From Uber to Netflix, innovation has always been a critical component of business success. But in today’s fast-paced world, staying ahead of the curve is more important than ever.
That’s where machine learning (ML) can help.
By leveraging the latest advancements in artificial intelligence and data analytics, ML enables us to automate and optimise a wide range of tasks. From predicting consumer behaviour to optimising supply chain logistics.
As a matter of fact, according to research done by Zippia:
- 85% of executives believe that ML and automation will give their companies a competitive advantage.
- Companies that adopted AI in at least one business function saw an average revenue increase of 66% within those functions.
- Marketing and sales saw the highest average increase at 79%.
- Strategy and corporate finance saw a 73% average increase.
And as machine learning continues to evolve, its impact on innovation becomes increasingly transparent.
That said, here are seven ways machine learning is revolutionising innovation:
1. Predicting Market Trends
One of the biggest advantages of ML is its ability to predict market trends.
According to research by AIMultiple, “Machine learning algorithms have been shown to outperform traditional forecasting methods in predicting financial markets, with one study finding that machine learning models produced significantly higher accuracy in predicting stock prices than traditional models.”
By analysing large volumes of data from various sources, including social media, online shopping patterns, and industry reports, with ML algorithms, you’re able to identify patterns and trends that would be difficult for experts to spot.
Take, for example, retailers that are able to use ML to predict which products are likely to be popular during certain times of the year.
By using your accumulated data from past sales, social media mentions, and other factors, ML algorithms can predict which products are likely to be in high demand and adjust inventory levels accordingly.
2. Optimising Workflows
Another significant benefit of using ML is having the ability to easily optimise workflows.
This includes using an ML model to analyse data from various sources for identifying areas of improvement and suggesting changes that can lead to increased productivity and efficiency. Sources can range from employee performance metrics to customer data and even workflow processes.
In the healthcare industry, ML algorithms can analyse patient data to identify trends and patterns that could help healthcare providers optimise their workflows. This gives priority to the patient’s care, while freeing up more capacity for all the staff involved.
In fact, research indicates that 73% of business leaders believe ML will double productivity for their employees, showcasing the potential for workflow optimization
By automating repetitive tasks and streamlining processes, healthcare providers can spend more time providing quality care to patients.
3. Personalising Customer Experiences
Personalisation is an essential factor for customer success.
And this applies to businesses across all industries.
Customer success refers to building long-term relationships with customers, while increasing customer satisfaction and loyalty.
It goes beyond providing customer service and instead aims to help customers achieve their goals by offering personalised guidance, training, and resources.The goal is to ultimately drive business growth through customer referrals and repeat business.
Using ML algorithms can help your business provide personalised experiences and improve customer success by analysing customer data to identify preferences, interests and needs. You’re able to go above and beyond in delighting customers at every point of interaction.
For example, online retailers can use ML algorithms to recommend products to customers based on their past purchases and browsing history. Or even offer them discounts and special offers for the products that they buy.
4. Improving Product Quality
ML can help businesses improve product quality by identifying areas for improvement.
By analysing customer feedback and product reviews, you’re able to learn what patterns and trends are able to help improve your products.
Look at the automotive industry, for example. ML algorithms can analyse data from sensors on vehicles to identify potential quality issues before they become major problems.
Where quality is concerned, Forbes reveals that:
- Machine learning-based automated quality testing can increase detection rates by up to 90%.
- Machine learning improves product quality up to 35% in discrete manufacturing industries.
By identifying issues early, manufacturers can take the right action to improve overall product quality.
5. Preventing Fraud
Whether the financial losses, damage to reputation, or the legal challenges, we all know that fraud is a major problem for any organisation.
Using ML can help you prevent these issues from happening.
By analysing large volumes of data from various sources, including financial transactions and customer behaviour patterns, ML algorithms can identify patterns of fraudulent activity and alert businesses before it occurs.
For example, financial institutions can use ML algorithms to detect fraudulent credit card transactions. By analysing customer behaviour patterns and transaction history, these algorithms can identify suspicious transactions and take the appropriate actions to make the organisation aware.
6. Enhancing Cybersecurity
The threat of cyber attacks continues to grow.
We can easily recall the 2013 target security breach that cost them over $200 million in damages, or the $1.4 billion in expenses that Equifax had to pay after 143 million customer’s information was stolen.
That’s why businesses are looking for the best ways to enhance their cybersecurity.
ML can help by analysing data from various sources, to identify potential threats and take proactive measures to prevent them. These sources include network traffic and user behaviour.
For example, ML algorithms can analyse network traffic to detect unusual activity that could indicate a cyber attack. By identifying potential threats early, businesses can take steps to prevent or mitigate the impact of cyber attacks.
According to Crowdstrike, there are numerous positive impacts of ML on cybersecurity:
Rapidly synthesise large volumes of data: ML can quickly analyse large volumes of historical and dynamic intelligence. This enables teams to operationalise data from various sources in near real-time.
Activate expert intelligence at scale: Regular training cycles allow models to continuously learn from their evolving sample population. This prevents recurring false positives and enforces expert-generated ground truth.
Automate repetitive, manual tasks: ML can alleviate security teams from mundane tasks, acting as a force-multiplier that enables them to scale their response to incoming alerts.
7. Streamlining Talent Acquisition
Finding and hiring top talent is a critical component of business success, but it can be time-consuming and costly.
ML can help by analysing candidate data to identify and qualify the best candidates for open positions.
For example, you use ML algorithms to analyse resumes, cover letters, and other application materials to identify candidates with the skills and experience needed for the position. It can also analyse social media profiles and online activity to assess a candidate’s fit with the company culture.
Using ML to streamline talent acquisition, businesses can save time and resources in the hiring process and ultimately build stronger teams. This not only benefits the company but also the employees who will be working alongside talented and qualified colleagues.
ML is changing The Way That We See innovation.
From predicting market trends to enhancing cybersecurity, the applications of machine learning are endless.
As a society, we should embrace this technology and the opportunities it presents for growth and progress.
Being an AI-enablement business, we’re constantly putting out helpful content on AI, ML, Data and tech-related content.
By staying informed and up-to-date on the latest advancements in these fields, we can all stay ahead of the curve and better understand the impact that these technologies are having on our world.
So, whether you’re a business owner looking to leverage machine learning for your organisation or a curious individual looking to learn more about these cutting-edge technologies, be sure to keep following our blog for all the latest updates and insights.