Three ways to validate the potential of AI in your business

It’s fair to say AI has taken over EVs as the most talked-about tech for 2024. 

Nvidia, a company that the average person on the street would have never heard of before a few months ago, is now the most valuable public company in the world. Apple shares recently hit a new high after rolling out their Apple Intelligence (AI) strategy and Microsoft continues to invest heavily in AI as it looks to stay ahead of the competition. 

This demand has also hit Australian shores. KPMG’s recent report found that three-quarters of Australian companies are using or piloting artificial intelligence in their financial reporting processes.

AI is everyone’s new favourite buzzword and much like its predecessor ‘sustainability’, it promises greater efficiencies and a brighter utopian-like future. Nowadays we’ve rightly coined this ‘greenwashing’, but I’ve recently wondered, have we entered the era of ‘AI washing’? 

How businesses can actually use AI

It’s useful for companies and organisations to look at AI through the lens of business capabilities, rather than technologies. As an experience officer specialising in how businesses can best incorporate AI into their operations, it’s important to have a framework for how we do this.

We can consider three pillars for improving how we validate the potential of AI, using automation, analysis and engagement. Through such, businesses can begin to not just improve customer and employee experiences but also the company’s bottom line.

Automation 

When we use AI, anything repetitive can be automated. It can remove the intricacies of mundane admin, or the cognitive load of monotonous tasks, whether that be for procurement or just for people working day to day. 

AI should be leveraged – not to replace – but to improve various business processes, making them more efficient and effective. 

We can use automation to create better employee or customer experiences, without impacting how we do business. In this process, AI will only adapt and learn, becoming more efficient at giving people time back in their day to do more important things. Here are some examples of where AI can make a big difference.

Supply chain management: Optimising inventory, predicting demand and improving logistics by analysing data and making real-time adjustments to improve profitability, mitigate risks and help speed up delivery.

Manufacturing: Monitor equipment for predictive maintenance, optimise production schedules, and ensure quality control, reducing downtime and boosting productivity.

Analysis

We can use analysis to better understand data, and in turn, our customers.

What if every time we went to an online retail store or even a bank, it knew what we needed? We already see this on social media when you’ve been looking for a pair of Nike trainers, and your ads become flooded with exactly that, more and more trainers. Risk assessment and calculations for customers and businesses delivered almost instantly. 

The trouble is without proper analysis, your feed begins to silo, and it undergoes an intense narrowing. Just because you liked one meme of an Australian breakdancer, doesn’t mean you need to see it 100 times more.

But, if we used AI to better analyse why something is funny to the user, or the reason why someone likes a particular retail product, we can create more tailored experiences in the user journey. It might be as simple as making sure the product they like is the first thing they see on a homepage, or even helping banking customers or mortgage brokers who repetitively fill out the same form. 

If a company can analyse data to make it more relevant to each user, it can create better customer experiences. Here’s some industries that could leverage this opportunity.

Financial analysis: AI can help detect fraud, manage risks, and forecast financial trends, informing organisations to assist in making strategic decisions.

Human resource: From screening resumes to scheduling interviews, and even conducting initial interview questions, we can streamline the recruitment process. Mixed with virtual staff it can assist with onboarding and answering questions when training new employees.

Engagement

A lot of people get frustrated at work and move on because it’s so repetitive and the engagement is lost internally. And the same goes with customers. Companies should ask themselves; how can we accurately track people’s engagement? 

I’ve worked with a top motorsport event where we developed analytical tools to assess e-retail channel health across the marketplace. We were able to make more targeted decisions through real-time insights, which resulted in a 400% increase in unique digital visitors, an increase in sales by 200% and growing attendance to the highest in event history year on year.

It’s about shifting how we think about engagement — making it easier and quicker by channelling AI into something truly useful for your organisation, much like in these examples.

Customer service: Imagine AI-powered chatbots handling customer enquiries, scheduling appointments, and tracking orders. This not only enhances the customer experience but also allows your team to focus on more complex issues and give customers a more personal experience.

Marketing: Create personalised marketing campaigns, analyse customer behaviour, create bespoke online retail experiences and automate content creation, leading to more effective and targeted strategies.

The bottom line

Without proper consideration, implementing new AI tools and features might not have the desired impact. Business should instead start simple.

If used correctly, AI can support three very important business needs: automating business processes, and gaining insight through data analysis, thereby, creating better experiences for their employees, customers and broader communities.

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