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.

Having done my Machine Learning certification in August 2019, I was fortunate to get an opportunity soon after to build and lead technology team that worked on AI / ML problems across the enterprise.

During the team build-out phase, I realized that many software engineers have completed a formal certification on machine learning to qualify themselves for a role in this emerging technology area where demand is expected to increase. There is also an unfounded assumption that all software engineers will work only on ML algorithms in future and demand for other skills will plummet. The reality is that not all software applications will be suitable machine learning candidates. Moreover, developing machine learning algorithms is only part of AI / ML technology lifecycle. There be massive software engineering needs outside of machine learning, particularly around data and SDLC automation to enable AI / ML technology. Having said that, familiarity of machine learning concepts will increase effectiveness of software engineers as all applications in near-future will interface with ML modules for certain functions.

Now, let’s address another question – is AI / ML just hype? To understand this, lets look at it through the lens of Gartner Hype Cycle. Since mid 1990s, a number of technologies fell by the wayside after inflated expectations in the beginning. However, a few like cloud computing, APIs / web services and social software went through the hype cycle but the reality after mainstream adoption was quite close to initial expectations. Looking at hypes since 2013, several technologies related to AI / ML have been at the top every year. Starting with big data and content analytics, we have seen natural language processing, autonomous vehicles, virtual assistants, deep learning and deep neural networks emerge at the top during the last seven years. And results from machine learning algorithms have already become part of our day to day life – like recommendations made by Amazon, You Tube or Netflix and chatbots available through a number of channels.

So, I believe AI / ML is real and will continue to disrupt mainstream industries. However, it will be different from other familiar technology disruptions in many ways:

• AI / ML technology will continue to evolve rapidly, driven by silicon valley innovation.
• New specialized areas of expertise will emerge every year that will require deep math understanding.
• Technology workforce will be under pressure as past work experience will be of limited value due to this fast evolution.
• Traditional enterprises will struggle to keep pace.
• Possibility of learning through data will undermine established business theories.

Finally, the overwhelmingly open source nature of this domain will lower entry barrier and promote start-ups to challenge established players. It will also give an opportunity for established organizations to adopt and manage this disruption. The choices made will determine whether an organization disappears like Blackberry, comes back with a bang like Microsoft or continue to hang-on like IBM. While this is primarily about embracing a relatively new technology domain, appropriate strategy around people and process will also be required to succeed. To summarize, organizations will have to create the right ecosystem and provide clarity on approach that encourages people to innovate.

In this blog series, I will articulate my thoughts around people, process and technology considerations while adopting AI / ML in a large enterprise:

• Technology functions that will require machine learning expertise.
• Business domains that will benefit from AI / ML.
• Challenges that enterprises should be prepared to encounter.
• Structure and governance to scale up adoption.
• ML technology platform.