What is AI and how should organisations be considering it?

AI is just about everywhere, but what actually is it, how can it help and hinder and which key considerations should organisations be thinking about?

What is AI and how should organisations be considering it?

AI is just about everywhere, but what actually is it, how can it help and hinder and which key considerations should organisations be thinking about?

What is AI and how should organisations be considering it, featured image, Clarasys

Meet the author

Ben Lover

Associate Director

Recently, it’s been impossible to open LinkedIn or browse the articles from your favourite thought leader without being confronted with headlines such as ‘AI WILL CHANGE THE WORLD AS WE KNOW IT’ and ‘ROBOTS ARE BETTER THAN YOU’.

I haven’t had such trepidation about technology since watching films like The Terminator as a youngster, albeit I was pretty excited about the world that Back to the Future painted. We’ll get those flying skateboards soon won’t we? They’re probably already available on Amazon Prime!

What is AI and how can we use it?

So, let’s start simply with what we believe AI to be, the opportunities it presents, and how we can navigate the road ahead.

To start with, applied AI is simply using data and technology to help us solve some real-world problems that would typically take us days, weeks, months, or even years to solve. Ultimately, the value of AI isn’t the technology itself, it’s the context we’d like to use it in and the problems it’s going to help us with. Machine learning and deep learning are all different ways of applying AI and simply, generative AI is a way of getting a response to a question we ask.

I’ve spent the last few years of my career exploring the classic Lead/Order to Cash, CRM, and Customer Experience (CX)-based world. So, let’s explore how we believe AI can help, challenge or complement us whilst delivering a great customer and employee experience.


Picture the scene: straight after our morning coffee, we log on and ask “ChatGPT, please draft me a marketing newsletter covering the latest and greatest products we’re launching this year”… and hey presto, thanks to generative AI, we quickly have a nicely worded email campaign ready to go, Easy right? Maybe.

Before you hit send, consider these questions: does the output really know your customers and what they want to hear? Will the tone be right? Humans know humans better than robots, don’t they? Where did this content even come from, and am I breaking a law I didn’t know existed by using it?!

OK, AI-generated social media and personalised marketing campaigns today usually need some human input, but it already offers massive time savings and the potential opportunities here are huge. We are also seeing businesses use AI to adopt personalisation at scale and use sentiment analysis to find out the products and experience they really want.

Product & Sales

I see a big opportunity here to adapt the way we tailor products to sell to new and existing customers using AI. AI offers a great opportunity to learn about what consumers really want, and what they like and dislike about our current offerings and the experiences we provide.

Predominantly in B2C, we are seeing the whole two-way interaction during the sales process for bespoke products and services being powered by generative AI. We can book an entire holiday with specific requirements and itineraries in the conversational manner that you’d get from a local travel agent.

As a simple use case, particularly in B2B, sales teams spend days rafting through records of data to understand where we may have some cross-sell and upsell opportunities for existing customers. Surely we can use machine learning and AI to make this whole process more efficient and effective, and ultimately deliver a better CX. We can be more sophisticated in learning about our customers’ buying habits which will help us predict what they’ll want in future.

We are already seeing plenty of tools available to plug into your CRM to crunch our sales data and learn from it. Let’s enable our sales teams to become relationship managers and use AI to learn and deliver even more attractive products and a better experience.

Legal & Contracts

The boring, but important stuff! How often are big sales deals held up by lawyers and small print? Surely we can speed up this whole process by creating tailored contracts and legal documents using generative AI? Wouldn’t it be great if the bots could agree and resolve gaps in contracts from both sides quickly without delaying the deal?


AI has already started to revolutionise the traditional way of billing and invoicing. Applying AI to accounting has huge potential as it can reduce calculations and manual operations, and again focus our minds on providing a better CX. 

Let’s use aspects of AI to help us learn about our customers’ habits – how they like to pay, how quickly they open and review what we’ve sent them, and can we better predict where there are going to be cash flow challenges? This will allow us to make better decisions for our customers and our business.

Customer Service 

AI will be used to improve the CX and create better interactions with consumers. Technologies, like chatbots and sentiment analysis, can help to streamline service teams, address customer requests more quickly, and proactively anticipate customer needs.

This area is already exploding in the market. It’s now rare if we’re not talking to AI bots as part of any sales and service of our favourite products. We can quickly gain the sentiment from our customers by the tone and number of expletives sent in the initial question or complaint – if we can give a really quick and accurate answer to the most common questions, great. AI can help us detect when a customer needs special attention and provide the human touch for the use cases that need it most. 

But what about the people?

The most common fear we see in the industry at the moment is about its impact on people. While generative AI on its own has a great deal of potential, it’s likely to be most powerful in combination with humans, who can help it achieve faster and better work.

Some considerations and key questions that businesses need to think about pretty quickly include:

Org structure 

  • What is the impact of AI on your traditional org structure and design?
  • If we’re using AI, how can it be embedded into job descriptions, roles, and outcomes teams are expected to deliver?

Skills and training

  • How do we help our teams understand the power of AI? 
  • Can we upskill our people to embrace, support, and train AI? Ultimately AI can free up time to focus on value add tasks
  • How do you manage the future workforce who have grown up with AI and already know more than us?

The journey

  • Can we give our people the time and opportunities to experiment with AI in their roles?
  • What can we do to encourage a positive mindset and reduce fear of change and ultimately job security?

To support the change effort, organisations are appointing their own AI champions and AI centres of excellence are being formed. This looks to build a foundation which will support the journey, keep organisations in touch with the latest industry trends, and help train and upskill people on the journey. 

Approaching the change

As a business, are you part of the AI revolution or the evolution? In other words,  are you going to make AI front and centre to help you deliver a great experience to your customers? Or, will you test this gradually and wait to see how the market evolves?

I would recommend taking some simple steps to explore the change:

  1. Identify the use cases – review your end-to-end internal process and customer journeys with the teams that know them best. Ask everybody to be clear on what is time-consuming at the moment and where you could speed up and deliver a better experience. AI initiatives are likely to deliver the most value where they span across teams/ processes with the customer/ employee experience at the centre.
  2. Prioritise and understand benefits – we can still apply agile principles here and agree the prioritisation factors for our backlog of AI initiatives. What will save us the most time? What will deliver the most value to our customers? What is likely to attract new customers? 
  3. Deliver iteratively – to put generative AI to work, companies can either use solutions out of the box or fine-tune them to perform a specific task depending on the complexity of the use case. We have the opportunity to rapidly build Proof of Concepts (POCs) to test our assumptions and investigate how AI could solve the identified use cases.

There is still plenty of uncertainty in the industry, especially relating to privacy and regulatory compliance. I’d recommend not doing too much at once in case your work has to be ripped up due to new regulations which are emerging rapidly as compliance catches up. Further, we have to assess and understand the impact of use cases on our people and processes. Therefore, build small use cases, test them, and roll them out quickly. 

  1. Measure – take a data-driven approach to measuring the ROI for the business and the impact of the initiatives being rolled out iteratively.
  2. Scale – when we’re confident and know we’re on the right track, we can consider scaling up some of our initiatives across functions and processes. 

Moving forward

Moving forward, it’s going to be a really interesting few months ahead as the industry explodes. For now, there are plenty of opportunities that allow us to improve ways of working and deliver an even better experience for our people and our customers. Be open-minded and methodical and let’s make sure we bring our people along for the ride.

To find out how we can help you with managing change, get in touch.

You might also like