12 Valuable Ways to Use AI in a Digital Product
As AI has grown, it has become easier for businesses of any size to access its potential benefits.. Here are some hypothetical examples that illustrate the breadth and depth of possible applications:
Smart CRM Systems
Customer relationship management goes beyond just maintaining a list of contacts. With AI, CRM systems can provide insights into customer behaviours, preferences, and even predict potential churn. By understanding what drives customers, businesses can tailor their strategies to enhance loyalty and drive growth.
Security is a top concern in today's digital age. AI can analyse transaction patterns in real-time, swiftly identifying any anomalies that could indicate fraudulent activity. If a user typically makes small, local purchases, and suddenly there's a high-value international transaction, AI systems can flag this for review, adding an additional layer of security.
With the explosion of user-generated content on social platforms, forums, and review sites, ensuring a safe and respectful environment is a challenge. Machine Learning-powered content moderation can automatically review, filter, and moderate vast amounts of content in real-time. Traditional keyword-based filters can be bypassed using simple tricks, but an ML-driven system can understand context. For instance, it can differentiate between benign usage and derogatory intent of certain words or phrases. Additionally, it can detect harmful images or videos, even if they are slightly altered to evade detection. By continuously learning from the content it reviews, the system becomes progressively better, ensuring platforms remain welcoming and free from malicious content.
Supply Chain Optimisation
Maintaining optimal stock levels is a delicate balancing act. AI can forecast demand by analysing sales patterns, seasonal trends, and even external factors like economic indicators. This means businesses can make informed decisions on stock ordering, reducing wastage from unsold goods and ensuring popular items are always available.
AI-powered search engines take user experience to a whole new level. By leveraging Natural Language Processing (NLP) and deep learning, these search engines can understand user queries in a more nuanced manner. This means more relevant results, even if the exact keyword isn't used. Additionally, they can offer content suggestions based on past searches, analyse user behaviour to enhance result rankings, and even understand complex, multi-part queries. This results in more accurate searches and happier users.
AI can process vast amounts of data from past sales, market trends, and even competitor analysis to provide accurate sales forecasts. This allows businesses to strategise effectively, plan promotions, and allocate resources where they're most needed based on patterns in data rather than purely intuition.
Automated Dataset Tagging
Organisations often sit on gold mines of data. But without proper tagging and classification, these data pools can be near impossible to navigate and utilise effectively.
Machine Learning can be employed to automate the process of data tagging. Let's take a media company with thousands of hours of video footage as an example. Instead of manually tagging each video with details like "outdoor," "nighttime," "crowded," or identifying specific people and objects, an AI system can process and tag the entire dataset efficiently. Not only does this save time, but it also introduces a consistency in tagging that might be hard to achieve manually.
As the AI system encounters and learns from more data its accuracy and granularity in tagging can improve. This makes subsequent searches and other attempts at data utilisation even more effective. Introducing this kind of consistent tagging to a dataset may make other approaches we've mentioned here possible with a dataset that otherwise would lack the detail to support it. For example, enabling advanced search and recommendation features within the aforementioned video footage database based on details that could only be gleaned from the actual content.
Adaptive Health and Fitness Recommendations
Machine Learning can revolutionise personal health by continuously adapting recommendations based on a user's progress and data. Instead of static fitness plans, imagine an AI system that learns from your performance. If you consistently surpass your 10,000 steps goal, the system might suggest a new challenge or tweak your dietary recommendations based on your increased activity level. On the flip side, if it detects irregular sleep patterns or increased resting heart rate, it might suggest relaxation exercises, adjusting goals to ensure you're not overexerting. By processing data over time, the system tailors its suggestions, ensuring they're always relevant and attuned to your current state.
Dynamic Learning Environments
Machine Learning can transform educational platforms into dynamic, responsive environments that adapt to each student's unique learning journey. Going beyond static curriculum, an ML-powered platform can recognise a student's strengths and weaknesses in real-time. If a student keeps making the same grammar mistake or struggles with a specific maths concept, the system can instantly provide additional resources or exercises tailored to address those precise issues. This ensures that learning is paced at the individual's needs, reducing frustration and enhancing comprehension.
Manufacturing & Quality Control
Ensuring product quality is paramount. Using computer vision, AI systems can scan products on manufacturing lines in real-time, identifying defects or inconsistencies. This not only ensures a consistent product quality but can also lead to savings by reducing wastage.
Chatbots for Customer Service
Chatbots are nothing new, but can often be the cause of frustration when the output of them is not in line with what the user needs. With an AI-powered chatbot, the user experience could be enhanced, with a much stronger likelihood of generating the desired answers. Chatbots could answer frequently asked questions more accurately, assist in real time with order tracking, and guide users more seamlessly through troubleshooting processes. The best part? They're always on, ensuring that something is always there to help.
Ever been on an online store and seen the "you might also like" section? That's a recommendation engine at work. By analysing user behaviour, past purchases, and browsing patterns, AI can suggest products or content that align with individual preferences. This means a more personalised experience for users and potentially increased sales/engagement for businesses.