Let’s explore two aspects that will provide insights into AI / ML relevance within technology and across enterprises. First, roles in technology that need to work on machine learning algorithms and second, areas within an enterprise that will benefit the most from AI / ML.
It is an incorrect assumption that all software engineers will work only on ML algorithms in future and demand for other skills will plummet. In fact, majority of current software engineering roles that do not require machine learning expertise will continue to exist in the future.
Software engineering functions that DO NOT require machine learning expertise: UI / UX development, interface / API development, rule based programming and several other client and server side components that requires structured or object oriented programming. In addition, there are others like database development and SDLC functions that are required for AI / ML technology lifecycle but don’t require deep machine learning knowledge. So, this leaves only data / feature engineering, data science and model deployment teams that absolutely require machine learning expertise. However, these are rapidly growing areas and demand for experts will continue to outpace many other areas.
Where can we leverage ML? Any use case where historical data can be used for making decisions but this data is so extensive that it is practically impossible for a human to comprehensively analyze the data and generate holistic insights will be a candidate for ML. The ML approach will be to leverage human subject matter expertise to source relevant data, determine the right data elements (features), select appropriate ML model and train the model to make predictions and propose decisions. A few examples:
- Sales & Marketing: Use data around customer behavior and make recommendations. We see this all the time from Amazon, You Tube, Netflix and other technology platforms.
- IT Operations: Use a variety of features to predict potential failures or outages and alert users / ops.
- Customer Service: Chatbots that use natural language processing to answer user queries.
- Intelligent Process Automation: Eliminate manual operations thereby optimizing labor costs and reducing operational risk.
- Cyber Security: Detect malicious activity and stop attacks.
- Anomaly detection: Every business domain needs to beware of anomalies and detecting them will reduce losses or accidents. It could be detecting defaults or money laundering or fraud for banks, detecting leak in a chemical plant, detecting a traffic violator, etc.
Every enterprise, large or small, is likely to have AI / ML opportunities that will result in bottom line benefits. In the next part, I will cover the typical challenges an enterprise faces during adoption.