing capabilities. This often translates to investments in high-performance hardware, such as GPUs or TPUs, and scalable cloud computing resources.

Networking and connectivity also play a vital role. AI applications often involve large data transfers and real-time processing, requiring high-bandwidth, low-latency networks. Cloud-based solutions can offer scalability and flexibility, but you should also consider on-premises infrastructure for sensitive data or applications requiring minimal latency. Establishing a reliable and efficient network architecture is crucial for seamless AI operations.

Make the move towards AI_

AI can often feel like a double-edged sword. If you aren’t using it, you’re already falling behind as usage increases among organisations. But if you rush to embed it, you could leave your business susceptible to risk.

However, when it comes to AI, the reward outweighs the risk. When you implement AI effectively, you can make that risk minimal.

By creating a long-term strategy for AI and doing the work to prepare your organisation, such as through infrastructure, data quality, governance and staff training, you stand a much better chance at securing long-term success that sets you apart from competitors.

And the first step to any long-term strategy is a business case. In the video below, our experts will tell you exactly how to build the case for AI in your organisation, in a way that finds valuable uses and gets stakeholders on board.