Unlocking business value with AI

The value of AI. What businesses want AI to do. Practical hints and tips.

"The rise of artificial intelligence (AI) is one of the defining trends in today’s business landscape. According to Larato’s research across more than 400 organisations, 16% view AI as the most valuable technology today, while a significant 46% see it as central to their future business goals. AI-powered tools like data analytics, automation, and customer interaction platforms such as ChatGPT and CoPilot are reshaping operations, driving efficiency and innovation".
Rev. Dr. Lucy Green Founder of Larato and Making a Difference initiative
Rev. Dr. Lucy Green
CEO, Larato
View AI as the most valuable tech now
0 %
Believe AI is central to growth 2024-2027
0 %
Struggle to access compute power
0 %

Introduction

Here, we explore the potential business value of AI as reported by over 400 businesses. We look at opportunities and challenges, practical examples, as well as some pros and cons to consider. 

The rise of artificial intelligence (AI) is one of the defining trends in today’s business landscape. According to Larato’s research across more than 400 organisations, 16% view AI as the most valuable technology today, while a significant 46% see it as central to their future business goals. AI-powered tools like data analytics, automation, and customer interaction platforms such as ChatGPT and CoPilot are reshaping operations, driving efficiency and innovation.

While only 15% of businesses currently see AI delivering acceptable value, nearly half believe it will be transformative in the next year. The shift is clear: AI is moving from automating processes to enhancing human efforts, improving productivity, and offering competitive advantages.

Yet, many businesses are at a crossroads with AI adoption. Of those who have started using AI, half have halted projects due to two major challenges:

  1. Limited access to compute power: AI demands significant processing power. Many businesses face challenges with the UK’s power infrastructure, but the rise of hyperscale data centres is helping to meet this demand more cost-effectively.

  2. Lack of integration into business strategy: For AI to truly add value, it needs to be embedded in everyday operations, not treated as a side project. It must align with strategic goals, such as improving customer service or streamlining logistics, to drive real results.

One: why AI matters to businesses today

What value businesses are looking for.

Our research found that the three top business priorities for 2025 are: 

  1. Improve customer experience
  2. Improve cybersecurity protection
  3. Improve operation efficiency


AI can contribute to each of these goals by enabling smarter decision making, boosting efficiency and helping businesses to scale at lower cost. 

Smarter Decision Making

AI can process data faster than humans, allowing businesses to make data-driven decisions in real-time. This can help with improving customer experience by providing insights in real time to customer service representatives. It can facilitate a better sales experience for customers too.  For example, a retailer could use AI to monitor sales data and customer behaviour, adjusting prices and stock levels dynamically to make sure buyers can purchase what they want and maximise profits during peak shopping periods.

Boosted Efficiency

By automating routine tasks, AI frees up employees to focus on more strategic, creative, and high-value activities. In a manufacturing environment, for instance, AI can handle routine quality control checks, allowing skilled workers to concentrate on more complex tasks like product development or process improvement. Manufacturers cited the use of AI in improving health and safety as an important step in the coming year.

Scalability

Once implemented, AI solutions can grow with the business. For example, a small online business might start using AI for customer support via chatbots. As the business grows, the same AI system can scale up to handle higher volumes of customer enquiries without needing to hire additional people. The businesses we spoke to have mixed opinions about this. For some, it is a key way to scale whereas others are concerned about losing skills, particularly administrative skills. 

Cost Savings

Automating labour-intensive processes with AI can significantly reduce costs. For instance, in a warehouse setting, AI-driven robots could automate inventory management, reducing the need for manual labour while improving accuracy and efficiency. Approximately one in three UK transport and logistics firms are investing heavily in these capabilities. 

Two: AI tools to transform your business

Adopt the right tools for the right reasons.

Choosing the right AI tools is essential for successful adoption. Our research showed that there is a a lot that businesses want to learn about AI, AI tools, and the benefits they could bring. So, here is a summary of some of the most common tools with pros, cons, and some examples of how businesses are using them now.

Machine Learning (ML) Platforms

  • Pros: ML platforms help businesses develop algorithms that learn from data, improving over time. For example, an e-commerce company could use ML to predict customer preferences and recommend personalised products, driving sales and enhancing the customer experience.
  • Cons: Training ML models requires large datasets and computational resources, which can be a hurdle, especially for SMEs.


Natural Language Processing (NLP) Tools

  • Pros: NLP is invaluable for applications like customer service, content creation, and sentiment analysis. For instance, a financial services company could use NLP to automatically analyse customer emails and quickly respond to complaints or queries, improving response times and customer satisfaction.
  • Cons: NLP struggles with language nuances and context, often requiring additional time and investment for fine-tuning. For example, an NLP tool may misinterpret a customer complaint if it’s written in informal or highly specific industry jargon, potentially leading to incorrect responses.


