AI & Machine Learning

When it comes to business, AI can be invaluable – whether it’s used to identify and target a potential customer base or streamline internal processes.   Already, a range of industries, from retail and banking to the security and legal sectors, are taking advantage of what AI can offer. The goal for future-thinking organisations is to make sure they have the right strategies in place so that they’re able to adopt these rapidly evolving AI capabilities. Here are three business needs were identified where AI could offer value.

When created from scratch, deep learning models require access to vast amounts of data and compute resources. This is a luxury that many can’t afford. Moreover, it takes a long time to train deep learning models to perform tasks, which is not suitable for use cases that have a short time budget. Fortunately, transfer learning, the discipline of using the knowledge gained from one trained AI model to another, can help solve these problems.

We can understand things in 1 dimension, 2 dimensions and 3 dimensions easily but Datasets can be very complex and hard to understand, especially if you don’t have the right tricks in your proposal. In machine learning, we sometimes need to make assumptions based on hundred or even thousand dimensions. Our brains just can’t do that, which is why machine learning helps us to recognize and learn patterns within data that humans can’t recognize. 

It’s vital to have the necessary data to make important business choices. Today’s technologies – such as artificial intelligence, business process automation, robotic process automation (RPA) and other automated tools – are creating a data-driven decision-making revolution. A growing number of businesses are taking advantage of RPA, which partially or fully automates human activities that are manual, rule-based, and repetitive, freeing up humans to focus on more pertinent tasks. RPA harnesses data so that you can take the guesswork out of your business decisions.

It’s common to see businesses of all sizes relying on people-power to complete tasks that today can and should be automated. To address this challenge, companies should consider deploying robotic process automation, or RPA. RPA uses bots to reduce manual workloads, freeing up teammates to work on more value-added tasks that ultimately enhance the customer experience and create greater job satisfaction. While RPA offers advantages, it can also present difficulties to deal with. Here is a look at the successes, challenges and best practices that other organizations may find helpful in their automation journey.

Choosing how to deploy a predictive model into production is quite a complex affair, there are different ways to handle the lifecycle management of the predictive models, different formats to stores them, multiple ways to deploy them and very vast technical landscape to pick from. Understanding specific use cases, the team’s technical and analytics maturity, the overall organization structure and its’ interactions, help come to the right approach for deploying predictive models to production.

Possibly most significantly, AI will be at the forefront of creativity – the force that ultimately drives the media business. Artists equipped with an AI-enabled feedback loop based on real-time, consumption metrics will up their creative batting average, which will thus increase production and commercial ROI. AI will influence all parts of the media value chain, helping content creators to be more creative, helping content editors to be more productive, and helping content consumers to find the content that matches their interests and current situation.

Management consulting tends to view itself as an elite, untouchable echelon of the business world. But it is vulnerable to the same market forces that are disrupting services everywhere. The consulting industry is at risk. With its deeply embedded business and mental models, many companies will be unable to make the jump. So how should consulting companies, or any in the services sector, adjust to this new, data- and AI-driven world? Follow the rules mentioned here.

The utilization of RPA in healthcare services can centralize and streamline different workflows. Shifting these routine tasks from human agents to bots can result in cost savings for healthcare providers. Also, automating crucial workflows will improve efficiency across the board. With this approach, healthcare professionals can spend the majority of their time on patient care and other critical activities. A major drawback of leveraging RPA in healthcare is that RPA can only process structured data and work with a rule-based approach. However, the advent of intelligent process automation (IPA) will make RPA smarter. 

The importance of industrial robots has increased largely in the automotive sector in the last couple of years. Due to this rise, the demand for mobile cobots has also increased. Though mobile cobots are currently in its initial stage of development, its significance is growing dramatically as they are not limited to perform single tasks. Deployment of mobile cobots helps in painting, spraying, and assembling of different parts of cars, due to which its demand has grown in the automotive sector. 

One of the most important steps when beginning any project at scale starts with determining the correct tasks to automate in a business. This is a critical first step in setting up robotic process automation (RPA) and getting the most bang for your buck. This is your opportunity to realise quick return on investment (ROI) and demonstrate the efficiency of RPA to stakeholders, while also evangelising a culture of continuous innovation within your organisation.