Here’s how businesses are using AI to grow

Guest Blogger
Here’s how businesses are using AI to grow

With so much information in the public domain about machine learning and Artificial Intelligence (AI), companies can be forgiven for being like ‘deer’s-in-the-headlights’, unsure of where to start.

Between 2020 to 2027, the global AI market is expected to grow at a CAGR of 42.2% and according to Gartner, “80% of all digital business industry visions will be powered by AI selected from AI industry use cases” by year 2023.

This is not to say that organisations need to rush into what is certainly a complex and confusing market. But there are a number of IT projects they can dip their toes into and harvest the low-hanging fruit, while setting on a practical path to improve essential hands-on skills incrementally.

The 8 Artificial Intelligence opportunities business should be considering:

1. Build a chatbot

Most companies know the top frequently-asked questions posed by their customers. This Q&A process can be handled by fairly easy-to-build chatbots, using tools like Microsoft QnA Maker. Aside from customer service, chatbots can also be used to create information resources for staff, including answering questions for new recruits about HR or other areas of the business.

Simply upload the relevant spreadsheets or URLs and QnA creates pairs of questions that can then be reviewed and trained, and then used as an API. As well as simple text-based bots, companies can also add branded pictures if they want customised looking material.

2. Marketing automation

The marketing department has increasingly become the earliest adopter of new digital technologies. This helps to explain why platforms like, Adobe Marketing Cloud and Dynamics 365 have taken leadership positions in offering clever machine-learning capabilities.

Features like being able to recommend relevant products and services for customers, showing personalised search results and curating sales leads have become standard. Companies can also create solutions to predict when a deal may be going cold, and even matching customers with the best customer service officers.

3. Reduce fraud and cyber risks

Data analytics has a huge role to play in fraud detection and cyber security today. But given most companies now have quite larger sets of data, achieving peace of mind – demands the ability to analyse at scale.

Machines can identify unusual activity such as multiple payments being made just under the trigger limit. The Commonwealth Bank’s recent ATM woes provide a good example.

Other clues that might ordinarily go unchecked include new merchants behaving differently, or phishing attacks that might lure unsuspecting users to share information that shouldn’t be asked for.

4. Inventory planning

Supply chain automation isn’t new, but machine learning is making it much more common. Instead of just historic sales data, machine learning lets you use data about the way customers research purchases on line, the impact of shopping habits, and other internal and external trends that affect purchase decisions plus managing inventory by forecasting demand.

For example, online retailers can use machine learning to predict with a high degree of accuracy what will sell in the next month, making it easier to reduce stock and free up more working capital.

5. Travel and logistics

People working out on the road are always looking for more efficient ways to cover their territory.

Whether it’s getting salespeople to prospects, deliveries to customers or picking the business location that will attract the most customers, routing and travel planning has a big impact on the bottom line.

There are a number of simple AI tools that can be applied to this problem.

For example, Bing and Google Maps provide predictive traffic services through their API’s to make maps showing distance and travel time to help figure out how many support calls an engineer could make in an hour, or finding the best time of day to make deliveries. Adding asset tracking and location data and you have the beginning of an AI-driven logistics solution.

6. Smarter maintenance

If your machine maintenance and repair schedules are driven by when things have already broken down, chances are you’re spending more time and money than the you need, while possibly causing yourself brand damage due to unhappy customers.

AI and machine learning can help companies make accurate predictions around the maintenance cycle, making it easier to coordinate site visits and the stocking of spare parts, leading to lower downtime and lower costs that impact the bottom line.

7. Recruitment

One of the more notable effects of today’s highly digitised work environment is the change in how we think about work. Another is the fact we now have access to data and digital tools that allow us to extract valuable insights about employees, teams and the workforce more generally.

Take the issue of workplace diversity. For years it’s been noted that a lack of gender diversity can have negative consequences for an organisation. Using AI, however, it’s possible to accurately measure those consequences. The technology can also flag for companies words or expressions that might diminish the effectiveness of a job advertisement – leading to fewer applicants.

8. Image recognition for manufacturing safety

We all know the building and manufacturing industries are full of dangerous machines and vehicles.

Using sensors, cameras and facial recognition technology makes it possible to know when equipment is being used incorrectly, or by someone who hasn’t acquired the appropriate certifications or training. Wearable digital devices, including eye-tracking devices are already being deployed as part of AI systems supporting better safety.

Brennan has supported many organisations in developing an IT roadmap that aligns directly with their business and what they’re trying to achieve. Feel free to contact one of our team today to discuss your business needs.

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