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There’s no escaping the waves artificial intelligence (AI) has made over the past 12 months. And many organisations have dipped their toes into the water. A recent shows 68% of Australian businesses have already implemented AI technologies and a further 23% are planning to in the next 12 months. This highlights how AI capabilities and implementations are only going to accelerate as organisations seek to further their core business outcomes and understand how AI can help them get there.

The same CSIRO report found that on average, organisations reported time savings of 30% across all AI-related initiatives. Furthermore, Goldman Sachs suggests AI adoption could lift global productivity growth by 1.5 percentage points over a 10-year period and drive a 7% (or US$7 trillion) increase in global GDP.

For many enterprises and telecommunications businesses, AI has the potential to offer valuable opportunities, including enhancing operations, safeguarding data, and enhancing customer experiences. As organisations grapple with increasing complexity and competition, the adoption of AI-powered solutions becomes an increasingly tantalising prospect for growth and innovation.

But as with any form of technology implementation, alignment with core business outcomes is critical. This article delves into three areas where AI has the potential to make inroads into your organisation. We take a look at how AI might reshape the way businesses operate and engage with their customers to drive efficiency, security, and satisfaction to new levels.

1. Network and Infrastructure Optimisation

Digital connectivity is the backbone of business operations. Yet the complexity of managing these systems can grow when enterprises expand their digital footprint through hybrid environments. Sophisticated solutions are required to maintain performance and prevent disruptions. AI has a role to play here in helping to optimise network traffic, monitor critical infrastructure, and manage energy consumption through a range of powerful tools:

Network Equipment Monitoring

Predictive monitoring and maintenance is a big deal in network performance. AI models can help predict potential issues by identifying patterns in the data that might signal a future disruption. For example, if a server shows increasing latency over time, AI can flag this as a potential problem and suggest pre-emptive maintenance before an outage occurs. This helps not only reduce downtime for the organisation but extends the life of the equipment, saving time and money in the process.

Infrastructure Management

AI can be used to create . These are virtual replicas of physical assets that mimic the real-time state of your infrastructure. Digital twins can help uncover anomalies in your infrastructure, such as temperature differences in a data centre or irregular power consumption. By using AI to analyse these instances, you can act before they escalate into large failures.

Dynamic Bandwidth Allocation

AI-driven dynamic bandwidth allocation can help optimise network traffic in real-time. As demand shifts throughout the day, AI models can identify patterns, and predict and respond by reallocating bandwidth to where it’s most critical and ensure the right level of resources are available. For instance, during a large video conference, AI can temporarily increase bandwidth to deliver an optimum experience for participants, while scaling back on less critical services that could be managed at a different time of day, e.g., data backups.

Energy Management

More efficient and sustainable use of power across enterprise operations can be managed using AI. For example, AI might recommend reducing power to underutilised servers or shifting energy-intensive processes to times when renewable energy is cheaper and more available. This can result in reduced energy costs and carbon footprint, contributing to both operational savings and corporate sustainability goals.

2. Cybersecurity Enhancements

There’s no need to state the obvious when it comes to the rising levels of cyber crime happening on a global scale. The question centres more on how to effectively protect critical assets and sensitive data, adopting a more intelligent and proactive approach to cybersecurity.

Threat Detection

Patterns are AI’s thing. They can help manage threat detection by continuously monitoring network traffic and analysing it for signs of unusual activity. By learning from historical data and real-time inputs, AI-powered threat detection systems can help identify emerging threats with greater accuracy and speed. This provides the critical resource of ‘more time’, enabling a timely response before significant damage can occur.

Automated Incident Response

If a security breach is detected, swift action is the difference between lost data and reputation protection. AI can help enhance this process by automating incident responses, including isolating compromised systems or blocking malicious traffic, while reducing the response time to seconds.

Pattern Recognition for Fraud Detection

Fraudulent activities often involve subtle and complex patterns that can be challenging for the human eye to detect. AI can be effective in identifying unusual behaviours that may indicate fraud due to its constant state of learning and adapting. This helps protect critical assets.

3. Customer Experience

AI can help organisations better understand their customers, anticipate their needs, and deliver more personalised experiences to resonate at a much deeper level than previously possible. But there is a balance to be maintained. While automating many aspects of customer experience can help drive quicker resolution times, people still appreciate interaction with people. Here are some of the ways in which AI can help drive some of those improvements and outcomes:

Voice Recognition Systems

AI-powered voice recognition systems can enable automated call routing and issue resolution, particularly in instances where answers to questions might be easily obtained. For example, AI can recognise a customer’s voice and sentiment and help route their call to the appropriate department, all within seconds. This makes the process not only more efficient but leads to a more satisfying and quicker resolution for the customer.

Targeted Marketing

With vast amounts of customer data now available, AI tools can help target customers with highly personalised campaigns. As with threat detection, AI is great at understanding patterns, including customer preferences, behaviours, and purchasing history. This can help with the design of more targeted marketing strategies to increase engagement, boost conversion rates, and ultimately drive more sales.

Predictive Sales Analytics

AI-powered predictive analytics can help forecast what customers are likely to purchase next based on their previous purchases. This means sales teams can tweak their approaches to offer the right products, at the right time. This helps build stronger, more personalised relationships with customers and create higher satisfaction and loyalty.

What Can We Expect in the Future?

Looking ahead, we can expect AI to become even more integrated into critical business functions. The ability of AI tools to analyse vast datasets in real-time and learn from each interaction will mean businesses can better anticipate the challenges that lie ahead and seize opportunities with greater agility and accuracy.

As AI becomes more pervasive, considerations around data privacy and ethics become more prevalent, particularly for customer experience initiatives, and the need for a skilled workforce to manage these advanced systems. Even though technology continues to play a greater role in how organisations are run, people remain and will always be a vital component in making that happen. But as with any new technology, the business use case should always lead and be the priority. Technology is the enabler to help make that happen, AI-powered or not.