FeatureArtificial Intelligence has been recognised as one of the central enablers of digital transformation in several industries. The transformation process seeks to leverage digital technologies to create or modify customer experiences and culture, and business processes, thus meeting customers’ changing needs and the market.
And this is where artificial intelligence comes into play. It can help companies become more innovative, more flexible, and more adaptive than ever. The promise of speed, ease, and cost optimisation, while simplifying complex processes and systems, places artificial intelligence as one of the most significant digital transformation drivers.
Intelligent process automation refers to tasks that are automated or optimised in part by Artificial Intelligence, Machine Learning algorithms, and robotic process automation.
Intelligent process automation is a process by which companies are training robots or computer applications to simulate routine or repetitive responses that would generally be given by employees, resulting in faster time to market and boosted business returns. IPA robots perform tasks by interacting with in house digital resources to carry out a business process just like humans do
Running a simple approach may give you more insights regarding the problem than a more complicated one
Intelligent Process Automation has thus been implemented by businesses in modern times to solve various business problems and needs to reduce costs and result in profitability. The main use cases in IPA are Processing Invoices, Payroll Transactions, Customer Support, Recruitment Process, Financial document analysis, and Insurance claims.
Best Practices
#1 Understand your data and business
Exploratory data analysis helps to determine the data quality and define reasonable expectations towards the project’s goals. Moreover, close cooperation with subject matter experts provides the domain’s insights, which are the key to obtain a complete understanding of the problem.
#2 Straightforward approach
Running a simple approach may give you more insights regarding the problem than a more complicated one, as simple methods and their results are easier to interpret. Moreover, implementing, training, and evaluating a simple model is way less time consuming than a sophisticated one.
Numerous different variables may influence the performance of artificial intelligence algorithms
#3 Define baseline
It is a good practice to have a simple baseline that helps in tracking the gain offered by complex strategies. Sometimes the benefit is minimal, and a simple method might be preferable for a given task for reasons like inference speed or deployment costs.
#4 Plan and track
Numerous different variables may influence the performance of artificial intelligence algorithms. Hence, tracking the trials becomes challenging, primarily if many people work together.
#5 Common sense
The final good practice is not to lose common sense, as blindly applying any rules might bring more harm than good.
How to measure ROI
Key performance indicators
Oftentimes, this is mandatory in ML training since there is typically a metric that you’re trying to optimise. However, in the case of segmentation for example, you need to know what the segments are going to be used for and what success criteria will be defined.
Benchmark of comparison
Once performance metrics are defined, maintaining a valid control group or holdout groups that are key to understanding the true impact that the ML and artificial intelligence models are contributing.
AI and ML also greatly help with medical research and drug discovery, as well as medical imaging and robotic surgery
Monitoring overtime
Many algorithms are updated on a regular basis as more training data is made available. This can be a blessing and a curse when it comes to implementing a ML model, as the models can change significantly over time and need to be monitored to ensure the targets are being met and the metrics for success are sustaining data changes.
Primary markets suited for adoption
E-commerce
Artificial intelligence is completely transforming the way we shop online. With the help of artificial intelligence-powered software solutions and machine learning, retailers are gaining an insight into their customers’ needs and preferences, so that they can optimise their content and ads and effectively target consumers.
Healthcare
Healthcare professionals can use artificial intelligence technology to automate data entry and other menial tasks, which not only eliminates the risk of human error, but also saves plenty of time that they can focus on providing their patients with the best possible care. AI and machine learning also greatly help with medical research and drug discovery, as well as medical imaging and robotic surgery.
Artificial intelligence is completely transforming the way we shop online
Transportation
Transportation is yet another area where artificial intelligence is completely changing the game. Driverless cars are no longer just a distant dream, as companies like Tesla, Mercedes-Benz, Ford and even Google are investing enormous amounts of money into building both semi-autonomous and completely self-driving vehicles.
Financial Services
Artificial intelligence is extremely beneficial to banks and other financial institutions. Not only can it save plenty of time on various repetitive tasks, but it can also detect and prevent fraudulent transactions and theft by analysing consumer behavior and potential financial risks.
Insurance
Artificial intelligence can also be of great help in the insurance vertical, as chatbots can be used to seamlessly manage insurance claims, provide personalised quotes, and manage payments, to name just a few of the use cases.
Exploratory data analysis helps to determine data quality and define reasonable expectations towards the project’s goals in close cooperation with experts.
