Taha Douaji, Machine Learning Engineer
7 mins
.
August 22, 2024
Image source: freekpik.com
Understanding how to use AI for your business can be a game-changer if you want to stay competitive. Since the advent of Generative AI, the adoption of AI has increased manifold in 2024. There's hardly a business professional who hasn't used generative AI in some form for their tasks.
According to Forbes, almost 97% of business owners (out of 600 respondents) believe ChatGPT will help their business. Beyond ChatGPT, firms are aggressively pursuing the integration of AI across a wide variety of technology products and business workflows.
And thus, AI in business is not just a trend but a necessity for companies aiming to boost efficiency and innovation.
Due to its increased accessibility, AI is being adopted across various industries for different use cases.
In this article, we take a look into the top 10 applications for AI in 2024. From ecommerce, marketing to self-driven vehicles, let’s understand how AI technology has become paramount for efficiency and innovation
Here’s what you will learn:
AI is a field within computer science whose primary goal is to help machines perform cognitive functions. These include understanding natural language, recognising patterns, and making decisions.
AI systems are capable of processing vast amounts of data that help them recognise patterns, make decisions and evolve based on new information. They range from simple rule-based systems to complex deep learning models that power everything from chatbots to self-driving cars.
AI integration in business is often met with hesitation, primarily due to concerns about widespread job loss. However, over the years, AI has transformed jobs rather than eliminating them. And thus, the key is learning to leverage AI to boost job performance and foster innovation.
Not surprisingly, large enterprises are already adopting AI technology to streamline various functions. These include process automation, enhancing customer service through chatbots, optimising supply chains, predictive analytics, personalised marketing, etc.
Let's understand how businesses are finding practical use cases for this technology.
AI algorithms analyse vast data based on user behaviour, purchase history, and browsing patterns to suggest products tailored to individual preferences.
More and more e-commerce businesses are adopting the power of AI-powered natural language processing to make communication sound as authentic as possible. These smart assistants are capable of engaging with consumers in real-time.
AI inventory management systems can forecast demand by analysing sales data, trends, and seasonality. They help optimise inventory levels and reduce overstock or stockouts.
Many e-commerce websites use AI to adjust product prices in real-time based on factors such as demand, competitor pricing, inventory levels, and customer behaviour. This ensures competitive pricing while maximising profits.
AI models Identify complex patterns in imaging data. They assist in analysing medical images such as X-rays, MRIs, and CT scans, thus improving diagnostic accuracy and early disease detection.
AI can accelerate drug discovery and optimise clinical trials by predicting molecular interactions. AI algorithms can help identify potential drug targets and design molecules that can interact with those targets.
AI systems can alert medical practitioners in case of any abnormal activity during patient care. For instance, remote patient monitoring (RPM) has been significantly growing to monitor patients.
AI can automate and streamline administrative tasks such as appointment scheduling and billing procedures. It facilitates efficient patient communication, feedback management, and enrollment processes, enabling healthcare providers to allocate more time to direct patient care.
AI-powered platforms can provide learners with adaptive learning opportunities while gauging learning patterns, strengths, and weaknesses. They use these data to provide customised, appropriate content and exercises
AI facilitates automated grading by following predetermined algorithms and criteria. These systems make sure that each student's work is judged fairly and without unintentional bias. They also allow educators to focus on more interactive teaching methods.
Artificial intelligence can also help develop, design, and digitise information based on the user's interest. Moreover, technologies like AR/VR are helping e-learning companies develop better video content.
Several AI-powered lead scoring tools are helping sales professionals get real-time updates on prospects and leads. These tools use machine learning algorithms that help ensure lead scores are accurate and up to date. These scores are based on user actions, such as page visits, downloads, form-fillup, etc.
Based on historical data, machine learning algorithms help sales teams forecast demands. This analysis helps them identify the next steps or actions to engage prospects while optimising their process
Sales professionals can also personalise their outreach and share relevant information with prospects based on lead scoring data. They are further assisted by generative AI tools like ChatGPT that help curate personalised messages.
AI helps with customer retention by predicting which customers are at risk of churning. It analyses their behaviour, engagement, and purchases to measure this. Sales teams can then take proactive measures to retain these customers.
AI marketing platforms analyse consumer data to create highly targeted marketing campaigns. They help marketing teams deliver personalised ads that resonate with specific audience segments.
Generative AI tools have become popular due to their ability to generate various types of content in a matter of seconds. These include social media captions, product descriptions, social media posts, blog content. They can also be trained to cater to brand voices.
