What is AI and Why Should Your Business Care?
From Academic Concepts to Business Solutions.
Artificial Intelligence (AI) and Machine Learning (ML) have become increasingly prominent in recent years. These technologies are more than just hype; they have the potential to revolutionize business operations, drive innovation, and create competitive advantages.
In this article, we will start by providing a brief overview of AI and ML to help you understand some of the formal definitions. However, we will also go beyond these definitions to offer practical advice on how these technologies can be implemented in ways that don't always adhere strictly to textbook categories. The implementation of AI and ML is often dictated by the unique needs of each company.
We will also provide specific examples of their use across different industries to illustrate how these technologies can be applied in real-world scenarios. Our discussion will focus on three key business areas:
Operational Efficiency
Customer Satisfaction and Loyalty
Innovation and Growth
First, let's delve into the details behind AI and ML.
Understanding AI and ML: The Academic View
AI refers to the ability of machines to mimic human cognitive functions like learning and problem-solving. ML (a subset of AI) allows machines to learn from data without being explicit programming.
There are three main types of ML (although the first two are by far the most commonly used):
Supervised Learning: This involves training a model on “labeled data”, where the input data and desired output are provided: during the training phase the model “learns” the features within the data that differentiate best between the different labels, so that when it is provided with new data, it can provide an appropriate label.
Example: a classic example here is spam detection, so given an email, predict if it should be labelled as “spam” or “not spam”. Here the model would have required a large number of example emails that had already been correctly labelled, so it could learn from them.
Unsupervised Learning: Here there are no specific labels provided: instead, the model identifies patterns and relationships within the data.
Example: Clustering algorithms can be applied to customer segmentation. In this scenario, no predefined labels are needed: the data can be analysed to identify the similarities between customer demographics and other behaviours to allow them to be grouped together.
Reinforcement Learning: This technique involves a model learning through trial and error, by taking actions in an environment and receiving rewards or penalties based on the outcomes.
Example: some of the most common use cases are in games and robotics.
This is a very high-level view to give readers a feel for some of the basic concepts, particularly from a business perspective. For those wishing to delve deeper into these areas, I would recommend the book "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig.
Understanding AI and ML: A More Nuanced View
While supervised, unsupervised, and reinforcement learning are common academic classifications, the practicalities of business applications typically involve more nuanced variations, beyond these textbook approaches.
For example, when no labelled data exists for a supervised model to be trained, it may be possible to firstly use an unsupervised technique such as clustering, in a semi-supervised manner, where human experts label some initial data points or clusters. The output of this clustering approach can then provide labelled data for supervised model training.
In other cases, expert domain knowledge may first be encoded as rules to make automated decisions. Then, as data builds up, this rules-based approach can transition to a learned supervised or reinforcement model that generalises beyond the manually created rules.
The true power lies in intelligently combining and orchestrating these techniques based on the unique business problem, available data, as well as any operational constraints. An important point is for businesses to remain open to nuanced approaches that blend machine learning with human expertise, rather than adhering strictly to textbook categories.
Benefits of AI and ML for Businesses
The following three areas are crucial for business success in today's competitive landscape. By taking advantage of AI and ML, organisations can gain a significant advantage in these areas:
Operational Efficiency
AI and ML can automate repetitive tasks, streamline processes, and optimise resource allocation, leading to increased productivity and cost savings.
Standard examples:
Predictive maintenance: predicting failures and schedule maintenance in a proactive manner.
Inventory prioritisation.
Fraud and anomaly detection.
More complex example:
In large enterprises there can be an overwhelming number of examples that fall into this category. In any organisation where there are large numbers of people performing a task, providing solutions for automating that work will clearly be beneficial, but in many circumstances, full automation is often not possible, but providing AI/ML tools that can help humans make more efficient and high-quality decisions can be tremendously valuable.
Customer Satisfaction and Loyalty
AI and ML allow businesses to personalise customer experiences, offer tailored recommendations, and provide more efficient and effective customer service.
Some standard examples:
Personalised product recommendations
Customer churn prediction
Behaviour prediction models
More complex example
In large enterprises with diverse customer touchpoints (e.g., websites, mobile apps, call centers, retail locations), AI and ML can be used to create a unified, customer experience. By analysing data from multiple sources, businesses can gain a 360-degree view of each customer's preferences, behaviours, and journey. This enables personalised interactions, targeted offers, and improving customer satisfaction.
Innovation and Growth
AI and ML enable businesses to uncover insights from vast amounts of data, identify new opportunities, and develop innovative products and services.
Some standard examples:
Market trend analysis
Financial forecasting
Marketing strategy optimization
More complex example
The ability of AI/ML to process massive amounts of data from disparate sources presents countless opportunities to uncover non-obvious insights that can spark innovation. For example, in marketing, AI can analyse consumer trends, social data, or customer feedback to identify unmet need for innovative new products or services.
Some Industry-Specific Examples
AI and ML are being used across all industries to drive transformation. Here are just a few specific examples:
Healthcare
Typically, the headline use cases within healthcare are related to AI-assisted diagnosis and drug discovery: which offer game-changing benefits.
From my own perspective, having worked within the healthcare industry for several years, I’m well-aware of the variety of use cases within this industry that are often overlooked:
The administration burden within healthcare is huge: for example, the medical coding systems are extremely complex, and so tools which aid better decision making (or full automation) can offer tremendous benefits.
Note: for other businesses areas with a high administration overhead, it is worth considering, ways in which AI could help to simplify these processes and lift this burden.
Retail
The retail industry has been an early adopter of AI/ML, using these technologies to better understand customer preferences and behaviors.
Some specific examples:
Personalised product recommendations.
Demand forecasting.
Inventory optimisation.
Finance
AI/ML plays a crucial role in the finance industry by enhancing risk management, improving operational efficiency, and enabling data-driven decision making.
Some specific examples:
Fraud detection.
Risk assessment.
Financial forecasting.
Manufacturing
Manufacturers are applying AI/ML capabilities across their operations and supply chains to drive process optimisations and cost savings.
Some specific examples:
Predictive maintenance.
Quality control.
Supply chain optimisation.
Why Your Business Should Care
In today's competitive landscape, businesses that fail to adopt AI and ML risk falling behind. These technologies can provide a competitive edge through increased efficiency, better customer experiences, and valuable data-driven insights.
To fully realise the benefits of AI and ML, it's crucial to customise these solutions to meet your specific business challenges and needs. Off-the-shelf solutions typically do not provide optimal value.
The key lies in bringing together your business leaders, domain experts, and AI specialists. This cross-functional collaboration ensures your AI solution is properly defined and aligns with your overall business objectives.
If you're ready to explore how AI and ML can drive growth and innovation for your business, contact us today. Visit our AI/ML services page for some specific information, or check out an overview of our full set of AI consulting services.