What is AI in business?
AI in business refers to the use of machine learning, algorithms and data-driven automation to carry out tasks traditionally handled by people, but typically faster and at a larger scale. From chatbots managing customer service enquiries to software that screens job applications, AI tools can help companies operate more efficiently. AI tools are now on the rise in talent acquisition, marketing and fraud detection.
The benefits of AI are clear, but there is a real risk of the human element getting lost. That is why more and more employers are wondering how they can embrace AI without losing the personal connections that build trust, both within their teams and with their customers.
The key to using AI effectively lies in understanding which tasks benefit from automation and which still require human judgement, empathy and oversight.
Where AI works best – and where people are essential
AI delivers the greatest value when used for tasks that require speed, consistency or the processing of large volumes of data. In recruitment, used responsibly, AI tools can help sift through resumes, flag promising candidates and schedule interviews, which can save hiring managers valuable time during the early screening stage. In customer service, chatbots can handle frequently asked questions and triage support requests. Marketing teams are also often using AI for tasks such as segmenting audiences, personalising offers and forecasting campaign results.
Despite the strengths of artificial intelligence, there are still many areas where human judgement cannot be replaced. Empathy, active listening and the ability to read tone or context, for example, are uniquely human skills that machines still lack. What’s more, sensitive conversations, thoughtful feedback and mentoring often require the kind of insight that only people can provide. Creative planning and long-term thinking also typically rely on collaboration and critical thinking, and these are abilities that come from people, not algorithms.
Ultimately, AI should be used to enhance, not replace, the work best done by people.
How to use AI in business without losing the human touch
Employers that make AI work well in their business tend to treat it as a support tool rather than a substitute for human interaction. The most effective approaches begin by using AI for behind-the-scenes tasks that do not require complex decisions or people skills. For example, administrative jobs like scheduling, sorting data, sending reminders and screening based on set criteria are well suited to automation. This helps free up employees to focus on the parts of their work that build relationships and make a real difference.
AI-generated outputs are often most helpful when they are used as just one step in the decision-making process. A candidate ranking tool, for example, might provide a helpful shortlist, but hiring managers still need to assess applicants with context and broader judgement. AI can detect trends and propose offers in customer interactions, but it is the staff on the ground who fine-tune those recommendations as needed, based on their real conversations and interactions with customers.
Take automated messages, for instance. They can still sound human if written in a clear and natural style, using everyday language and having people review content before it is sent. This approach can help make AI-generated messages feel warmer and more genuine. Many employers, therefore, train their staff to adjust AI-generated copy so it reflects the brand’s individual voice and style. This helps keep communication consistent and personal across the board.
Ethical considerations and the importance of transparency
As AI becomes increasingly common in customer and candidate interactions, transparency matters more than ever. People want to know when they are dealing with a machine, especially in situations that involve personal information or emotional stakes. One way businesses can build trust is by clearly stating when AI is being used, whether in online chats or during the hiring process.
There is also a growing awareness of how AI tools can unintentionally reflect bias. Customers also want to be confident that automation is not affecting fairness or privacy. Since AI tools learn from past data, they can unintentionally reflect or reinforce existing biases in recruitment. This may result in certain demographic groups being favoured in hiring or differences in how customer queries are handled. Employers can address this by regularly reviewing the tools they use to ensure they are inclusive and balanced. Such an approach helps businesses to protect both their brand reputation and the people who engage with their services.
Preparing employees to work with AI
Introducing AI into the workplace is not just about adding new software – it also requires a cultural shift and developing new skills. Employees need to understand how AI works, when to rely on its results and where human judgement should still take precedence.
To start with, employers can support this by offering relevant training on artificial intelligence. For example, teams might learn the difference between rule-based automation and machine learning, how to assess the reliability of AI-generated outputs or how to recognise and correct potential errors or biases. Offering real-world examples, such as interpreting customer sentiment or adjusting a candidate shortlist, can make these lessons more practical and engaging.
Clear and ongoing communication is equally important. Some employees may worry that AI could make their roles redundant, especially if their current tasks are being automated. Managers can build trust by reinforcing that AI is intended to support their work, not replace it.
Involving staff in decisions around new AI tools, gathering their feedback and highlighting the ways AI can reduce their workload can also ease concerns. For example, employees may welcome automation that allows them to focus more on creative projects, strategic planning or customer relationships.