Robotic Process Automation (RPA)

  • Pros: RPA is great for automating repetitive tasks without altering existing systems. For example, an insurance company could use RPA to automate claims processing, speeding up approvals and reducing manual errors.
  • Cons: RPA is limited to rule-based tasks and struggles with more complex processes. For example, if an insurance claim involves a unique situation, RPA might not be able to handle the complexity, requiring human intervention. Additionally, our research has shown a high level of scepticism about RPA that has stemmed from previous bad experiences. Most of these were attributed to a business not scoping out its requirements well enough. This led to unwelcome surprises when the automations were implemented.

 

Computer Vision

  • Pros: Computer vision excels at interpreting visual data and is widely used in manufacturing (quality control), healthcare (diagnostics), and retail (inventory management). For example, a supermarket chain might use computer vision to track stock levels on shelves, ensuring timely restocking and reducing out-of-stock incidents.
  • Cons: It requires a lot of training data, and its accuracy can be influenced by environmental factors. Initial setup costs can also be high. For instance, a healthcare provider implementing AI-powered diagnostic tools may face significant upfront investment to train the system on thousands of medical images.


Deep Learning Frameworks

  • Pros: Deep learning is excellent for processing complex, unstructured data like images, audio, and text. For example, a tech company developing self-driving cars would rely on deep learning to process and interpret vast amounts of visual and sensor data to make driving decisions.
  • Cons: Deep learning models demand substantial computational power and data, making them expensive and complex to implement initially.

Three: integrating AI into your business strategy

How to harness the value of AI.

Since 46% of businesses cited AI as the most valuable technology to their future success, here are some tips about integrating AI into your business strategy.

  1. Make AI part of your strategy: don’t have a separate strategy for AI. Its value and usage needs to have a clear role in your overall strategy.
  2. Define Clear Objectives: start by outlining what you want AI to achieve. For instance, if your goal is to improve customer service, model the service experience you want to deliver, then identify how AI can help make that a reality. AI-powered chatbots could offer instant responses to common customer queries, freeing up staff for more complex tasks.
  3. Prioritise Change Management: introducing AI often means rethinking workflows. Prepare your teams with proper training and communicate clearly how AI will support their roles, rather than replacing them. For example, in logistics, AI might optimise delivery routes, but staff still play a crucial role in managing customer relationships and problem-solving.
  4. Build a Scalable Infrastructure: investing in scalable solutions like cloud-based AI platforms can solve issues around compute power. For smaller businesses, cloud platforms offer access to AI tools without significant upfront costs, making AI adoption more feasible.
  5. Partner with Experts: collaborating with AI specialists, technology providers, or even universities can help businesses gain the expertise they need. For instance, a retail company might work with a tech firm to develop AI-driven inventory management solutions that streamline stock control, reducing waste and improving availability.

Four: the future of AI in business

What to expect…

As AI continues to advance, we can expect several key trends:

  • Ethical considerations: it is essential that AI is transparent and based on ethical practice. Expect this to be a central theme in the development of this technology and its usage. You can find out more about some of the key ethical issues here.
  • More Accessible AI Tools: Low-code and no-code platforms will make AI more accessible, enabling businesses to build solutions without needing extensive technical knowledge. For example, a small accounting firm could use a low-code AI platform to automate routine bookkeeping tasks, freeing up time for more complex client advisory work.
  • Increased Focus on Ethical AI: With AI’s growing presence in daily operations, businesses will need to ensure transparency and fairness in their systems. Bias, security, and societal values will become central considerations. For instance, an HR department using AI for recruitment will need to ensure that the system is free from bias and promotes diversity.
  • Industry Expansion: While AI has made significant strides in finance and retail, other sectors like healthcare, agriculture, and education will see more widespread AI adoption, unlocking new opportunities for innovation. For example, AI could be used in agriculture to monitor crop health, predict yields, and optimise water usage, leading to more sustainable farming practices.

Action plan (dos and don’ts)

Look at your options

Embrace AI as a core part of your strategy

Evaluate how it can enhance both operational efficiency and customer interactions

Consider the ethical implications of using AI in your business

Leverage local expertise for a smooth transition and sign up for #MakingADifference

Delay

Don’t delay AI adoption due to concerns about complexity or potential job disruption. The longer you wait, the more your AI-powered competitors will pull ahead.

Don’t take its output at face value. It makes mistakes. Check. Test. Check. Test. You get the picture.

Increase your revenue, develop your market, grow your business today.

Ready to find out how we can work with you to help meet your business objectives and bridge the gap between buyers and sellers with our market-leading intelligence and decades of strategy experience.