Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. A few years ago, AI & ML was just the stuff of sci-fi? Today, these technologies are driving the ‘Mobile-First’ and ‘Big Data’ revolutions and we are seeing them being included in more and more modern devices in both our home and work environments.
But firstly, let’s start by defining these two innovations as they are not quite the same thing.
Artificial Intelligence is the broader concept of computer systems performing tasks or jobs that would normally require a human to finish. Some of these jobs include; speech interpretation and recognition, basic leadership and visual perception.
Machine Learning is really just a subset AI based which involves giving machines access to data and letting them learn for themselves, without the need for additional programming. So, rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using data and algorithms that give it the ability to learn how to perform the task. The idea being, the more data it processes, the smarter it gets.
Companies have already started incorporating these two technologies into their business systems and processes. The one’s you’ve most likely come into contact with are ‘Chat Bots’ but as these technologies continue to refine and evolve, more and more companies will adopt AI in more areas of their business.
Despite its relative nascence as an enterprise technology, the application of AI in a business setting is seemingly limitless. As the technologies continue to refine and evolve, it’s fair to wager that the theoretical will transform into the practical, and more companies will adopt AI in more areas of their business.
The following are a few examples of how AI and ML technologies are being adopted to help improve key business processes:
Automating the candidate search process – Recruitment agencies and HR departments are using ML & AI to automatically analyse resumes and filter digital communications from applicants. These smart systems are capable of responding automatically to candidates that are clearly not a fit for the job, while also sorting the best candidates to the top of the pile.
Organisations are also using AI to create and manage online job postings on their websites and mobile apps. Rather than creating a single general ad to be used on multiple sites, AI systems can create hundreds of different job postings customised for different platforms and handle the responses for each job site in different ways. Once roles are filled, these bots can remove the postings and replace them with new ones.
Supply Chain Management & Planning
With the ubiquity and expediency of data in today’s global economy, supply chain networks have become more flexible in their ability to accommodate more geographically dispersed elements, forming a broader supplier network. However, that trend only exacerbates the complexity of managing the flow of parts and goods through the supply chain in an efficient manner. By using machine learning (ML) algorithms, AI can help to better anticipate changes in lead times, predict price fluctuations, and recommend certain suppliers. Ultimately, this is making organisations more proactive than reactive in managing their supply chains.
The call center has become a customer engagement hub where companies must seamlessly manage multiple channels to deliver consistent and personalised interactions with its customers. Contact centers using AI can automate analysis of historical customer interaction across each channel (e.g., phone, live chat and email), and optimise agent forecasting activities. This would mean minimising understaffing and the associated customer churn, and overstaffing and the associated unnecessary staffing costs. Another use case is analysing previous phone conversations with the help of ML and speech analytics to determine keywords associated with customer churn. Using this capability, AI can help call centers notify agents when a customer uses one of these keywords and determine customer churn risk. This, in turn, alerts the agent to take the next-best action to minimize the chances of losing the customer.
5 tips for app developers using ML & AI
- Selecting the appropriate ML approach is key to success – the simpler the model, the easier the learning process, and the more accurate the predictions.
- It’s always a good idea to involve a data scientist in the project in order to choose the right ML method and criteria for the best results.
- It’s all about the data. The more high-quality data the algorithm has access to, the more accurate the results and suggested actions will be. As a coder you should therefore avoid subsampling and use all available data. Improper data collection will produce poor results. Remember – “Rubbish in – rubbish out!”
- It is essential to take into consideration the business model and production capacities of the client while building machine learning algorithms.
- Machine learning algorithms require proper and careful testing. This should be taken into account when planning the costs and timing of projects.
Do you have an app development project you’d like to discuss?
If you’re thinking of using machine learning and AI as part of your business app development project then get in touch and we can talk you through our experience of using these technologies to power apps.