For many organisations, adopting AI also presents a chance to support training and career development. Employees who become confident in using new technology may be better equipped to take on evolving responsibilities as the business grows and changes. This shift can support a culture of continuous learning and prepare workers for future changes in the workplace.
Keeping the customer experience human
As AI becomes more involved in customer engagement, it is still important to keep the experience personal. People want to feel heard, valued and understood, especially when their issue is complex or sensitive.
A practical way to balance efficiency with empathy is to use AI as the first point of contact, as it is useful for sorting queries or providing fast responses. However, when the situation requires more understanding or flexibility, a real person should be ready to step in. Although automated replies can offer immediate support, a personalised follow-up from a team member builds trust and shows that the business genuinely cares about its customers.
Many companies find success by designing their customer service process with this in mind. For instance, a chatbot might greet the customer and ask a few questions to assess the nature of their request. If the issue is routine, it can be resolved on the spot. But if the query is complex or indicates frustration, the customer is smoothly handed off to a live agent.
It is also important to ensure that AI’s messaging is reviewed and adapted before being used with customers. A technically correct answer might still come across as cold or impersonal if it lacks warmth or does not reflect the brand’s tone. Taking time to fine-tune messaging shows that the business genuinely cares – and this perception can make a lasting difference in customer loyalty.
Using AI effectively in recruitment
Hiring is one of the most common areas where AI is being applied, but it is also one where the human touch matters most. When used appropriately, AI can help make the hiring process faster and more efficient without losing sight of the candidate experience.
Many employers now rely on AI to help scan resumes, sort applications or schedule interviews, especially when dealing with large volumes. These tools can be very helpful for saving time. But if businesses lean too heavily on automation, they risk missing out on strong candidates, especially those with non-traditional backgrounds or career paths that do not fit neatly into an algorithm.
That is why human review is so important. AI might flag someone based on keywords, but it takes a person to understand the full picture, like recognising transferable skills or spotting a potential cultural fit that the software might overlook.
AI can also play a role in screening assessments or video interviews, but these results once again need to be seen as just one part of the overall process. At the end of the day, good hiring decisions are based on more than data. They require understanding people, reading between the lines and making judgement calls about a candidate’s motivation, communication and potential.
Supporting small businesses with AI
For small businesses, where time and resources are often stretched, finding ways to work more efficiently can make a real difference. The good news is that AI-powered tools are no longer reserved just for big companies. In fact, they are becoming more affordable and easier to use, even for teams with limited budgets.
Many small business owners are using AI to take care of everyday tasks like booking meetings, managing customer enquiries or sending out marketing emails. Automating these kinds of jobs can free up valuable time, so staff can focus on what really matters: building strong customer relationships and growing the business.
For businesses that are just getting started with AI, it helps to begin with one or two simple tools and build from there. As the team gets more comfortable, you can gradually explore new ways to use AI that fit your workflow. With the right support and a clear understanding of what AI can and cannot do, even the smallest businesses can take advantage of what this technology has to offer, without losing their personal touch and unique voice.
Ultimately, AI can help improve an organisation’s performance by making operations more efficient and taking care of repetitive tasks. But it is still the human element – in particular aspects like empathy, judgement and creativity – that builds strong relationships. When businesses are transparent about using AI and approach it strategically, they can combine the best of both worlds and enjoy a more streamlined workplace that still feels personal and connected. And when used the right way, AI becomes a powerful tool that supports your people, rather than replacing them.
For more support with using AI in your business and other topics, explore our wide range of employer resources.
Frequently Asked Questions
What are some examples of AI in business?
AI is commonly used in recruitment (such as for resume screening), customer support (through chatbots) and marketing (via predictive analytics and content personalisation). AI-powered psychometric testing is also becoming increasingly popular.
Is AI replacing jobs?
In most cases, AI is transforming roles rather than replacing them entirely. Although more repetitive tasks are increasingly being automated, many jobs are evolving to focus more on strategy, communication and human interaction.
How can employers prepare staff for adopting AI?
Preparation includes providing training in AI literacy, encouraging open feedback and clearly communicating that AI is there to support human contributions, not replace them.
What is the best way to get started with AI in my business?
Start with simple, repetitive tasks that do not require any judgement, such as scheduling, reminders or routine data sorting. Choose tools that are easy to use, and make sure your team understands how they work.
Do employees need technical skills to work with AI?
Not necessarily. Most AI tools are designed to be user-friendly. However, employees still need to receive basic training on how to interpret results, spot errors and understand when human expertise and decision-making is needed.