AI is reinventing market research with tools that automate survey ideation, survey programming, data collection, data analysis, and data reporting.
Predictive analytics helps marketers identify patterns and trends from historical and current customer data. This enables them to anticipate customer needs, optimise targeting, and identify upselling and cross-selling opportunities.
AI systems can analyse large amounts of historical data to identify patterns and anomalies that may indicate fraud. The methods include pattern recognition, data verification, biometric authentication, etc.
AI-driven algorithms analyse vast amounts of market data to execute trades at optimal times. They help maximise returns while minimising risks.
AI-powered intelligent document processing systems are here to revolutionise financial paperwork. They expedite financial workflows by automatically extracting and processing large volumes of data. For instance, Pedant is an IDP system that uses machine learning algorithms to extract relevant data from invoices and delivery notes.
AI algorithms help robots intuitively interact with humans. They improve user experience across applications like healthcare, IOT, manufacturing, transportation, etc.
Natural Language Processing, a part of machine learning, help robots better interpret human instructions. Techniques like sentimental analysis, syntactic parsing, etc., are major contributors.
AI-powered robots use this capability to detect objects within the perimeter. It also helps robots to understand the size & shape of a particular object and assists their interaction with the object. This functionality is used for pick-and-place operations, surgical assistance, obstacle detection, cargo handling, etc.
AI monitors network traffic in real-time, to detect potential security threats like malware, phishing attempts, and unauthorised access. It can sift through vast amounts of data to detect subtle anomalies that might indicate a cyberattack.
AI algorithms can analyse user behaviour from their usage pattern. These patterns help reveal anomalies that may indicate a security breach. For instance, AI can identify behaviour peculiarities around lambda times, localities, and access manners.
AI streamlines security incident response by automating triage, enabling faster detection and remediation of threats. Machine learning prioritises alerts, reducing the burden on employees and allowing them to focus on critical issues.
AI recruitment tools automate the initial stages of recruitment by screening resumes and assessing candidate fit based on predefined criteria. They can even conduct preliminary interviews using chatbots.
By leveraging AI platforms, companies can implement real-time performance tracking, and maintain a high level of workforce engagement.
AI can automate repetitive tasks, reduce errors, and create a seamless experience for new employees. HR professionals can implement personalised onboarding kits to complete the initial paperwork in a short time.
Companies can align resources with future projects using AI-based predictive analytics.
Chatbots can communicate with customers and solve recurring queries in real-time through NLP. Several consumer-facing websites and applications offer personalised solutions and support 24/7 with the help of AI-poweredmarchatbots.
AI-powered tools can analyse vast customer data from various sources to detect emotions. These include emails, chats, reviews, social media interactions, etc. They help businesses respond appropriately to negative feedback.
With AI, ticketing systems can automatically categorise and route customer support queries to the appropriate department. This ensures faster resolution times and improved efficiency.
Interactive voice response (IVR) systems with voice recognition AI reduce the need for manual intervention and speed up call resolution.
AI remains a buzzword for many. There is still limited awareness around its current practical applications and people often fail to distinguish it from related concepts like machine learning and data science. Organisations must bridge this gap by facilitating workshops that highlight AI’s practical benefits and showcase real-world applications.
AI-driven operations must start with strong leadership and align with the organisation's vision. Senior executives should be transparent, securing buy-in by building awareness. Developing a business-led use case roadmap, communicating the strategy clearly, and holding management accountable for its successful implementation is paramount.
As the demand for AI adoption grows, companies often find themselves at a relative disadvantage due to skill shortages. Organisations can combat these challenges by curating in-house training programmes or investing in third-party services. They can also consider partnering with academia or outsourcing certain aspects of their AI projects.
While AI systems thrive on data, the availability and quality of data can be limited. Inaccurate data can hinder the precision of even the most advanced AI models. Implement stringent data quality controls and ensure enhanced data cleansing.
Build a flexible technological architecture to support the integration and scalability of AI solutions. Ensure it is adaptable to evolving AI technologies, can handle large data volumes, and integrates seamlessly with existing systems. While integrating AI into outdated legacy systems can pose technical challenges, these can be navigated through the strategic use of APIs and middleware.
As AI continues to evolve, its potential to transform business operations, customer experiences, and innovation will only grow. Embracing AI can lead to significant advancements in how businesses manage resources, engage with customers, and develop new products. Future developments in AI promise even greater opportunities for growth and efficiency, making it an indispensable tool for modern businesses aiming to succeed in a rapidly changing world.
